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|Title:||An intelligent warehouse postponement decision support system for efficient e-commerce order fulfillment||Authors:||Leung, Ka Ho||Advisors:||Choy, K. L. (ISE)
Ho, George (ISE)
|Keywords:||Business logistics -- Management
Business logistics -- Information technology
|Issue Date:||2019||Publisher:||The Hong Kong Polytechnic University||Abstract:||The rise of omni-channel and e-commerce online shopping has reshaped the entire retail and logistics industry. Though numerous benefits are brought by such e-shopping trend, the e-retailers and logistics service providers (LSPs) now face noticeable challenges to meet the tight requirements of e-commerce order processing as demanded by e-retailers and end consumers. To capture the market pie of e-commerce logistics business, LSPs are struggling to transform their business from handling traditional large lot-sized shipment orders to e-commerce parcel-based, discrete orders. The fundamental differences among traditional and e-commerce logistics orders (e-orders), in terms of arrival frequency, delivery requirement, urgency, and the number of stock-keeping-units (SKUs), have created enormous handling difficulties for LSPs in processing e-orders efficiently in their distribution centres using conventional order processing flow. In view of the need for LSPs to improve their internal core competence in processing e-orders so as to grasp today's e-commerce logistics business opportunities, this research is performed with an objective of improving LSPs' e-order handling efficiency through re-engineering of their e-order operational flow in distribution centres. The re-engineering of e-order processing flow is achieved by the implementation of "Warehouse Postponement Strategy" (WPS), a proposed operational strategy in this research, having an aim to "delay the execution of logistics operations until the last possible moment". By consolidating the e-orders and subsequently releasing the consolidated orders at the right timing, a LSP would be able to deploy the WPS in distribution centres for handling e-orders efficiently. However, the re-engineering of e-order operational flow through the introduction of WPS in strengthening the internal competence of LSP is effective only if the decision-makers can manage to (i) consolidate similar e-orders logically, and (ii) release the consolidated orders at the most appropriate timing.
As neither of the above-mentioned decisions can be made manually, an E-commerce Fulfillment Decision Support System (EF-DSS) is proposed in order to provide LSPs with decision support in determining (i) "How to group the e-orders", and (ii) "When to release the grouped e-orders". The issue of "How to group the e-orders" is tackled with a GA-rule-based approach to group e-orders based on the similarity of storage locations of ordered items, whereas the problem of "When to release the grouped e-orders" is solved by a novel autoregressive-momentum-moving average-based Adaptive Network-Based Fuzzy Inference System (AR-MO-MA-ANFIS) approach, integrating the autoregressive, momentum and moving average elements of time series data into the modeling of ANFIS. The feasibility of the proposed system is validated through three case studies conducted in third-party LSPs based in Hong Kong. The system reveals a significant improvement in terms of the order handling efficiency and resource management. Though there has been a noticeable growth in both business-to-customers (B2C) and business-to-business (B2B) e-commerce retail activities in recent decades, the mainstream literature in dealing with e-commerce operating activities has been lacking. The major contribution of this research is in the design and application of an e-commerce operations-oriented decision support system that integrates the wider concept of the proposed Warehouse Postponement Strategy for effective e-order fulfilment in distribution centres. A practical roadmap of WPS implementation is provided in this research, enabling logistics practitioners to deploy WPS effectively as well as opening up a new area for researchers to take e-commerce operating inefficiencies into account in research on warehouse decision support.
|Description:||xx, 295 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P ISE 2019 Leung
|URI:||http://hdl.handle.net/10397/81467||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of Nov 13, 2019
Citations as of Nov 13, 2019
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