1M. Sc. [Engg.] Student, Department of Mechanical and Manufacturing Engineering, M. S. Ramaiah School of Advanced Studies, Bangalore, 560 058
2Asst. Professor, Department of Mechanical and Manufacturing Engineering, M. S. Ramaiah School of Advanced Studies, Bangalore, 560 058
3Senior Manager-Production, WEG Industries India Pvt. Ltd, Hosur-635 110
Online published on 18 February, 2020.
As the world of business continues to change rapidly and dramatically, organisations are also subjected to continuous changes. Starting from 21st century, the concept of a global market place is influencing the manufacturing organisations throughout the world in contrasting ways. However with the product life cycles becoming shorter, inventory management is becoming as dicey as to predict one's future. Moreover owing to the uncertainties in demand of products, managing inventory levels is becoming highly complex and hence crucial to a company's survival. Since fewer mathematical models have been used to manage inventories, an attempt has been made to apply models to manage inventories in the tool warehouse.
In the study, 10% reduction in the total supply chain cost through the development of a tool management system has been targeted. Inventory related data has been collected from April 2011 to October 2011. After analysing the demand patterns, tools have been classified into certain and uncertain demand category of products. Under certain demand, EOQ Model, complete aggregation model and tailored aggregation model has been used for low, medium and high valued products respectively. On the contrary, uncertain demand products have been modelled using safety stock and cycle service levels. Finally comparative analysis has been carried out between legacy and used models in terms of total supply chain cost.
Results revealed 7% savings from the EOQ model for low value products; 21% savings from aggregation model for medium value products; 18% savings from tailored aggregation method for high value products and 23% savings from probability model for products with uncertain demand. In addition, most frequently used carbide inserts have been aggregated and corresponding optimal order frequency and lot sizes have been arrived. Further, the study can be carried out for other categories of products in the selected energy sector company.
EOQ, Complete Aggregation Method, Tailored Aggregation Method, Cycle Service Level