Thèse préparée par Mahdi El Alaoui El Abdellaoui

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 :

Partners or/​and fun­ders : Bosch group, elm.leblanc