Manufacturing Control System Development for an In Vitro Diagnostic Product Platform

The current in vitro diagnostic design process is a combination of methods from engineering disciplines and from government regulatory agencies. The goal of design processes that have been developed is to ensure that a new product meets the user’s expectations and is safe and effective in providing its claimed benefits and proper functioning, otherwise known as the essential design outputs. In order to improve the ability of designers and auditors to ascertain the safety and efficacy of a product, the use of design controls has been adopted that specify a method of evaluating the design process at several key stages. The main objective of this research was to examine the resolution and architectural details necessary to build an adequate manufacturing control system to assure the EDO outputs in large IVD instruments in the company under study. The control system is the defined inspections and test processes to delineate between acceptable and unacceptable product before release for sale. The authors reviewed current design control regulatory requirements within the IVD industry, as well as design controls in other regulated industries. This research was completed to determine what opportunities could be transferred to large in-vitro IVD instruments using an IVD manufacturer as a case study. In conclusion, the research identified three areas where a properly configured EDO can add value within IVD instrument design and manufacture, namely: (1) development of a control system which is fit for purpose; (2) a mechanism to manage and proliferate key design knowledge within the organisation and thereby manage outsourced services; and (3) implementing a scaled engineering change process because changes impacting EDO naturally require extra regulatory and engineering oversight.

Improvement of Delivery Reliability by an Intelligent Control Loop between Supply Network and Manufacturing

Manufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturing control’s decisions. In addition to the requirements, the concept describes the structure of the system as a control loop, a reinforcement learning-based controlling element as the central decision-making component, and the integration into the existing production IT landscape of a company as well as with latest internet of things (IoT) devices and cyber-physical systems. The benefits of the concept were elaborated in expert workshops. In summary, this approach enables an effective and efficient response to events from the supply network through smarter manufacturing control, and thus more resilient manufacturing.

Application of a Simulation-Based Digital Twin for Predicting Distributed Manufacturing Control System Performance

During the last years, several research activities and studies have presented the possibility to perform manufacturing control using distributed approaches. Although these new approaches aim to deliver more flexibility and adaptability to the shop floor, they are not being readily adopted and utilised by the manufacturers. One of the main challenges is the unpredictability of the proposed solutions and the uncertainty associated with these approaches. Hence, the proposed research aims to explore the utilisation of Digital Twins (DTs) to predict and understand the execution of these systems in runtime. The Fourth Industrial Revolution is leading to the emergence of new concepts amongst which DT stand out. Given their early stage, however, the already existing implementations are far from standardised, meaning that each practical case has to be analysed on its own and solutions are often created from scratch. Taking the aforementioned into account, the authors suggest an architecture that enables the integration between a previously designed and developed agent-based distributed control system and its DT, whose implementation is also provided in detail. Furthermore, the digital model’s calibration is described jointly with the careful validation process carried out. Thanks to the latter, several conclusions and guidelines for future implementations were possible to derive as well.

Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation

In manufacturing systems, a state of high resilience is always desirable. However, internal and external complexity has great influence on these systems. An approach is to increase manufacturing robustness and responsiveness—and thus resilience—by manufacturing control. In order to execute an effective control method, it is necessary to provide sufficient information of high value in terms of data format, quality and time of availability. Nowadays, raw data is available in large quantities. An obstacle to manufacturing control is the short-term handling of events induced by customers and suppliers. These events cause different kinds of turbulence in manufacturing systems. If such turbulences could be evaluated in advance, based on data processing, they could serve as aggregated input data for a control system. This paper presents an approach how to combine turbulence evaluation and the derivation of measures into a learning system for turbulence mitigation. Integrated in manufacturing control, turbulence mitigation increases manufacturing resilience and strengthens the supply network’s resilience.