Optimal lot-sizing algorithms on stochastic demand at the retailer

Abstract

Purpose - The purpose of this study is to explore the concept of the economic lot sizing and the time cycle period of reordering. The stochastic demand is quite common in the real environment of a cement retailer. The study compares three methods to obtain the optimal solution of a lot-sizing ordering from the real case of the previous study where the dataset is collected from the area of some retailers at Banda Aceh Province of Indonesia. Design/Methodology/Approach - The problem model appears when the retailer with shortage has to fulfill the lot size in the optimal condition to the stochastic demand while at the same time has the backlog condition. Moreover, when the backorder needs the time horizon for replenishment where this condition influences the holding cost at the store, many retailers try to solve this problem to minimize the holding cost, but on the other side, it should fulfill the customer demand. Three methods are explored to identify that condition: A Wagner-Whitin algorithm, the Silver-Meal heuristic, and the holding and ordering costs. The three methods are applied to the lot sizing when there is a backlog. Findings - The results of this study show that the Wagner-Whitin algorithm outperforms the other two methods. It shows that the performance increases around 27% when compared to the two other methods in this study. Research Limitations/Implications - All models are almost approximate and useful to determine the cycle period on stochastic demand. Practical Implications - The calculation of the dataset with the three methods would give the simple example to the retailer when he faces the uncertainty demand models. The prediction of the calculation is done accurately than the constant calculation, which is more economic. Social Implications - The calculation will contribute to much better predictions in many cases of uncertainty. Originality/Value - This is a initial comparative model among other methods to achieve the optimal stock and order for a retailer.