Effective use of Data and Decision Analytics to Improve Order Distribution in a Supply Chain

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Our submission consists of a short case and its accompanying teaching notes and laboratory guide, which is taught as part of the course “Operations Focused Data, Analytics & IT” in a master programme to train business analytics professionals. We attempt to use the case to expose our students to the Data and Decision Analytics Framework which helps the students identify the actual cause of business problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to improve the order distribution for a logistics service provider. 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, Decision Analytics, Inventory Management, Strategy


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

Research Areas

Intelligent Systems and Decision Analytics


Teradata University Network

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Winner of Teradata University Network (TUN) Teaching Innovation Award 2013

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