IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
Edited by: Prof. Jay Lee
1. Introduction
For the past decade, the impact of web-based technologies has added “velocity’ to
the design, manufacturing, and aftermarket service of a product.
Today’s
competition in manufacturing industry depends not just on lean manufacturing, but
also on the ability to provide customers with lean service and life-cycle costs for
sustainable value. With emerging use and applications of internet and wireless
communication technologies, the impact of e-intelligence is forcing companies to shift
their manufacturing operations from the traditional factory integration philosophy to
an e-factory and e-supply chain philosophy. It transforms companies from a local
factory automation to a global enterprise and business automation.
The
technological advances for achieving this highly collaborative design and
manufacturing environment is based on multimedia type web-enabled engineering
tools and a highly reliable communication system for enabling distributed procedures
in concurrent engineering design, remote operation of manufacturing processes, and
operation of distributed production systems. This transition is dependent upon the
advancement of next-generation manufacturing practices on “e-manufacturing ”
which is focused on the use of internet and tether-free communications technologies
to make things happen collaboratively on a global basis.
Figure 1 illustrates the evolution of product and manufacturing innovation and the
future trend from 1980s to today and future. Nowadays product focus has been
changed from information and computer intelligence to knowledge and e-intelligence
in a tether-free environment as need to fulfill the fickle customer demands.
Manufacturing focus is shifting from factory automation to business automation for
high velocity performance. Quality focus is addressing e-service for customer
solutions and asset optimization where as in the past it was mentioned as TQM and
six sigma for business process as the manufacturing is not beyond the factory floor.
These trends necessitate new knowledge generation and manufacturing strategies in
using tether-free technologies to impact next-generation products and manufacturing
systems. As production systems are characterised by high velocity business
automation, it is necessary to monitor and control the automated manufacturing
systems to obtain an optimised outcome anywhere and anytime
These transitions demand the development of (1) next-generation manufacturing,
maintenance and service sciences and practices, and (2) the education and training
of knowledgeable leaders, engineers and general workforce. The new benchmark
for competitive manufacturing companies is a paradigm shift to web-enabled
engineering focused on e-intelligence for integrated product design, manufacturing,
and service. These transformations require a new breed of leaders, engineers and
scientists who possess interdisciplinary knowledge and skills, and are internationally
astute in technical, social, economical, and cultural issues in a global environment.
3. e-Manufacturing: Definition and Principal Requirements
Today’s customers demand more customized products, faster delivery, and instant
access to their order status. Lead times of products must be slashed. A company’s
capability to answer customer orders and bring about solutions to their needs
primarily depends on their production speed, not their warehouse capacity, or their
quickness to accept and process the orders. Because of this, many companies
started outsourcing their production units to focus on the marketing and servicing
side of the business to satisfy their customers. However, this is not an ultimate
solution as they are still dependent on the production capabilities and
responsiveness of their suppliers. If these suppliers cannot find a solution and
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
improve their production speed, customers will move to another option, and today,
that is easy with access through today’s global, open and web based market.
1980s
Product
Focus
Intelligent
Mechatonics (data &
control intelligence)
1990s
Product that Thinks
and Links
(information
&
computer
intelligence)
2000-2010
Products that Learn,
Grow,
Reconfigure,
Sustain in a Tether-Free
Environment (knowledge
& e-intelligence)
Manufacturing
Focus
Factory Automation
(flexibility)
Enterprise
Automation
(agility)
Business Automation
(velocity
and
manufacturing)
Quality
Focus
SPC & TQM
For Mfg Process
(factory)
Six-Sigma
for
Business Process
(enterprise)
E-service for Customer
Solutions
and
Asset
Optimization
(customers)
e-
Figure 1: Evolution on Product, Manufacturing and Quality
e-Manufacturing uses e-intelligence and web-enabled tools (prediction,
transformation and optimization and synchronization) to integrate information from
equipment, suppliers and factory floor, Enterprise Resource Planning (ERP) and
Supply Chain Management (SCM) to achieve maximum business performance.
Transformation: abilities to transform data from machine or device level to
reconfigurable web-level application formats (SQL, XML, HTML, etc). At this level,
many web-enabled applications can be performed. For example, we can perform
remote machine monitoring and performance calibration. Experts from machine tool
manufacturers can assist users to analyze machine measurement data and perform
prognostics for preventive maintenance. Users from different factories or locations
can also share this information through these web tools. Everyone shares the same
set of data formats without language barriers.
Machine performance prediction: abilities to predict the degradation of machine or
process performance for prognostics. A watchdog agent, which has been
pioneered by the Center for Intelligent Maintenance Systems (IMS), provides
continuous monitoring and prognostics of asset degradation, and enables companies
to rapidly evaluate assets’ performance.
Its output represents the quantitative
measures of the performance of the product or equipment at a given state. By
comparing performance value at different states, it can predict and assess the
condition and performance of complex and sophisticated machine systems. To
effectively apply this methodology in various kinds of products and machines, its
integration with working environment (i.e., sensors, actuators, controllers, and human
interfaces) needs to be further developed.
