Hype Cycle for IT Evolution in Manufacturing,
2018
Published: 1 August 2018
ID: G00340295
Analyst(s): Marc Halpern, Simon Jacobson, Rick Franzosa
Manufacturers must adopt appropriate IT to enable a digital business. This
new Hype Cycle analyzes IT evolution to help IT organizations plan their
technology adoption roadmaps.
Table of Contents
Analysis.................................................................................................................................................. 3
What You Need to Know.................................................................................................................. 3
The Hype Cycle................................................................................................................................ 3
The Priority Matrix.............................................................................................................................5
Off the Hype Cycle........................................................................................................................... 6
On the Rise...................................................................................................................................... 7
AI-Enabled Material Informatics...................................................................................................7
Blockchain in Manufacturing....................................................................................................... 8
Cyber Physical Systems........................................................................................................... 10
IP Protection in 3D Printing....................................................................................................... 12
At the Peak.....................................................................................................................................14
Digital Thread........................................................................................................................... 14
Multienterprise MDM.................................................................................................................16
Digital Twin............................................................................................................................... 18
All-in-One Supply Collaboration Platforms.................................................................................20
Cloud-Native CAE, Simulation and Virtual Prototyping.............................................................. 21
Sliding Into the Trough.................................................................................................................... 23
Cloud-Native CAD.................................................................................................................... 23
MPM Frameworks.................................................................................................................... 24
Product Innovation Platforms....................................................................................................26
IT/OT Convergence and Alignment........................................................................................... 28
Cloud Computing in Manufacturing Operations.........................................................................30
Model-Based Manufacturing.....................................................................................................31
Internet of Things for Manufacturing Operations........................................................................33
3D Printing in Manufacturing Operations...................................................................................35
Cloud-Native PLM Applications................................................................................................ 37
Industrial Operational Intelligence..............................................................................................39
Master Data Management........................................................................................................ 41
Supplier Quality Management Applications............................................................................... 43
Asset Performance Management..............................................................................................44
Asset Performance Management in Manufacturing Operations................................................. 46
Digital Manufacturing................................................................................................................ 48
Climbing the Slope......................................................................................................................... 51
Synchronized BOMs................................................................................................................. 51
System Engineering Software................................................................................................... 52
Product Cost Management.......................................................................................................54
Quality Process Management Applications............................................................................... 55
Real-Time SPC Applications..................................................................................................... 57
Simulation and Test Data Management.....................................................................................58
Plant Engineering and Design................................................................................................... 60
MES Applications for Discrete Manufacturing............................................................................62
MES Applications for Process Manufacturing............................................................................63
Product Requirements Management.........................................................................................65
Entering the Plateau....................................................................................................................... 67
Enterprise Manufacturing Intelligence Applications.................................................................... 67
Simulation and Virtual Prototyping............................................................................................ 68
Parts and Materials Search and Selection................................................................................. 69
Appendixes.................................................................................................................................... 71
Hype Cycle Phases, Benefit Ratings and Maturity Levels.......................................................... 72
Gartner Recommended Reading.......................................................................................................... 73
List of Tables
Table 1. Hype Cycle Phases................................................................................................................. 72
Table 2. Benefit Ratings........................................................................................................................ 72
Table 3. Maturity Levels........................................................................................................................ 73
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List of Figures
Figure 1. Hype Cycle for IT Evolution in Manufacturing, 2018..................................................................5
Figure 2. Prioritiy Matrix for IT Evolution in Manufacturing, 2018..............................................................6
Figure 3. Priority Matrix for Discrete Manufacturing and PLM, 2017...................................................... 71
Analysis
What You Need to Know
The pace of commercial IT advances for manufacturers has never been greater. The impact of these
advances is positive if adopted rationally, and destructive if the technologies are adopted without
careful consideration of business readiness. For example, the "Hype Cycle for Innovation in
Manufacturing Industries, 2018" suggests that manufacturers are still formative at capitalizing on
the many technologies on the Slope of Enlightenment in this Hype Cycle.
Manufacturers face a particular challenge, as they are constrained by deeply ingrained processes
and practices. Evolution demands new ways of creating, using, communicating and managing new
and existing categories of data for many different roles. Therefore, new and evolving technology
opportunities can make IT roadmap planning difficult, inhibiting the pace at which manufacturers
can adopt IT opportunities.
This Hype Cycle provides manufacturers with decision-making support to rationally adopt
technologies by providing insight into the calculated technology-related risks appropriate for their
companies.
The Hype Cycle
This Hype Cycle addresses opportunities for manufacturers to evolve their manufacturing IT and
differentiate their performance through technologies that support product life cycles including
design, engineering, manufacturing, service and continual product improvements. Each technology
capability or innovation profile (IP) on this Hype Cycle reflects an opportunity. The selected IPs
enable greater product innovation, improve product quality, enable greater operational efficiencies,
and reduce product life cycle costs. They also enable greater ability for manufacturers to adapt and
change with changes to market conditions, customer preferences, suppliers and partners.
The technologies climbing from the Innovation Trigger are emerging from R&D and becoming
formative opportunities. Among these, digital twin and digital thread have generated the greatest
excitement for manufacturers. Digital twin and digital thread are most relevant to manufacturers that
have embraced or now plan to adopt Internet of Things (IoT) strategies for manufacturing operations
and product monitoring during service life. Artificial intelligence (AI)-related technology applied to
material informatics and cyber physical systems are seeing fledgling commercialization. Use of
blockchain technology by manufacturers is just emerging as well. Use of artificial intelligence for
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design were judged to be not at the state of feasibility to be introduced to the Hype Cycle yet.
Technologies to protect intellectual property when using 3D printers to produce parts are gaining
greater interest.
Many of the IPs sliding down the Trough of Disillusionment mostly reflect opportunities with the next
generation of cloud-native applications across design, manufacturing and product life cycle
management (PLM). These are taking longer than expected for market growth because
manufacturers are locked into legacy environments that their "day-to-day" business operations
depend on. A smooth migration path to the new generation is not obvious. Enabling these IPs
requires substantial reorganization, rethinking data management, and process re-engineering — as
early adopters are discovering. Low adoption of standards for cloud implementations and for data
representation plus few proven implementation guidelines further inhibit adoption of these IPs. The
cultural ramifications of adopting the cloud as a mainstream IT platform are also significant. Also,
the new-generation cloud-native design and manufacturing capabilities are sliding into the Trough
because they need more time to functionally match their on-premises counterparts.
Most investment and focus appear to be on IPs climbing the Slope of Enlightenment. The ascent of
those IPs primarily support:
■
Improved product quality
■
Greater manufacturing efficiency
■
More streamlined coordination between design and manufacturing activities
The IPs climbing the Slope of Enlightenment are growing in value, availability and presence. As
more manufacturers adopt them, the users request more enhancements, which increases their
appeal to more prospective users, helping in the ascent. IPs for simulation and virtual prototyping
and parts and material search and selection are nearly off the Plateau of Productivity because their
use is widespread. However, there is still room for growth.
Technical opportunities that have permeated the market for decades, such as drawing tools, 3D
modeling and 3D visualization, and specifications management in process industries are not
included on this Hype Cycle. They are beyond the Plateau because their broad availability and
almost ubiquitous use across manufacturers renders them less as a differentiator and more of a
necessity. Yet, some of those technologies no longer represented, such as 3D modeling and 3D
visualization, are key building blocks for newer IPs such as digital twin, which is positioned near the
Peak of Inflated Expectations today.
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Figure 1. Hype Cycle for IT Evolution in Manufacturing, 2018
Source: Gartner (August 2018)
The Priority Matrix
The Priority Matrix summarizes business impact versus maturity for technology investments that
manufacturing CIOs should be prioritizing. Gartner judges the technologies closest to the upper-left
corner of the matrix as having the greatest business impact with the most maturity for broad
adoption. The innovation opportunities through technology adoption presented on the Priority
Matrix are a mix of applications with a specific focus, such as system engineering, or a platform
adoption, such as product innovation platforms or manufacturing process management (MPM)
frameworks.
In general, the platform opportunities will take longer to implement. That's why, although Gartner
predicts that some of them, such as digital twin, master data management, product innovation
platforms and MPM frameworks, will not be mainstream for five to 10 years, they should be started
sooner. System engineering software is a focused category of software. However, the planning
necessary to succeed with it will take substantially longer than implementing the software. Earlyphase technologies such a cyber physical systems, artificial intelligence and blockchain
technologies hold great promise to revolutionize manufacturing companies, but have a long journey
toward mainstream adoption.
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Figure 2. Prioritiy Matrix for IT Evolution in Manufacturing, 2018
Source: Gartner (August 2018)
Off the Hype Cycle
This is a new Hype Cycle. Therefore, all innovation profiles are newly introduced.
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On the Rise
AI-Enabled Material Informatics
Analysis By: Michael Shanler
Definition: AI-enabled material informatics solutions are software and services that apply the
principles of informatics to materials science by applying learning techniques to materials-related
big data to better predict results by materials characteristic. Typically, the insights generated are
applied to the use, selection, discovery and development of materials in downstream engineering
and product development and manufacturing.
Position and Adoption Speed Justification: This technology is positioned early on the Innovation
Trigger phase on the hype curve because today, most manufacturers with R&D programs are just
starting to explore these solutions. Only in the last five years have specific informatics software
solutions for materials R&D emerged where advanced analytics and data science are engineered
into the solutions. Most prior iterations of material informatics were simply loosely packaged
materials databases with light search functionality. Today, the majority of clients have implemented
a materials research capability using newer approaches. Generally, clients either (1) build a
customized, proprietary in-house system; (2) outsource the work to a CRO that performs material
science, or (3) modify a "drug discovery" oriented cheminiformatics application or electronic
laboratory notebook (ELN). In most cases, these standard approaches do not support the possibility
of using newer tech like machine learning or advanced analytics.
As investments in materials engineering and science increase, and software engineers begin to
embrace data-science and machine learning capabilities, the footprint of AI-enabled material
informatics solutions will expand and become more prominent. The desired capability is now part of
strategic plans at many small and large enterprises including medical device, diagnostics equipment
manufacturers, food and beverage, oil and gas, materials, chemicals, crop science, battery,
electronics manufacturers, automotive, and aerospace and defense.
Many clients are looking to replace their current "standard" approach and move to newer and more
advanced solutions. They want to better leverage the output and data streams from these solutions
for downstream engineering software, such as CAD, CAE and PLM. This process integration will
further fuel innovations and inspire new startups to bring more capabilities to market. We also
expect established engineering software companies to look toward these smaller materials and
informatics companies as potential acquisition targets, or areas where they build their own
capabilities, as the synergies between materials informatics and R&D engineering enable a broader
perspective on overall product life cycle.
User Advice: CIOs should work with R&D and manufacturing business leads to understand what
capabilities need to be augmented in the roadmap. While advanced analytics applications represent
a new space, the R&D staff required to operate these systems must be more scientific in nature, at
least in the beginning. It will take 5-10 years before these systems are simplified enough to mesh
more seamlessly into product development processes, at which point these systems will be
mainstream for R&D users. The value of these systems is reliant on properly harmonized or
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arranged data, especially if the system will "learn" based on existing datasets. Today, most new
instances will likely be targeted toward specific R&D functional areas and disciplines. Before
extending the application to the rest of the R&D enterprise or beyond, the R&D materials informatics
strategy must align to the R&D product life cycle strategy, with a focus on improving informatics
data literacy.
Business Impact: The most immediate impact will be on the value of the R&D product portfolio.
Making investments in advanced materials informatics should improve the overall time to market
and, in particular, reduce the amount of time it takes to innovate with new materials. There is also an
innovation benefit: several clients have already claimed that these types of tools have yielded
materials as candidates that were previously undiscoverable. Secondary benefits include improved
quality and regulatory compliance.
Benefit Rating: Moderate
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Sample Vendors: Citrine Informatics; DataRobot (Nutonian); Enterra Solutions; Exabyte.io; Tilde
Materials Informatics; Uncountable
Recommended Reading: "How to Make Smarter Decisions About Artificial Intelligence in Life
Science R&D"
"Deliver Artificial Intelligence Business Value: A Gartner Trend Insight Report"
"Artificial Intelligence Is Closer to CIOs Than They May Think"
"Craft an Artificial Intelligence Strategy: A Gartner Trend Insight Report"
Blockchain in Manufacturing
Analysis By: Stephen E. Smith
Definition: A blockchain is an expanding list of cryptographically signed, irrevocable transactional
records shared by all participants in a network. Each record contains a time stamp and reference
links to previous transactions. With this information, anyone with access rights can trace back a
transactional event, at any point in its history, belonging to any participant. A blockchain is one
architectural design of the broader concept of distributed ledgers. This profile addresses the
blockchain from the perspective of the manufacturing industry.
Position and Adoption Speed Justification: Blockchain technologies are still emerging and not
ready for widespread use. There are pilots happening in the areas of control of counterfeit parts and
goods, transparency in the supply chain, improvement of warranty management and enhancement
of quality and loyalty programs. Those manufacturers that engage with blockchain will ultimately be
able to leverage what has essentially become digital product/brand benefits that can provide value
in the form of additional customer/buyer confidence. The combination of technologies, the diversity
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of the participants, and the requirement for detailed standards are likely to combine in a way that
delays cross-value chain adoption for some time. The governance requirements for complex
multienterprise supply chains will make it more likely that stand-alone blockchain implementations
will gain traction faster.
User Advice:
■
Prepare for the blockchain by continuing to tighten up on standards and requirements as they
relate to product sourcing, manufacturing, assembly, distribution, storage and payment.
■
Participate in standards boards (where appropriate) to ensure that you are in alignment with the
rest of the industry, your suppliers and your customers, in case you are asked or required to
participate in a blockchain.
■
Identify the unique points of value and risk in your value chain, engage cross-functionally within
your organization and map these points to known industry pilots in order to assist in building the
business case.
Business Impact: A manufacturing blockchain is well-suited to bring additional order and the right
balance of transparency, security and process discipline to the ecosystem. The blockchain is poised
to both open up opportunities for new manufacturing business models and provide solutions to
some perennially challenging issues in manufacturing value chains. Simplifying traceability is a
major benefit as any given manufacturing industry value chain is complicated. Ingredients, parts,
components, labeling, formulas and finished goods change hands throughout the chain.
Additionally, blockchain secures the handling of contracts, payments, warrantees and customer
information during these exchanges, often across borders where different regulations can multiply
the complexity. Therefore, manufacturers will be able to guarantee the provenance and authenticity
of products, and to leverage that ability for competitive advantage. Regulations and shifting
customer demands on transparency may make some aspects of what the blockchain can deliver
the cost of doing business.
In addition, the blockchain may create new opportunities for manufacturers to monetize different
activities and processes in the value chain. For example, designs for products can be securely
distributed, used and properly accounted for as manufacturers expand their business model
thinking to further leverage their design and formulation of intellectual property.
Benefit Rating: High
Market Penetration: Less than 1% of target audience
Maturity: Emerging
Sample Vendors: Blockverify; Chronicled; Everledger; loyyal; Provenance; Ubims; Warranteer
Recommended Reading: "Blockchain Will Drive Digital Branding in Consumer Goods
Manufacturing"
"What CIOs Should Tell the Board of Directors About Blockchain"
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"Market Trends: How Blockchain Impacts Different Vertical Industries"
"Blockchain Trials Across Industries Show a Market in Transition"
"Market Guide for Blockchain Consulting and Proof-of-Concept Development Services"
Cyber Physical Systems
Analysis By: Simon F. Jacobson; Alexander Hoeppe
Definition: Cyber physical systems (CPS) blend physical/mechanical automation with algorithmic
knowledge to be self-adaptive and automatically reconfigurable. CPS rely on distributed computing,
interconnected information systems and prescriptive analytic techniques to add new dimensions of
interpretation, judgment and expertise to manufacturing operations.
Position and Adoption Speed Justification: CPS are core to future visions of smart manufacturing
and Industrie 4.0. They represent confluence of physical and virtual worlds through an internet of
services to connect people, products and processes within the manufacturing function, as well as
extended supply chain to create a self-adaptive and autonomous production capability. This will
change the dynamics across multiple industrial ecosystems and be done by automating
unstructured processes, shortening cycle times, and improving product and service quality.
Deployments will be a distributed environments that extensively uses IoT, cloud services, machine
learning and secure high-speed networks to orchestrate data and processes in real-time across
company and geography boundaries.
Eventually CPS will replace its foundational precursors: conventional production process control
and automation, materials handling systems (plus the sensor networks or machine networks), and
transactional workflow systems to promote real-time information gathering and processing.
Combining multiple platforms and systems underscores the need for interoperability standards.
Several proposed draft frameworks exist for standards proposed by the United States' National
Institute of Standards and Technology (NIST) and Germany's Industrie 4.0 platform, which has
produced the RAMI 4.0 framework for digital transformation in various industries beyond
manufacturing. Beyond upgrading IT and OT, revamping factory layouts and identifying where to
judiciously automate process and data flows in/across the value chain is needed. This new level of
orchestration and operating model overhaul will drive manufacturers to revisit their corporate
production systems to ensure alignment and integration with other internal constituencies and value
chain partners.
A Utopian view for CPS includes integration with the smart city, where power consumption during
commuting hours is balanced. Factories would slow their production lines so public transportation
could consume more energy resources. This cross-industry and cross-ecosystem approach will
take years to come to fruition. We anticipate that other sectors (smart cities, for example) will
advance ahead of manufacturing. The near-term increased pursuits of digital supply chains over the
past 12 months — and more tactically — the rise of strategic projects for IoT and advanced
analytics are encouraging and push CPS forward on this year's Hype Cycle. The lengthy time frame
to the Plateau of Productivity represents the obstacles created by the variance of factory layouts
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and production styles — not to mention the maturity levels for both — to fulfilling the full vision for
CPS.
User Advice: When seeking to establish a CPS, pursue the following actions:
■
Incubate small scale pilots to push the potential impacts on connected products and
orchestration of manufacturing processes. Partner with suppliers and distributors in these pilots
as necessary.
■
Cultivate partnerships with academia, consortiums and local government to help shape policy
and future-state capabilities.
■
Investigate the potential with your supply base, business and market environment, especially on
the smart ecosystem or urban manufacturing proposition.
■
Promote the use of standards and implementation recommendations to manage complexity of
implementation scalability, and extensibility and to ensure compliance conformity cybersecurity, and machine safety.
■
Build a governance model based on reference architectures such as CESMII, RAMI 4.0, IIRA or
combinations, allowing to understand the impact of new IoT use cases and supporting CPS and
other technologies.
