Product Lifecycle Management Michael Grieves Pdf Download
Karri Pretty
The digital twin concept gained recognition in 2002 after Challenge Advisory has hosted a presentation for Michael Grieves in the University of Michigan on technology. The presentation involved the development of a product lifecycle management center. It contained all the elements familiar with the digital twin including; real space, virtual space and the spreading of data and information flow between real and virtual space. While the terminology may have changed over the years the concept of creating a digital and physical twin as one entity has remained the same since its emergence. While its commonly thought to be developed in 2002, digital twin technology itself has actually been a concept practiced since the 1960s. NASA would use basic twinning ideas during this period for space programming. They did this by creating physically duplicated systems at ground level to match the systems in space. An example is when NASA developed a digital twin to assess and simulate conditions on board Apollo 13.
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When Dr. Michael Grieves first presented his visionary research of a conceptual digital model underlying product lifecycle management at a conference in 2002, he had the inclination it could potentially drive precision manufacturing.
A digital twin is a virtual representation of a piece of equipment across its lifecycle. Although the application and purpose of digital twins differ from one application to another, data from sensors are typically used to map and analyze how the object responds to the physical world. Statistical and mechanistic modeling can be used to simulate, monitor, diagnose, predict and recalibrate outcomes of a jet engine, a physical plant, a city or even a living heart.
The pay-out, according to Grieves, was that the effective use of information became a replacement for the probability of wasting physical resources, such as time, energy and material. It generated efficiency across the product lifecycle, from the design and manufacturing, to the operating and disposal phase.
Machine Design: Today there are many versions of what constitutes a digital twin. What requirements should be met before we can actually label it a digital twin? Which elements should be present?
But again, you want to drive it from the use case. What use of that information am I going to have? And unfortunately, sometimes on the data side we get caught up with massive amounts of data and no information. We need to be conscious of the fact that we need to be able to use that information and create value in the organization, by either reducing costs or increasing functionality and capability. Or else we ought not to be doing that sort of thing.
MD: This brings us to the role of the engineer and decision-making. How are digital twins influencing decision-making in the design phase? And how does it then follow on through into the production cycle?
MD: By extension, can we talk about the idea of the digital twin as an exact replica or description of reality versus being the tool that does predictive analysis and drives reality? How will the digital twin influence future decision-making?
Digital Twins are all the rage. They seem to be more popular than Pokemon Go was in July of last year. Nearly every article, blog post, or story about Digital Transformation or the Industrial Internet of Things (IIoT) mentions Digital Twins. The term Digital Twin has been around since 2003 when Dr. Michael Grieves introduced the concept. Since its introduction, the term Digital Twin has evolved considerably from its rather engineering design-centric roots.
Today, the industry refers to any digitized version of virtually anything physical as the Digital Twin of that item. Yet, exactly what that Digital Twin represents and how the information is used can vary widely. With Google getting almost 3 Million hits on the term in less than a second, it is no wonder there is so much confusion about Digital Twins. Over the next series of blog posts, LNS Research hopes to provide some clarity as to how we see Digital Twins impacting manufacturing and asset-intensive industries. In this first post, we will explain the differences in how we see the Digital Twin concept impacting products versus assets.
Product lifecycle management (PLM) is the discipline of taking an idea through to the actual realization of that idea into a product and then ultimately even its disposal. PLM is the evolutionary outgrowth of computer aided engineering and/or design (CAE/CAD) just as enterprise resource planning (ERP) is the evolutionary outgrowth of manufacturing resource planning (MRP II). PLM is product-centric. When Dr. Grieves introduced the Digital Twin concept in 2003, it was also product-centric.
As little as two years ago, most of the Digital Twin dialog in the market was about smart connected products; focused on products sold into the consumer market. Embraced by the major PLM providers, the Digital Twin discussion focused initially on the product design activity, then the manufacturing process activities, and finally expanded to incorporate product feature engineering for expanded value creation.
The value of the Digital Twin was seen initially in using virtual prototyping to reduce product development costs. Following that was assessing manufacturability to reduce production costs, and in creating a virtual product to aid in customer acquisition and market share development.
These twins are more complex than PLM-centric product twins and will require far more processing capabilities to use effectively. In the next blog post, I will expand on the functionality and capabilities LNS believes will be critical elements of these asset-centric Digital Twins.
Simply put, product lifecycle management is a key factor in determining which products have long-term success and which fail to achieve their potential. Whether your product development process involves OEM partners or a select few component suppliers, optimizing your product lifecycle management has an impact on your bottom line.
Effective product LCM brings together the many suppliers, departments and employees involved with a product's design and production in order to streamline activities. The ultimate goal is to deliver a product that outperforms its competitors, is highly profitable, and lasts as long as market desire and technology lifespan permit.
This growth in collaboration requires companies to gain tighter control of their production runs, while also incorporating more data and feedback into their development processes. Product lifecycle management aims to provide timely and reliable information flow between partners to ensure all functional groups have the correct information to deliver a successful product that achieves long-term life span and product evolution goals.
The overarching goal of product lifecycle management is to expand the profitable period of your product while maintaining customer loyalty as you introduce new products or retire old ones. Some key reasons to use LCM include:
LCM means better planning for future regulatory and technology changes, product enhancements, geographic expansion, customer needs and service requirements. Keeping your suppliers and OEM partners connected helps you manage market updates more effectively by understanding the broader market implications.
For example, new European IVD Regulations have more explicit and stringent requirements that extend across the entire product lifecycle, including post-market activities around monitoring/surveillance, corrective action and reporting.
Micaela Wochner, Head of Customer Operations, together with a team of professionals, manages all day-to-day interactions with and for our OEM partners: the right delivery, at the right time, compliant, in the right quality, and the right price in the hands of our customers. Micaela joined Tecan in 2012 as Program Manager. In 2015 she developed the Customer Operations team from the former Product Management and Program Management team thus sharpening the customer focus of Tecan Partnering. Micaela joined Tecan with 20 years experience in the Healthcare Industry in Commercial, Clinical Affairs and Project and Program Management roles.
With digital twin technology, which uses advanced analytics and ML models, marketers can now create a digital twin of customers (DToCs) using historical and real-time customer data. Unlike static entities or customer profiles, they are dynamic avatars continuously updated with real-life user data.
Such virtual avatars not only simulate customer behavior but also provide context and predictions for future marketing endeavors. With the rise of cookieless marketing and data privacy regulations, DToCs are a great way to understand prospects and implement data-driven marketing strategies.
Fast forward to 2002, Michael Grieves introduced the concept of digital twins as part of product lifecycle management. The term was formally introduced by John Vickers, a NASA principal technologist, in 2010.
Digital twins integrate Industry 4.0 technologies like automation, artificial intelligence, big data, and 3D printing to conduct simulations, solve performance problems, and enhance customer experiences.
Digital twins tell you how objects function under different environments, following the product lifecycle from design and development to disposal. They reduce the time to market by helping you optimize product design and decision-making.
Formula 1 racers consistently use digital twins to test different configurations before a tournament. They attach multiple sensors to their race cars and study suspension, aerodynamics, and other factors that could increase their chances of winning.
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