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Executive Overview
This report looks back on the implementation strategy of Industrie 4.0 at the time it was defined and reviews what progress has been made over the years, both in the discrete and process industries. We discuss the early experiments, the ones realized and those that are still needed. We review successive implementation attempts and successes, initially covering only part of the scope of the Industrie 4.0 vision. We describe how recent developments in combining digital twins, modular automation and agent-based flow control realized the vision of flexible, configurable manufacturing plants producing high-mix, low-volume products. We discuss successful implementations at industrial scale.
This report describes ongoing developments enabling make-to-order production in global, configurable and flexible manufacturing networks. The ongoing development of standardized and encrypted product definition information must be completed to realize end-to-end digital product definitions that can be executed on any plant with suitable capability without risk of loss of intellectual property. Together, current realizations and ongoing work will enable the full Industrie 4.0 vision.
The report concludes with the potential opened by the capabilities discussed and their associated benefits. We discuss the applicability in European and American contexts. For fast-moving goods, ARC believes this is a breakthrough in both contexts and we predict major benefits over time using these approaches.
Foresight of the Industrie 4.0 Strategy and How It Evolved
In 2013, the Industrie 4.0 Working Group, a consortium of German authors that became known as Platform Industrie 4.0, published their “Recommendations for implementing the strategic initiative INDUSTRIE 4.0.” The dual strategy to become a leading market and a leading supplier includes the following key features:
Development of inter-company value chains and networks through horizontal integration.
Digital end-to-end engineering across the entire value chain of both the product and the manufacturing systems.
Development, implementation and vertical integration of flexible and reconfigurable manufacturing systems with businesses.
The question the report poses regarding the third point is: “How can cyber-physical systems be used to create reconfigurable manufacturing systems?” It provides the answer as well: “A set of IT configuration rules will be defined that can be used on a case-by-case basis to automatically build a specific topology for every situation, including all the associated requirements in terms of models, data, communication and algorithms.”
The Smart Manufacturing and Manufacturing Renaissance concepts in the US have very similar goals although the philosophy to realize them may differ somewhat. This report discusses the potential of applying Industry 4.0, also from an American perspective. Early experiments with modular production were followed by a long pause precipitated by several factors.
The Importance of Process Equipment Assemblies
The first of these factors is the availability of modular process equipment assemblies (PEAs) -- a prerequisite for the Industrie 4.0 concept to work. “Process” in this case must be interpreted as the generic term for a manufacturing process, not as process versus discrete manufacturing. PEAs can dynamically be reorganized and connected to execute a bill-of-process (BoP) for a discrete product or a recipe of a batch process. Although less likely, it could also execute a continuous process to produce a product with specific qualities that cannot be produced on a different configuration of the line. In discrete and batch processing, PEAs can be placed adjacent to a network of conveyor belts or on islands in the production hall and mobile autonomous robots (MARs) can provide flexible intralogistics and bring the intermediate product or work in progress (WIP) to the next relevant production unit.
To enable fast plant reconfiguration, modular plants also require modular automation. Instead of programming automation systems for each reconfiguration, the PEA comes with an interface exposing its catalog of capabilities and an orchestration layer requests services from the different PEA to execute the production steps. Internally PEA translates the requests to setpoints of its controllers. This concept took a few years to develop and to standardize (module type package or MTP in the process industries and BaSys in the discrete industries). The PEAs can now be delivered with their modular automation packages and a catalog of services it can provide. Reconfiguration of plants by connecting PEAs only requires a high-level product definition for the orchestration layer.
Both process and batch industry applications were successful. They are being used in chemical pilot plants used to shorten time to market for new product variants. Modules are taken from an inventory owned by a single company. Also, the integration of package units into brownfield plants was very successful. The integration of two brands of automation systems is done far more quickly using modular automation. These topics are discussed in detail in this ARC report. In the discrete industries, industrial-scale production has been implemented, and will also likely be implemented soon in the CPG industry, as we will discuss below.
Foundation: Production Flow Control in Modular Plants
The Industrie 4.0 vision must be constructed bottom up and start with production flow control in modular plants. It can be implemented by placing PEAs on islands in a production hall to create what is colloquially called a ballroom. Digital twins of the units inform of the capabilities the unit has, its current state (such as producing, stopped, idle etc.), and any time-dependent or constant production conditions.
Mass customization and increasingly agile supply chains with shorter fulfillment times require managing increasing variability. To reach full make-to-order production, optimize fulfillment, and minimize storage, production orders contain a mix of product variants or different products. These are not organized in campaigns for identical products, but in a sequence of incoming orders. Each production order is associated with an individual bill-of-process (BoP) specifying the production capabilities required and the production steps to execute.
A capability matching service determines which equipment can execute the production operation’s next step. Then a scheduling service determines which operations will take place next on which equipment, reduces bottlenecks, and optimizes flow. An execution engine executes the BoP and takes care of orchestrating shop floor resources in real time to meet the production schedule. When bottlenecks emerge orders can use upfront defined priorities, or real-time scheduling priorities from the execution engine.
A network of conveyor belts or autonomous mobile robots (AMRs) takes care of the transport of the materials and the finished products from and to on-site storage. This process is virtualized using digital twins of AMRs, digitally scheduled and executed using a logistics dispatcher, which is the equivalent to the production execution engine.
In the implementation we witnessed recently, the information exchanges between matching service, executors, scheduler, and equipment are made visible in close-to-real-time in a similar way as mobile phones represent text message exchanges between people. For example, an intermediate product ready to leave an equipment will let the BoP execution engine know it is ready for the next step, and BoP execution engine will assign the next optimal production station, and the logistics dispatcher will assign a robot to bring it there. Each step leaves a message in the trail, while steps in the work order will gradually show up in green, to indicate completion. This provides insight to the operator or supervisor as to what the “system” does. Industry’s experience with automatic optimizers indicates this is of critical importance for adoption by personnel, and making sure the system remains switched on. In the implementation we have seen, BopEx was deployed on Siemens’ automation-adjacent Industrial Edge Platform indicating BoP execution engine’s efficiency in terms of compute and memory resources.
Table of Contents
Executive Overview
Foresight of the Industrie 4.0 Strategy and How It Evolved
The Next Level: Integrated Networks of Flexible Plants
Conclusions
Recommendations
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