Business Impact: The destiny of CPS will ultimately be predicated on a blend of operating models,
science, engineering, supply chain and technology; it will carry transformative impacts across:
■
Risk: Medium — Many building blocks for CPS are too early in their life cycles to associate
with risk as pilots are being incubated. However, companies that lag in their convergence and
alignment of IT and OT to create transparency and efficiency will be left behind.
■
Technology Intensity: High — Requires costly upgrades, mastery of artificial intelligence, the
IoT and capability for integrating and managing information to optimize production and
distribution costs at a magnitude larger than today's systems can handle. The rapid proliferation
of IoT data alone will challenge existing OT information infrastructures and disrupt existing
approaches to security, process automation and data integrity.
■
Organization Change: High — Self-adaptive and automatically reconfigurable systems change
the nature of which decisions are made and implemented and by whom they're made.
■
Process Change: High — Supply chain convergence must be mastered and SLAs for the value
network will be rewritten and redefined across partners with enhanced risk profiling enhanced,
service levels, inventory and pricing components. Also, evolving regulatory compliance and
data ownership policies will drive changes to data governance and privacy policies.
■
Competitive Value: High — CPS can dynamically reconfigure product supply networks to
accommodate variability, capture new opportunities or achieve other outcomes that add value
to the customer.
Benefit Rating: Transformational
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Market Penetration: 1% to 5% of target audience
Maturity: Embryonic
Sample Vendors: Axoom; Bosch Software Innovations; Fujitsu; Google; Hitachi; IBM; Microsoft;
Schneider Electric; Siemens
Recommended Reading: "Understanding the Five Stages of Gartner's Maturity Model for
Manufacturing Excellence"
"Driving Digital Business Transformation for Industry Leadership: A Supply Chain Perspective"
"The Internet of Things Revolution: Impact on Operational Technology Ecosystems"
"Cool Vendors in Digitalization Through Industrie 4.0"
"Magic Quadrant for Industrial IoT Platforms"
"Toolkit: Self-Assess Your Manufacturing Operations Maturity"
"Artificial Intelligence Will Make Manufacturing Operations Smarter — But a Learning Curve Comes
First"
IP Protection in 3D Printing
Analysis By: Marc Halpern
Definition: Intellectual property (IP) protection for 3D printing refers to practices and supporting
technologies that protect creations from anyone attempting to illegally use or distribute that
innovation through 3D printing. Such creations include inventions, literary and artistic works,
designs and symbols, and names and images used in commerce.
Position and Adoption Speed Justification: Stealing IP using 3D printing has gained visibility as
3D printing emerged from prototyping and became a set of technologies that can produce objects
and products from a broad range of materials. Gartner's 2014 prediction about the level of IP theft
was substantially higher than the current estimate of $1.03 billion in counterfeit 3D printed objects
during 2018. Although the actual level of IP theft is much lower than the prediction, the need for IP
protection of 3D printed parts will continue to increase as the cost of 3D printing continues to
decline while the quality of printed parts continues to improve. Although few technical solutions
have appeared on the market that address this challenge, the established ones appear to be
growing. Therefore, while IP protection remains a research and development (R&D) activity, the
established commercial solutions are proving to be viable businesses. As yet, no approach to the
challenge has gained the level of attention that would place IP protection at the Peak of Inflated
Expectations.
User Advice: CIOs serving enterprises with a vested interest in protecting IP from theft by 3D
printers should be investing in this technology. IP protection can be addressed from multiple
perspectives. Some key perspectives are: (1) securing content such as 3D models used to create
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3D prints; (2) creating markers on the objects that authenticate those objects; and (3) search
engines that compare 3D objects.
Gartner recommends that clients consider digital asset management software and product data
management software to control access to digital content, which is the source 3D data needs to
create 3D prints. CIOs should monitor the market for vendors that embed markers on 3D prints as
those vendors become increasingly available.
For example, Applied DNA Sciences promotes its use of DNA to mark genuine products with visible
or invisible signatures that, when screened, identify the product as genuine. This is a feasible
approach, although Applied DNA Sciences is not yet widely known, nor is the technique proven on
a significant scale. 3D geometric search technology (such as that available through HCL
Technologies [Geometric] and Siemens PLM Software) shows promise in detecting the illegal use of
content to print counterfeit parts. While such technology is in the early adoption phases for sourcing
parts, extension to IP protection is a possibility.
In addition, concerned parties will need to tighten governance of sourced parts to ensure that the
parts and materials they purchase are from the original sources that own the IP. If the parties cannot
validate that the IP belongs to those sources, then they should not be partnering with those
sources. Besides the ethical issues, enterprises knowingly using or suspected of using counterfeit
parts will be vulnerable to lawsuits or charges of criminal activity.
Business Impact: Enterprises that do not protect their IP lose competitive advantage, resulting in
financial losses and lost growth opportunities. To prevent such setbacks caused by 3D printing, they
must either redefine their business strategies to reduce the potential impact of IP theft or introduce
steps into manufacturing, service and sourcing to ensure that original and replacement parts are not
counterfeit. This is likely to increase the costs of designing, 3D printing, sourcing and maintaining
products. Some companies may redefine their business value propositions and business strategies
in order to emphasize value-added services and their ecosystems that serve customers, rather than
prioritizing the physical content of products as the key value proposition.
Implementation of IP protection practices will lengthen design, R&D and manufacturing processes.
Nevertheless, such DNA marking or alternative processes will increasingly become an integral part
of 3D print creative processes.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Sample Vendors: 3D Control Systems; Applied DNA Sciences; Authentise
Recommended Reading: "Predicts 2018: 3D Printing and Additive Manufacturing"
"Adopting 3D Printing for Industrial Parts Has Key Impacts on CAD and PLM Priorities"
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At the Peak
Digital Thread
Analysis By: Simon F. Jacobson; Rick Franzosa; Marc Halpern
Definition: A digital thread is a framework to trace the evolution of information and allows users to
access, integrate, organize and transform disparate technical and knowledge-based data from
multiple operational and enterprise-level systems. This enables an integrated view of a product
and/or the processes and their evolution over its respective life cycle.
Position and Adoption Speed Justification: The digital thread weaves its way on to the Hype
Cycle this year before the Peak of Inflated Expectations. While the digital thread is not new, its
nascent nature on the continuum is a result of limited adoption and deep but targeted focus on
connecting engineering and production on configure-to-order (CTO) and engineer-to-order (ETO)
supply chains with massive potential to support other supply chain models and industry value
chains. This includes upstream order management, downstream to delivery, customer service (e.g.,
installation, maintenance) activities and limited adoption — not to overlook products with
embedded software.
Many of the systems of record (SOR) that a producer would use to draw upon for the necessary
information and knowledge to create the metadata structure for a digital thread are already in place.
These can include, but are not limited to, manufacturing execution systems (MES), product life cycle
management (PLM) applications, quality management systems (QMS) and ERP. As the number of
connected products expand IoT platforms, edge devices and sensors will also play a role. The
overall intensity of data raises the importance of analytics for simulation and pattern analysis too.
The pace of which the digital thread reaches the plateau will be a factor of how manufacturers can
use the data to add context to decision making, manage traceability and create knowledge as
products and services evolve over a long life cycle. This requires enterprises to focus on the key
elements of improving how information is accesses and managed.
■
First, the digital thread is often confused with a digital twin. They are complementary in nature.
A digital twin represents a unique object while the digital thread encompasses a wider time
horizon and provides history and context specific to a product and/or process's life cycle. This
dynamic nature can impact how standard work is executed, suppliers engaged, and products
and processes concurrently improved.
■
Second, current data exchange activities are time consuming and expensive. Accessing the
data and achieving the consistency of workflows to synchronize bills of material (BOMs) and
bills of process (BOPs) need to evolve to a point where automated tools for verification and
validation exist. When it does the acceleration of supplier readiness assessments and
technology transfer can benefit.
User Advice: Supply chain leaders and CIOs looking to invest in and manage the digital thread
should:
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■
Focus on building the digital thread as a representation of a product and the manufacturing
processes that make it over their life cycles instead of confining it to engineering and
production.
■
Overcome the absence of a complete data model by investing in a diverse collection of
standards to capture and normalize data from different systems. These include standards for
the exchange of product models (STEP) and QIF files, as well as MTConnect and OPC to
access machines and production equipment. Reference models from the digital Manufacturing
and Design Innovation Institute (DMDII) and National Institute of Standards and Technology
(NIST) are providing testbeds and reference models too.
■
Diligently map value streams and identify the relationships of different digital assets and data
points necessary to gather knowledge across a product and/or process's life cycle and
determine what feedback loops are necessary.
■
Don't become hostage to a single vendor platform for your digital thread(s).
Business Impact: A digital thread connects multiple information sources across design and
manufacturing by linking design requirements (i.e., CAD and other digital models for machine
tolerances or inspection plans) with production outcomes. While the concept of a digital thread is
more familiar to producers of complex and highly engineered design and execution, a better
understanding of how product and process content evolves helps designers and planners better
understand requirements and optimize solutions to control of product characteristics, production
equipment (and settings/recipes) and suppliers. It directly impacts cost and quality, but also impact
compliance requirements (e.g., International Traffic in Arms Regulations [ITAR], Food Safety
Modernization Act [FSMA]), can reduce complexity (of managing multiple specifications, suppliers,
etc.) and improves knowledge management. It also can disintermediate any inconsistencies and
reduce profit loss, scrap/rework and other potential customer-facing risk.
As currently implemented, the digital thread is a decision support tool for improving production
quality and throughput on a local basis, with longer-term potential for agility across functions with
the following impacts:
Risk: Medium — Compliance challenges: Edge devices and sensor proliferation heighten
cybersecurity concerns while regulations such as ITAR and FSMA loom. There's also IP risk and
concerns from OEMs and brand owners for revealing their process structures and suppliers for fear
of disintermediation.
Technology intensity: High — Automated tools for verifiable and validated synchronization/data
access are still lacking. The data management for a digital thread requires cloud services and
setting collaboration rules with suppliers for accessing their data. This can help optimize a range of
activities, including machine and equipment performance and scheduling. Also, there is an not only
an impact on tooling and production costs, but also changeovers.
Organization change: Medium — Initially it will have subfunction and operational impacts; longterm potential to deepen cross-functional collaboration models; and short-term focus on harvesting
skills for data access and model creation.
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Process change: High — BOM changes might be a normal occurrence. However, the
synchronization of different BOMs — and the subsequent alignment of BOPs and standard work, as
well as where new SLAs that bind responses from suppliers to provide data transparency and
ultimately impacts traceability downstream once products are in the market.
Competitive value: High — Cost optimization and time savings come from shortened decision
cycles and improved agility on both global and local bases. Accelerating innovation and bringing
products to market are also not to be overlooked.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: Aras; AVEVA; Dassault Systèmes; Hexagon Manufacturing Intelligence; iBASEt;
Microsoft; Optimal Plus; Siemens PLM Software; Wipro
Recommended Reading: "EQM Hubs Unite Quality Management IT Systems Across the Value
Chain"
"Innovation Insight: Manufacturers Need MPM and MBM to Innovate Digitally Enabled Design
Through Production"
"Video: Connecting the Digital Thread at GE Transportation"
"Harvest the Value of Smart Manufacturing in the Supply Chain, Not the Factory"
"How to Deploy Manufacturing Process Management for Digital Manufacturing"
Multienterprise MDM
Analysis By: Michael Patrick Moran
Definition: Multienterprise MDM is a technology-enabled discipline that supports the shared
governance of common master data assets used across ecosystems, business networks, B2B
integration, multienterprise analytics and collaborative business process enabling and is used within
a number of multienterprise business applications. An example is the governed semantic
consistency of product data used within multiple PLM suites across a consortium. This style of
semantic reconciliation supports multienterprise-governed data sharing at an extreme scale.
Position and Adoption Speed Justification: Multienterprise data models are relatively new and
often not used alone, but as part of a business solution, for example, managing inventory status
levels as shared master data across a partner ecosystem to support the implementation of vendormanaged inventory (VMI). They sit at the heart of the more scalable multienterprise business
offerings in the market today. Multienterprise data-model-based MDM will likely be adopted to
support the scale that comes with managing the often extraordinarily large number of devices and
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volumes of data associated with the Internet of Things (IoT) and, now, ecosystems and digital
business.
A complex technology and discipline, it appears to be stalled within the broader market so we
continue to hold its current position on the Hype Cycle. We anticipate an acceleration once end
users demand the extreme scale information-based B2B, cloud-to-cloud, hybrid and network-tonetwork (N2N) collaboration, and therefore ready to address the challenges of interenterprise data
sharing. It is quite possible that blockchain will be a general purpose technology (GTP) that acts as
a catalyst for the development and deployment of these more specific purpose technologies. We
anticipate these requirements to stem principally from the focus on digital business and supporting
partner ecosystems.
User Advice: Select multienterprise MDM solutions based on current market offerings that target
specific business processes (such as VMI, claims adjudication and so on) or non-industry-specific
solutions (e-invoicing). Some vendors will incorporate a multienterprise data model as part of this
and will, therefore, be capable of supporting a multienterprise MDM program. These vendors are in
the minority and their technology maturity is quite low. Until or if these offerings mature, use
traditional MDM solutions and disciplines, because they can scale upward to a degree and leverage
existing business process networks, hubs or other integration initiatives. At that point, more
multienterprise business applications may emerge that are built on multienterprise data models and
MDM. Should this occur, internal legacy MDM solutions, MDM capability within legacy applications
as well as legacy point-to-point integration tools could be retired.
Note that your need for enterprise-centric MDM is likely to decline as multienterprise MDM grows,
but it will not go away entirely. This will depend on where the bulk of your business is operated from,
on the grid or network or behind your firewall. Vendors do not yet generally offer a stand-alone
multienterprise MDM capability; it is often driven by a specific focus on industry business process
needs or sets of processes/integrations — such as Liaison Technologies' Liaison ALLOY Platform
for Healthcare offering for collaborative data integration and management across healthcare
ecosystem networks and, more recently, SAP's newly introduced Asset Intelligence Network
solution for the energy industry's data-sharing needs.
Business Impact: Achieving semantic consistency for data across organizational boundaries can
be approached by a variety of means, inclusive of point-to-point and ad hoc methods (greater in
number and take substantial effort to maintain) to advanced APIM and iPaaS offerings making use
of AI and ML to multienterprise MDM. The primary benefit of multienterprise MDM over other forms
of integration/governance is scalability and automation. Multienterprise MDM supports a much
more efficient and effective manner of integration and governance of semantics across the network
of enterprises collaborating in multienterprise business processes. This discipline and its supporting
technology together are enablers for the more complex shared or collaborative business processes
delivered with multienterprise applications. Without multienterprise data models and MDM, such
applications do not scale well when built on top of traditional enterprise-centric data models. The
inconsistency in design typically doubles to triples the amount of data needed to ensure process
consistency.
Benefit Rating: High
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Market Penetration: 1% to 5% of target audience
Maturity: Adolescent
Sample Vendors: E2open; Hubwoo; IBM; Liaison Technologies; One Network; SAP; TrueCommerce
Recommended Reading: "Supply Chain Brief: Update on the Multienterprise Business Process
Evolution"
"How to Architect a Multicloud-Capable Hybrid Integration Platform"
"Market Guide for HIP-Enabling Technologies"
Digital Twin
Analysis By: Alfonso Velosa; Marc Halpern; Benoit J. Lheureux
Definition: A digital twin is a virtual representation of a real object. Digital twins are designed to
optimize the operation of assets or business decisions about them, including improved
maintenance, upgrades, repairs and operation of the actual object. Digital twins include the model,
data, a one-to-one association to the object and the ability to monitor it.
Position and Adoption Speed Justification: The idea of modeling the operational behavior of
things and processes continues to gain traction.
■
For operators of assets (aircraft, buildings, power plants, windmills), digital twins are starting to
gain adoption. Their primary near-term use is lowering maintenance costs and increasing asset
uptime.
■
For product OEMs, digital twins are beginning to proliferate for connected products (cars, lights,
stereos). The primary near-term use of digital twins is differentiation and to help the enterprise
manage warranty costs, support channel partners and better understand customer experiences.
Hundreds of millions of things will have digital twins within five years.
The digital twin profile has moved closer to the Peak of Inflated Expectations, in part due to heavy
promotion by technology and service providers. Although about 5% of enterprises have started
implementing digital twins, less than 1% of assets have digital twins.
User Advice: CIOs should identify and prioritize opportunities to use digital twins for business
outcomes. To do this, consider the following:
■
Business outcomes: Determine with business leaders the outcomes (financial, innovation,
productivity) they hope to realize by exploiting digital twins. Leverage design thinking to identify
potential business models.
■
Objectives: Work with IoT teams to review your strategy and establish an IT vision for digital
twins. Align it with the enterprise's digital transformation strategy.
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■
Technology: Start with asset models based on key business uses. Build the system
representation, applying physics and function features as appropriate. Determine what data is
necessary to "feed" the models and the types of analytics needed. Use standards where
possible, but don't let their dearth limit innovation.
■
Stakeholder engagement: Engage the business unit to build their business twin strategy. This
may require discussions on the nature of digital twins, their value, and issues such as the cost
of software asset life cycle management. Use design thinking exercises to help develop the
models and user experience.
■
Digital ethics: Work with business and legal teams to establish a policy on ownership of the
digital twin models and data, as well as who may participate. Ensure this digital ethics policy
helps engage partners and customers about what data may be shared and monetized.
■
Business case: Align with business objectives, to identify a portfolio of digital twin initiatives that
provide short (~1 year) and midrange (~5 year) paybacks.
■
Risk analysis: Create a threat and opportunity analysis of the current business ecosystem,
incorporating digital twin development by competitors or partners.
Business Impact: Digital twins are transformational as they enable business to optimize or
transform their current business models. In the next decade, digital twins will become the dominant
design pattern for solutions.
For example, they enable superior asset utilization, service optimization and improved customer
experience. They create new ways to operate, such as consumption of physical outcomes instead
of the capital expenditure acquisition of industrial assets. And they will open up new ways to
monetize data.
Digital twins will challenge most enterprises to change their thinking from a hardware-centric to a
hardware-plus-software-centric perspective. This includes the implications on operating business
models, product management costs, and risks on unethical data use.
Finally, digital twins' impact will extend beyond assets. People within the supply chain are currently
being modeled and analyzed. The digital twin of organizations has been used to optimize the
business decisions for customer experience, cost optimization and portfolio management.
