Abstract
This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries.
Highlights
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The objective of this study is to propose a new novel way to plan the replenishment process between the supplier and the warehouse of any company that has a seasonal demand with a reasonable forecasting accuracy of the end product’s demand
The callot sizes of the future weeks can be useful, especially when the initial forecast sent to delivery or not in the week, and if so, what size lot we would need
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Replenishment planning is necessary to balance service levels, inventory ordering and holding costs. The decision-maker must react to the customer’s dynamic needs, and at the same time keep the costs as low as possible. A clear picture of the situation on the ground is necessary to optimize the system
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