Data-driven inventory control leveraging big data
About 70% of the total order volume is generated by about 25% of the SKUs, while the last 10% of volume is generated by about 50% of rarely ordered SKUs.
The volume, velocity, and variety of supply chain data being received by companies are rapidly increasing. The need to extract and use whatever value these data have in order to make improved supply chain control, warehousing, and logistics decisions in (near) real-time has become a requirement to meet competitive pressures.
Pre-packaging of fast-moving inventory.
Pre-packaging of fast-moving inventory can be a powerful inventory management strategy to reduce turnaround time and warehousing cost while simultaneously increasing service levels.
In this project we explore together with Macy's Systems and Technology, the efficiency and efficacy of various pre-packaging strategies combining real-world requirements, both in terms of flexibility and speed with data-driven decision making approaches. Based on historical customer demand and expected future demand for a specific product the optimal quantity of stock to be pre-packaged is to be determined. Pre-packaged items can be shipped significantly faster, requiring often only the application of an address label. At the same time, pre-packaging items that are out of demand can be very costly due to inventory holding costs and space restrictions. Determining the optimal tradeoff is a hard problem as product life can be short, so that historic demand data is effectively not available. To overcome this we will be using auxiliary product data and various machine-learning strategies to more accurately determine the optimal pre-packaging strategy. The final goal of this project is a big data inventory management and pre-packaging framework, which leverages state-of-the-art analytics on real-time order data as well as hundreds of millions of historical and auxiliary data points, going beyond traditional, static inventory policies.
09/2015: Official start of project