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Discussion Forum 1
Pushpinder Singh Toor (2117060)
University Canada West
CMPT 641: Digital Transformation
Prof. Afshin Doust
October 11, 2024
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Digital twins are an emerging technology that is disrupting the way industries perform
their planning, operations and asset management. Developed for modern factories and predictive
maintenance ABB (2023) defines the digital twin as a full virtual copy of any system or asset
that can synchronize with design information along real/time running data. If more complex use
were to be employed, say simulations, diagnostics and prediction is done.
Digital twins aid the review and interrogation of 3-D designs to assure fit during design.
According to ABB (2023), in these simulations, the mechanical, thermal and electrical behaviour
are considered and coupled directly. This makes it faster to build prototype and reduces time to
market and increase product quality.
For any of these types off system, we need to check all physical links while making this
system 3D model that is little solution to test the working prototype with actual fit up of whole
working LCD display interface in final enclosure and total size foot print into provided space by
end consumer. With model-based design, engineers can connect digital twins of parts and model
how the parts function together, revealing everything from how data is transferred to how
controls are run and mechanical and electrical operation are coordinated. This process reduces
customer downtime and physical onsite connect time.
By combining historical and real-time operating data with predictive algorithms, digital
twins are great at predictive maintenance. This connection gives details about the health of the
equipment and how it might break down, which helps with planning maintenance better and
lowering unexpected downtime (ABB, 2024). As an example, ABB has created digital twin apps
for ship maintenance that keep an eye on important equipment and model how systems age based
on how they are used in real life.
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Digital twins are useful because they can combine data from different sources over the
whole lifecycle of an object. ABB (2024) stresses how important it is to have a central digital
twin directory that can connect data saved in different places. This all-around method makes it
easier to make smart decisions and keep getting better. Grieves (2017) postulates that this
lifecycle-wide data merging is what makes digital twins different from older simulation and
modelling methods.
Digital twins are used in many different ways in business settings. ABB has used digital
twins to improve the way materials are handled in a number of places, such as mines, power
plants, and ports. According to ABB (2024), these programs simulate the flow of materials, make
data sharing automatic, and improve inventory and quality control.
Looking forward, digital twins are becoming an integral part of not only production but
also maintenance. As per ABB (2024), the company is in the process of developing a platformagnostic digital capability, spanning hardware to cloud across all industries. Which will enable
even more innovation and pace with business operations.
So digital twins are a really new thing for industries that manufacture and maintain
things. They deliver a visibility, predictability, and optimisation previously unseen by bridging
the physical and digital world. This allows for smarter, more efficient and longer-lasting
industrial operations.
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References
ABB [ASEA Brown Boveri]. (2023, July 16). Digital twin applications. Industrial
Software. https://new.abb.com/industrial-software/features/model-predictivecontrol-mpc/digital-twin-applications
Grieves, M. (2017). Virtually Perfect: Driving Innovative and Lean Products Digital
Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex
Systems. https://polytechnic.purdue.edu/sites/default/files/files/Fall16%20Grieves%20%20Digital%20Twin%20Mitigating%20Uppredicatable%20systems.pdf
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital twin driven prognostics and
health management for complex equipment. CIRP Annals, 67(1), 169–172.
https://doi.org/10.1016/j.cirp.2018.04.055
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Comments
#09 Oct, 11:32 pm (Nabina Khadka)
Your post offers a good overview of the standard changes online scheduling software is
having on receptionists, and the way businesses function It nicely captures some of the key
trends like cloud-based client management, chatbots based on RPA, mobile responsive interfaces
and integrated payment systems. This is really true, and these new ideas do help make things
smoother and keep customers happier.
You might look further at the potential impact on workforces. There are both upsides and
downsides to automation, as you can well imagine — the impact of automation on job loss, and
the evolution of what a receptionist will look like in years to come. According to Woodruff et al.
(2024), AI and automation are not only taking away our tasks, but they also build up jobs and
opportunities. For example, instead of paying for labor to manually make appointments, we
might use that same energy to run bots and algorithms — on the back end at least — while
shifting less rote, more challenging, relationship-based tasks that require emotional intelligence
to a former receptionist turned "digital concierge" who can swoop in when problems or
opportunities arise. This transformation poses challenges and opportunities to the workforce skill
development and upskilling of employees in todays digital age. Just goes to show how we always
have to keep learning and adapting to new technology.
Reference
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Woodruff, A., Shelby, R., Patrick Gage Kelley, Rousso-Schindler, S., Smith-Loud, J., & Wilcox,
L. (2024). How Knowledge Workers Think Generative AI Will (Not) Transform Their
Industries. https://doi.org/10.1145/3613904.3642700
2. Replied to Aneet Pal Kaur on October 11, 2024, 12:05 PM
Walmart is a prime example of this as they use technology to deliver better customer service and
run their business more efficiently than ever. AI-driven chatbots, online appointment booking
systems and voice automation have transformed the way customers interact with Walmart. This
has not only reduced waiting times but also improved overall customer satisfaction. These
technology advances improve customer experience and convenience while also helping Walmart
make best use of its staff and lower costs.
