Voici les éléments 1 - 10 sur 24
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    Sustainable business growth: exploring operations decision-making
    Purpose: The objective of this paper is to explore how operations decision-making may keep the growing firms within the boundaries of corporate and societal sustainability. Design/methodology/approach: We classify operations decisions during growth periods according to the three dimensions of the triple bottom line (economic, social and environmental). By means of a longitudinal case study of a family-owned wood construction firm that is in a process of intense growth, we identify, visually represent and analyse the complex sequences of selected managerial operations decisions. Findings: Our empirical data suggests that operations decisions made by managers during growth periods follow specific patterns. From our analysis, we derive various research propositions that investigate how a well-understood and therefore efficient and effective decision-making process can facilitate sustainable business growth. Research limitations/implications: Our findings offer opportunities for future studies to zoom in on specific parts of the decision-making process during growth periods. Moreover, given the exploratory nature of our study, future research should test hypotheses derived from our research propositions. Practical implications: This study investigates operations decision-making during growth, which is crucial for guiding companies through this complex transition phase. Originality/value: This conceptual and empirical analysis explores new theory and contributes to the vastly under-researched subject of sustainable business growth.
  • Publication
    Accès libre
    Investing in disaster management capabilities versus pre-positioning inventory: A new approach to disaster preparedness
    Disaster preparedness has been recognized as a central element in reducing the impact of disasters worldwide. The usual methods of preparedness, such as pre-positioning relief inventory in countries prone to disasters, are problematic because they require high investment in various locations, due to the uncertainty about the timing and location of the next disaster. Investing in disaster management capabilities, such as training staff, pre-negotiating customs agreements with countries prone to disasters, or harmonizing import procedures with local customs clearance procedures, has been recognized as a way to overcome this constraint. By means of system dynamics modeling, we model the delivery process of ready-to-use therapeutic food items during the immediate response phase of a disaster, and we analyze the performance of different preparedness scenarios. We find that pre-positioning inventory produces positive results for the beneficiaries, but at extremely high costs. Investing in disaster management capabilities is an interesting alternative, as it allows lead time reductions of up to 67% (18 days) compared to a scenario without preparedness, at significantly lower costs than pre-positioning inventory. We find that the best performance can be achieved when combining both preparedness strategies, allocating part of the available funding to disaster management capabilities and part to pre-positioning inventory. We analyze 2828 such combined scenarios to identify the best mix of preparedness strategies for different levels of available funding. On the basis of our findings, we provide recommendations for relief organizations on how to allocate their preparedness budget.
  • Publication
    Métadonnées seulement
    Analyzing the Efficient Execution of In-Store Logistics Processes in Grocery Retailing - The Case of Dairy Products
    (2013-8-2) ;
    Teller, Christoph
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    Kotzab, Herbert
    In this article, we examine in-store logistics processes for handling dairy products, from the incoming dock to the shelves of supermarkets and hypermarkets. The efficient execution of the in-store logistics related to such fast-moving, sensitive, and essential items is challenging and crucial for grocery retailers' sales, profits, and image. In our empirical study, we survey in-store logistics processes in 202 grocery supermarkets and hypermarkets belonging to a major retail chain in central Europe. Using a data envelopment analysis (DEA) and simulation, we facilitate process benchmarking. In particular, we identify ways of improving in-store logistics processes by showing the performance impacts of different managerial strategies and tactics. The DEA results indicate different efficiency levels for different store formats; the hybrid store format of the small hypermarket exhibits a comparatively worse performance in the analyzed execution of in-store logistics processes. The process simulation analysis reveals that the strategic and tactical design of in-store logistics processes (such as store locations/layouts, capacity management, reorder time, order period, and safety stock factors) lead to substantial service performance improvements (such as higher on-shelf availability combined with reduced inventory obsolescence costs). The results also show marginal improvements in the performance figures when delivery delays and damage to products are reduced.