Benefit Rating: Transformational
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Sample Vendors: ANSYS; Cognite; Dassault Systèmes; Flutura Decision Sciences and Analytics;
GE Digital; IBM; Microsoft; Particle; PTC; Siemens PLM Software
Recommended Reading: "Five Approaches for Integrating IoT Digital Twins"
"Exploiting Digital Twins to Drive Ecosystem Strategies"
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"Four Best Practices to Avoid Digital Twin Failures"
"Digital Twins Will Impact Economic and Business Models"
"Innovation Insight for Digital Twins — Driving Better IoT-Fueled Decisions"
"Top 10 Strategic Technology Trends for 2018: Digital Twins"
All-in-One Supply Collaboration Platforms
Analysis By: Kaitlynn N. Sommers
Definition: An all-in-one supply collaboration platform is a multienterprise network plus a suite of
applications that comprehensively support all interactions with all suppliers across the various
process domains. This includes order management, e-invoicing, supply chain visibility, collaborative
planning, risk assessment, complaint management, quality management, service entry, shopping
cart, sourcing and supplier information management. Application platform as a service (aPaaS) may
be included to support extensibility.
Position and Adoption Speed Justification: Gartner continues to see demand from buying
organizations for packaged, horizontal portals that provide a single, "go to" place for all interactions
with suppliers. The demand is not just for single-sign-on and user authentication capabilities, but for
a broad platform that supports all collaborative supplier processes. Quality, product design, supplier
information and risk management, sourcing, demand signal and order management, planning,
transportation management, supply chain finance, e-invoicing and indirect P2P suites are all in
scope for this type of solution. Buyers seek these horizontal portal "suites" to replace aging,
proprietary portals and to automate and digitize paper-based processes. They want something
simple to deploy, and the "all in one" marketing message used by several vendors resonates with
them. Although technology vendors are rising to meet demand, they all offer only partial solutions
now, and roadmaps for future development.
As it is currently conceived, the horizontal supplier portal market will fail. There is no suite on the
market today that has applications for every domain relating to supply chain collaboration, as it
difficult for a single technology vendor to deliver strong products across a broad spectrum of
processes. Additionally, most buyers have domain-specific point solutions in place and face internal
resistance from stakeholders to replace them. Integration of these existing solutions into a single
horizontal platform that offers the missing modules requires master data management, data
mapping, integration, and upgrade coordination.
User Advice: The conceptual simplicity of an all-in-one supply collaboration platform is appealing,
but there is a trade-off for that simplicity. No single vendor provides best-in-class support for the
broad spectrum of processes associated with supplier collaboration, and product capabilities are
uneven.
Application leaders modernizing procurement should:
■
Use Gartner's Pace-Layered Application Strategy to help decide when differentiating and
innovating functionality is and is not needed.
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■
Deploy solutions that support logical clusters of multienterprise processes — for example all
things finance or procurement — rather than seeking an "all in one" solution.
■
Carefully vet the capabilities of any prospective supplier portal solution in terms of applications,
business services, tenancy, B2B messaging, delivery options (on-premises, cloud or hybrid),
multichannel integration and security.
■
Organizations with multiple ERP systems must consider the cost and difficulty of integrating a
supply collaboration platform to each ERP.
■
Consult with current suppliers for input on vendor selection. The fees, scalability and user
interface vary widely across vendors.
■
Consider adding contractual obligations for supplier support in your all-in-one supply
collaboration provider contract's terms.
■
Pay special attention to vendor viability and long-term roadmaps to reduce risk. The broad
scope of the solution and extensive involvement of suppliers make switching costs for supply
collaboration solutions of any scope, high.
Business Impact: The notion of a single place in which to interact doesn't add as much value as
one might think, because the users differ from one domain to the next. For example, the individuals
working on quality issues will not be the same people as those checking the status of invoices.
There really is no business case — other than convenience for IT — for giving a supplier's quality
professionals and accounts receivable managers the same URL to interact with customers.
Benefit Rating: Low
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Sample Vendors: E2open; JAGGAER (POOL4TOOL); LiveSource; SAP (Ariba); SourceDay;
SupplyOn; Tradeshift
Recommended Reading: "Supply Chain Brief: Supply Chain Convergence — End-to-End Risk
Management Becoming a Must-Have Capability"
"Technology and Solutions for Supply Chain and Operations Primer for 2018"
"Explore and Understand Gartner's Supply Chain Management Applications Stack"
Cloud-Native CAE, Simulation and Virtual Prototyping
Analysis By: Marc Halpern
Definition: Cloud-native computer-aided engineering (CAE), simulation and virtual prototyping,
architected to run efficiently on the cloud, offer the ability to predict, understand behavior or virtually
experience any modeled system or process for any given environment within which it operates.
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Position and Adoption Speed Justification: A few leading edge commercial vendors and
university researchers have been commercializing these capabilities. Both cloud-native applications
and CAE software are not new. The evolution is architecting cloud-native CAE and simulation
software. Leading edge commercial vendors and universities initiated the new platform. The growth
of digital business, particularly the potential for cloud-native CAE to support digital twin strategies
heightens the interest in this technology. However, it is expanding in the market slower than
expected due to functionality gaps and ingrained user behaviors, although it provides more
flexibility and access to compute power. Some CIOs will have concerns about security, performance
and overall costs.
User Advice: CIOs should question whether those capabilities are cloud-native or mainstream
software ported for cloud use. A cloud-native capability should run more efficiently on the cloud
than mainstream software that was ported to run on the cloud, but was not architected for it. IT
organizations must reconsider their positions regarding security risks associated with such cloudnative applications. If they still conclude that this is a risk, they should explore options to mitigate
those risks, since the benefits of cloud-native design applications are significant. If corporate policy
prohibits such software, the IT organization should encourage a review of those policies.
When CIOs become comfortable with adopting such software, they should perform total cost of
ownership (TCO) assessments to compare the costs with owning on-premises software before
adopting. If the TCO for cloud-native design software is higher than on-premises costs, the
company should include the value of the business benefits as part of the analysis. For this category
of software, IT organizations must also study the cost of execution time for simulations since cloud
service providers charge for the use of cloud infrastructure.
Some early users report that cloud infrastructure as a service (IaaS) providers charge for usage
based on the priority that users assign to the simulation and the level of use for the cloud services.
The higher the priority and use level, the higher the cost. However, since several vendors have
established partnerships with IaaS providers to enable more predictable costs, the IT organization
should identify the presence or nature of such relationships with IaaS providers, if those
relationships exist.
When adopting this technology for use with digital twins, users must carefully consider the degree
of detail that makes sense to gain insight that supports key design, maintenance, or product life
cycle decisions.
Business Impact: Users should find that throughput for simulations will be faster, because such
software has been architected to take advantage of multiple processors simultaneously.
Collaboration with internal colleagues, suppliers, customers and other value chain partners will
become easier as the new generation of software typically includes collaboration capabilities, online
design communities and social networking. Also, users will likely experience fewer revision control
problems because they are all working with one instance of design content.
For the IT organization, cloud-native software should lower the costs of maintaining the latest
versions of software since the software providers enable the upgrades and cloud facilities typically
have stronger security infrastructure than what most companies provide for themselves.
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Benefit Rating: High
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Sample Vendors: Autodesk; ESI Group; OnScale; Rescale; SimScale; UberCloud
Recommended Reading: "Top 10 Strategic Technology Trends for 2018: Digital Twins"
"IoT Enriches PLM With 360 Degrees of Product Life Cycle Data"
"Innovation Insight for Digital Twins — Driving Better IoT-Fueled Decisions"
"Product Innovation Platforms: The Foundation of Product Design and PLM in the Digital Business
Era"
"Cool Vendors in Product Design and Life Cycle Management, 2016"
Sliding Into the Trough
Cloud-Native CAD
Analysis By: Marc Halpern
Definition: Cloud-native computer-aided design (CAD) is a design software architected to run on
the cloud and is used to create architectural or electromechanical designs. It typically includes
fundamental product data management functions, including revision control and access control for
the design content it creates.
Position and Adoption Speed Justification: Startup companies and mainstream providers of
design software have been introducing cloud-native CAD increasingly but the adoption has been
low despite the excitement. In recent years, all of the new CAD applications that Gartner has seen
are cloud-native. Large companies have continuing albeit diminishing discomfort with having
sensitive design data outside the four walls. Existing investments in current on-premises design
solutions and infrastructure prove to be a bigger inhibitor. Therefore, while they experiment with
cloud-native CAD, they do the majority of their work with on-premises CAD software.
The transformation to cloud-native CAD will take years in large companies because millions of CAD
users have deeply ingrained culture and processes for using on-premises CAD software. However,
cloud-native CAD appeals to small companies because its use requires only a browser, and without
an investment in expensive hardware with extensive computing power. However, cloud-native CAD
developers are still working to catch up with the functionality of the mainstream on-premises CAD
software, therefore, veteran user communities await the confidence that performance and
functionality will meet their needs.
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User Advice: CIOs should encourage users to experiment with cloud-native CAD today and
compare their experiences with their current on-premises software. IT organizations must evaluate
cloud-native CAD against their criteria for IT security as well as performance and functionality. If
they still conclude that this is a risk, they should explore options to mitigate those risks since the
benefits of cloud-native design applications regarding agility and flexibility of use are significant.
If corporate policy prohibits accessing cloud-native software, the IT organization should encourage
a review of those policies. When a manufacturer becomes comfortable with adopting such software,
they should do a total cost of ownership (TCO) assessment to compare the costs with owning onpremises software and weigh the benefits and cost trade-offs of cloud-native CAD versus onpremises CAD before adopting cloud-native CAD. When selecting a partner, users and CIOs must
evaluate the developmental roadmap of the considered cloud-native CAD suppliers as many
suppliers are still catching up in terms of design functionality with mature on-premises CAD
software.
Business Impact: For the IT organization, cloud-native design software should lower costs of
upgrading and maintaining design software. In addition, costs for hardware to run CAD should
decline since truly native CAD software enables scalable performance superior to "thick clients"
needed for on-premises CAD. However, they should also expect to make higher payments to
vendors if they decide to adopt it. The IT organization will likely find that their design data will be
more secure rather than less secure, because there is only one copy of any given design model, and
cloud facilities typically have stronger security infrastructure than what most companies provide for
themselves.
Users will find that collaboration with internal colleagues, suppliers, customers and other value
chain partners will become easier, because the new generation of software typically includes
collaboration capabilities, online design communities and social networking. In addition, users will
likely experience fewer revision control problems as they are all working with one instance of design
content.
Benefit Rating: High
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Sample Vendors: Autodesk; Dassault Systèmes; Onshape
Recommended Reading: "Product Innovation Platforms: The Foundation of Product Design and
PLM in the Digital Business Era"
"Three Styles of Digital Business Platforms"
"Four Best Practices to Avoid Digital Twin Failures"
MPM Frameworks
Analysis By: Rick Franzosa
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Definition: A manufacturing process management (MPM) framework links the virtual world of
product and manufacturing process design to the physical world of manufacturing execution and
transactions, including product and process design, manufacturing operations, material
requirements planning and sourcing.
Position and Adoption Speed Justification: MPM frameworks are evolving as manufacturers
attempt to transition their IT infrastructure from disconnected niche applications used to design
products and processes to a more harmonized environment that streamlines the creation and
sharing of design and manufacturing data. Creating digital consistency across multiple software
applications to streamline processes and information improves cost, time and quality performance.
This concurrently touches business process and IT changes. The end result is a more structured
approach to handling frequent engineering and process changes and managing the synchronization
of multiple bills of materials (BOMs) and work instructions across the life cycle of a product and
manufacturing process.
Vendors are investing in platforms to accelerate the advancement of MPM frameworks, but there is
an absence of true end-to-end solutions. The benefits will increase as frameworks begin to mature.
As implementations take advantage of these new capabilities and manufacturers modify processes
to capture, analyze, measure and archive data, these MPM frameworks will continue to show
progress in benefit realization. In the meantime, specialty vendors continue to fill the gaps among
ERP, PLM and MES. Until there is additional movement on end-to-end solutions, movement on the
Hype Cycle will slow.
User Advice: Manufacturers in all industries can benefit, although the complex engineering and
configure-to-order segments must give this high priority. Given the complexity of their product and
manufacturing process designs, as well as large capital investments in factories and equipment, this
investment can help improve their design and manufacturing activities on a global scale. They must
define best-in-class standardized workflows and data needs of supporting roles in product
development and manufacturing as the first steps to defining the interfaces between PLM, ERP,
MES and sourcing applications. This shouldn't rule out other industries from starting to plan.
Producers in chemicals, pharmaceuticals and life sciences should look at the frameworks that
encompass formulations, recipes, and specifications that describe the product and manufacturing
processes.
This is a long-term investment that IT professionals and business operations managers should plan
in multiple phases, such that each delivers business value and enables subsequent steps. They
should define business performance metrics related to reductions in cost, time and product defects
to monitor progress. Education, services, change management consulting and training will be as
important (or more important) than software when planning for adoption, as will be master data
management/data rationalization tasks. Implementing standard process approaches for the major
touchpoints among ERP, PLM and MES can help accelerate the process as MPM framework
support becomes available.
Business Impact: MPM frameworks streamline the processes and supporting workflows from
design to production, as well as continuously improve design for manufacturability in discrete
industries and process robustness in process industries. Manufacturers seeking to achieve or
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maintain a competitive edge through design and innovation, while consistently manufacturing
compliant products right the first time, must adopt an MPM framework. This can streamline the
process for introducing new products because it reduces the risk of rapidly introducing changes to
works in progress. It also provides design visibility to manufacturing planners and feedback from the
factory floor to engineering to continuously improve design for manufacturability.
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Sample Vendors: Anark; AspenTech; AVEVA; Dassault Systèmes; Geneo; iBASEt; Oracle;
Proplanner; SAP; Siemens
Recommended Reading: "Market Guide for MPM and MbM for Discrete Manufacturing"
"Market Guide for MPM and MbM for Process Manufacturing"
"How to Deploy Manufacturing Process Management for Digital Manufacturing"
"Innovation Insight: Manufacturers Need MPM and MbM to Innovate Digitally Enabled Design
Through Production"
"Digital Manufacturing Requires a New Look at Old Systems"
Product Innovation Platforms
Analysis By: Marc Halpern
Definition: A product innovation platform is an IT infrastructure intended to cultivate continuous
creativity, inspiring new and better products throughout full life cycles and across generations of a
product.
Position and Adoption Speed Justification: The market recognizes a product innovation platform
as the emerging means of delivering product design and life cycle management capabilities. PLM
and design software providers have been evolving product innovation platforms by incorporating
layers of analytics, visualization, social networking, and modeling and simulation (M&S) tools into a
single infrastructure — one infrastructure for each vendor. These platforms are becoming
increasingly flexible and extensible, albeit more progress needs to be made on their openness.
Manufacturers are demanding the capabilities but few, if any, have adopted such platforms because
they are so deeply invested in their current product development and PLM environments, it is hard
for them to transition to the product innovation platform vision. Therefore, adoption has been slower
than Gartner expected.
User Advice: Chief technical officers and IT managers should be aware of product innovation
platforms as these platforms continue to evolve and are adopted to enhance product design,
manufacturing activities and service. Since these solutions run in the cloud, performance will be a
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priority, particularly for graphics-intensive visualization, and modeling and simulation activities.
Therefore, IT leaders should be investing in virtualization that utilizes graphics processing units
(GPUs) as well as CPUs to maximize performance and utilize compute resources as much as
possible. IT leaders must also be careful of costs, because the licensing of cloud-based solutions is
more expensive than on-premises solutions, accumulated over a period of five years or more. When
assessing the financial feasibility, users must weigh the trade-offs of higher usage costs versus
potentially lower infrastructure and support resource costs, plus added business value.
As a caution, Gartner clients using multiple product innovation platforms report that the platform
strategy complicates the ability to share content across them because the platforms are not yet
open enough. Given insufficient openness of some of the platforms, adopters risk "lock-in" over
years of use. Therefore, users must push for greater use of standards and better interfaces to
reduce the IT complexity of working across them. In addition, users should be cautious about
adopting subscription licensing with these platforms because subscriptions give vendors more
leverage over users when negotiating contract renewals. As a precaution, adopters should negotiate
terms and conditions that support an exit strategy when entering a vendor relationship.
Business Impact: Product innovation platforms give manufacturers greater agility when adapting to
changing business conditions. For example, business intelligence supported by big data integrated
into the design environment helps designers and engineers make more market-savvy, cost-effective
design decisions. Today, new product development teams often understand market intelligence and
cost implications after they have made crucial decisions, resulting in more product costs and
delays. Social networking enables a greater infusion of new ideas and domain expertise earlier in
the product development phase. Therefore, with product innovation platforms, engineers, designers,
scientists, sourcing experts, manufacturing experts and others will find it easier to leverage
resources to help them in their work. Cloud support, enhanced by the virtualization of compute
resources (employing GPUs as well as central processing units), will enable more realistic
simulations of design alternatives — further enhancing insights about PLM-related priorities and
improving decision making. Cloud enablement will also give stakeholders greater flexibility to enable
PLM-related activities at any time and from anywhere.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: Aras; Autodesk; Dassault Systèmes; Onshape; Oracle; SAP; Siemens
Recommended Reading: "IoT Enriches PLM With 360 Degrees of Product Life Cycle Data"
"Predicts 2017: Digital Business Will Pervasively Transform Product Design and PLM Across
Manufacturing"
"Digital Business Is Transforming New Product Development Priorities"
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"Product Innovation Platforms: The Foundation of Product Design and PLM in the Digital Business
Era"
IT/OT Convergence and Alignment
Analysis By: Simon F. Jacobson; Kristian Steenstrup
Definition: Gartner defines operational technology (OT) as the hardware and software that detect or
cause a change of state through the direct monitoring and/or control of physical devices, processes
and events in the enterprise. IT/OT convergence reflects the growing use of standard IT
technologies within OT. Alignment is the necessary organizational response to these changes.
Position and Adoption Speed Justification: In the past 12 months there has been an increased
pace of change and urgency to accelerate factory modernization and industrial automation projects.
A sizable backlog of initiatives that improve the accessibility to and analysis of a broader range of
production data — as well as automation and control of production processes — is finally receiving
attention.
Integrating transactional and standards-based IT applications with the time-based, granular and
dynamic OT world has been happening for over a decade through Internet-Protocol (IP)-based
networking and nonproprietary operating systems. Today's efforts are focused on creating smart
factories by augmenting OT with IoT. It is being done through analytics, virtualization/cloud and
sensor-based technologies. This convergence and integration of IT and OT is much further along
than the alignment. Alignment is the process of synchronizing standards, support processes,
security and architecture plans to build in compatibility between the IT and OT systems. This,
however, does not necessarily mean that IT departments and OT management groups need to be
one integrated organization. Nor does integrating IT and OT systems have to be dependent on
alignment (but, ideally, it should).
Instead, alignment adjusts the relationship among traditional custodians of OT systems and other
groups that deal exclusively with technology — usually the IT department. As factory and plant-level
modernization initiatives evolve, the alignment becomes critical. The preponderance of OT has been
managed outside of corporate IT and the line of business. Each group works differently and has
unique lexicons and motivations that have created large gaps in knowledge management (e.g.,
processes, standards and documentation), security, physical connectivity and governance.
