Data Analysis of Retailer Orders to Improve Order Distribution

Publication Type

Book Chapter

Publication Date

2015

Abstract

Our paper attempts to improve the order distribution for a logistics service provider who accepts order from retailers for fast moving consumer goods. Due to the fluctuations in orders on a day to day basis, the logistics provider will need the maximum number of trucks to cater for the maximum order day, resulting in idle trucks on other days. By performing data analysis of the orders from the retailers, the inventory ordering policy of these retailers can be inferred and new order intervals proposed to smooth out the number of orders, so as to reduce the total number of trucks needed. An average of 20 % reduction of the total number of trips made can be achieved. Complementing the proposed order intervals, the corresponding new proposed order size is computed using moving average from historical order sizes, and shown to satisfy the retailers’ capacity constraints within reasonable limits. We have successfully demonstrated how insights can be obtained and new solutions can be proposed by integrating data analytics with decision analytics, to reduce distribution cost for a logistics company.

Discipline

Business | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Publication

Reshaping Society through Analytics, Collaboration, and Decision Support: Role of Business Intelligence and Social Media

Volume

18

Editor

Iyer, Lakshmi S.; Power, Daniel J.

First Page

211

Last Page

238

ISBN

9783319115757

Identifier

10.1007/978-3-319-11575-7_15

Publisher

Springer Verlag

City or Country

New York

Additional URL

http://dx.doi.org/10.1007/978-3-319-11575-7_15

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