Synchronization with supply chain systems: synchronize manufacturing
performance with e-business tools and supply chain systems. The synchronization
element provides integration with e-business systems including Customer Relations
Management (CRM), Supply Chain Management (SCM), B2B e-Commerce systems.
For example, by knowing the degradation of machines in the production floor, the
operation supervisor can estimate its impacts to the materials flow and volume and
synchronize it with the ERP systems. The revised inventory needs and materials
delivery can also be synchronized with other business tools such as the Siebel CRM
system. For example, when a cutting tool wears out, the information can be directly
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
channeled to the tool suppliers, and also update the database for tool performance
management. In this case, cutting tool companies no long sell cutting tools, but
instead, sell cutting time. In addition, when the machine degrades, the system can
initiate a service call through the service center for prognostics.
3. Needs and Opportunities in e-Manufacturing
Today, even with the best implemented lean manufacturing practices, many
companies still face the following problems, which are all interrelated to each other:
1) Defect parts, 2) High downtime, 3) High energy utilization and cost, 4) Long
changeover and ramp up time, 5) Long lead time for new product realization (long
part and process design, and validation periods), 6) Slow decision making (high
inventory, slow scheduling, etc.).
e-Manufacturing can bring solutions to these problems by addressing the following
research and development issues: 1) Development of intelligent agents for
continuous, real time, remote and distributed monitoring and analyses of devices,
machinery and systems to provide the first and most needed element of predictive
maintenance via offering real time information about machine’s performance status
(health condition), its capability of producing quality parts (or completing its tasks),
etc, 2) Development of remote, distributed and web-based quality control systems
and their integration with intelligent predictive agents described above in order to
identify quality variations and their causes in real time, 3) Development of D2B
(device-to-business) platform for complete transformation, optimization and
synchronization of plant floor problems, issues, and solutions with higher level
production, maintenance and transaction scheduling systems, inventory control
systems, supply chain systems and with ERP for dynamic scheduling of production,
maintenance, human and other resources, dynamic inventory monitoring and control,
optimization of energy/power utilization, etc., 4) Development of virtual design
platform for collaborative part, process, tooling design among suppliers, design and
process engineers and as well as customers for fast validation and decision making
In order to effectively implement the e-Manufacturing platform described above in
various manufacturing environments (food processing, metal fabrication, petrochemical processing, paper/tissue production, aircraft industry, etc.), the followings
need to be accomplished and integrated:







Data from various machinery, devices and processes must be processed
(analyzed) locally.
Data from disparate systems and machinery needs to be merged (i.e.
correlated)
Data quality need to be ensured, may be, by having data quality indexes
Advanced prediction methods and tools need to be developed in order to
detect degradation, performance loss or trend of failure not faults,
breakdowns, etc.
For flexibility of data processing, analysis, remote monitoring and control of
devices on the plant floor existing tether-free technologies (wireless
communication, wireless sensor, vision systems, etc.) should be utilized
Performance issues of tether-free (wireless) communication and network
architecture should be analyzed and ensured in terms of security, safety,
reliability, robustness, and scalability. Data assurance -getting correct data to
correct user, never allowing data to incorrect user- need to be provided
For wider and easier implementations, new standards need to be Identified
and developed. Existing efforts to develop standards should be leveraged via
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002

active participation and involvement (i.e. IEEE 802.xx standard committees,
MIMOSA, etc.)
Identify the needs and areas in terms of interfacing factory floor side of emanufacturing with ERP, SCM, EAM systems for smooth synchronization and
business automation
4. Roadmap and Recommendations for e-Manufacturing
 The business models as a result of e-Manufacturing technology should be
first clarified whether it is going to be (a) Service based, (b) Life-cycle
guarantees, and if it is financially justified
 Engage control suppliers & sensor manufacturers- provide solutions, not just
technology
 Identify transition from Legacy (current) systems- Need migration path
 Identify new applications (e.g., tracking materials, assets) and possible
advantages (improved ramp time, etc.)
 Identify the requirements for work force needed to support e-Manufacturing
practices (i.e. interdisciplinary knowledge and skills, international
understanding, etc,)
 Develop, impact and participate for new academic curricula on eManufacturing courses, workshops, and training material
 NSF programs on e-Manufacturing should be developed to provide funding
for fundamental research and educational activities
 Additional opportunities for exchanging information between manufacturing
and tether-free communities in forms of workshops, standard groups, web
based discussion groups, etc should be organized.
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
E-Manufacturing Business Models
Sub Group Members:
1.
2.
3.
4.
5.
6.
7.
8.
Ke-Zhang Chen
Barry Crook
James Dantalis
Walter Dooley
David Lowe
Simon Snowden
Vince Thomson
Aaron Walsh
Think Tank Discussion summary:
1. Steps in defining your business model
Step 1 Definition of a business
 Who is the customer?
 What is our business?
Step 2 E Creation
 How to use movement of information to enhance value
(benefit/cost)
Step 3 Self Assessment
 Which businesses or parts of a business can deliver
better value to the customer using ‘e’ ?
2. Business models Benchmarking
A. Customer-business groupings
B. Competition assessment
C. How does/where can ‘e’ deliver value to outdo our
competitors?