Progress continues and manufacturers are evolving their disciplines as the importance of an
integrated manufacturing capability that can respond to the supply chain increases. Also technology
providers have helped expose the issues (and the subsequent need to assimilate toward a solution)
too. Hybrid projects and common standards continue to emerge but are often hostage of culture at
both a corporate and plant level. Many initiatives are still site-centric versus fully documented and
deliberate. Scalability, cost and skills need attention as knowledge management hangs in the
balance. Because of this until the corporate realization of the impact of IT/OT convergence runs the
risk of being recognized long before alignment is undertaken.
User Advice: It is recommended that supply chain leaders responsible for manufacturing operations
strategy and performance seeking to converge and align IT and OT take the following actions:
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■
Steer governance toward responsibility and scale, not ownership and accountability. Realize
now that any desired success with managing assets, assimilating to cloud computing and
improving the overall reliability of manufacturing operations long term means bringing these
domains together through new organizational models and technology roadmaps
■
Pinpoint where the IT organization should collaborate with the OT worlds in supporting and
managing OT systems, and create joint integration plans where data flow benefits need to be
realized. Consortia reference architectures and industry standards each can play a role.
■
Determine whether existing talent can support future architectures that blend IT and OT
components. Alignment, not duplication, is key, as not all practices and processes can be
transferred to the OT world and, likewise, to the IT world.
Business Impact: IT and OT are inseparable, and the criticality of line of business giving it added
attention is massive. Their alignment and integration are a paradigm shift that emphasizes culture
and governance ahead of technology, impacting:
■
Risk: High — Not acknowledging the convergence and alignment between the two domains
with overall manufacturing strategies will perpetuate cultural and systems divides that increase
downtime and create security concerns.
■
Technology intensity: High — Standards-based IT technologies have existed in the OT world.
However with augmentation by IoT, incorporation of advanced analytics, and the need for
increased security managing the increased complexity, pace of change and the demands on the
information that is generated by OT systems ups the intensity.
■
Organization change: High — Alignment of IT and OT does require new and/or innovational
organizational designs, job description changes, certifications, reskilling and recruitment of new
competencies
■
Process change: High — Realignment of responsibility across IT and groups traditionally most
involved in managing OT will result in changes to security, technology acquisition and upgrades,
knowledge management and governance.
■
Competitive value: Medium — Increasing leverage of information assets and operational
efficiency improvements enables better availability of factory/plant assets — and data which
can open, new collaboration opportunities.
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: AVEVA; Brock Solutions; Factora Solutions; GE Digital; Inductive Automation;
Microsoft; PTC; Rockwell Automation; Siemens; Tata Consultancy Services
Recommended Reading: "2018 Strategic Roadmap for IT/OT Alignment"
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"Show the Value of OT and IT Alignment, and Realize Digital Business Results"
Cloud Computing in Manufacturing Operations
Analysis By: Rick Franzosa; Simon F. Jacobson
Definition: Cloud computing is a style of computing in which scalable and elastic IT-enabled
capabilities that support manufacturing operations are delivered as a service using internet
technologies.
Position and Adoption Speed Justification: Business initiatives for connected factories and digital
supply chains — as well as ongoing advances of IoT and software provider offerings — have made
cloud inevitable in manufacturing operations. This continues to accelerate cloud computing's
positioning on the Hype Cycle:
■
Although there's acknowledgment of lower initial cost the demonstration of the long term total
cost of ownership (TCO) benefits are still being evaluated and established by customers and
vendors. Buyers have apprehensions for control of cloud costs and vendor monetization of
data. We anticipate as pricing models evolve, user buying habits will change and adoption will
further increase further.
■
Provider offerings have evolved across all segments spanning quality management,
manufacturing execution systems (MES), production planning and varying degrees of analytics.
Edge computing is also maturing. Not to be overlooked are new market entrants that are taking
a "cloud-first" approach — often building their applications in Amazon or Microsoft's cloud.
Regardless of progress, concerns over data security, latency, and exchange (between onpremises and cloud) persist;— as do fears related to those on scalability and service levels
Cloud's progress across manufacturing operations is varying by industry, use case, and enterprise.
Early adoption has been seen in multisite activities spanning quality (internal and supplier-facing
processes) and asset performance management (APM). It's extremely unlikely that manufacturers
will soon totally abandon on-premises models for mission-critical applications, so the hybrid cloud
model will persist.
User Advice:
■
Identify use cases for cloud computing with broad applicability to enhance existing process
capabilities and overcome IT skills deficiencies across multiple sites without compromise to
capacity utilization or quality. Minimize disruption through hybrid deployments that leverage
existing on-premises systems.
■
Demand that candidate vendors provide clarity around their cloud offerings — especially the
pricing, data ownership and long-term TCO — but also focus on service and security models as
well.
■
Establish a clear understanding of the expected benefits of a move to the cloud. Benefits and
trade-offs (how much control to give up) should be well understood. Cloud computing involves
many components (spanning software as a service through business process as a service), and
some aspects are more mature than others.
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Business Impact: Cloud computing innovates manufacturing new services for technology
consumption and information access models, and contributes toward a flexible and agile
manufacturing network with the following impacts:
■
Risk: Medium — Risk is variable depending on the characteristics and nature of the processes
and/or cloud service used.
■
Technology intensity: High — Over time, the cloud will reduce on-premises IT total cost of
ownership (hardware and personnel). Other factors such as service-level agreements and
security. will require more consistent attention and can change the nature of the relationship
with key providers.
■
Organization change: Low — Specializations will be needed based on capabilities enabled by
cloud computing.
■
Process change: High — Clarity on functional requirements are needed to avoid the mass
customization of cloud-based applications to site-specific needs. This includes clearly defined
change controls and an understanding of touchpoints with other functions (e.g., planning and
logistics).
■
Competitive value: Medium — Will increase an organization's ability to improve processes,
identify variability and scale across internal and external manufacturing partners.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Early mainstream
Sample Vendors: 42Q; Amazon; iTAC Software; InfinityQS; Microsoft; Oracle; Plex; SAP; Siemens;
Tulip
Recommended Reading: "Toolkit: Defining the Functional Requirements for Manufacturing System
Selection"
"Cool Vendors in Manufacturing Operations, 2017"
"Survey Analysis: Multisite MES Delivering Benefits Today With Cloud(s) on the Horizon"
"Supply Chain Management Moving to the Cloud: The Steps to Take and the Benefits You Can
Expect"
Model-Based Manufacturing
Analysis By: Rick Franzosa; Marc Halpern
Definition: Model-based manufacturing (MbM) refers to the use of digital models, rather than
analog content, to plan, validate and manufacture products.
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Position and Adoption Speed Justification: MbM is rooted in fragmented and disconnected
applications such as: computer numerical control (CNC) machining, machining validation software,
tools to simulate and visualize factory operations, programmable logical controllers (PLCs), robotics
programming tools, factory design applications, drawing and documentation creation and
management software, and software for managing product and processing bills of materials
(BOMs). Niche vendors in these application areas have been acquired by larger vendors.
Capabilities have increasingly been consolidated into a common infrastructure that supports a
broader range of manufacturing technologies.
Integrated MbM environments are relatively new. Interest in digital twins is also elevating the interest
in MbM. Systemic adoption has been slow, except by early adopters such as large aerospace and
defense, automotive and industrial machinery. These companies face challenges implementing
MbM. although they are making progress. Vendors that support process manufacturing are also
developing MbM capability. For example, model-based predictive control is something that many of
the pharma companies' quality by design (QbD) initiatives are built around to ensure validated
processes and critical control points are known and monitored. Although MbM is an expensive
investment compared with other IT opportunities, the costs to implement it are declining as
experience with it grows and software providers learn to deliver the capabilities more economically.
User Advice: IT professionals responsible for supporting manufacturing operations and
manufacturing executives in industries where production involves large capital investments should
be developing a roadmap and integrated architecture for MbM. Manufacturing managers should
team up with involved IT personnel to phase in the implementation of MbM.
MbM should be phased in rather than adopted as a single, large IT project.
Infrastructure implementers must plan for interfaces to PLM, manufacturing execution systems
(MES) and ERP software so that key content, such as computer-aided design (CAD) models, BOMs/
recipes, factory assets, material masters and lead-time information are available to support MbM. In
some instances, core applications might need replacement. This alone will have its challenges;
organizations will need to prepare their users for significant configuration effort and less-thanoptimal results as these tools eventually become more robust. There will also be a significant effort
in data cleansing, mapping and rationalization between systems that have been interfaced/
integrated at a high level that will now be more intertwined in a true MbM environment.
Business leaders need to support the effort and explain at a corporate level why MbM is important.
They need to adjust job performance metrics in ways that encourage manufacturing personnel to
adopt MbM, but also collaborate with R&D and engineering to ensure that efforts are aligned. The
implementation of MbM is imperative to providing a strong foundation to support of IoT/Industrie
4.0 initiatives connected to design/NPI. As the adoption of MbM grows, there will also be
opportunities to encompass these benefits across the supply chain from supplier management
through customer support.
Business Impact: Manufacturers design and simulate their manufacturing processes, material
flows and worker movements, and factory layouts. MbM can save substantial money if it detects
defects in factory layout or manufacturing operations. Some manufacturers that have made the
investment report a 20% to 30% reduction in the cost to scale up production. It most visibly
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benefits companies when users detect errors in the factory layout that would normally involve
significant cost and time to correct. It also saves considerable cost and time by reducing the
number of iterations necessary to program PLCs, machine tools and robots, and lays the
groundwork (foundation) for the implementation of IoT in the future.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: AspenTech; Autodesk; Dassault Systèmes; iBASEt; PTC; Schneider Electric;
Siemens
Recommended Reading: "Market Guide for MPM and MbM Technology for Process
Manufacturing"
"Market Guide for MPM and MbM Technology for Discrete Manufacturing"
"Innovation Insight: Manufacturers Need MPM and MBM to Innovate Digitally Enabled Design
Through Production"
Internet of Things for Manufacturing Operations
Analysis By: Simon F. Jacobson; Scot Kim; Eric Goodness
Definition: The Internet of Things (IoT) is the network of dedicated physical objects that contain
embedded technology to communicate, sense or interact with their internal states or the external
environment. IoT comprises an ecosystem that includes assets and products, communication
protocols, applications and data and analytics. In manufacturing, operations IoT is a core building
block for digital supply chains and smart factories.
Position and Adoption Speed Justification: The IoT has a clear home in manufacturing operations
and its hype is massive. The spread of use cases across asset performance, energy management,
quality, traceability and improved visibility of production performance carry easy to quantify benefits
that appeal to most production styles and environments (i.e., "greenfield" or "brownfield" capacity).
It is also continually fueled by the ever-expanding pipeline of vendor startups, platform marketing,
consortia (and their reference architectures and implementation guidelines), and lowered costs of
sensors and edge devices. Not to be overlooked is IoT as a cornerstone of several nationally driven
industrial policy initiatives like Industrie 4.0 and Made in China 2025.
For these reasons the IoT hype is not fading anytime soon — but in 2018 it has moved further
toward the trough. Despite the IoT's crux/key position/center of future production capabilities that it
carries enterprises vary in their adoption rates, it is being held back by the following such as:
■
Misaligned benefits and objectives between operations and supply chain. High IoT activity
(pilots, etc.) does not translate into scale. A large majority of successes remain isolated. This
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site-centric approach to IoT perpetuates a furthering of shop-floor to top-floor endeavors, for
cost and quality-based improvements at risk of losing out of productivity gains, derived from
connecting production performance and automated processes with the end-to-end supply
chain.
■
Cost and scale. Site-level projects might continue to rise, yet the variances of IT and OT
systems, in and across sites, often require different interfaces and integration points. Accessing
data from a lathe, extruder or edge device, all require different approaches. This common
happenstance hinders repeatability and scale, while driving up costs and skepticism. It has also
exposed the need to invest in security, integration, ensure repeatability and/or scale of solutions
and develop integration standards so the ease of access that is promised at the onset of several
initiatives can be realized.
■
Technology immaturity. IoT offerings from established and new entrants continue to proliferate
across the market. The matching of capabilities from technology against the roadmaps
marketed, as well as the viability of all offerings continue to be questioned.
■
Uncertainty of how to manage the increased volume of data. Architecting at the edge and
accessing more granular data sources provide value only when there is a planned and
purposeful use of the data and it is tied to the processes it represents. Additionally, issues on
data ownership and monetization rights are just starting to percolate. This can stymie
ecosystem cultivation and the advancement of IT/OT and line of business collaboration models.
If these challenges are not overcome, the IoT in manufacturing operations could languish in the
trough in the coming years. This will prolong a majority of companies from realizing their
transformative potential.
User Advice: Manufacturers that simply view the IoT as a site-level phenomenon, risk losing out on
larger opportunities. They should:
■
Segment use case pursuits into those that will enhance the core of operations as well as those
that will foster future innovation and process capabilities.
■
Break the shackles of site-specific benefits with a stage-based maturity continuum that
integrates manufacturing and supply chain performance objectives. Deconstructing IoT and
then mapping data, processes and deployments in line with performance maturity will
accelerate toward a networkwide view needed for long-term planning, success and connecting
manufacturing with customer value.
■
Ensure alignment between IT, OT and line-of-business stakeholders to lessen concerns on
scalability and integration costs with clear documentation of projects. This ensures accurate
budgeting of resources, identifying the role of standards (e.g., MTConnect, OPC) and
clarification of expected benefits.
Business Impact: The IoT will optimize product supply through improving reliability across
networks of factories that are both vertically and horizontally integrated with the supply chain,
enabling new ecosystems, transparency of processes and information and unique revenue building
opportunities.
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Risk: High — Using IoT to augment OT exposes challenges of scalable implementation and
business cases (e.g., time and capital outlay) as well as security and compliance challenges.
Additionally, the engineering and new equipment and processes, against ongoing pressures to
reduce costs and boost margins, require strong prioritization of projects and validation of use
cases.
■
Technology intensity: High — Both IT and OT are impacted. New ways of automating
information and process flows may be required. Underestimating integration requirements is a
risk.
■
Organizational change: Medium — Over time, there will be some revised organizations with
new specializations as IT and OT converge and align, and not just integrate.
■
Process change: High — Processes and operating models for information access and
ownership will affect intercompany and intracompany collaboration. This impacts improved
decision making for servicing demand, manufacturing flexibility, external collaboration, products
and services and data access and/or security policies.
■
Competitive value: High — There is faster decision making based on available information,
new options for customer intimacy and monetization.
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: Cisco; GE Digital; Inductive Automation; Microsoft; PTC; Rockwell Automation;
Savigent Software; SAP; Siemens; Tulip
Recommended Reading: "Four Best Practices to Manage the Strategic Vision for the Internet of
Things in Manufacturing"
"Internet of Things Primer for 2018"
"Harvest the Value of Smart Manufacturing in the Supply Chain, Not the Factory"
"Magic Quadrant for Industrial IoT Platforms"
"Implementing and Executing Your Internet of Things Strategy: A Gartner Trend Insight Report"
3D Printing in Manufacturing Operations
Analysis By: Rick Franzosa; Pete Basiliere
Definition: 3D printing (3DP) in manufacturing operations refers to the use of 3DP to produce a
finished item, subassembly or intermediate product. It can also be used to print tools, fixtures, dies
and molds used during the production of finished goods.
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Position and Adoption Speed Justification: The growth of 3DP in manufacturing remains strong,
with early adopters showing how the technology can change both design and production
processes, and thereby supporting Industry 4.0 implementations. For example, rapid prototyping
has always been a solid use of 3DP and remains a stronghold for the technology. The concept of
augmented manufacturing, which leverages 3D printing to make conventional assembly and
production operations more cost-effective, is well on its way through the continuum.
Commercial-scale production environments — high-mix, high-volume, for example — remain
constrained by material availability/integrity and cost. These operations will be challenged to change
from existing methods for finished goods or intermediaries until 3DP can demonstrate a positive
impact on TCO and productivity.
The main adopters for producing intermediates or finished goods with the technology are discrete
industries. Here, use cases for cost and time reductions (without compromise to quality) are
expanding beyond prototypes to component production, tooling and aftermarket parts. In process
segments, adoption is accelerating because the technology offers an alternative to carrying large
spare parts inventories for capital assets.
User Advice:
■
Take an expansive view of the cost impact of 3DP's use in manufacturing. Identify whether the
reduction of development and production costs, as well as increased quality, will be offset in
high procurement and logistics costs.
■
Identify, during product planning and design phases, whether 3DP is a suitable means, or if
traditional manufacturing processes should be considered.
■
Identify to what degree 3DP can be integrated into the existing processes (PLM, SCM) by
applying criteria such as size/complexity of 3D-printed items, required raw materials, material
handling constraints, energy consumption, software/hardware integration
■
Include all stages of the product life cycle when considering use of 3DP, not just the production
process design. In some cases, the value will be in the production of parts that cannot be made
by any other means. In other cases, 3DP may play a minor role in the product life cycle.
■
Find hybrid approaches that insert 3DP for certain production steps or finishing close to
customers, including outsourcing/3DP service bureaus, etc.
■
Ensure that you do not overlook how demand is translated and loaded into manufacturing.
Supply chain and IT leaders will need to support 3DP integration with end-to-end supply chain
to meet specialized demand in a timely manner to ensure on time and on budget delivery.
■
Evaluate the impact of 3DP on your extended supply chain, and not just the sourcing of parts
but also the impact (time and cost) that 3DP could have on supply depots.
Business Impact: Understand the following impacts of 3DP on manufacturing operations:
■
Risk: High — IP protection is required and experimentation is needed to identify the product
lines and production styles for which 3DPs are most suitable. Examine whether co-development
with suppliers can ensure reliability of supply and materials.
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■
Technology intensity: Medium — Technology intensity ties more to the materials that are used
in the production process. Structural integrity is a massive factor when considering quality over
a product's life cycle. Investment in libraries to maintain geometric product representations
could be needed.
■
Organization change: Low — In the short term, roles and job descriptions do not change.
■
Process change: High — 3DP can remove multiple steps in bringing a product to market. It will
change several processes spanning manufacturing network design and logistics networks, but
will also expand into material sourcing and supplier management.
■
Competitive value: High — Improved cost position, higher design reuse, faster product launch
and introduction, better aftermarket services, improved product quality, and greater consistency
between contract manufacturers all indicate high competitive value.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: Azoth; Dassault Systèmes; SAP; Siemens PLM Software
Recommended Reading: "Adopting 3D Printing for Industrial Parts Has Key Impacts on CAD and
PLM Priorities"
"3D Printing Opportunities and Uses Primer for 2017"
"Survey Analysis: Capital and Operating Costs Are Driving and Inhibiting Enterprise 3D Printer
Sales"
"What 3D Printing Means for Your Supply Chain"
Cloud-Native PLM Applications
Analysis By: Janet Suleski
Definition: Cloud-based product life cycle management (PLM) applications refer to PLM software
that is designed to run on cloud infrastructures.
