Product Overview

During the summer of 2021, Sumana G., Chief Technology Officer of South Central Railway, was reviewing the annual productivity reports of field employees. This was an annual exercise that was crucial to central planning as it helped identify potential weaknesses and possibilities for improvement. Sumana knew that evaluating the productivity of store personnel would be the most challenging task because Indian Railways (IR) managed over 280,000 items stocked in 215 depots across the country. While reviewing the time sheets, Sumana quickly realized that field officers were spending a significant time amount of time on materials purchase, especially items purchased locally by field offices. On further inquiry, field officers revealed that retrieving data from the stores database based on item descriptions posed considerable challenges, and in most cases, the search results were not very useful. Sumana was quick to realize that an artificial intelligence (AI)-based search engine could solve this problem.

Learning Objectives

The central objectives of the case are the following: 􀀁 Understand the material management system challenges of large, geographically dispersed corporations that maintain massive inventories. 􀀁 Discuss the practical applications of AI-based solutions and various text-based clustering techniques. 􀀁 Understand the complexities of a text-based clustering problem, especially the limitations of a lexical similarity-based approach and the advantages of a hybrid approach.