Varying lot-sizing models for optimum quantity-determination in material requirement planning system

Lot sizing is one of the three input components of material requirements planning (MRP). In practice, lot-sizing models (LSM) are not often utilised to solve real-life scenarios due to different assumptions, limits, and the extent to which these conditions are valid. Therefore, for many practitioners, a way out is to adjust some traditional method to their present situation rather than vary different lot-sizing models. This study was designed to develop a standalone lot-sizing module (LST-MOD) with a graphic user interface (GUI) to determine the most suitable ordering policy. Six (6) LSM were analysed based on relevance and complexity. Since lot-sizing is an exogenous decision, the lotsizing component of the material requirement planning system (MRP) was decoupled. This resulted in the development of a standalone module using Python programming language. Thereafter, using an end product and its sub-items, the LSTMOD was used to obtain total inventory cost. By varying different lot-sizing models, those with high flexibility performed better than those with little or no flexibility. This approach showed that for an organisation with little financial strength, it is possible to develop in-house lotsizing module to vary simultaneously several ordering policies under multiple conditions. The output will assist management in their decision planning process and ultimately contribute to the actualisation of their strategic goals.