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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.
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.
Road network performance (RNP) is a key element for urban sustainability as it has a significant impact on economy, environment, and society. Poor RNP can lead to traffic congestion, which can lead to higher transportation costs, more pollution and health issues regarding the urban population. To evaluate the effects of the RNP, the involved stakeholders need a real-world data base to work with. This paper develops a data collection approach to enable location-based RNP analysis using publicly available traffic information. Therefore, we use reachable range requests implemented by navigation service providers to retrieve travel times, travel speeds, and traffic conditions. To demonstrate the practicability of the proposed methodology, a comparison of four German cities is made, considering the network characteristics with respect to detours, infrastructure, and traffic congestion. The results are combined with cost rates to compare the economical dimension of sustainability of the chosen cities. Our results show that digitization eases the assessment of traffic data and that a combination of several indicators must be considered depending on the relevant sustainability dimension decisions are made from.
Purpose
The purpose of this paper is to explore the impact of information technology (IT) on supply chain performance in the automotive industry. Prior studies that analyzed the impact of IT on supply chain performance report results representing the situation of the “average industry.” This research focuses on the automotive industry because of its major importance in many national economies and due to the fact that automotive supply chains do not represent the supply chain of the average industry.
Design/methodology/approach
A research model is proposed to examine the relationships between IT capabilities, supply chain capabilities, and supplier performance. The model divides IT capabilities into functional and data capabilities, and supply chain capabilities into internal process excellence and information sharing. Data have been collected from 343 automotive first-tier suppliers. Structural equation modeling with partial least squares is used to analyze the data.
Findings
The results suggest that functional capabilities have the greatest impact on internal process excellence, which in turn enhances supplier performance. However, frequent and adequate information sharing also contributes significantly to supplier performance. Data capabilities enable supply chain capabilities through their positive impact on functional capabilities.
Practical implications
The findings will help managers to understand the effect of IT implementation on company performance and to decide whether to invest in the expansion of IT capacities.
Originality/value
This research reports the impact of IT on supply chain performance in one of the most important industries in many industrialized countries, and it provides a new perspective on evaluating the contribution of IT on firm performance.
This article introduces the Database for Estimation of Road Network Performance (DERNP) to enable wide-scale estimation of relevant Road Network Performance (RNP) factors for major German cities. The methodology behind DERNP is based on a randomized route sampling procedure that utilizes the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) in combination with the tile-based HERE Maps Traffic API v7 and a digital elevation model provided by the European Union’s Earth Observation Programme Copernicus to generate a large set of independent and realistic routes throughout OpenStreetMap road networks. By evaluating these routes using the PHEMLight5 framework, a comprehensive list of RNP parameters is estimated and translated into polynomial regression models for general usage. The applicability of these estimations is demonstrated based on a case study of four major German cities. This case study considers network characteristics in terms of detours, infrastructure, traffic congestion, fuel consumption, and CO2 emissions. Our results show that DERNP and its underlying randomized route sampling methodology overcomes major limitations of previous wide-scale RNP approaches, enabling efficient, easy-to-use, and region-specific RNP comparisons.
This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect ‘risk’ into the supplier selection problem. Secondly, since we allow for integer variables in our model—in contrast to the classical Markowitz portfolio theory—we use the ε-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution.
Considering climate change, recent political debates often focus on measures to reduce CO2 emissions. One key component is the reduction of emissions produced by motorized vehicles. Since the amount of emission directly correlates to the velocity of a vehicle via energy consumption factors, a general speed limit is often proposed. This article presents a methodology to combine openly available topology data of road networks from OpenStreetMap (OSM) with pay-per-use API traffic data from TomTom to evaluate such measures transparently by analyzing historical real-world circumstances. From our exemplary case study of the German motorway network, we derive that most parts of the motorway network on average do not reach their maximum allowed speed throughout the day due to traffic, construction sites and general road utilization by network participants. Nonetheless our findings prove that the introduction of a speed limit of 120 km per hour on the German autobahn would restrict 50.74% of network flow kilometers for a CO2 reduction of 7.43% compared to the unrestricted state.
Unternehmen sind häufig mit Situationen konfrontiert, in denen schnell Entscheidungen bezüglich der Auswahl mehrerer Handlungsalternativen gefunden werden müssen. Mathematische Verfahren können hierbei unterstützen, z. B. für die Ermittlung einer ersten Diskussionsbasis. Verfügbare Softwarelösungen errechnen zwar häufig optimale Ergebnisse, zeigen jedoch Schwächen bei der praktischen Anwendbarkeit. So ist eine Einarbeitung in komplexe Optimierungssoftware für die teilweise sporadisch auftretenden Probleme in der Regel für Unternehmen nicht möglich, unter anderem auch unter Anbetracht der teilweise hohen Kosten der Standardsoftware und dem benötigten hohen Einarbeitungsaufwand. Gerade Problemstellungen in Fachbereichen, die nicht auf mathematische Problemlösung spezialisiert sind, münden daher regelmäßig in Ineffizienzen. Basierend auf den in der Literatur diskutierten Lösungsansätzen wurde ein praxisorientierter Ansatz zur Entscheidungsunterstützung für Rucksackprobleme bzw. 0–1 Probleme mithilfe von Genetischen Algorithmen (GA) entwickelt und technisch in Microsoft Excel® umgesetzt. Ein Praxistest bei einem chinesischen Textilunternehmen belegt erhöhte Effektivität und Effizienz. Die Software kann kostenfrei nach MIT Lizenz unter http://www.solvega.de/ heruntergeladen werden.
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.