Position and Adoption Speed Justification: Cloud-based PLM was introduced in the late 1990s
as a software-as-a-service model available on either a private cloud or in the public cloud
infrastructure. It gained momentum after Autodesk announced PLM in the cloud as a strategic
direction in 2011. Today, the majority of vendors developing PLM-related applications choose the
cloud as their development platform. These vendors include, but are not limited to, Aras, Arena
Solutions, Dassault Systèmes, Oracle, PTC, SAP, Selerant and Siemens. The interest in cloud-based
PLM is still rising, and adoption is accelerating, though it should be noted that the adoption of
cloud-based PLM applications is not as prevalent in process industries, such as food and beverage,
as it is in discrete industries. Many manufacturers maintain a hybrid approach to PLM-application
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deployments, adopting new PLM functionality in the cloud while simultaneously maintaining
mainstream PLM functionality on-premises. For the near and midterm, expect use of hybrid onpremises and cloud-native approaches to dominate, with a shift toward more use of an expanding
number of cloud-native applications over time.
User Advice: Intellectual property (IP) rights are a big concern across all process and discrete
manufacturing industries. Legal departments in particular may have concerns about product data
being stored in the public cloud. Although data security needs to be addressed, IT and business
executives should not allow it to be a major obstacle to cloud adoption. Some experiences suggest
that the cloud might safeguard IP better than on-premises applications and databases due to the
use of stronger encryption capabilities by service providers. IT and business process teams must
collaboratively define the use cases for cloud-based PLM adoption. In doing so, they must consider
the potential benefits, total cost of ownership and enhancement of process capabilities.
IT strategists across the value chain should first be exploring cloud as a platform to provide
additional support and value to teams without a permanent central location or whose members
travel frequently and are responsible for activities in the field, in factories and with partner firms.
Manufacturers wishing to get started with cloud-based PLM should first adopt it for collaboration
and engineering change workflow. These users get the immediate benefit of accessing PLM from
remote locations to shorten development cycle time. IT procurement specialists should evaluate
long-term total cost of ownership and contract negotiations. Since cloud licensing is most likely to
be SaaS or subscription-based, customers pay for the right to use the software and no longer own
it. Therefore, CIOs must carefully negotiate contract terms and conditions to safeguard their PLM
data. Projecting out five years or longer, vendor pricing for cloud applications may be higher than
today's classic paid-up licensing models.
Business Impact: Cloud computing improves a user's ability to access PLM applications and
collaborate. It streamlines the ability to connect new users to PLM software. Some Gartner clients
report additional benefits in mergers and acquisitions, where cloud applications may allow them to
effectively standardize application access and use across newly acquired businesses. Other
adopters have reported that cloud-based PLM has simplified collaboration across partners,
suppliers and customers. Although multiyear software access costs are likely to increase, IT
organizations should be able to reduce the costs of upgrading to new versions of the applications
and adjust the cost of accessing applications based on fluctuations in need. IT departments should
also remain aware that API marketplaces will be increasingly important as more product-centric
applications move to the cloud. For APIs from marketplaces, IT organizations may want to select
only vendors that have known partnerships with one another so that they do not have to continually
validate data synchronization.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: Aras Corp.; Arena Solutions; Autodesk; Dassault Systèmes; Oracle; PTC; SAP;
Selerant; Siemens
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Recommended Reading: "IoT Enriches PLM With 360 Degrees of Product Life Cycle Data"
"Plan Your Exit When Negotiating a SaaS Contract, or Risk Cloud Vendor Lock-In and Higher
Costs"
"Toolkit: Minimize SaaS Risk and Cost Using This Toolkit to Efficiently Negotiate Optimal SaaS
Contract Terms and Conditions"
"Gartner's Five-Stage Maturity Model for Achieving PLM Excellence in Supply Chain"
"Best Practices to Avoid Obsolescence of PLM Applications and Data"
"Roadmap for CIOs to Harmonize Applications for a Digital PLM Platform"
Industrial Operational Intelligence
Analysis By: Simon F. Jacobson
Definition: Industrial operational intelligence (OI) incorporates and builds on top of the core of
enterprise manufacturing intelligence (EMI) applications by adding workflow, advanced analytics
techniques and data management to improve agility and decision support in manufacturing
operations.
Position and Adoption Speed Justification: In manufacturing operations, OI subsumed and
expands upon the aggregate, visualize, analyze, contextualize and propagate capabilities of EMI
applications. It is now portrayed as the platform for visibility into production performance and
decision support. It allows manufacturers to leverage IT and OT data from multiple production
sources to increase efficiencies and create agility across the manufacturing network.
Also, artificial intelligence (AI) heightens the awareness and importance of OI. Interest and
experimentation with machine learning or predictive and prescriptive analytics reflect the changing
dynamics of this market. The large demand for advanced analytics in manufacturing outweighs the
supply of proven and complete offerings that pushes OI toward the trough. A large portion of
established vendors are either launching their next-generation platforms or integrating different
acquired or repackaged technologies; smaller, industry-specific and/or purpose-built use case
offerings to create data models or contextualized streams of real-time data are taking advantage of
the opportunity.
What will push OI toward the Slope of Enlightenment is the endeavor for a single platform to
manage manufacturing operations in real time. It will facilitate real-time situational awareness,
predictive what-if capabilities, automates data and information flows, and event-driven
collaboration. Operational errors attributed to imperfect information or lack of information at the
point of decision making will mostly be a thing of the past. That is contingent upon a forwardlooking perspective that includes a more holistic and actionable business context of production
information, allowing process stakeholders to continuously improve and optimize operations.
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Broader adoption still faces a slew of hurdles: reliance on legacy solution (Excel) dashboards and
portals, poor integration and alignment of IT and OT, inconsistently defined metrics and work
processes, as well as unwillingness to share data across sites.
User Advice: Identify candidate vendors that can support real-time environments. OI is more than
traditional reporting and isn't easily achieved with traditional business intelligence (BI) applications,
or data warehousing techniques and skill sets.
■
Recognize that deploying industrial OI has both OT and IT impacts and, therefore, requires
governance. It should be undertaken as part of a broader initiative that converges and aligns IT
and OT.
■
Ensure that your industrial OI investment supports information usage, value and dissemination
that matches the speed of your operations by defining how real-time OI needs to be. Consider
what information is required for what decisions and the speed and impact of those decisions on
cost, quality and process capabilities.
Business Impact: OI delivers manufacturers the ability to mine, model, manage and simulate
operations data over an extended hierarchy of time scales and business priorities that go
significantly beyond the production environment. It's the foundation for a closed-loop capability that
enables a knowledge-based enterprise where stakeholders at several levels have access to the
kinds of analytics they would not otherwise have. This creates situational awareness for all
stakeholders and enables faster and better decisions based on high-quality information that's
extracted and distilled from multiple data points.
Critical areas of impact include:
■
Risk: Low — OI builds on existing analytic investments in manufacturing environments.
■
Technology intensity: High — A key capability of industrial OI is to define and maintain
persistent functional and operational models (or relationships) that create understandable
business context for users. It is directed specifically at the day-to-day and minute-to-minute
operational decisions made in the course of the processes that run a business — unlike the
more traditional, offline use of historical data to make tactical and strategic decisions. Advances
in cloud models, mobility, data storage/architecture and analytics applications will further
intensify over time.
■
Organization change: Medium — Currently OI is a decision-support tool and capability that
requires traditional IT and OT factions unite on use cases, knowledge transfer and application
ownership. As decisions become more automated, the organizational impacts will increase. Not
to be overlooked is the development of skill sets for data management by manufacturers.
■
Process change: Medium — OI will enhance situational awareness for multiple operations
roles through improved decision-management tools. It requires identifying suitable use cases
that can span scenarios across new product design and introduction (NPDI), asset performance
management, energy efficiency, quality, operational risk management, contract manufacturing,
customer sales and aftermarket services, and location decisions when focusing on specific
benefit opportunities.
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■
Competitive value: High — OI provides the ability to detect leading indicators that alert
companies to unplanned variability and risk, reduce the lag between events and responses, and
accelerate problem solving, as well as improve the quality of decisions made by individual
stakeholders.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: AVEVA; Dassault Systèmes; IP Leanware; Microsoft; Optimal Plus; Plataine;
Rockwell; Savigent Software; SAP; Sight Machine
Recommended Reading: "Transform Manufacturing Swarm IoT Data Into Revenue-Generating
Insights"
"Industrial Analytics Revolutionizes Big Data in the Digital Business"
"Artificial Intelligence Will Make Manufacturing Operations Smarter — But a Learning Curve Comes
First"
Master Data Management
Analysis By: Bill O'Kane; Michael Patrick Moran; Simon James Walker
Definition: Master data management (MDM) is a technology-enabled business discipline in which
business and IT work together to ensure the uniformity, accuracy, stewardship, governance,
semantic consistency and accountability of the enterprise's official shared master data assets.
Master data is the consistent and uniform set of identifiers and extended attributes that describes
the core entities of an enterprise, such as existing customers, prospective customers, citizens,
suppliers, products, assets, sites, hierarchies and the chart of accounts.
Position and Adoption Speed Justification: A trusted version of master data domains remains a
central component in the pursuit of digital business goals. MDM is a strategic program that can
require several years. The need for business case creation and program management, and the
requirement to deploy information governance, restricts MDM's success to organizations that fulfill
these requirements. The technical challenge is to align the technical capabilities of MDM (such as
data integration and data quality) in a fashion that supports business requirements.
The market penetration of MDM as a whole is still low due to the technical and organizational
complexity of implementation (often compounded by a lack of understanding of the differences
between master data and application data), but the technical profiles for MDM for some singledata-domain implementations (such as customer and product data) have now reached the Slope of
Enlightenment ahead of the position for MDM overall. Demand is again being driven by the pursuit
of the "360-degree view" of critical data, but MDM is now firmly in the Trough of Disillusionment as
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organizations better understand the challenges, but are most often unable to surmount them
without substantial external guidance.
User Advice: Organizations with complex or heterogeneous application and information landscapes
typically suffer from inconsistent master data, which in turn weakens business process integrity and
outcomes. Any number of business applications may be affected, including customer-facing,
supplier-facing, enterprisewide and value chain applications.
If your business strategy depends on the consistency of data within your organization, you will likely
consider MDM as one enabler of this strategy.
Companies investigating MDM should:
■
Ensure a clear "line of sight" to business benefits and sponsorship. Understand which business
initiatives require better master data to succeed, and explain the need for MDM to appropriate
stakeholders.
■
Identify one or more solutions for the most important master data in your organization, such as
customer, product and financial data, based on business process enablement and optimization.
Plan on using the solution(s) for at least the next several years as changing incumbent MDM
solutions can be quite challenging. Look for solutions that support a holistic implementation and
end-user experience across domains, use cases and implementation styles.
■
Identify the architectural role that each implemented MDM solution will play in your approach to
enterprise information management (EIM). Use MDM as an opportunity to implement sound
information architecture fundamentals, such as canonical transaction formats for master data
domains as part of a well-managed data integration practice.
■
Use previous experiences in dimensional data development for business intelligence initiatives
to identify your organization's most fragmented but reused data domains. Begin your MDM
efforts with those domains and expose newly managed master data early in analytics platforms.
■
Avoid confusion and hype related to MDM and ensure that it is supported with the appropriate
level of discipline and technology, for example, application data management.
Business Impact: Leading organizations that create a strategy to implement MDM and supporting
technology that is well-thought-out, holistic and business-driven will be able to deliver significant
business value. They will do so in terms of enabling competitive differentiation and business growth,
improved customer experience, reduced time to market and delivery on operational efficiency, and
by meeting governance, risk management and compliance requirements.
MDM strategies that are linked to strategic IT enterprise transformation efforts (such as ERP and
CRM implementations) provide significant additional value to those efforts. Conversely, MDMcentric business cases are often used to highlight opportunities for significant business process
optimization.
In some cases, we have seen the need for MDM to trigger improvements in other areas such as
data quality, information governance, enterprise metadata management, although conversely, we
have also seen those programs initiate the need for better master data management.
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Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: IBM; Informatica; Oracle; Orchestra Networks; Riversand Technologies; SAP;
SAS; Semarchy; Talend; TIBCO Software
Recommended Reading: "Magic Quadrant for Master Data Management Solutions"
"Use the 7 Building Blocks of MDM to Achieve Success in the Digital Age"
"Mastering Master Data Management"
"Use the Gartner MDM Maturity Model to Create Your MDM Roadmap"
Supplier Quality Management Applications
Analysis By: Simon F. Jacobson; Sam New
Definition: Supplier quality management applications are a subset of quality management system
(QMS) functionality. These applications help companies ensure supplier compliance with applicable
requirements and enforce standards for all materials, components, subassemblies and assembled
goods — including related services and documentation — from all tiers of suppliers.
Position and Adoption Speed Justification: Supplier quality initiatives continue to be prioritized by
manufacturers. The ongoing focus is toward a supply management system that binds policy
(including compliance), process and a holistic technology strategy that extends quality's reach
upstream into multiple tiers of suppliers.
The software provider market has elevated the focus on supplier quality in the past 12 to 18
months. On the one hand, QMS vendors — especially as the market moves more and more
functionality to the cloud — have prioritized this functionality on their roadmaps. Some buyers
prioritize supplier quality modules and functionality as part of the first phase of functionality selected
and deployed. Some companies redeploying their entire QMS applications are placing supplier
quality as a key component of their broader functional requirements due to overlapping processes
including audits and complaint management. On the other hand are the investments in this
functionality made by ERP, product life cycle management (PLM), manufacturing execution systems
(MES) and other supply chain providers.
The net result of these activities are challenges in the form of functional overlap, heightened
concerns on master data and data exchange (especially when change controls tied to product
designs are involved), and internal culture. Specifically, the supplier quality group in many
organizations does not work in parallel with sourcing and procurement organizations, which creates
misalignment. Further adding complexity are the differences in industry-specific requirements (e.g.,
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AIAG and the U.S. Food and Drug Administration [FDA]) and the ability to expand functionality
across multiple tiers of suppliers (not just first tier).
The widespread interest in rounding out the core technology for supplier quality pushes this
technology forward on the Hype Cycle this year but shouldn't signal that the market is settled.
However, the imperatives to tighten the grip on supplier management and improve quality are
anticipated to continue pushing this technology forward.
User Advice:
■
When making the business case for an integrated QMS, be sure that the cost benefits can be
realized without disruption to product or service differentiation.
■
Don't overlook the initial hard benefits that come from integration and visibility. However, eye
longer-term integration with broader supply management initiatives where quality is portrayed
against other key performance indicators (KPIs) to ensure a 360-degree view of supplier
performance.
■
Overcome technological immaturity by developing service-level agreements (SLAs) to drive
adoption of a single system. In some cases, simply building the usage of a specific software
package into supplier scorecards and/or risk assessments can be a good starting point.
Business Impact: Supplier quality information is critical to corporate quality, risk management and
supply chain, operations and engineering functions. These functional organizations can leverage
supplier quality data to confirm compliance with product and service requirements and regulations
as defined by customers. Additionally, the information from these applications helps overall supplier
development and risk initiatives. Supply chain sourcing and procurement teams can use supplier
quality management information to reduce costs and risk, segment the supplier base or target
suppliers with collaborative continuous improvement programs. For example, operations and
engineering can use supplier quality data (including upstream supplier, in-process quality and test
data) to help pinpoint areas for manufacturing process and design improvements.
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: EtQ; FastFit360; iBASEt; Intelex; JAGGAER; Nulogy; Omnex; Oracle; Plex;
Siemens PLM Software
Recommended Reading: "Improve Multitier Supplier Quality by Leveraging Visibility"
"Best Practices for Improving the Governance of Supply Chain Quality"
Asset Performance Management
Analysis By: Nicole Foust
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Definition: Asset performance management (APM) is a market of software tools and applications
for optimizing operational assets (such as plant, equipment and infrastructure) essential to the
operation of an enterprise. It uses data capture, integration, visualization and analytics to improve
operations, maintenance timing, and which maintenance and inspection activities to perform on
mission-critical assets. APM includes the concepts of asset strategy and risk management,
condition monitoring, predictive forecasting and reliability-centered maintenance.
Position and Adoption Speed Justification: Some aspects of APM have been practiced for more
than 10 years, mostly by the largest companies in a handful of industries. Its broader adoption has
been stalled until recently by a combination of internal and external factors, including provable ROI,
budget access, skills, delegation of responsibilities and maturity of technology. Previously, there
was a need to build your own or apply complex mathematical tools to the problem.
Recently, APM has become more productized and is maturing into a more accepted part of
business. This is, in part, due to rapid innovation in enabling technologies such as IoT, advanced
analytics and algorithms in asset-intensive industries. These are widening the scope and decreasing
the deployment cost, aiding more widespread awareness and use of APM. The promise of reduced
maintenance cost and downtime, coupled with higher levels of operational reliability, is attracting
other industries. APM adoption is progressing at a varied pace among industries. Those that
depend on the success of their assets such as manufacturing, utilities and natural resources
industries tend to be further along in their asset management strategy, and usually invest more
heavily in APM. Other industries that rely on physical assets to some degree, such as retail and
public sector, are beginning to embark on this journey, but may not invest as heavily in APM
solutions.
User Advice: Asset-intensive industries' CIOs seeking the next level of asset performance
improvement should deploy APM. However, they should recognize that APM is characterized by a
variety of approaches, including analyzing performance history to develop databased maintenance
strategies; using advanced analytics to detect patterns and predict equipment failure; and in some
instances, simply using visualization of real-time operating and condition data to make better
decisions.
APM typically, but not necessarily, follows the deployment of enterprise asset management (EAM).
However, CIOs should not expect to get APM capabilities from the EAM vendors themselves,
although some EAM vendors continue investments in this area. This means that, in many
circumstances, third-party products may need to be interfaced into EAM.
Good data — that is, historical service data and operational data — is a necessary condition for
successful APM projects. Therefore, organizations looking to invest in APM should also expect to
make investments in information management infrastructure to capture operational data where it
doesn't exist today. APM leverages the convergence of IT and operational technology, and will
require resources familiar with both worlds' data structures and communication conventions. In
some instances, companies looking at APM projects will benefit from cloud-based approaches to
data sharing and multiparty collaboration.
