Managing a Retail Store and the Associated Warehouse with a Knowledge-Driven Approach

For today’s retail stores that directly serving final customers’ demand have some special characteristics and restrictions, such as varieties of goods yet limited space in a retail store, a customer order includes various products but each of them is with a small number of quantities, products with different features like seasonal, etc. The competitiveness of the retail store relies on the utilization of the space and fulfillment of the customer demands. To increase the competitiveness, the retail store is normally linked with an associated (small) warehouse. The intelligence of managing the associated warehouse for the retail store has an extraordinary meaning in today’s dynamic environment. This research use data-driven approach to analyze historical customer order to unearth the relevance, customer buying behavior, time series, seasonal, etc. Then by using knowledge-driven approach (ontology) to define the classes and properties, relations between retail store, warehouse, and products, the knowledge can be shared, reused, and communicate in the company. Finally, via inference engine with Semantic Query-enhanced Web Rule Language (SQWRL) to infer the correlations from the ontology and find out the knowledge and logics. The research helps the retail store quickly delivery the products and lets the retail store and its warehouse possess the ability to respond to the various changes.