Distribution network optimization for consumer goods economy packs requiring consolidated packing of multiple stock keeping units sourced from multiple production facilities

• Main Author: Syed Zia Abbas Rizvi
• Other Authors: Rohit Bhatnagar
• Format: Theses and Dissertations
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Manufacturing
• Online Access: http://hdl.handle.net/10356/60558
• Institution: Nanyang Technological University

Consumer goods supply chains faces challenges caused by proliferation of diverse stock keeping units and these challenges are intensified when coupled with aggressive marketing campaigns, which offers economy packs of similar and multiple products packed as one unit at discounted price to boost sales and revenues. These situations causes planning, supply chain configuration and supply decisions harder to make. A key challenge is to select the number and locations of warehouses from existing supply chain network to execute consolidated packing operations as a postponement strategy and distribute these economy packs to the stores selling these products while ensuring all the trade-offs between various logistics costs are evaluated and the network is configuresd to deliver best logistical performance. These challenges are confronted through development of an optimization model, which considers inventory, transportation and packing operations cost at warehouses and inbound & outbound legs of products journey to final customers. The model is capable of analyzing the trade-offs between these costs and considers the effects of pooling inventories requirements, long haul bulk shipments and centralization/decentralization of packing and distribution operations to make supply chain configuration decisions. The model is implemented in relatively simpler settings and executed for a range of products demand characteristics scenarios i.e. combinations of demand levels and variability to develop insights on the obtained results and verifies that the model results are aligned with theory and efficiently analyzes the logistic cost trade-offs.