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Business Impact: APM is a critical investment area for asset-intensive industries, including
manufacturing, mining, oil and gas, transportation and utilities. Successful APM deployments can
deliver measurable improvements in availability, as well as reduce maintenance and inventory
carrying costs. Most APM projects are executed on the premise that data-driven decisions will
improve equipment reliability and, therefore, reduce operational risk. Some benefits include
improved uptime and cost savings can be substantial, typically delivering benefits measured in
millions of dollars per year.
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Adolescent
Sample Vendors: ABB; AspenTech; AVEVA; Bentley Systems; GE Digital; IBM; MaxGrip; SAP; SAS;
Siemens
Recommended Reading: "Market Guide for Asset Performance Management"
"Financially Optimized Maintenance Planning Using Asset Performance Management"
"2018 CIO Agenda: A Utility Perspective"
"Mapping a Route to Asset Management and Reliability"
"2017 CEO Survey: Digital Can Be a Hard Sell for CIOs in Asset-Intensive Industries"
Asset Performance Management in Manufacturing Operations
Analysis By: Simon F. Jacobson; Kristian Steenstrup; Nicole Foust
Definition: Asset performance management (APM) encompasses the capabilities of data capture,
integration, visualization and analytics. These capabilities are tied together for the explicit purpose
of optimizing the performance of assets to increase availability, minimize costs and reduce
operational risks. APM includes the concepts of condition monitoring, predictive forecasting and
reliability-centered maintenance.
Position and Adoption Speed Justification: Although APM has been practiced for more than 10
years in a handful of industries, its broader adoption has been stalled until recently by a
combination of internal and external factors. These factors include cost/budget, skills, delegation of
responsibilities and maturity of technology. In prior years, there was a need to build your own or
apply complex mathematical tools to the problem.
APM is maturing into a normal part of the business for three reasons, beginning with manufacturers'
ongoing remit to maximize capacity utilization and eliminate downtime. As production runs shorten
and, in other instances, new production lines are commissioned, the reliability of operations is vital.
The second reason is the technology itself. APM has become more productized. This is, in part, due
to rapid innovation in enabling technologies. These include the Internet of Things (IoT) — which
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makes a whole new set of assets and equipment in and across factories accessible — advanced
analytics and algorithms in asset-intensive industries. These are widening the scope and decreasing
the deployment cost, aiding more widespread awareness and use of APM. The third reason — and
related to the technology itself — is that OEMs and machine builders are developing smarter
machines and new service offerings to support APM. Here, cloud is having an impact by lowering
the barriers to the use of APM itself as well as the emergence of APM as a service offering.
User Advice: Recognize that APM is characterized by a variety of approaches. These include
analyzing performance history to develop data-based maintenance strategies, using advanced
analytics to detect patterns and predict equipment failure, and in some instances, simply using
visualization of real-time operating and condition data to make better decisions. For example,
improved equipment availability will benefit multiple stakeholders spanning management roles
responsible for reliable supply, production planners seeking to manage both short- and long-term
production schedules, and maintenance teams that can shift to maintenance by exception.
Do not expect to get APM capabilities from most enterprise asset management (EAM) vendors,
although some are making strides in this area. This means that, in many circumstances, third-party
products may need to be interfaced into EAM. APM typically, but not necessarily, follows the
deployment of EAM.
Anticipate investments in information management infrastructure to capture operational data where
it doesn't exist today. APM leverages the convergence of IT and operational technology (OT), and
will require resources familiar with both worlds' data structures and communication conventions.
For many manufacturers, this is as much a cultural shift as it is a technological shift, and
reorganizing some processes should be considered. In some instances, companies looking at APM
projects will benefit from cloud-based approaches to data sharing and multiparty collaboration.
Business Impact: APM is a critical investment area for manufacturers seeking measurable
improvements in availability of their factories and/or individual production units. The improved
uptime and cost savings can be substantial, typically delivering benefits measured in millions of
dollars per year (this includes reducing spare parts inventory carrying costs). Successful APM
deployments deliver measurable improvements in availability as well as reduce maintenance and
inventory carrying costs. Most APM projects are executed on the premise that data-driven decisions
will improve equipment reliability and, therefore, reduce operational risk.
Impacts to consider are:
■
Risk: Low — APM is a decision support tool that results in low-risk improvements in asset
management decision making, and reductions in unplanned downtime and costs.
■
Technology intensity: High — Analytics are central to APM deployments, can be quite
complex and often require specific knowledge of the tools, as well as access to multiple
sources of data from different time horizons.
■
Strategic policy change: Low — Improved asset performance is a long-standing
organizational goal for most manufacturers.
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■
Organization change: Medium — Although new skills may be required for OT support and
significant organizational change is not required, there is the potential for greater OEM
involvement in the asset-monitoring and decision-making processes that affect the prevailing
maintenance culture.
■
Process change: Medium — Increased reliance on data-based decision making means less
reliance on expert-driven decision processes. In addition, better failure prediction erodes the
reliance on preventative maintenance processes. Thus, shifting to maintenance by exception
will be gradual.
■
Competitive value: High — High levels of asset predictability translate into more predictable
production and agility. For many organizations, a 1% reduction in equipment downtime can
translate into million-dollar returns.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: ABB; AspenTech; AVEVA; Bentley Systems; GE Digital; IBM; QiO; SAP; SAS;
Siemens
Recommended Reading: "Market Guide for Asset Performance Management"
"Magic Quadrant for Industrial IoT Platforms"
"2018 Strategic Roadmap for IT/OT Alignment"
"Mapping a Route to Asset Management and Reliability"
"The Internet of Things Is Accelerating Asset Performance Management Innovation and Adoption"
"Using Advanced Analytics to Predict Equipment Failure"
Digital Manufacturing
Analysis By: Simon F. Jacobson; Rick Franzosa; Marc Halpern
Definition: Digital manufacturing orchestrates the virtual and physical domains to support
concurrent design of process and product, production execution and continuous improvement, and
to plan and operate manufacturing as an integral part of a value network.
Position and Adoption Speed Justification: Digital manufacturing has expanded beyond its
origins of using 3D models for validating factory designs and manufacturing processes. The rise of
digital business, hype of artificial intelligence (AI) and ongoing interest in 3D printing have changed
the requirements of digital manufacturing. Manufacturers continue to focus on digitizing — that is,
improving or automating existing processes with newer technologies. In some cases, this is a
euphemism for continuing forays to eliminate paper and nonvalue-added processes in factories.
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Depending on the complexity and production environment, this can translate to visualization or
more complex projects for synchronizing bills of materials (BOMs), upgrading plant infrastructure (to
capitalize on the Internet of Things [IoT]) and critical applications or deploying new systems
altogether (e.g., manufacturing execution systems [MES]). These are precursors for longer-term
initiatives and concepts such as digital twins, digital thread and manufacturing process
management/model-based manufacturing (MPM/MbM) .
While the costs of entry for certain technologies have decreased with digital business' evolution and
there continues to be activity in the form of projects and corporate initiatives, the time to plateau is
not accelerating and there is no advancement on the continuum this year for the following reasons:
■
Scalability is a multiyear effort that require manufacturers to develop specific funding and
deployment roadmaps to ensure scale within plants and across the supply chain.
■
Many standards need ratification (e.g., data formats and exchange). Consortia testbeds and
reference architectures (e.g., Plattform Industrie 4.0) are just emerging, and require
understanding and time to adoption.
■
Upgrading of existing factory automation and OT layers is often overlooked and prolongs
investment approval.
■
Ensuring adoption requires upskilling the manufacturing workforce to succeed with new
technologies and interaction paradigms. This is a complex change management initiative.
While dormant this year the subactivities of digital manufacturing and the market's urgency are not
dissipating. It is highly anticipated in the next year that initiatives will progress further and the
fundamental shift and highly visible results on offer will emerge on a broader scale.
User Advice: Supply chain leaders responsible for manufacturing operations strategy must:
■
Deliberately align digital initiatives in factories with the value streams they support. The goals
are to improve flexibility, cost and quality, as well as accelerate commercialization and improved
decision making.
■
Take a life cycle approach to investments. Focus on those that will boost the core operations
first and build from there. This will help identify which applications and data sources need
access or upgrading to garner consistency of data — tempering a strong desire to completely
"clean house" when tackling these initiatives.
■
Recognize that this transition is as cultural as it is technological. Supply chain, manufacturing
and IT must come together and share responsibility for new processes and systems and
technological enhancements — especially with production activities being decoupled across
the network.
Business Impact: Not all manufacturers are ready to sustain this shift and have different initiatives
to succeed. However, all manufacturers must be mindful of these impact areas:
■
Risk: Medium — The risk of technology continues to decrease, but technology acquisition
without architectural consideration creates further inertia. In parallel, the low risk is balanced by
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the potential challenge of individual sites capitalizing on the lowered costs and making
investments in isolation from an enterprise strategy.
■
Technology intensity: High — Concepts like the digital thread and digital twins, while alluring,
ask questions of the foundational building blocks of manufacturing systems architecture. While
elements of digital manufacturing are already in place, upgrading and integrating them could
come first. Regardless of projects, the importance of establishing a foundation of master data
(which requires cleansing), metrics and analytics, and feedback loops across applications and
processes is necessary to capture the benefits of a broader digital manufacturing architecture.
■
Organization change: Medium — In the short term, roles and organizational structures will
have minimal change. As more advanced manufacturing techniques become mainstream, the
skill sets for managing will differ from current job descriptions and the upskilling needs
attention. Traditional organizational boundaries among IT, line of business and business
functions need to be shed for seamless collaboration.
■
Process change: High — In its most basic sense, removing paper and manual work will create
enough change. Broadly, some value streams, product flow paths and supporting business
processes could require slight re-engineering (quality), while others require complete reinvention
(e.g., planning, network design and bill of material conversions) as feedback loops and
analytical requirements morph.
■
Competitive value: High — Improved time, transparency and reliability of operations as well as
reuse of designs allow for better flexibility to respond to varied demand and lower costs.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: AspenTech; AVEVA; Dassault Systèmes; GE Digital; iBASEt; Microsoft; PTC; SAP;
Siemens; Wipro
Recommended Reading: "Harvest the Value of Smart Manufacturing in the Supply Chain, Not the
Factory"
"Understanding the Five Stages of Gartner's Maturity Model for Manufacturing Excellence"
"Toolkit: Self-Assess Your Manufacturing Operations Maturity"
"Toolkit: Defining the Functional Requirements for Manufacturing System Selection"
"How to Deploy Manufacturing Process Management for Digital Manufacturing"
"More Than Digital Is Driving the Factory of the Future"
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Climbing the Slope
Synchronized BOMs
Analysis By: Marc Halpern
Definition: Synchronized bills of materials (BOMs) capabilities refer to associating and updating
equivalent items from different BOMs such as engineering, manufacturing, sales/marketing and
service, where each of the BOMs can be labeled and structured differently.
Position and Adoption Speed Justification: Commercial software vendors have made significant
progress in delivering synchronized BOM capabilities. Best practices for synchronizing BOMs are
also advancing. In recent years, some manufacturers have made progress using semantic search
technologies to make synchronization more efficient. Some easy-to-use cloud-native solutions are
also emerging. Adoption is accelerating, although it still remains low as compared to demand.
User Advice: This technology best suits leading-edge manufacturers of complex engineered
systems such as transportation vehicles, aircraft, defense systems, heavy machinery, complex
healthcare equipment and other electromechanical systems. This includes aftermarket services,
such as maintenance, repair and overhaul (MRO). CIOs at these manufacturers must be ready to
work consultatively with software vendors that have a record of executing well on joint software
development efforts. CIOs working for manufacturers should be prepared to invest extensively in
data architecture, master data management techniques, and training and cultivating talent through
implementation experience. Where appropriate, manufacturers and service organizations should
ensure that synchronized BOM capabilities can manage software, firmware and embedded
application components as a part of products and systems.
Business Impact: Manufacturers and service organizations investing early anticipate that
successfully deploying synchronized BOM strategies increases the efficiency of using BOM content
throughout the product/service life cycle. This shortens the time from design completion to product
manufacturing. Accurate BOM synchronization reduces scrap, rework, inventory shortages,
expediting and imperfect customer orders, and it helps ensure continuity of supply. The enterprise
efficiencies generated will enhance the benefits obtained through reductions in cost and time
required to identify, access and use BOM items. Gartner estimates that synchronized BOM
capability has the potential to reduce improper part selections by more than 80% throughout the life
cycle when servicing products and systems. In addition, BOM synchronization is key to enabling
efficient traceability and tracking genealogy of complex assemblies. For example, governance of
organizations such as the U.S. Department of Defense, Federal Aviation Administration (FAA), Food
and Drug Administration (FDA), etc. requires such traceability.
Areas of impact include the following:
■
Risk: High — technology and best practices for synchronizing BOMs are in early stages of
commercialization. Companies risk setbacks while on the learning curve for this type of
initiative.
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■
Organization change: Medium — synchronizing BOMs eliminates manual work, but the roles it
impacts and the relationships among those roles do not need to change. However, it might
eliminate the roles responsible for transforming BOMs from one use case (such as engineering
BOMs) to another (such as manufacturing and service BOMs), although it might add roles to
maintain and update business rules for BOM translations as needed.
■
Process change: Medium — when synchronizing BOMs works properly, manufacturers can
eliminate steps in manual BOM translation processes. However, it might require changes to
process steps to ensure that the business roles and attributes associated with each item in a
BOM are comprehensive.
■
Competitive value: High — when properly working, synchronized BOM technology will reduce
errors in product data and streamline design-to-manufacturing workflow substantially. This
translates to faster times and lower costs when introducing products to market. Few product
data errors during product service mean faster service and fewer mistakes, resulting in higher
perceived brand value.
■
Industry disruption: High — synchronized BOM technology will accelerate the product time to
market to such a degree that the manufacturers adopting it will be able to introduce products
noticeably faster than competitors and at lower costs.
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: Aras; Arena; Dassault Systèmes; iBASEt; openBOM; Proplanner; Siemens
Recommended Reading: "Toolkit: Defining the Functional Requirements for Manufacturing System
Selection"
"Toolkit: Assess Your PLM Digital Platform Maturity in Discrete Manufacturing"
"Market Guide for MPM and MbM for Discrete Manufacturing"
"How to Deploy Manufacturing Process Management for Digital Manufacturing"
"Best Practices in Bills-of-Material and Recipe Management"
System Engineering Software
Analysis By: Marc Halpern
Definition: System engineering software enables the design, modeling and simulation of systems
that can include related physical parts, software logic and processes.
Position and Adoption Speed Justification: The concept of product functional analysis and
system modeling has been present for decades. However, the rapid rate of infusing software in
physical products has elevated the importance of system engineering as commercial software.
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During the past five years, several PLM vendors have incorporated system engineering into their
product suites. Also, larger software vendors have been acquiring smaller specialty system
engineering software vendors.
However, due to the complexity of the many design domains found across a system, PLM vendors,
while making progress, do not yet meet the complete needs of the user community. In addition,
software vendors and early adopters are on a steep learning curve regarding organizational
requirements, new roles, processes and best practices to succeed with enterprisewide system
engineering initiatives. This steep learning curve inhibits expanded use of the software but as
experience grows, best practices are also increasing.
User Advice: Large manufacturers of complex engineered product systems involving operational
technology should be investing in this class of software. Industries with the greatest need include
consumer electronics, military electronics, aerospace and defense products, transportation
vehicles, industrial equipment and heavy machinery. Ideally, these large manufacturers should use
system engineering software with product requirements management software.
Manufacturers should prioritize the quality of interfaces between requirements management
software and system engineering software when choosing among candidate software providers.
This integration should provide mapping between requirements and the technical specifications that
meet the requirements. Additionally, integration of the system engineering software itself should
enable users to link critical design parameters from the system models to the technical
specifications.
Manufacturers with less complex products should audit their performance in requirements
management and in the ability to systemically define and design product platforms. Designing a
product platform involves using system engineering techniques that can "mix-and-match" parts and
technologies within the platform to create many variants of a product. If they are not satisfied with
the results of those audits, they should invest in system engineering, product portfolio management
and product requirements management to buttress their performance at designing their products as
systems.
Planners of system engineering programs using such software should be conscious of differences in
hardware development which is done by sequential project structures and development of
"onboard" software which applies agile or scrum development methods. They must orchestrate the
hardware and software development carefully, with great sensitivity to the interconnections between
hardware features and evolving software logic.
Planning for change management including organizational change, roles, processes and practices
needs to be part of the initiative.
Business Impact: Increasing the role of system engineering has the potential to transform how
manufacturers produce products. Successful manufacturers will be able to create new classes of
products and a greater variety of products within a single product family much more rapidly. They
will be able to efficiently convert current engineer-to-order business to more scalable configure-toorder business. System engineering is also essential to cope in the emerging age of IoT and digital
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business. System engineering also advances best practices for developing digital twins and how
digital twins connect to their physical counterparts.
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Sample Vendors: BigLever Software; Comet Solutions; Dassault Systèmes; ESTECO; Jama
Software; MathWorks; Modelon; Phoenix Integration; PTC; Siemens
Recommended Reading: "Five Approaches for Integrating IoT Digital Twins"
"Digital Business Is Transforming New Product Development Priorities"
"Product Innovation Platforms: The Foundation of Product Design and PLM in the Digital Business
Era"
Product Cost Management
Analysis By: Marc Halpern
Definition: Product cost management technology predicts and captures estimates of the cost of
products, systems or solutions over their life cycles.
Position and Adoption Speed Justification: Leading edge software developers have been
attempting to introduce these applications since the early 1990s. However, market development has
lagged because companies have been slow to build databases and support workflow that
accurately characterize the predicted and estimated cost of materials, parts, services and
processes. Predictive costing with additive manufacturing as well as predictive costing for
subtractive manufacturing and assembly have been evolving. Since most Gartner clients see great
potential in cost governance as provided by niche vendors, major PLM vendors have incorporated
predictive costing of products, services and solutions into their software platform. These vendors
include Dassault Systèmes, Siemens, Oracle and SAP.
User Advice: CIOs must investigate cost management software if their companies are interested in
cost governance for predicting the costs of parts, products and product manufacturing. To calibrate
the software for accurate predictions, they must monitor, document and continuously validate the
cost of labor, procurement, materials handling, equipment maintenance, related services and so on.
Stand-alone product cost management offerings will be faster to install and learn, but
manufacturers will need to integrate this with other classes of software to ensure that all costrelated data is collected and validated on a recurring basis. A best-in-class deployment would
include integration with ERP and manufacturing execution systems (MES), supply chain
management (SCM), service planning and design software. Integrated solutions will take longer to
deploy, but offer a higher probability that relevant cost-related data is visible and comprehensive.
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CIOs should work with business leaders to identify subject matter experts to create a central group
that provides ongoing oversight and governance to costing activities, including cost management
software performance. This could be either a governance group or a center of excellence.
