Optimizing Inventory Carrying Cost and Ordering Cost Using Rank Order Clustering Approach for Small and Medium Enterprises (SMEs)

EasyChair Preprint 4642

Abstract

Inventory is one of the main assets for any organization, be it large enterprises or small and medium size enterprises. Therefore, decisions related to inventory directly affect the revenue generated by an organization. This work is intended to find a sufficient level of control over each inventory item and to alleviate inventory management issues of SMEs. In this paper, Rank Order Clustering (ROC) algorithm is used to classify multi-item inventory under few clusters. Proposed framework is tested on a medium scale gear manufacturing firm which manufactures 40 different types of planetary and customized gear boxes. The results demonstrate accurate assessment of proposed methodology to form clusters of similar components which being used in different assemblies. This technique helps to identify similar components in order to aggregate requirement of components as well as to formulate specific inventory policy to cut inventory carrying cost for each component. Lot sizing techniques can be tested further to optimize inventory carrying cost and quantity discounts can also be considered to reduce unit cost.

Keyphrases: Inventory Management, Rank Order Clustering, Small and Medium Enterprises (SMEs)

@booklet{EasyChair:4642,
  author    = {Ganesh Narkhede and Neela Rajhans},
  title     = {Optimizing Inventory Carrying Cost and Ordering Cost Using Rank Order Clustering Approach for Small and Medium Enterprises (SMEs)},
  howpublished = {EasyChair Preprint 4642},
  year      = {EasyChair, 2020}}