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Understanding the robustness of optimal FMCG distribution networks

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Logistics Research

Abstract

Distribution network design is about recommending long-term network structures in an environment where logistic variables like transportation costs or retailer order sizes dynamically change over time. The challenge for management is to recommend an optimal network configuration that will allow for longer term optimal results despite of environmental turbulences. This paper studies the robustness of cost-optimized FMCG (fast-moving consumer goods) distribution networks. It aims at observing the impact of changing variables/conditions on optimized logistic structures in terms of the optimal number and geographical locations of existing distribution centers. Five variables have been identified as relevant to the network structure. A case study approach is applied to study the robustness of an existing, typical, and optimized FMCG network. First, distribution network data of a German manufacturer of FMCG are recorded and analyzed. A quantitative model is set up to reflect the actual cost structure. Second, a cost optimal network configuration is determined as a benchmark for further analysis. Third, the variables investigated are altered to represent changes, both isolated (ceteris paribus) and in combination (scenario analysis). Each one of the variables investigated proves to be fundamentally able to suggest a change of the optimal network structure. However, the scenario analysis indicates that the expected changes will by and large compensate each other, leaving the network in near optimal condition over an extended period of time.

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Notes

  1. For an overview of the recent changes and reactions in Germany compare Otto [28].

  2. According to Mulvey et al. [27], we will observe the “solution robustness” of the optimal distribution structure.

  3. A similar approach to represent transportation costs and their dependence on distance and tonnage can be found in Tempelmeier [37].

  4. Theoretical background to the EOQ model and to the safety stock policy that is applied by Dryco is presented, for instance, in Chopra and Meindl [7].

  5. An approach, as proposed by Croxton and Zinn [8], to integrate inventory holding costs into the above presented network optimization model is to estimate the holding costs related to a certain network configuration by using the Square Root Law: in case that the demand variance for a product is the same at all customer locations and that the demands for a product at all customer locations are uncorrelated, savings due to centralization of inventory are proportional to the square root of the ratio of the new number of stocking locations over the original number of stocking locations [13]. As we did not suppose the two conditions to meet perfectly the situation of Dryco, we derive inventory holding costs for given network configurations analytically as presented above.

  6. For a more comprehensive overview of the solution procedure we refer to Drezner and Hamacher [11] and Eiselt and Sandblom [12]. An introduction to the Lagrangian relaxation method for solving integer programming problems is given by Fisher [14].

  7. The output is measured in tons. As factories are specialized in the production of different segments of consumer goods, one or two factories cannot produce 100% of the total distributed tonnage.

  8. Certainly, larger changes per variable lead to optimal networks consisting of 4 or more MDCs, but these threshold values are not reported in this analysis.

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Authors and Affiliations

  1. Department of Business Administration, esp. Controlling and Logistics, University of Regensburg, Universitätsstraße 31, 93053, Regensburg, Germany

    Florian Kellner, Andreas Otto & Andreas Busch

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  1. Florian Kellner
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  2. Andreas Otto
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  3. Andreas Busch
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Correspondence to Florian Kellner.

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Kellner, F., Otto, A. & Busch, A. Understanding the robustness of optimal FMCG distribution networks. Logist. Res. 6, 173–185 (2013). https://doi.org/10.1007/s12159-012-0097-6

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