Business Impact: Businesses can realize ROI with cost management software because its
predictions provide design guidance to reduce costs of manufactured parts and products. Some
businesses use the predicted costs for negotiating the costs of parts and systems with suppliers.
They also use the predicted costs to negotiate the costs of outsourced manufacturing services.
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: 3C Software; aPriori; Boothroyd Dewhurst; Cognition; Dassault Systèmes;
FACTON; Oracle; pVelocity; SAP; Siemens
Recommended Reading: "Adopt Predictive Costing to Deliver Winning Products at Greater Profit"
Quality Process Management Applications
Analysis By: Simon F. Jacobson; Sam New
Definition: Quality process management applications are a subset of quality management systems
(QMSs) that digitally represent standard operating procedures that govern, support and enforce
conformity to quality standards. These range from internally defined business rules to International
Organization for Standardization (ISO) or other industry-specific and customer-mandated quality
standards.
Position and Adoption Speed Justification: Quality process management applications are a
nucleus to a robust enterprise quality management system. They provide predefined workflows to
manage wide-ranging set of cross-functional and cross-application processes spanning
nonconformance management, change controls, failure modes and effect analysis (FMEA) and
audits.
The market continued to be active in the past 12 months. Manufacturers, as part of a pursuit of
"enterprise" quality management strategies, are formulating plans to upgrade, replace and
consolidate outdated and custom-made systems to a single platform. By doing so they will be able
to harmonize and add common structure to the various methods and procedures that have
traditionally been enforced on a functional or localized basis. In some industries, this has driven a
replacement market, whereas, in targeted situations, a new supply chain or business unit will select
a new system completely.
Likewise, software providers continue to morph their offerings toward full-scale, reconfigurable
platforms with common process and data definitions. The cloud has established itself as a core
requirement by a large majority of buyers too, and the vendors have met this with offerings available
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in either Amazon or Salesforce's environments. Also, the infusion of capital from private equity
firms, expansion into the QMS space by some vendors and the emergence of new providers, not to
mention some consolidation to expand platform offerings in industries like life sciences, are all
positives for the space.
Last year, the combination of market developments and the aggressive nature of buyers and the
evolving provider capabilities have pushed this class of QMS applications further along the Hype
Cycle continuum. Despite strong activity that is continuing this space being dormant this year. Why?
The time to plateau is still belabored by targeted deployments for specific processes (e.g., auditing
and corrective actions) versus wider support of an ecosystem of parallel processes and general
absence of a disciplined approach to "enterprise" quality architecture that balances standard
processes and ongoing change due to external conditions (e.g., regulatory requirements).
Furthermore, despite the positives, some other provider-centric challenges have been exposed.
These include their inability to offer pricing that matches cloud offerings, immaturity in the services
channels and lack of clearly defined deployment roadmaps (adding industry-specific [i.e., product
registration] or advanced functionality [i.e., analytics] atop the core set of QMS workflows and
processes). For the advanced functionality, buyers are slightly concerned about expansion beyond
core QMS functionality to support more end-to-end initiatives, such as track and trace in the supply
chain as well. That will test the partnering mettle of many providers. The nature of the buyer is not
changing and we anticipate many of these risks will be addressed in the next year, which will then
push the market further along the continuum.
User Advice:
■
Architect a multiphase deployment that starts with a single process, like nonconformance
management, change control or audits, and use the ROI to then expand into multiple
processes.
■
Look for a solution that aligns with your overall enterprise quality management approach.
Today's quality process management vendor should have (at minimum) some support for cloud
computing, a wide library of APIs and analytics, as well as providing a long-term roadmap for
other QMS functionality such as learning management and support for some supplier-facing
activities.
■
Where suitable, extend process support to suppliers and trading partners to improve
collaboration (such as advanced product quality planning [APQP]) and regulatory compliance
(such as cGMP processes enforced by the U.S. FDA).
■
Scrutinize candidate vendor pricing models, especially for cloud-based solutions, to ensure
there's benefit in the agreement.
■
Urge candidate vendors to provide a multi-phase roadmap that will enable your organization to
deploy core QMS functionality and then scale to more advanced capabilities.
Business Impact: Corporate operations and supply chains continue to be exposed by gaps in their
quality processes and supporting data, with the financial performance implications and risks only
increasing. Businesses that have multinational and offshore manufacturing centers are particularly
vulnerable to negative brand impact from quality issues (such as lead paint in children's toys or poor
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quality in automobile tires). A stringent quality-compliance program supported by robust tools can
prevent unsafe, dangerous or shoddy products from reaching the market.
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: CEBOS; EtQ; Intelex; MasterControl; Omnex; Oracle; Pilgrim Quality Solutions;
Siemens; Sparta Systems; Veeva Systems
Recommended Reading: "Supply Chain Is Missing the Mark on Quality"
"Improve Multitier Supplier Quality by Leveraging Visibility"
"EQM Hubs Unite Quality Management IT Systems Across the Value Chain"
Real-Time SPC Applications
Analysis By: Simon F. Jacobson
Definition: Real-time statistical process control (SPC) applications provide a subset of quality
management system (QMS) functionality. These applications measure, monitor and analyze
production processes. This ensures compliance to predefined and validated tolerances and gives
users an understanding of where and when variability occurs. In turn, this boosts process
capabilities by eliminating undesired results and establishing consistent output.
Position and Adoption Speed Justification: SPC applications continue to be of interest to Gartner
clients and are progressing on the continuum this year. Customers are increasingly drawn to
tracking, trending and understanding production process performance against predefined
tolerances of acceptability in near real time. As product portfolios expand and production flexibility
becomes the norm, this establishes a fact-based platform for companies to identify and eliminate
variability in products while improving the understanding of their production processes. Also driving
the newfound interest are broader efforts to mitigate risk and improve traceability across the supply
base.
Manufacturers want a consistent usage of SPC applications across sites but are often held back by
desktops full of legacy systems that support basic SPC functionality versus sophisticated analysis
capabilities (e.g., pattern analysis). Other challenges include harmonizing datasets across sites
(especially when part and specification-level data is required) and nurturing skills to interpret the
data.
To meet the changes in buyer habits, many stand-alone SPC vendors continue to make significant
investments in their product roadmaps. Desktop and/or client/server applications are giving way to
cloud-based offerings. The functionality and tools for data analysis are expanding to support
streaming data, automating the adjustment of predefined tolerances and control limits, and
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incorporating data lakes. Despite substantial steps forward, stand-alone providers are competing
against conventional MES vendors (with SPC modules) and new market entrants (that support SPC,
among other analysis techniques).
User Advice:
■
Evaluate SPC's fit within your overall manufacturing system architecture as well as
manufacturing's fit within your product supply processes. This will help you determine the kind
of provider to invest in.
■
Use SPC to provide the foundational basis for data-driven continuous improvement in product
processes but do not confine efforts to production lines. Develop feedback loops further
upstream to R&D and engineering and into the supply base. This can widen the reduction of
errors and increase yield. It also impacts how processes are designed and validated. While this
requires an investment in analysis expertise and automation, the net results, however, can drive
greater cost savings and improve NPI.
■
Connect the dots between the data generated in SPC applications and continuous
improvement programs (e.g., TPM and TQM) to drive consistent production throughput in a
more systemic fashion by detecting waste and improving how operations are controlled. This
requires shifting the lens of SPC from its traditional bottom-up and plant-centric investment to
top-down programs to systemically improve quality in the supply chain.
Business Impact: Using SPC applications to identify and eliminate root causes of production
variability will improve production stability, lower costs and cut the risks of high impact of poor
quality on business performance, customer service and brand image.
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: camLine GmbH; DataNet Quality Systems; Hertzler Systems; InfinityQS;
Northwest Analytics; Optimal Plus; PQ Systems; Predisys; SAS; Schneider Electric
Simulation and Test Data Management
Analysis By: Marc Halpern
Definition: Simulation and test data management are the capabilities to capture, organize, and
reuse content and knowledge from engineering analysis, virtual prototyping, other types of
simulations and physical testing for additional simulation and decision making.
Position and Adoption Speed Justification: The corporate demand is growing for infrastructure to
manage simulation and test content. Discrete manufacturers in industries such as automotive are
moving fastest at this. This topic is actively discussed at engineering and product life cycle
management (PLM) conferences, as manufacturers increasingly adopt these capabilities. In
addition, the "mind share" of analytics in product design and PLM practice is raising the visibility of
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this capability. Digital twin strategies further advance interest in this technology since it supports the
use of digital twins to make design decisions.
While a minority of candidate manufacturers have invested so far, an increasing number are
inquiring about best practices. Expected growth in digital twin initiatives will also accelerate
adoption of simulation and test data management capabilities. Several of the visionary, independent
providers of these capabilities such as Enginuity PLM (acquired by Dassault Systèmes in 2008),
LMS Engineering (acquired by Siemens in 2012) and MSC Software (acquired by Hexagon in 2017)
are being absorbed into simulation and test "ecosystems" of the vendors that acquired them. This
absorption of the capabilities reflects both the strategic importance of this capability for some
industries and its movement up the Slope of Enlightenment. The long learning curve inhibits rapid
movement of this technology up the slope.
User Advice: CIOs working for manufacturers with strict regulatory demands for simulation as part
of design certification, such as automotive and aerospace firms, should adopt these capabilities to
manage simulation and test content. These CIOs should favor software suppliers and service
providers with computer-aided engineering (CAE) and testing-domain experience and expertise for
their shortlists of candidate vendors.
Manufacturers interested in integrating these capabilities into overarching product life cycle
activities should select tools compatible with available enterprise business infrastructures. IBM,
Microsoft, Oracle and SAP, or PLM vendors such as Dassault Systèmes, PTC and Siemens, provide
such infrastructure. Aras is developing such capabilities in partnership with a major automotive
OEM. Since the learning curve is steep, planning and training should be an important factor when
planning implementation activities and budgets. CIOs should also be planning for the emergence of
these capabilities on a cloud platform by 2022.
Business Impact: Capable simulation and test data management increases the efficiency of
regulatory compliance, captures knowledge and improves the ability to share and reuse simulation
content as well as reuse lessons learned from simulations. Gartner estimates a 70% decrease in
time needed for a manufacturer to capture, organize, and share simulation and test content. This
makes design processes leaner and improves a product development organization's ability to add
quality and to reduce product life cycle costs. Gartner estimates a 40% cost improvement to
evaluate design alternatives.
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: Altair; Ansys; Aras; Dassault Systèmes; Esteco; Hexagon; Siemens
Recommended Reading: "Four Best Practices to Avoid Digital Twin Failures"
"Digital Business Is Transforming New Product Development Priorities"
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"Enhance Business and Manage Risks With Appropriate Simulation and Computer-Aided
Engineering Use"
"Product Simulation and Test Content Management Requirements to Accelerate Product Design"
"Manufacturers Need More Than Product Data Management to Use Simulation Content Efficiently"
Plant Engineering and Design
Analysis By: Marc Halpern
Definition: Plant engineering and design refers to a suite of 3D modeling, drawing and simulation
software used to conceptualize, define, document and evaluate a plant that will be newly built or is
being upgraded. It includes mathematical tools for simulating part and material flows, chemical
processes, worker safety and analyze life cycle costs.
Position and Adoption Speed Justification: Manufacturers have been using software applications
to design and improve plants for more than 30 years. Although several of the leading vendors in this
category have existed for so long, growth and capabilities have been slow to moderate compared
with other software categories in the manufacturing and product life cycle management Hype
Cycles.
However, digital manufacturing motivates rethinking plant engineering and factory design because,
while connected devices in factories are not new, concepts such as Industrie 4.0 encourage
manufacturers to reconsider how their factories operate. This encourages manufacturers to rethink
the factory itself and the nature of software needed to plan, design and construct manufacturing
facilities. In addition, the concept of "virtual commissioning" or running a physical plant from a
virtual model or "digital twin" is gaining mind share. These developments create hype around plant
engineering and design again. Although some interfaces and standards exist for sharing data, they
are not yet sufficient. In addition, coordination between the engineering firms contracted to design
and build the plants and their customers remains a challenge.
Particular market accelerators and inhibitors influence the rate of growth. Accelerators include the
need to design plants more quickly so they can be easily constructed in the lowest possible cost
and shortest possible time. Software providers are streamlining this support by applying digital twin
concepts, increased support for 3D scanning, and improvements in capabilities to convert scanned
"point clouds" to 3D models. Interest in the use of simulation technologies to streamline factory
activities and processes is also growing buttressed by improvements in the simulation technology
capabilities, ease-of-use and performance.
User Advice:
■
Those responsible for complicated facilities at large manufacturers with ongoing capital
improvement programs should be considering plant engineering and design software.
Manufacturing experts and engineers responsible for doing the plant design should be
interested as well.
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■
Given the software's broad scope, IT organizations must structure the implementation as an
orchestrated set of projects, such that later projects build off previous ones and each project
delivers measurable business value. Given that the software vendors often sell these
applications either in suites or a la carte, it is best to first determine the scope of work to be
done and then decide how to purchase the software.
■
IT strategists must prioritize software used to create and visualize models of equipment when
planning factory design software implementations. The strategies to align operational
technology with information technology must now also be a strategic priority. They should be
adopting digital twin concepts, scanning technologies and simulation technologies as
appropriate.
■
Implementers should validate the reliability of importing geometric and nongeometric data into
the environment of choice. They must also determine how they will standardize the
convergence of operational technology and information technology. Also, engineers and
designers should use these models to simulate plant construction sequences and plant
operations. Moreover, stakeholders in such software need to build a competency in relevant
simulation capabilities.
Business Impact: Manufacturers that have adopted this software report cost and time savings on
original factory designs and factory upgrades. Examples of savings come from:
■
Detecting wrong selections of equipment and piping before making investments.
■
Avoiding problems in factory layout that cause material flow, people movement or safety
problems.
■
Avoiding problems when moving equipment and materials in and out of factories during
upgrades or new construction.
■
Reducing engineering changes resulting from errors in sharing data that lead to time and cost
overruns during construction.
■
Plant design models can support "virtual commissioning" or testing automation software on the
design model to identify automation bugs before enabling the automation to operate in the
actual plants. This can save cost and time when bringing the plant to production.
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: AspenTech; Autodesk; AVEVA; Bentley Systems; Dassault Systèmes; Intergraph;
PTC; Siemens; SolidPlant
Recommended Reading: "Four Best Practices to Avoid Digital Twin Failures"
"IoT Enriches PLM With 360 Degrees of Product Life Cycle Data"
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"Innovation Insight for Digital Twins — Driving Better IoT-Fueled Decisions"
"How to Deploy Manufacturing Process Management for Digital Manufacturing"
"Market Guide for MPM and MbM for Discrete Manufacturing"
"Innovation Insight: Manufacturers Need MPM and MBM to Innovate Digitally Enabled Design
Through Production"
MES Applications for Discrete Manufacturing
Analysis By: Rick Franzosa
Definition: Manufacturing execution system (MES) applications are a specialist class of productionoriented software that manages, monitors and synchronizes the execution of real-time, physical
processes involved in transforming raw materials into intermediate or finished goods. They
coordinate this execution of work orders with production scheduling and enterprise-level systems
like ERP and product life cycle management (PLM). They also provide feedback on process
performance and support traceability, genealogy and integration with process history.
Position and Adoption Speed Justification: MES applications remain at the top of the list of
prioritized manufacturing investments for companies seeking control and consistency across their
manufacturing operations. MES applications for discrete manufacturing are starting to show
movement on the Hype Cycle. This is due partly to:
■
A renaissance in MES caused by manufacturers looking to replace legacy MES environments
with newer systems that can more easily integrate with IoT/cloud platforms
■
New integrated functionality being released by vendors that have acquired MES assets
■
Further realization that ERP and PLM do not provide all of the technology answers to modern
manufacturing environments.
This does not eliminate the possibility of emerging technologies in plant automation, process
management and/or analytics integrated directly with system-of-record platforms. ERP could
eliminate the need for traditional MES in some industries, but the tasks performed by MES systems
will continue to be automated and performed. The applications that perform these services may just
go by another name.
User Advice: Concentrate on the following when identifying an MES application that's right for your
environment:
■
Functional capabilities a product can offer in support of the process issues that need a solution,
and the degree of domain specialization required. Keep in mind that what you choose may not
actually be called MES.
■
MES applications are enterprise solutions. Use this opportunity to standardize processes and
view your manufacturing capability as value creation for the business. Define global process
templates that will speed deployments and foster standardization. Evaluate vendor capabilities
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to support the enterprise, including multisite support, remote hosting and hybrid cloud-based
capabilities.
■
While defining your enterprise strategy is key to success in MES deployment, base initial
investments on boosting plant-level capabilities. This will satisfy initial ROI concerns and
provide a platform for longer-term benefits.
■
Do not assume that the vendor solution called "MES" is an MES solution in your context, nor
that the solution you require is actually an MES by any definition.
Business Impact: MES applications boost manufacturing reliability by digitally enforcing processes,
methods and procedures. Because MES applications also provide the genealogy and traceability
information required for compliance, they serve as a strategic nucleus for broader manufacturing
architectures, including IIoT. MES data is also an important resource for determining discrete
product quality. When scoped, implemented and integrated properly with other processes for
product supply, MES applications can support the growth goals of the business while removing
costs and complexity, driving process optimization, improving product quality and directly impacting
the bottom line.
The advent of production-worthy cloud-based MES capability from new and existing vendors gives
manufacturers options in building a manufacturing network, adding the benefit of scalability and
flexibility across multiple sites and leveraging other cloud based information platforms (IoT, for
example) to provide more holistic sources of manufacturing data for better business decision
making.
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: 42Q; Cogiscan; Dassault Systèmes; Eyelit Manufacturing; iBASEt; iTAC Software;
Oracle; Plex; SAP; Siemens
Recommended Reading: "Magic Quadrant for Manufacturing Execution Systems"
"Toolkit: Defining the Functional Requirements for Manufacturing System Selection"
"Survey Analysis: Multisite MES Delivering Benefits Today With Cloud(s) on the Horizon"
"Market Guide for MES/MOM Implementation"
"Digital Manufacturing Requires a New Look at Old Systems"
MES Applications for Process Manufacturing
Analysis By: Rick Franzosa
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Definition: Manufacturing execution system (MES) applications are a specialist class of productionoriented software that manages, monitors and synchronizes the execution of real-time physical
processes involved in transforming raw materials into intermediate or finished goods. They
coordinate this execution of work orders with production scheduling and enterprise-level systems
like ERP and product life cycle management (PLM). They also provide feedback on process
performance and support traceability, genealogy and integration with process history.
