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Logistic activity, in particular transportation, produces green house gases (GHG). For different purposes GHG need to be allocated to objects. This paper studies how to allocate the GHG volume of a transportation process (delivery tour) to the single shipments moved by the process. First, it identifies classes of generic allocation schemes and presents 15 allocation methods. Second, since the majority of these methods has not been designed for allocating GHG, we apply and compare them in the short distance transport context within a numerical example. The aim is to study how the schemes perform according to criteria. We suggest using causality, efficiency, empty core robustness, symmetry, individual rationality, coalition stability, ease of application, and set robustness as appraisal criteria and attempt to mainstream the discussion by recommending selected allocation methods.
The assessment of greenhouse gas (GHG) emissions of supply chain activities is performed to create transparency across the supply chain and to identify emission-cutting opportunities. Literature provides several generic and case study approaches to estimate GHG emissions. But research often focuses on products. This paper sheds light on how the greenhouse performance of a fast-moving consumer goods (FMCG) distribution network depends on several (FMCG specific) variables to set up a “CO2 network footprint”. Within a quantitative computational study, the distribution network footprint of an existing FMCG manufacturer is analyzed. Three options being fundamentally able to reduce total GHG emissions are identified: number of distribution centers, performance of the engaged logistics service provider and shipment structure. First, transportation processes for the investigated FMCG manufacturer are analyzed to derive GHG emissions caused by different distribution shipments. Second, initial data are manipulated to simulate variable changes, that is, different logistics structures. Third, results are reported and analyzed to show up how different changes in logistics structures may reduce GHG, without technological propulsion or use of regenerative energy.
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.
GHG network analysis FMCG
(2013)
Purpose
Tooling is a common component of an industrial product’s manufacture. Specific tooling is devised to serve the fabrication of a particular product, while generic tooling can be used in the manufacture of multiple products. In the latter case, companies are confronted with the problem of fairly allocating the indirect costs of the tooling. This article studies how to allocate costs of generic tooling to single production orders.
Methodology
Ten allocation methods (AMs) are described that are in principle suited to the distribution of generic tooling costs to production orders. Since the presented methods have for the most part been discussed in differing contexts, we apply them to a specified generic tooling problem for comparison. Evaluation of the various methods is based on 16 criteria. Reasoning is supported by a computational Monte Carlo simulation. Furthermore, we suggest using the Analytical Hierarchy Process (AHP) to elaborate one final proposition concerning the most preferable allocation scheme.
Findings
The article reports the single allocation rules’ performances for different allocation scenarios. The described characteristics refer to fairness, efficiency, and simplicity as well as to empty-core performance. Using AHP analysis allows for the aggregation of the rules’ criteria ratings. Thus, especially suitable allocation schemes for the problem at hand are identified.
Practical implications
An allocation is required for budgeting reasons and also for the definition of projects’ bottom-up sales prices. Selecting the “right” AM is important, as a suboptimal AM can result in unfair allocation vectors, which will act as incentives to stop using the common resource, potentially leading to higher total costs.
Originality/value of the article
Research on the comparison of AMs is typically performed for certain purposes, such as enterprise networks, horizontal cooperative purchasing scenarios, or municipal service units. This article will augment the research evaluating AMs by introducing a novel set of evaluation criteria and by providing an in-depth comparison of AMs suited for the allocation of generic tooling costs.
In this paper, we propose a combined methodology for the selection of distribution centres (DCs) by integrating and balancing economic, environmental and social sustainability aspects. The analytic network process (ANP) permits to systematically evaluate possible DCs on the basis of a situation-specific decision structure. A systematic development of the decision networks, which is one of the core challenges using the ANP, is supported by the process analysis method which we extend for that purpose. The validity and soundness of the proposed framework are demonstrated by means of a case study. The case study results reveal that omitting aspects of sustainability can lead to unfortunate results.