Data Analysis of Retailer Orders to Improve Order Distribution

Publication Type

Conference Paper

Publication Date



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.


Data Analytics, inventory policy, order distribution


Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics


Analytics 2013

City or Country


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