Comparing inventory policies for replenishment planning: a simulation study

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Soo, C-L, 2025. Comparing inventory policies for replenishment planning: a simulation study. DBA, Nottingham Trent University.

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Abstract

Problem Statement: In the dynamic context of supply chain replenishment, selecting an effective inventory policy is critical for better performance. However, a lack of comprehensive comparative analysis of the legacy and emerging approaches leaves a gap in this research paper, necessitating further research to guide policy selection.

Purpose: This research aims to compare the supply chain performance under various inventory policies and identify influential factors for their selection. It explores the mechanisms of these policies under multiple parameters, including re-order level, quantity, buffer size, lead times, safety stock, lead time factor, and variability.

Design/methodology/approach: This research uses a comparative simulation study design in the AnyLogistix (ALX) dynamic simulator, to provide risk-free experiments before implementation, for three inventory policies. The simulation analysis uses demand data collected over prolonged demand periods from three variable industry cases for comparison without human intervention. The simulator applies the statistical variation of lead time/demand with comparison experiments for supply chain performance evaluation.

Findings: The simulation results show that the Re-order Point (ROP) inventory policy is more effective than both Make-to-Availability (MTA) Dynamic Buffer Management (DBM) and Demand-Driven Material Requirements Planning (DDMRP), during prolonged demand intervals. Furthermore, DDMRP outshines MTA DBM when there is an anticipated spike in demand. Regarding adjusting buffer parameters, MTA DBM proves easier to handle than DDMRP. Another key observation is evidence of a reduction in Service Level by Revenue (SL) with an increase in Supply Variation (SV) for Transportation Lead Time (TLT).

Originality/value: This work grants a richer understanding of inventory policy selection, especially in rapidly changing business environments. Furthermore, these data-driven insights offer guidelines for choosing inventory policies in various contexts, which are presented in the form of an innovative policy selection decision table. While practitioners will find the table a valuable and pragmatic tool for decision-making, its design and foundation also pave the way for further research.

Limitation: While simulation can project performance outcomes based on quantitative causality and statistical impact, it may not replicate the dynamic nature of human decision making in real business contexts. There is a need for further research to combine qualitative case studies with quantitative simulation to broaden this analysis.

Item Type:	Thesis
Creators:	Soo, C.-L.
Contributors:	Name Role NTU ID ORCID Stratton, R. Thesis supervisor MAN3STRATR https://orcid.org/0000-0001-5437-4233 Kian, R. Thesis supervisor SMI3KIANR https://orcid.org/0000-0001-8786-6349
Date:	January 2025
Divisions:	Schools > Nottingham Business School
Record created by:	Jeremy Silvester
Date Added:	15 May 2025 15:39
Last Modified:	16 May 2025 14:52
URI:	https://irep.ntu.ac.uk/id/eprint/53586