Position and Adoption Speed Justification: MES applications remain at the top of the list of
prioritized manufacturing investments for companies seeking control and consistency across
manufacturing operations. In process manufacturing, MES vendors have amassed decades of
experience in managing processes via input from equipment and sensors. Process MES vendors
also have provided process visibility to upstream systems/users to ensure compliance. Such
experience gives these vendors an edge in being able to support Internet of Things (IoT) devices.
MES applications for process manufacturing are starting to show accelerated movement on the
Hype Cycle. This is due in part to:
■
A renaissance in MES caused by manufacturers looking to replace legacy MES environments
with newer systems that can more easily integrate with IoT/cloud platforms
■
Further integration to applications such as LIMS and enterprise labeling
■
New integrated functionality being released by PLM, ERP and automation vendors that have
acquired MES assets
This does not eliminate the possibility of emerging technologies in plant automation, process
management and/or analytics integrated directly with system-of-record platforms (ERP) could
eliminate the need for traditional MES in some industries, but the tasks performed by MES systems
will continue to be automated and performed. The applications that perform these services may just
go by another name.
User Advice: Concentrate on the following when identifying an MES application that's right for your
environment:
■
Do not assume that the vendor solution called "MES" is an MES solution in your context, nor
that the solution you require is actually an MES by any definition.
■
Functional capabilities a product can offer in support of the process issues that need a solution,
and the degree of domain specialization required. Keep in mind that what you choose may not
actually be called MES.
■
MES applications are enterprise solutions. Use this opportunity to standardize processes and
view your manufacturing capability as value creation for the business. Define global process
templates that will speed deployments and foster standardization. Evaluate vendor capabilities
to support the enterprise, including multisite support, remote hosting and hybrid cloud-based
capabilities.
■
While defining your enterprise strategy is key to success in MES deployment, base initial
investments on boosting plant-level capabilities. This will satisfy initial ROI concerns and
provide a platform for longer-term benefits.
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Business Impact: MES applications boost manufacturing reliability by digitally enforcing processes,
methods and procedures. Because MES applications also provide the genealogy and traceability
information required for compliance, they serve as a strategic nucleus for broader manufacturing
architectures. MES data is also an important resource for determining product quality. When scoped
and implemented appropriately to integrate with other processes for product supply, MES
applications can support the growth goals of the business while removing costs and complexity.
This, in turn, drives process optimization, improves product quality and directly impacts the bottom
line. Although these systems are well-positioned to support IoT and similar technologies,
businesses need to weigh these advancements against the possibly enormous capital expenditure
of replacing plant automation equipment with newer, smarter devices.
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Sample Vendors: ABB; AspenTech; GE Intelligent Platforms; Honeywell Process Solutions; Körber
Medipak Systems; Oracle; POMS; Rockwell Automation; Savigent Software; Schneider Electric
Recommended Reading: "Magic Quadrant for Manufacturing Execution Systems"
"Toolkit: Defining the Functional Requirements for Manufacturing System Selection"
"Survey Analysis: Multisite MES Delivering Benefits Today With Cloud(s) on the Horizon"
"Market Guide for MES/MOM Implementation"
"Digital Manufacturing Requires a New Look at Old Systems"
"Harvest the Value of Smart Manufacturing in the Supply Chain, Not the Factory"
Product Requirements Management
Analysis By: Marc Halpern
Definition: Product requirements management (PRM) captures the voice of the customer and other
requirements needed for product performance, manufacturing, regulatory compliance and service.
Position and Adoption Speed Justification: Product requirements management is growing in
importance, particularly as software becomes more prevalent in physical products. Although the
majority of manufacturers still manage product requirements on spreadsheets, an increasing
number of manufacturers are exploring and adopting these capabilities to be integrated with their
product life cycle management (PLM) infrastructure. The capabilities from niche vendors are also
becoming more comprehensive and user-friendly. This is putting more pressure on the incumbent
software providers with large market presence to modernize.
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As best practices and experience with product requirements management continue to evolve,
software developers are evolving this class of capability to a new generation of functionality that
captures requirements more dynamically to capture how requirements continuously change and get
redefined due to changes in market requirements or to reflect discoveries during the design process
of how to make products better, deliver them faster, or at lower cost. Gaming technology is also
starting to be leveraged to create a virtual prototype so developers can understand how customers
will use a product. This provides early feedback on product concepts to help design and marketing
teams evaluate how well a proposed product fits customers' perceived needs.
User Advice: All manufacturers, particularly those whose products are complex engineered
systems, should evaluate this capability — either as a best-of-class solution or as one integrated
with their PLM infrastructures. Product development leaders must be careful to factor customer
experience into product requirements management. If the requirements are simply a checklist of
features and functions to be fulfilled but they are fulfilled in a way that make the product awkward to
use, then the product is likely to fail in the market. Therefore, manufacturers should augment
formalized product requirements management with gaming technologies, virtual prototypes, or
physical prototypes to validate that a product fulfills requirements in ways that please customers.
Equally as important, companies should explore what solutions allow them to enter and track
regulatory/quality requirements with alerts to noncompliance.
Manufacturers should be exploring changes to job performance metrics and business processes
that would improve the new product development and introduction team acceptance of more
disciplined requirements management.
Business Impact: Product requirements management software aids the capture of what customers
expect from products. It also shortens the time needed to verify that a product design meets
customer needs. It is particularly useful for notifying product development teams when product
requirements change. In particular, consumer goods manufacturers see benefits from capturing
market input for both new and existing products. It was once thought of as the beginning for a
product, but PLM vendors are formalizing the ideation stage with idea management scoring and
capabilities. This formalized approach ensures better tracking of ideas and traceability back to the
original origin whether that source was an individual, a focus group, or a bigger community. Equally
formalized, PLM vendors also offer capabilities to generate a product specification from an
approved product requirement, thereby allowing greater capabilities to track, control, compare and
change. Incorporating gaming technology offers better insight into requirements that will provide
customers with a great experience because gaming technology enables simulation that gives
manufacturers insight into product ease-of-use and productivity.
In summary, requirements capabilities improve the processes by which marketing and product
development teams work together to make requirements more visible. This reduces the number of
costly design changes resulting from requirements not met. Elimination of those unnecessary
design changes reduces the cost and time to deliver products to market. Worse, not meeting
requirements could mean products fail in the market.
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
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Maturity: Adolescent
Sample Vendors: BigLever Software; Dassault Systèmes; IBM Rational; Jama Software; No Magic;
Oracle; PTC; Siemens
Recommended Reading: "Digital Business Is Transforming New Product Development Priorities"
Entering the Plateau
Enterprise Manufacturing Intelligence Applications
Analysis By: Simon F. Jacobson
Definition: Enterprise manufacturing intelligence (EMI) applications provide decision support to
various operational and business roles by synthesizing and analyzing information from highly
granular, manufacturing-related data sources and making it visible and understandable through
dashboards and portals.
Position and Adoption Speed Justification: EMI applications will reach the plateau and come off
the Hype Cycle in the next 18 months. This should not come as a surprise for two reasons:
■
There is no shortage of manufacturing data, and enterprises want to access and use more of it.
The efficiency improvements and cost returns from simply visualizing production performance
are often more than enough to justify the investment(s) at the site level. However, the growing
demands for widespread agility and decision support beyond the plant and in the supply chain
are being met by a wider range of analytics options that are capable of providing more than
basic, post facto visibility into operational performance through descriptive and diagnostic
dashboards. They are capable of broadening the understanding and knowledge of production
processes through predictive, prescriptive and cognitive analytic techniques across multiple
sites. This is threatening the longevity of incumbent, plant-specific EMI deployments.
■
The market is flooded with point applications, service provider frameworks, modules from both
ERP and MES vendors, and emerging offerings from IoT vendors — with little parity between
providers. Over the past few years, we have highlighted that EMI applications would be
subsumed by industrial operations intelligence (IOI) as the provider market for factory-level
analytics evolves. At one point it was common for a provider to have offerings in both camps.
Roadmaps and offerings have evolved from point solutions to platforms with sophisticated
analysis and workflow tools, data management, and scalable deployment models (e.g., cloud).
As a result, the core functionality that differentiated EMI — aggregating, analyzing, visualizing,
contextualizing and propagating production data — is now core functionality to establishing a
wider IOI platform. This has commoditized and limits the value that a stand-alone EMI
application will provide.
User Advice: When evaluating analytics for manufacturing operations, supply chain leaders
responsible for manufacturing operations strategy and performance must:
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■
Recognize that EMI applications provide basic visibility into operational performance through
descriptive and diagnostic dashboards. The efficiency improvements and cost returns from
simply visualizing production performance are often more than enough to justify the
investment(s). Continue to use these applications to provide a fact-based description and
diagnosis of production process performance and to spur continuous improvement across
easy-to-quantify metrics such as OEE and energy consumption.
■
Going forward, appraise providers that incorporate base EMI capabilities as part of a widerreaching platform that supports a diversified set of analytic techniques, use cases and data
sources. Start with your incumbent vendors.
Business Impact: EMI applications are a low-risk, high-reward investment. They help overcome the
visibility hurdles that stand in the way of understanding line- or site-level performance as it relates to
manufacturing costs, asset availability and capabilities, energy management, and other factors that
can constrain responsiveness.
Benefit Rating: Moderate
Market Penetration: More than 50% of target audience
Maturity: Mature mainstream
Sample Vendors: Aptean; Dassault Systèmes; OEEsystems; Parsec; PTC (ThingWorx); Rockwell
Automation; SAP; Schneider Electric; Shoplogix; VIMANA
Recommended Reading: "Helping Manufacturers Move Beyond Visibility to Advanced Analytics"
Simulation and Virtual Prototyping
Analysis By: Marc Halpern
Definition: Simulation and virtual prototyping are the modeling of parts, products, systems, facilities
and processes using software tools, predicting how they will behave and assessing their
acceptability, without building and testing actual prototypes.
Position and Adoption Speed Justification: Product life cycle management (PLM) software
companies and specialty computer-aided engineering (CAE) software companies continue to invest
in expanding the capabilities of this technology for established simulation of mechanical and
electrical behavior. An increasing number of the well-established software providers in this category
now promote themselves as providers of virtual prototyping capabilities, rather than staying with the
CAE label. The growing mind share surrounding digital twins bolsters the visibility of this technology
since simulation is an important capability associated with digital twins. Simulation requires
substantial education and experience among users to become efficient with it. CIOs seem to lack
sufficient knowledge about the business value. However, as user spending continues to increase,
CIOs are motivated to learn. Consolidation in this space continues as new vendors continue to
emerge with new capabilities. For example, simulation supporting autonomous vehicle development
has become a top priority. Consequently, new simulation vendors continue emerging to support
autonomous vehicles.
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User Advice: CIOs and other IT professionals responsible to support new product development
software in markets where customers prioritize product reliability and ergonomics should make
investments to support this type of capability. Top priorities for CIOs include growing demand for
access to compute power and network bandwidth. Managers need education on the business
value, while engineering teams need training on best practices, proven approaches and technical
details. Additionally, manufacturers should plan to leverage the technology for digital twin strategies
across manufacturing, service and marketing as well as product development. Virtual prototyping
should be done in tandem with failure mode and effects analysis.
Business Impact: Simulation and virtual prototyping eliminate the need for a substantial
percentage of testing. For example, in the automotive industry, simulation can reduce millions of
dollars of physical prototype testing to hundreds or thousands of dollars of simulation. Engineers
increasingly use simulation as part of the design process, enabling smarter design decisions, further
reducing time and cost to bring new products to market. In addition, simulation has played a key
role in reducing the time for new vehicle development from five years during the 1980s to three
years and less. Now automotive companies use simulation to guide and validate designs of
autonomous vehicles. Other industries such as industrial equipment, aerospace, medical devices,
etc., benefit from simulation and virtual prototyping with reduced cost and time to develop new
products.
Benefit Rating: High
Market Penetration: More than 50% of target audience
Maturity: Mature mainstream
Sample Vendors: Altair; ANSYS; Comet Solutions; Dassault Systèmes; Hexagon; SAP; Siemens
Recommended Reading: "Digital Business Is Transforming New Product Development Priorities"
"Enhance Business and Manage Risks With Appropriate Simulation and Computer-Aided
Engineering Use"
"Managing Prototypes More Than Activities Yields Greater Product Development Success for
Manufacturers"
"Product Simulation and Test Content Management Requirements to Accelerate Product Design"
Parts and Materials Search and Selection
Analysis By: Marc Halpern
Definition: Parts and materials search and selection software for direct materials sourcing refers to
applications used to identify parts and materials to purchase — and to coordinate supplier and
manufacturer selection, parts selection, procurement, and quality or compliance. It enables
searching for parts that may or may not be in catalogs, with visibility to preferred suppliers. This can
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also include evaluations and "scorecarding" of supplier performance and the quality of the parts
delivered.
Position and Adoption Speed Justification: These capabilities are readily available to all users.
The functionality has been steadily advancing to include visual search as well as mainstream textual
search. Users can request that the software identify parts of shape similar to geometric models that
users submit to the system. Therefore, users can now use a combination of geometric search and
textual search for additional context to identify the parts they seek. Specialty vendors, large product
life cycle management (PLM) vendors and publishers of parts catalogs that include computer-aided
design (CAD) models and parts specifications are absorbing more direct materials sourcing
functionality related to PLM. Independent vendors have been mostly acquired or marginalized.
Based on a sampling of client interactions, the majority of Gartner manufacturing clients are using
these capabilities, and the functionality is becoming more accepted and mature.
User Advice: All manufacturers should investigate and use these capabilities to accelerate the
selection of suppliers and parts during product development and introduction. Since these tools
primarily support parts and supplier selection, they should typically be used in tandem with software
capabilities that specialize in supply chain logistics. As a prerequisite, manufacturers must define
processes and relationships that integrate sourcing with the roles of engineers and designers in
product development. Procurement must understand design considerations and the use of the
parts it buys to communicate more successfully with suppliers. Engineers must be trained to
understand the cost sensitivities and factors that influence the success of relationships with
suppliers. Management should understand the sourcing implications, including single versus
multisourcing, supply part or materials' life cycle risk, the implications of "nearsourcing" or sourcing
in diverse geographies.
As environmental regulatory mandates — such as Restriction of Hazardous Substances (RoHS),
Waste Electrical and Electronic Equipment (WEEE) and Registration, Evaluation, Authorisation and
Restriction of Chemicals (REACH) — become increasingly important, manufacturers should adopt
visibility of relevant regulatory data as an important criterion for selecting sourcing capabilities.
Manufacturers should also consider supplier performance metrics that summarize a supplier's track
record in making environmental and sustainability data visible.
Business Impact: Direct materials sourcing software enhances a manufacturer's leverage when
negotiating with suppliers, which leads to reduced material and service costs by linking ongoing
supplier performance firmly to the inclusion of supplier parts in new products. Moreover, by
identifying currently used components included in new designs, manufacturers can reduce
inventory levels, respond with greater agility to shifts in demand or respond to supply chain risks.
Direct material sourcing software streamlines the process of identifying alternative parts when
standard parts are not available.
Benefit Rating: Moderate
Market Penetration: More than 50% of target audience
Maturity: Mature mainstream
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Sample Vendors: CADENAS PARTsolutions; Dassault Systèmes; IEEE GlobalSpec; Thomas
Recommended Reading: "Predicts 2017: Procurement and Sourcing Technology"
"Magic Quadrant for Strategic Sourcing Application Suites"
Appendixes
Figure 3. Priority Matrix for Discrete Manufacturing and PLM, 2017
Source: Gartner (July 2017)
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Hype Cycle Phases, Benefit Ratings and Maturity Levels
Table 1. Hype Cycle Phases
Phase
Definition
Innovation Trigger
A breakthrough, public demonstration, product launch or other event generates significant
press and industry interest.
Peak of Inflated
Expectations
During this phase of overenthusiasm and unrealistic projections, a flurry of well-publicized
activity by technology leaders results in some successes, but more failures, as the
technology is pushed to its limits. The only enterprises making money are conference
organizers and magazine publishers.
Trough of
Disillusionment
Because the technology does not live up to its overinflated expectations, it rapidly becomes
unfashionable. Media interest wanes, except for a few cautionary tales.
Slope of
Enlightenment
Focused experimentation and solid hard work by an increasingly diverse range of
organizations lead to a true understanding of the technology's applicability, risks and
benefits. Commercial off-the-shelf methodologies and tools ease the development process.
Plateau of Productivity
The real-world benefits of the technology are demonstrated and accepted. Tools and
methodologies are increasingly stable as they enter their second and third generations.
Growing numbers of organizations feel comfortable with the reduced level of risk; the rapid
growth phase of adoption begins. Approximately 20% of the technology's target audience
has adopted or is adopting the technology as it enters this phase.
Years to Mainstream
Adoption
The time required for the technology to reach the Plateau of Productivity.
Source: Gartner (August 2018)
Table 2. Benefit Ratings
Benefit Rating
Definition
Transformational
Enables new ways of doing business across industries that will result in major shifts in industry
dynamics
High
Enables new ways of performing horizontal or vertical processes that will result in significantly
increased revenue or cost savings for an enterprise
Moderate
Provides incremental improvements to established processes that will result in increased revenue
or cost savings for an enterprise
Low
Slightly improves processes (for example, improved user experience) that will be difficult to
translate into increased revenue or cost savings
Source: Gartner (August 2018)
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Table 3. Maturity Levels
Maturity Level
Status
Products/Vendors
Embryonic
■
In labs
■
None
Emerging
■
Commercialization by vendors
■
First generation
■
Pilots and deployments by industry leaders
■
High price
■
Much customization
■
Second generation
■
Less customization
■
Maturing technology capabilities and process
understanding
■
Uptake beyond early adopters
■
Proven technology
■
Third generation
■
Vendors, technology and adoption rapidly evolving
■
More out-of-box methodologies
Mature
mainstream
■
Robust technology
■
Several dominant vendors
■
Not much evolution in vendors or technology
Legacy
■
Not appropriate for new developments
■
Maintenance revenue focus
■
Cost of migration constrains replacement
■
Rarely used
■
Used/resale market only
Adolescent
Early mainstream
Obsolete
Source: Gartner (August 2018)
Gartner Recommended Reading
Some documents may not be available as part of your current Gartner subscription.
"Understanding Gartner's Hype Cycles"
"Best Practices to Avoid Obsolescence of PLM Applications and Data"
"Digital Manufacturing Requires a New Look at Old Systems"
"Harvest the Value of Smart Manufacturing in the Supply Chain, Not the Factory"
"IoT Enriches PLM With 360 Degrees of Product Life Cycle Data"
"2018 CIO Agenda: Global Perspectives in Heavy Manufacturing"
"Best Practices for Developing a Successful Business Case for Digital Product Life Cycle
Management"
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"Predicts 2018: Expect Generational Impact on Product Design and Manufacture"
"Redesign the IT Operating Model to Accelerate Digital Business"
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