  • Publication
    Métadonnées seulement
    Rapid Modeling for Sustainability - Foreword
    (2013-3-1)
    Hilmola, Olli-Pekka
    ;
    ;
    Vandaele, Nico
    It is our pleasure to introduce this special issue of Decision Support Systems on ‘Rapid Modeling for Sustainability’. It brings together core papers on Rapid Modeling, focusing on the rising dimensions of sustainability, which is raised on top of technical and financial modeling. Rapid Modeling refers to the basic models behind the structured process of decision making to manage and improve time based performance. As it was originally applied for manufacturing systems and supply chains, it has spread through decision making areas like private and public services, office operations and new product development. All these areas cope with a substantial effort of short and/or on-time delivery. Rapid refers to strong calculation possibilities so that various what-if scenarios can be run in a short period of time. In this way Rapid Modeling is very well suited to support both individual as group decision making processes. Therefore, we are honored to edit this special issue for Decision Support Systems.
  • Publication
    Métadonnées seulement
    A hybrid analysis method for multi‐class queueing networks with multi‐server nodes
    (2013-3-1) ; ; ;
    Fichtinger, Johannes
    There is no doubt that Rapid Modeling based on queueing theory serves the purpose of understanding complex manufacturing systems. It enables managers to integrate operational performance measures when evaluating alternative process designs. This helps decision-makers to avoid the mistake of focusing mainly on short-term financial results instead of finding sustainable solutions. However, we are facing several limitations of state-of-the-art algorithms for queueing systems. In particular, the precision of the estimates of the performance measures vary under different conditions, and information about the distribution of the output variables is available only for a few special cases. In this paper, we propose a set of recursive equations to describe the behavior of multi-class, multi-server queueing systems: ∑ Gi/Gi/m. We will show the application to simulation and propose a hybrid decomposition method for queueing networks of ∑ Gi/Gi/m stations. The proposed method is intended to deliver better estimates of the performance measures than the available decomposition algorithms and, at the same time, to be faster and easier to implement than full simulation. We illustrate the performance of the hybrid solution by comparing the results of discrete event simulation with the results of a software package implementing the hybrid algorithm and software packages applying alternative algorithms.
  • Publication
    Accès libre
    A meta-analysis of humanitarian logistics research
    Purpose - This paper gives an up-to-date and structured insight into the most recent literature on hu-manitarian logistics, and suggests trends for future research based on the gaps identified through structured content analysis. Design/methodology/approach - We use a quantitative and qualitative content analysis process to analyse the characteristics of the existing literature. We identify the most studied topics in six structural dimensions, and present gaps and recommendations for further research. Findings - We found that existing humanitarian logistics research shows too little interest in continuous humanitarian aid operations, in slow onset disasters and man-made catastrophes. While several papers address different phases of disasters, very few focus particularly on the reconstruction following a disaster. Empirical research is underrepresented in the existing literature as well. Research limitations/implications - While five of our structural dimensions are inspired by previous reviews, our sixth dimension (situational factors) is derived from a theoretical framework we developed and which has never been tested before. The validity of our study could therefore be increased by testing this framework. Originality/value - We analyse the broadest set of papers (174) ever covered in previous literature reviews on humanitarian logistics. We conduct a quantitative analysis of the papers in order to analyse the situational factors which have mostly been studied so far in literature. This paper is also the first in humanitarian logistics to use content analysis as the main methodology to analyse literature in a structured way, which is of particular value to the academic community as well as practitioners. Outstanding Paper Award 2013 winner (Emerald)
  • Publication
    Métadonnées seulement
    Batch Sizes Optimization by means of Queueing Network Decomposition and Genetic Algorithm
    Batch sizes have a considerable impact on the performance of a manufacturing process. Determining optimal values for batch sizes helps to reduce inventories/costs and lead times. The deterministic nature of the available batch size optimisation models reduces the practical value of the obtained solutions. Other models focus only on critical parts of the system (e.g., the bottleneck). In this paper, we present an approach that overcomes important limitations of such simplified solutions. We describe a combination of queueing network analysis and a genetic algorithm that allows us to take into account the real characteristics of the system when benefiting from an efficient optimisation mechanism. We are able to demonstrate that the application of our approach on a real-sized problem with 49 products allows us to obtain a solution (values for batch sizes) with less than 4% relative deviation of the cycle time from the exact minimal value.
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