No abstract

Purpose: Demand Driven Material Requirements Planning (DDMRP) aims to deal with variability by adjusting inventory levels while maintaining, or even increasing, customer service levels. This approach bridges the push and pull approaches. Even though it first made its appearance in 2011, research in this field remains relatively limited. This paper aims to measure the spatiotemporal evolution of the DDMRP, its scope and context of implementation, and the research lines studied in that field in order to identify areas that still need to be addressed by future researchers.Design/methodology/approach: The systematic literature review approach adopted in this paper examines research dealing with the DDMRP approach published in different languages between 2009 and 2020. To-date papers focused on the performance analysis and comparison, what differentiates this study is the focus on the scientific evolution level of DDMRP, the parameters, and contexts that should be more studied.Findings: The results show that DDMRP is not yet a mature method and that the robustness of the approach still needs to be tested. More research is also required to determine scientifically some setting parameters, how the proposed DDMRP could be implemented in different industrial contexts with existing information systems.Originality/value: Based on the evolution analysis of DDMRP, this study outlines its current state of maturity and its different shortcomings under a broader vision to make this method more complete on the scientific and industrial level.

Demand Driven Material Requirements Planning (DDMRP) is a recent method mixing push and pull flow management. Although it claims to be the solution to traditional methods' limitations, the DDMRP method works at infinite capacity: manufacturing or supply orders are launched according to a logic of replenishment of stocks defined as buffers. This article proposes an evaluation of capacity management using visual charts developed by simulation. These charts correlate the bottleneck resource's loading rate to a service rate by considering one of the DDMRP method parameters, the Decoupled Lead Time (DLT). The charts are a decision support tool. They allow identifying to which loading rate the DLTs are representative of the flow times of manufacturing orders and which capacity level to use. We study different workshops, including a real industrial case. Our results show that it is better to control the flow times by adjusting capacity rather than adjust the DLT parameter.

G uill a u m e D ess e vr e a , J a c q u es L a m ot h e b , R o b ert P ell eri n a , M a h a B e n Ali a , Pi err e B a ptist e a a n d Vi n c e nt P o m p o n n e c a D é p art e m e nt d e M at h é m ati q u es et d e G é ni e I n d ustri el, P ol yt e c h ni q u e M o ntr é al, M o ntr é al, C a n a d a; b U ni v ersit é d e T o ul o us e, C e ntr e G é ni e I n d ustri el, I M T Mi n es Al bi, Al bi, Fr a n c e; c Pi err e F a br e O p ér ati o ns, C astr es, Fr a n c e

Home care centers face both an increase in demand and many variations during the execution of routes, compromising the routes initially planned: robust solutions are not effective enough, it is necessary to move on to resilient approaches. We create a close to reality use case supported by interviews of staff at home health care centers, where caregivers are faced with unexpected events that compromise their initial route. We model, analyze and compare three resilient approaches to deal with these disruptions: a baseline approach without any collaboration, a distributed collaborative approach, and a centralized collaborative approach, where we propose a centralization and sharing of information to improve local decision-making. The latter reduces the number of late arrivals by 11% and the total time of late arrival by 21%, and also halves the number of routes exceeding the end of work time (contrary to the distributed collaborative approach, due to the time wasted reaching colleagues). The use of a device, such as a smartphone application, to centralize and share information thus allows better mutual assistance between caregivers. Moreover, we highlight several possible openings like the coupling of simulation and optimization to propose a more resilient approach.