Human front desk workers are replaced by automated options––that itself proves how customer
service has evolved in the 21st century digital era. This might concern some who think that the
human element will be lost in customer ties, but it actually lets staff focus on complex and
productive work. Walmart's plan demonstrates how technology can play a role in helping stores
to keep pace as the world becomes increasingly digital-focused, serving new customer demands
from online orders while streamlining their operations.
Given how quickly everything has to change in the retail industry, Walmart´s technology-focus
strategy could serve as a model for others seeking to improve their customer service and tastes
(Sagar, 2024). However, businesses also need to identify a balance between automation and
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more personalized one-on-one customer experiences so as to ensure they are covering all the
bases with their customers while maintaining a positive brand reputation.
Reference
Sagar, S. (2024). The Impact Of Digital Transformation On Retail Management And Consumer
Behavior The Impact Of Digital Transformation On Retail Management And Consumer
Behavior. Issue 1. Ser, 26, 6–14. https://doi.org/10.9790/487X-2601010614
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Replied to Simran on October 12, 2024, at 1:30 am
Hello Simran,
This excellent post covers recent developments in business process automation for customer
interaction. The content is divided up by themes like online appointments, AI-driven chatbots
and voice technology. The author does a good job breaking down how these techs are making
workflows more efficient, cutting costs and enhancing customer service.
The following is an illustrative contribution about offering a more personalized experience in
automated customer interactions.
Sure, automation has made that efficiency possible, but there is a rising tide of richer
personalized automated experiences being pushed into the mainstream (Jaffery, 2022) . Tailored
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interactions — AI and machine learning capabilities can now review massive volumes of data
about a distinct customer. This means more than salutation of customers by their names, they
know what you would like to order, your purchase history and even better can anticipate the next
need.
For example, in the case of e-commerce a section of AI based recommendation engines are being
used not only for recommending products based on browsing preference but also local events
and weather. All of this customization to the customer level can do wonders for improving
customer engagement and satisfaction.
Sentiment analysis is also seeing increased integration with automated customer service systems.
Such systems can interpret the tone and emotion in customer messages or voice calls, and
prioritize things that are more urgent or sensitive for human intervention — i.e. walk a finer line
between efficiency of delivery against empathy (Jaffery, 2022) .
Quite, but as systems continue to evolve privacy matters and there are ethical issues around using
data. Balancing personalization and privacy will remain a challenge for businesses going
forward.
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Reference
Jaffery, B. (2022). Connecting Meaningfully in the New Reality. In Deloitte. Deloitte.
https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/deloitte-analytics/ca-en-omniaai-marketing-pov-fin-jun24-aoda.pdf
4. Replied on October 13, 2024 at 10:35 am
Better known as digital twins, they are the virtual mirror images of physical assets, and
transforming how things get built and taken care of. In manufacturing, they can be used to
digitally prepare and position parts so that mistakes are eliminated and performance is improved.
They also simplify the convergence of mechanical, electrical and software pieces in a digital
environment shortening the design cycle.
In predictive maintenance, digital twins leverage real-time data and analytics to monitor the
health of equipment, predict when it will need a tune-up or fail altogether, allowing businesses
greater control over repair schedules. This tactic reduces unplanned downtime, extends asset life
and helps schedules maintenance more efficiently. The ability to simulate the larger part of
unobservable factors, favors accurate diagnosis (Perno et al., 2022)
Digital twins make us smarter to take data-guided decision-making. It saves businesses time and
money. It ultimately allows for real-time observation, intricate diagnostics, and predictive
measures. This changes how things are made and, importantly, it changes the way manufacturing
is managed. The smarter way of running factories is via digital twins, which employ predictive
analytics to maintain uptime, increase production rate and extend life cycle for assets.
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Reference
Perno, M., Hvam, L., & Haug, A. (2022). Implementation of digital twins in the process
industry: A systematic literature review of enablers and barriers. Computers in Industry,
134, 103558. https://doi.org/10.1016/j.compind.2021.103558
5. Replied on October 13, 2024 at 10:45 am
A key tenant to train AI is a replica of the digital twins directs in intelligent manufacturing
industry, and that emerging trend became reality with predicting maintenance using digitical
twins. The models combine data fusion, iterative optimization and interactive feedback across
cyberspace to the real world (Zhong et al., 2023). Check a form of condition-based maintenance
performed by monitoring the equipment that continuously evaluate from its status to trends and
done preventive other than predictive.
This requires data capture, status monitoring and prediction of health as well as maintenance
planning. Sensors acquire environmental and equipment information, which provide data to
enable their status estimation now as well anticipation in the sequences of time. The Fault
detection uses both model-based and data-driven methods. For example, maintenance is planned
by using health forecasts which are usually in percentage form or remaining useful life.
This strategy maximizes decision-making by optimizing the trade-off between safety and cost
within Industry 4.0 construct thereby improving Equipment Reliability along with Operational
Efficiency
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Reference
Zhong, D., Xia, Z., Zhu, Y., & Duan, J. (2023). Overview of predictive maintenance based on
digital twin technology. Heliyon, 9(4), e14534.
https://doi.org/10.1016/j.heliyon.2023.e14534