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Multi-method decision support framework for supply network design

Giuseppe Timperio, Robert de Souza, Boy Panjaitan Bernado, Sumit Sakhuja and Yoseph Sunardhi

A chapter in The Road to a Digitalized Supply Chain Management: Smart and Digital Solutions for Supply Chain Management, 2018, pp 197-219 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: In a business context characterized by increased competition due to digital transformation and technological breakthroughs, massive penetration of online purchasing, greater customers' expectations and switch of manufacturing paradigms, the role of logistics has become today more critical to firms than ever before. One critical initiative that logistics providers can undertake in order to gain a truly competitive edge is to optimally seize and utilize key resources such as storage and transportation assets. This paper is an effort in that direction and proposes a decision support framework to design a cost effective supply network in the last leg of deliveries. A case study based approach about a postal service provider in Southeast Asia is presented to showcase the applicability of the proposed framework. By leveraging on the integration of data analytics, network optimization and simulation, this work highlights the advantages of adopting a holistic approach to decision making for the used case. Results show that number of storage facilities, and their locations, affect speed and cost-effectiveness of last mile distribution. For the case at hand, 18% of savings in transportation and warehousing cost with no impact on service level can be achieved by reducing the number of facilities in the network from 9 to 4. By reading the present paper, decision makers will gain insights on how to address challenges related with supply network design, transportation costs reduction and optimization of overstretched transportation routes.

Keywords: Network Design; Network Optimization; Last Mile Logistics; Transportation Optimization (search for similar items in EconPapers)
Date: 2018
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