The new industrial revolution, “Industry 4.0” complicates the decision making in manufacturing systems due to many challenges, such as short product life cycle, data integration, and consideration of sustainability. Companies need help to overcome these challenges and deploy a smart factory.

 

This Ph.D. falls within this context and proposes a new methodology for implementing Industry 4.0-oriented decision support systems.  First, we identify and classify the decision problems of a manufacturing system and examine their interactions.  Then, we study their main evolutions in Industry 4.0 and propose cartography to represent them.

 

On this basis, using enterprise modeling, we aim to develop agile and rapidly reconfigurable decision support systems adapted to Industry 4.0. Furthermore, we identify gaps in existing enterprise modeling frameworks and propose a new Industry 4.0-oriented enterprise modeling methodology called «MEMO I4.0».

 

MEMO I4.0 offers a structured two-step approach. The strategic/tactical step is to create the basic modules and enrich the library of modules. The operational step is to build the integrated model and evaluate performance. Finally, the application of MEMO I4.0 on industrial case studies of the company «elm.leblanc» to derive decision support systems has shown its ability to considerably reduce the development time of modeling projects and its compatibility with Industry 4.0.

 

Keywords: Industry 4.0, Smart manufacturing, Enterprise architecture, Enterprise modeling, Manufacturing systems, Digital twin, Simulation, Optimization, Decision support systems, New key performance indicators.