Title : Methodology of enterprise modeling oriented Industry 4.0: Application for decision support systems in manufacturing systems
Beginning of thesis : 2018
End of thesis : 2021
Abstract : With the advent of the new industrial revolution known as Industry 4.0, manufacturing systems became equipped with new technologies (e.g., the internet of things, cloud computing, augmented reality). They play an essential role in transforming traditional manufacturing systems into smart manufacturing systems. However, this context increases the complexity of decision-making due to many challenges such as the short product life cycle, the integration of data in all processes, and the necessity to consider the sustainability dimension in the development of manufacturing systems. Medium and large companies need help to meet these challenges and succeed in implementing a smart factory.
For this purpose, in this thesis, we develop a new methodology for implementing decision support systems adapted to the industry 4.0 environment. First of all, we identify the manufacturing system’s decision problems and classify them according to four main factors: time horizon, activity type, problem branch, and problem family. Next, we examine the interactions between decisions and draw a cartography of decisions to provide academics and practitioners with a holistic view of decision problems and their interactions. Afterward, we study the main evolutions of decision-making within Industry 4.0 and their impacts on the key performance indicators (KPIs), the time horizon of decisions, and potential integrations of decisions.
Once we understand the decisional aspect in manufacturing systems and the new paradigms emerging, we review the classical enterprise frameworks and the new one proposed to describe the manufacturing system from the conceptual analysis of industrial activity to implementing a Decision Support Systems (DSS). Besides, we identify fundamental gaps, notably to carry out the modeling up to the implementation of an agile and quickly reconfigurable DSS.
To fill this gap, we focus on developing a new methodology of enterprise modeling oriented Industry 4.0 (MEMO I4.0) meant for companies with a long-term vision about future orientations. These companies usually operate in a structured, standardized framework based on their DNA and guidelines.
MEMO I4.0 has four key principles: Agility, Modularity, Interoperability, and Robustness. It proposes a structured approach that consists of two main stages.
The strategical/tactical stage concerns the development cycle to create the modules used to build an integrated model at the operational level. Newly conceived modules are added to a library of modules to allow for knowledge capitalization. The operational stage concerns the execution cycle to generate an integrated model and assess the system performance. Furthermore, MEMO I4.0 is a framework based on four dimensions, namely life-cycle, genericity degree, scale, and view. Each stage is organized according to the life cycle phases (Definition, design, Implementation, and Maintenance) to structure the modeling process. The genericity degree is about the reusability of modules. The scale concerns the modeling granularity level of modules. The view acts as a support to generate a compatible model regarding needs, objectives, system features.
Finally, based on MEMO I4.0, we develop all the simulation modules and validate the simulation model against the field reality of the Drancy plant of the elm.leblanc company belonging to bosch Group. We perform several case studies to derive a simulation-based DSS and Optimization/Simulation-Based DSS. According to the manager’s requests, we update the required modules (if needed) and integrate them to evaluate the performance of manufacturing systems. To assess the performance, we use either classical KPIs (e.g., productivity) or new KPIs 4.0 (e.g., Ergonomy). Our results show that MEMO I4.0 can considerably reduce the time spent in the development cycle of simulation projects and demonstrate its compatibility with Industry 4.0 needs.
Keywords : Industry 4.0, Decision support systems, Enterprise modeling, Simulation and Optimization
Thesis defense :Automne 2021
Supervisor :
-
Xavier DELORME : Professeur Mines Saint Etienne, Limos
Partners or/and funders : Bosch group, elm.leblanc