Pull-production system in a lean supply chain: a performance analysis utilizing the simulation-based optimization

2018 ◽  
Author(s):  
Satie Ledoux Takeda Berger ◽  
Enzo Morosini Frazzon ◽  
Apolo Mund Carreirao Danielli
Keyword(s):  
Supply Chain ◽  
Performance Analysis ◽  
Production System ◽  
Simulation Based ◽  
Pull Production ◽  
Simulation Based Optimization ◽  
Lean Supply Chain ◽  
Pull Production System ◽  
A Performance

System dynamics modelling of fixed and dynamic Kanban controlled production systems: a supply chain perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jagan Mohan Reddy K. ◽  
Neelakanteswara Rao A. ◽  
Krishnanand Lanka ◽  
PRC Gopal
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Cycle Time ◽  
Production System ◽  
Demand Uncertainty ◽  
Production Systems ◽  
Decision Variable ◽  
Content Type ◽  
Pull Production ◽  
Kanban System

Purpose Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty. Design/methodology/approach In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios. Findings From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible. Research limitations/implications In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size. Originality/value This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

scholarly journals Simulation-Based Optimization of a Two-Echelon Continuous Review Inventory Model with Lot Size-Dependent Lead Time

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1014
Author(s):  
Ibrahim Alharkan ◽  
Mustafa Saleh ◽  
Mageed Ghaleb ◽  
Abdulsalam Farhan ◽  
Ahmed Badwelan
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Shop Floor ◽  
Lot Size ◽  
Continuous Review ◽  
Inventory Costs ◽  
Size Dependent ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Total Inventory

This study analyzes a stochastic continuous review inventory system (Q,r) using a simulation-based optimization model. The lead time depends on lot size, unit production time, setup time, and a shop floor factor that represents moving, waiting, and lot size inspection times. A simulation-based model is proposed for optimizing order quantity (Q) and reorder point (r) that minimize the total inventory costs (holding, backlogging, and ordering costs) in a two-echelon supply chain, which consists of two identical retailers, a distributor, and a supplier. The simulation model is created with Arena software and validated using an analytical model. The model is interfaced with the OptQuest optimization tool, which is embedded in the Arena software, to search for the least cost lot sizes and reorder points. The proposed model is designed for general demand distributions that are too complex to be solved analytically. Hence, for the first time, the present study considers the stochastic inventory continuous review policy (Q,r) in a two-echelon supply chain system with lot size-dependent lead time L(Q). An experimental study is conducted, and results are provided to assess the developed model. Results show that the optimized Q and r for different distributions of daily demand are not the same even if the associated total inventory costs are close to each other.

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