Visual charts produced by simulation to correlate service rate, resource utilization and DDMRP parameters

Abstract

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.

Stok Yönetiminde Min/Mak, Ekonomik Sipariş Miktarı ve DDMRP Politikalarının Simülasyonla Kıyaslaması

“…vd Arena simülasyon uygulamasıyla (https://www.rockwellautomation.com/en-us/products/software/arenasimulation.html) çalışarak biri sanal diğeri fiili üretim ortamında darboğazlı ve değişkenlik içeren iki vakayı karşılaştırmışlardır (Dessevre, Baptiste, Lamothe, & Pellerin, 2023).…”

“…Desserve vd simülasyona ek olarak fiili bir vakayla karşılaştırma yapmışlardır (Dessevre, Baptiste, Lamothe, & Pellerin, 2023). Benzer şekilde İstanbul'da yerleşik orta ölçekli üretici-perakendecide yapılan DDMRP uygulamasında başarılı sonuçlar elde edilmiştir (Uluçay, 2014).…”

Analysis of a new dynamic capacity management approach in DDMRP: Application on a real industrial case

“…For example, Martin, Lauras, Baptiste, Lamothe, Fouqu and Miclo (2019) develop a process control and a decision-making tool to adjust buffer parameters, Lee and Rim (2019) propose an alternative model for the safety stock calculation, while Achergui, Allaoui and Hsu (2020) develop an algorithm to solve the optimization problem of minimizing storing costs for uncapacitated buffer positioning and (Damand, Lahrichi & Barth, 2023) propose a multi-objective genetic algorithm to determine a set of parameters related to DDMRP. Recently, Dessevre et al (2021) propose a visual tool to correlate service rate, resource utilization and DDMRP parameters, that helps to choose a capacity solution among others and Azzamouri, Baptiste, Pellerin and Dessevre (2022) analyze the impact of a periodic review of DDMRP stock buffers, while Cuartas and Aguilar (2023) develop a hybrid algorithm based on reinforcement learning to determine the optimal time and quantity to buy a product and Martin, Lauras and Baptiste ( 2023) propose an experimental design to compare different multi-parameter sizing policies.…”

“…Studies on DDMRP are both axiomatic and empirical (Bagni, Godinho-Filho, Thürer & Stevenson, 2021), but many issues remain to be tackled scientifically, especially from complex environments (Velasco Acosta, Mascle & Baptiste, 2020), industrial sectors (Dessevre, Lamothe, Pellerin, Ali, Baptiste & Pomponne, 2023), and about the implementation process (Orue, Lizarralde & Kortabarria, 2020). Azzamouri, Baptiste, Dessevre and Pellerin (2021) present more details in a systematic review.…”

“…One of the main obstacles to switching from push to pull production is capacity management for the short-term horizon. This has been underlined by the case study of Dessevre, Baptiste, Lamothe and Pellerin (2021). The partner is a manufacturer and a distributor of dermo-cosmetic products, and its bottling line can work two or three shifts a day.…”