3. Implementation technologies
A. What capabilities are required (best practices)
B. Identify needs
C. Analyze gaps in capability
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
4. Processes to realize E-Manufacturing
A.
B.
C.
D.
Order fulfillment
New product introduction
Customer service
Infrastructure provision
5. Issues
A.
B.
C.
D.
E.
F.
Evaluation of information flow versus value
Integrated systems for information flow
Most systems are hybrids of existing technology tools.
No industry specific system available.
Need tools to define systems and best components.
Often a mismatch in system design and technical
capability, e.g., web design and communication
bandwidth
G. Metrics to measure value (output-input)
6. Supply chain models
A.
B.
C.
D.
E.
Manufacturer controls all parts and materials.
Manufacturer controls assembly of subsystems.
Manufacturer controls product specification only.
Supplier controlled inventory
Vendor managed inventory
7. Product development models
A. Inside company
B. Partnership
8. Other Issues
A. Customized information systems
B. Public versus confidential information
C. Security
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
9. Needed Tools:
A. Simulation
B. Modeling systems: languages, strategies, processes…
C. Best Practice Database







Standard Template
Case Studies (solutions)
Business model/description
Metrics
System solutions
Key issues - do’s and don’ts
Information systems design
i.
Modules
ii.
Performance measurement
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
Emerging Technologies / Tools and
Methodologies
Sub group Members:
1.
2.
3.
4.
Dr William A. Estrem
Dr K.W. Chan
Dr. Y.C. Kao
Dr. H.C. Lau
Think Tank Discussion summary:
1. Critical Infrastructure for Manufacturing Enterprises
A.
B.
C.
D.
E.
Business Continuity and Disaster Preparedness
Security management (Physical and Information Security)
Facilities Management
Energy systems management
Communication Management
2. Advanced Telecommunication
A.
B.
C.
D.
E.
Mobile
Wireless
Broadband
Satellite
Convergence of Voice/Video/Data
3. Open system, standards and interoperability
A. Open Control Architecture
B. Open source code – Linux, Apache
4. Information Aggregation, Analytics and Predictive Engineering
A.
B.
C.
D.
E.
F.
Data warehousing/marts
Data mining
Knowledge Management
Indexing
Document management
Data query engine
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
5. Distributed Computing
A.
B.
C.
D.
E.
Message Oriented Middleware
Publish and Subscribe
Peer to peer
Web services
Enterprise Application Integration
6. Telematics / Telemetry
A. Human Machine Interface
B. Instrumentation and Transducers
C. Supervisory Control and Data Acquisition (SCADA)
7. Collaborative tools
A.
B.
C.
D.
E.
F.
Interactive grids
Computational grids
Immersive virtual reality
Telepresence
Simulation
Synchronous and asynchronous
 Technology Matrix:
 Business functional value vs. Applicable Emerging Technologies /
Tools and Methodologies
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
Critical Issues
Sub Group Members:
1.
2.
3.
4.
5.
6.
Felix Chan
Bill S.Y. Tsai
Rohan Dalton
Mark Stevens
Der-Baan Perng
Kuang-Chao Fan
Think Tank Discussion summary:
1. Different critical success factors in developing a
supply chain system in different countries:
A. Identify areas for performance improvements (where
and what)





visibility - info sharing and processing
response time
fill rate
accuracy
trust
B. Tools and technologies for Performance measurements
(what and how)
C. Data collection and collation (which and where)
2. SME Roles
A. financial benefits and constraints
B. technical risks, barriers and knowledge know how
C. partnership gaps
3. Security and data transparency within a supply chain
system
A. how much data to give out
B. system failure/reliability
C. trust
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
4. What functions will increase value, reduce product
variations and differentiate customer services
5. Data transmission barriers
A. cost
B. bandwidth
C. availability
IMLF 2002 – E Business and E Manufacturing Think Tank led by Prof Jay Lee
8th – 10th February 2002
International Collaboration /
Test Bed Sharing
Sub Group Members
1.
2.
3.
4.
Prof. S. Wadhwa
Prof. Yoke San Wong
Dr Jianguo Wang
Prof. Jay Lee
Think Tank Discussion Summary
Collaboration Focus: Joint R&D, Share Experiences, Knowledge
A. Best Practices (E-Manufacturing)
 Supply Chain Management (SCM): Models, Experiences
 CIM Systems and Beyond: Models, Experiences
 New Business Models, Test Beds, Experiences
B. Integrated Performance Metrics
 Integration of Performance Measures: SCM
 Decision-Information Systems: Value of Information
 E-Manufacturing: New quantitative/qualitative measures
C. Enabling Technologies
 Agents for Planning and Control, DSS
 Machine /Device Interfaces
 Agents for Machine Prediction and Prognostics
D. Tool Libraries
 Simulation, AI: Intelligent Manufacturing, Maintenance
 Optimization, Improvement Strategies
 Tools for Collaborative Decision Making
E. Demo Models to Promote E-manufacturing Concepts
 New Value Propositions,
 Change Motivation,
 Mindset Evolution Towards Innovation