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As it is today, many of product lifecycle processes, from design, to process planning and engineering, manufacturing are siloed because different software tools, models and data representations are used, and often by many different teams across different organizations and geographic locations. To achieve the goals of smart manufacturing, these product lifecycle processes and manufacturing functions need to be connected and integrated to increase process automation, responsiveness and efficiency, and to reduce human errors. Furthermore, because of connected smart products, this lifecycle is now being extended beyond the four-wall of the factories, into customers’ operation environment.
Digital thread refers to the communication framework for integrating production functions across the product chain and integrating product data for digital models. It does so by enabling data flow and integrated view of the product’s data throughout its lifecycle across different stages, from design, to manufacturing, and now to operation, and even to end-of-life and recycling of the product, as illustrated
in Figure 2.
With the digital thread, we can digitally streamline the production functions to increase speed and efficiency, and reduce human errors. With it, we can readily collect all relevant data about an individual product, from design specification common across product instances, manufacturing data specific to each product, data about its operation and maintenance, etc. These data can be used to build models for individual product. These models, built and supported by all the data about the product, dynamically reflecting the current state and its history of a product in its full lifecycle, constitute the so-called digital twin of the physical product.
With the digital twin, we can more readily assess its capabilities and performance, and discover deficiencies though analytics and simulation; we can optimize manufacturing and operations processes including better fault detection and diagnosis, and predictive maintenance; we can improve product design and process engineering; and we can even precisely determine product recall scope to reduce recall cost since they can trace back quality factors for each and every product.
IIoT for manufacturing – enabling new capabilities
Within the value, product and asset chains described previously, the manufacturing, the operation and maintenance and the supply chain management functions deal with physical objects directly and are naturally areas that that IIoT will bring the most impact.
By applying the IIoT technologies to the manufacturing environment, we can collect large among of data at near real time covering machine operational states, performance indicators, process parameters, environmental data, quality measurements, all of which reflecting the real time state and performance of the machines and the quality of the products that are being made.
Through analytics, we can optimize the manufacturing processes through realtime monitoring, fault detection and diagnosis, predictive maintenance, precise OEE, online quality assurance, energy efficiency management and worker safety monitoring.
The analytics result can be provided to MES to help to dynamically optimize its management of the manufacturing capacity and resources. It will also help update the digital model of individual products as they are being manufactured.
Of course, the same technology are being applied to deployed machines and equipment in the fields to achieve the similar benefits – needlessly to say, this is a primary application of IIoT.
So in summary, IIoT enables smart manufacturing by providing the necessary insights to its decision-making, scheduling and planning systems and it also enables the establishing of digital thread and the building of the digital twin in the process as well.
Finally, by leveraging technologies in low-cost digital tagging, sensors and wireless communication, we can track the movement of material, parts or products in the supply chain, monitoring not only their location but their environmental condition, including temperature, humility, vibration etc. to ensure their quality and on-time delivery. Any exception can be timely reported back to ERP systems to dynamically adjust the production plan.
So far we have dealt with data analytics that mostly require fast response where streaming analytics is most fitting. However, batch oriented Big Data further extend the power of analytics in optimizing the manufacturing processes.
With the application of Big Data, we can perform macroscopic batch analytics, longitudinally across time and horizontally across many manufacturing functions and processes. We use these analytics to identify performance bottlenecks and inefficiency in the manufacturing processes, to build models for streaming analytics to capture meaningful patterns in the manufacturing process in near real time and for engineering simulations, and finally to understand product usage patterns and customer preferences.
(In the final installment of this series of blogs, we will share some high level thoughts how to combine architecturally the IIoT technologies into the smart manufacturing setting.)
About Your Guest Blogger:
Dr. Shi-Wan Lin is the CEO and a co-founder of Thingswise, LLC, a startup providing streaming industrial analytics solutions purposely built for IIoT systems and Smart Manufacturing – as a turnkey solution adapting and innovating key IT technologies to meet OT’s stringent requirements in reliability, performance and security, deployable from the edge to the cloud.
Dr. Lin co-chairs various technical groups for the Industrial Internet Consortium (IIC), the Architecture Joint Task Group between Plattform Industrie 4.0 and the Industrial Internet Consortium and the National Institute for Standards & Technology (NIST) Cyber-Physical Systems Public Working Group. Dr. Lin is a lead editor and contributor to the Industrial Internet Reference Architecture published by IIC.
Previously, he worked for Intel for 10 years last as a Principal Engineer/Technologist in the Strategy and Technology Office in its Internet of Things Group and before then Sarvega, Inc (a Web Service/SOA/Security startup), Lucent Technologies and Motorola. Dr. Shi-Wan Lin has 20+ years’ experience in system architecture, Big Data, analytics, enterprise software, Cloud Service, system security and trust, telecommunications and wireless data communications.
The opinions expressed in this series of blog reflect his personal view and observations and he is solely responsible for them.