Exploring the Intersection of PLM and Industry 4.0

It is often said that product lifecycle management (PLM) is an enabler to Industry 4.0. Enabled digital platforms include PLM, ERP, MES and many other processes, tools and technologies leveraging the Industrial Internet of Things (IoT), augmented reality, simulation, additive manufacturing, automated robotization, and more. This is about connecting the dots between product development and the manufacturing execution world as well as embracing the value of connected assets.

Some claim that PLM and the “Industry 4.0 revolution” are converging toward a new data continuity paradigm. Digitalization opens the door to new opportunities, extending data lifecycle management continuity across the wider enterprise. Industry 4.0 promises to bring about the rise of new business models, leading to new data-driven product and service offerings. Others forecast that Industry 4.0 and PLM will align and mature to maximize organizational value across the enterprise, from:

• The program office to the sourcing department
• The research and development office to the assembly line
• The factory to the field, where products are maintained and services are delivered to end users

Industry 4.0 (or Industrie 4.0 as it is also referred to in Europe) emerged in 2011 as one of the key initiatives to support the German government’s high-tech strategy to develop its manufacturing sector and include greater automation, modularity and robotization—and take full advantage of advances in networks, connectivity and wireless communication. Since then, Industry 4.0 has evolved into the idea that production lines must be flexible to adapt to mass production as well as be reconfigured to enable product personalization from customer-driven configuration, optimized logistics, data reuse, analytics and improved production processes. Most of these requirements and objectives align, link or extend typical PLM principles and their associated value expectations.

Looking at Industry 4.0 from an enterprise data continuity perspective, it is essential to understand how the data lifecycle is managed throughout and across interfaces.

Platform Industrie 4.0, a German industry network, promotes the transition from engineering and operations into a capability-based continuous engineering process and operations as the key enabler for various Industry 4.0 systems. According to one of the Industrie 4.0 publications, the components of Industry 4.0 can clearly be characterized as a means to an end, with consideration for a combination of:

• Resources, skills and capabilities (realized via methods, functions, and more, which are specified via classifications, dictionaries, catalogs, etc.)
• Production processes (which leverage these resources)
• Contextual models (engineering data, requirements, BOM, CAD, simulations, virtual and physical products, etc.)
• Feasibility checks (alignment of capabilities and contextual models)

Industry 4.0 is both a vision and an innovation framework for production optimization and interoperability, which focuses on strategy implementation, closed loop feedback, data mining, automation, expansion into AI, machine learning, sensors, industrial IoT, and more. It’s a platform where IT and OT converge, leveraging available data into insights, feeding back enterprise platforms with operational information. According to Gartner, IT and OT are defined as follows:

• Information Technology (IT): common term for the entire spectrum of technologies for information processing, including software, hardware, communications technologies and related services; in general, IT does not include embedded technologies that do not generate data for enterprise use.
• Operational Technology (OT): hardware and software that detects or causes a change, through the direct monitoring and/or control of industrial equipment, assets, processes and events.

Typically, enterprise solutions include either or both IT and OT elements to manage enterprise data as it gets authored and shared across functions. OT solutions consume and generate data from machines, robots, and assets used in the field and in the factory. PLM solutions imply that a PLM platform is being used to manage product data; it also refers to the discipline of managing data lifecycle and changes throughout—data created and consumed by both IT and OT. If one of these is the right hand of manufacturing organizations, then the other is the left hand. Having the two perspectives in sync and aligned from a maturity point of view is essential as both contribute to data-driven value creation and optimization (leading to competitive advantage and in turn organizational health).

Industry 4.0 builds upon the concept of data lifecycle, bringing it to the next level from an asset automation perspective, leveraging the physical-virtual connectivity in the field of manufacturing. The notion of data lifecycle clearly expands into the world of production planning, simulation and execution, as well as into the realm of asset management (any resource that is used or reused on the shopfloor). With Industry 4.0, the focus shifts in scope, as well as from upstream (new product development) to downstream (production and asset management).

New product development data flows into smart factories and smart operations and as part of smart products—all of which are fueled by the very same data created and passed on downstream of the design office. Data continues to be transformed throughout the process, including and building from product and business data into production and operational data, such as:

• CAD, mechanical and electrical content
• Software, firmware, operating systems and other TCU-embedded systems
• BOM, configured product data, filtering from 150 to 100 percent
• Bill of process, assembly/manufacturing resources, process simulation and load balancing data
• Assembly work instructions and technical publications, including 3D and 2D documents
• Supplier deliverable information, alternative parts, sourcing and commercial traceability information, and meeting requirements for compliance, maintenance and warranty support
• Material property information, including simulation results and other compliance data
• Product quality information

Most of the above data are by-products from engineering, manufacturing engineering, product line delivery management and associated upstream deliverables. All of the data is typically managed according to PLM principles of BOM configuration management, with associated processes that follow data lifecycle and change business rules. Aligning PLM with Industry 4.0 requirements implies answering many data management questions, including:

• How does data and product connectivity contribute to feeding smart factories and enhancing the product lifecycle practice?
• How can feedback loops and data integration upstream and downstream of new product development be improved to contribute positively to the circular economy?
• How can downstream waste be reduced by improving data traceability and accessibility, with better product analytics throughout its lifecycle?
• How can manufacturing planning and execution be linked more tightly?
• How can engineering, manufacturing and service/maintenance process operations be more robustly connected and integrated?
• How can time to market be reduced, with both product development and production cost optimization, integrating co-creation from the design office to the shopfloor?
• How can the digital and the physical worlds be further aligned, delivering more customer-centric products enabled by horizontally integrated PLM strategies, taking an ecosystem approach to collaboration and leveraging more agile and continual release processes?
• How can prelaunch costs be reduced and how can downstream manufacturing improvements be made?

Linking PLM systems and IoT can be misleading: it is not simply about feeding data from factory assets to PLM systems, but rather managing the lifecycle of this data and multiple versions or data points—making sense of unstructured data is not usually the strength of the PLM practice. On the contrary, PLM is about managing structured data in the form of items and BOM structures. Processing unstructured information through data lake analysis, machine learning, and other analytics tools is a different science.

Once patterns of information are identified, root causes are analyzed, and data corrections are identified, they need to be fed back to MES, ERP and PLM solutions for corrective actions. This is primarily where the “converge” will occur—ensuring that PLM processes, ERP and MES interfaces can integrate such feedback loops is a core element of working toward systems engineering. Model-based systems engineering (MBSE) incorporates such feedback loops, data consolidation and translation from unstructured to structured, following the PLM principles of lifecycle and change management from one iteration to another.

What are your thoughts?