Agarwal Automobiles: Fuel Station Forecasting and Inventory Management
(4 pages of text)
On June 1, 2016 a student was preparing for a summer job with Agarwal Automobiles, a vehicle fuel station owned by his father. The student had taken courses that covered supply chain management, including inventory management and forecasting. His father had suggested that, as a summer project, the student examine the fuel station’s retail inventory management practices with the intention of replacing the current simple rules with a more rational and complex planning model. The student needed some ideas to use as a guide toward a better ordering policy. The available data for the previous six months suggested that the company held an average ending inventory of ₹2.1 million worth of fuel products to maintain average daily sales of ₹0.52 million. The challenge was to reduce the inventory levels, while maintaining a high customer service level in fuel sales.
This case is suitable for a postgraduate level production and operations management course that discusses item level forecasting as well as the formulation of inventory management policies and models in a joint-product-ordering scenario. It can also be used in a graduate level quantitative technique or management science course to demonstrate the use of the mixed integer linear programming model for complex inventory planning problems, using a spreadsheet-based optimization tool like OpenSolver. The case provides opportunities to explore analytical techniques in a real business scenario. After completing the case, students should be able to do the following:
- Perform quantitative business forecasting, including model selection and decomposition methods used in seasonality and trend analysis.
- Manage the inventory of multiple products in a retail scenario, including economic order quantity and periodic review policy for joint ordering, under maximum order size and separation constraints.
- Apply the mixed integer linear programming technique, using a spreadsheet-based solver to formulate and solve an inventory management problem dynamically by using the forecast data, under capacity and separation constraints.
India, Small, 2016
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