A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. (January 2018)

• Record Type: Journal Article
• Authors: Leung, K.H.; Choy, K.L.; Siu, Paul K.Y.; Ho, G.T.S.; Lam, H.Y.; Lee, Carman K.M.
• Is Part Of: Expert systems with applications. Volume 91(2018)
• Page Start: 386
• Page End: 401
• Publication Date: 2018-01
• Subjects: E-commerce logistics -- O2O retailing -- Order fulfilment -- Business process re-engineering -- Warehouse postponement applications -- Expert systems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.09.026
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The e-commerce internal order processing flow is streamlined and re-designed. A GA-rule-based system for efficient e-commerce order fulfilment is proposed. An optimal order processing plan is generated by genetic algorithm technique. A system implementation shows a significant order processing time reduction. Abstract: In today's world of digitization, the rise of the e-commerce business around the globe has brought a tremendous change not only in our purchasing habits, but also to the entire retail and logistics industry. Given the irregular e-commerce order arrival patterns, limited time for order processing in e-fulfilment centres, and the guaranteed delivery schedules offered by e-retailers, such as same-day or next-day delivery upon placing an order, logistics service providers (LSPs) must be extremely efficient in handling outsourced e-commerce logistics orders. Without re-engineering the order fulfilment processes, the LSPs are found to have difficulties in executing the order fulfilment process due to the tight handling requirements. This, in turn, delays the subsequent processes in the supply chain, such as last-mile delivery operations, consequently affecting customer satisfaction towards both the retailer and the LSP. In view of the need to improve the efficiency in handling e-commerce orders, this study aims at re-engineering the fulfilment process of e-commerce orders in distribution centres. The concept of warehouse postponement is embedded into a new cloud-based e-order fulfilment pre-processing system (CEPS), by incorporating the genetic algorithm (GA) approach for e-commerce order grouping decision support and a rule-based inference engine for generating operating guidelines and suggesting the use of appropriate handling equipment. Through a case study conducted in a logistics company, the CEPS provides order handling solutions for processing e-commerce logistics orders very efficiently, with a significant reduction in order processing time and traveling distance. In turn, improved operating efficiency in e-commerce order handling allows LSPs to better align strategically with online retailers, who provide customers with aggressive, guaranteed delivery dates.