A simulation test bed for production and supply chain modeling

This research considers inventory replenishment in a stochastic, multi-echelon supply chain involving both production and distribution functions. Simulation is used to compare distribution/material requirements planning (DRP/MRP), re-order point (ROP) and Kanban (KBN) replenishment strategies. Additional experimental factors include the demand pattern and the existence of manufacturing capacity constraints. Trade-off curves between inventory and delivery performance are generated. Statistical techniques, including analysis of variance (ANOVA), are then used to compare the areas under the trade-off curves and determine the relative dominance among the replenishment strategies. The methodology is used to identify both main and interaction effects. With seasonal demand, DRP/MRP performance is found to be best, followed by ROP and KBN, respectively. Without seasonal demand, the relative performance ranking depends on the presence of capacity constraints. Without capacity constraints, ROP performs best, followed by DRP/MRP and KBN. With capacity constraints, the ranking is reversed. This difference in behaviour can be explained using queuing analysis.

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“…This test bed was designed to be simple, flexible and transparent (Enns and Suwanruji 2003). It consists of two main modules.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies

Suwanruji

Enns

2006

International Journal of Production Research

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“…It is relevant to mention the existence of papers or books in the literature focusing on tutorials of simulation or simulators related to other PCP systems, that is, regarding other systems of order release. For example, a simulator of MRP and DRP was developed by Suwamuji (2003) using electronic spreadsheets, VBA and Arena in an integrated manner. The simulator also allows to compare the performance of MRP/DRP with the reorder point system and with the Kanban system.…”

Section: Introductionmentioning

confidence: 99%

Use of workload control in production planning and control: modeling on a simulation software

2021

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This paper aims at presenting a structure of implementation of the Workload Control (WLC) approach on a commercial discrete event simulation software by comprehensively demonstrating how to construct and simulate this approach. This research was developed considering the lack of studies fully describing this implementation on this kind of software, which hinders the dissemination and use of the WLC approach by managers. Initially, the logic was developed on a dedicated and structured language and then converted to the simulation software. A detailed description of the WLC implementation contributes to the business domain by facilitating its replication and application in other manufacturing environments, reducing the project time needed to develop the base model and allowing managers and/or researchers to focus directly on the adjustments of the model to the environment being modeled. In the academic domain, this paper addresses a gap in the WLC literature concerning the lack of tutorials for simulation and the lack of information, in the existing papers, regarding the development of the computational model.

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“…Simulation-based methodologies can help decision-makers by modelling the flow of material and information in planning and controlling complex decisions. The simulation also discusses issues such as the stochastic environment, non-stationary demand, multiple performance criteria, capacity-constrained resources, assembly line coordination, and allows modelling without any assumption or constraint (Enns and Suwanruji, 2003).…”

Section: Introductionmentioning

confidence: 99%

Modelling and improvements of production processes using OptQuest and process analyser

Aka

2019

IJIMS

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In this study, alternative situations can be evaluated with process analyser and OptQuest without the need to reconstruct the system. The process analyser provides the running and comparison of alternative scenarios built by changing variable and parameter values in a simulation model. OptQuest module is an optimal search simulation tool that looks for the best solution through input variables and constraints. The aim of this study is to show that process analyser and OptQuest can be used in improving of production processes. Process analyser and OptQuest provide computational simplicity with their user-friendly interfaces in improving production processes. According to current production process simulation model, a large number of iterations are performed in the direction of resource and capacity constraints, and the results that provide the best performances are determined. Finally, the best response values are achieved when the number of package maker (PM) and capacity of package are two.

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A simulation test bed for production and supply chain modeling

Cited by 18 publications

References 7 publications

Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies

Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies

Use of workload control in production planning and control: modeling on a simulation software

Modelling and improvements of production processes using OptQuest and process analyser

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