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Manufacturing-Execution-Systeme (MES) werden in der Fertigungsindustrie dazu eingesetzt, einen durchgehenden Informationsfluss zwischen den an der Fertigung beteiligten Systemen herzustellen. Von der Vernetzung aller an der Produktion beteiligten Systeme wird dabei eine gesteigerte Produktivität auf Shop-Floor-Ebene erwartet. Beim Durchlauf des beispielhaft gezeigten Fertigungsauftrages wurden durch den Einsatz von MR-CM©, einem von der Maschinen-fabrik Reinhausen entwickelten und implemen-tierten MES, bereits einige Produktivitätspotenziale im Vergleich mit der konventionellen Abwicklung ohne Einsatz des MES deutlich. Diese resultieren im Wesentlichen aus der Eliminierung von nicht wertschöpfenden Schnittstellenproblemen zwischen den an der Auftragsdurchführung beteiligten Maschinen. Die erzielten Produktivitätszuwächse bestehen im Kern aus einer Verkürzung der Maschinenrüstzeiten, was zu einer Reduktion der Produktionskosten und einer Zunahme der Produktionsflexibilität führt und zu einer Reduzierung der Werkzeugbestände sowie der damit verbundenen Kapitalbindungskosten.
Die Projektauswahl stellt eine Herausforderung insbesondere für mittelständische Industrieunternehmen dar. Abhängigkeiten verschiedener Alternativen und vorgegebene Investitionsbudgets führen bei Anwendung von in der Praxis standardmäßig eingesetzten Verfahren regelmäßig zu suboptimalen Projektkombinationen. In enger Abstimmung mit einem Automobilzulieferer wurde ein Ansatz zur Projektauswahl unter Berücksichtigung von Ressourcen und laufenden Projekten entwickelt sowie technisch umgesetzt. Implementierte Praxistests belegen Verbesserungen bei der Projektauswahl, aber auch bei der Budgeteinhaltung, der Fixkostenreduzierung sowie der Angebotspreisbestimmung.
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)