4,395 research outputs found

    From ERP to advanced resource planning: Improving the operational performance by getting the inputs right.

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    In this paper, we show that the planning and decision support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP package, may be greatly enhanced by including an Advanced Resource Planning (ARP) module as an add-on at the midterm planning level. This ARP module enables to estimate the impact of variability, complexity and dynamic system behavior on key planning parameters. As such, it yields realistic information both for short-term planning purposes and for reliable lead time quotations. We show how dynamic behavior impacts the operational performance of a manufacturing system, and discuss the framework for incorporating the ARP module into the ERP system.Planning; Operational performance; Performance; International; Science;

    Low carbon manufacturing: Characterization, theoretical models and implementation

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    Today, the rising of carbon dioxide (CO2) emissions is becoming the crucial factor for global warming especially in industrial sectors. Therefore, the research to reduce carbon intensity and enhance resources utilization in manufacturing industry is starting to be a timely topic. Low carbon manufacturing (LCM) can be referred to the manufacturing process that produces low carbon emissions intensity and uses energy and resources efficiently and effectively during the process as well. In this paper, the concepts of LCM are discussed and the LCM associated theoretical models, characterization and implementation perspective explored. The paper is structured in four parts. Firstly, the conception of low carbon manufacturing is critically reviewed then the characterization of low carbon manufacturing is discussed and formulated. Third part, the theoretical models are developed with initial models by using the theory from supply chain modeling and linear programming solutions (LP). The models show the relationship of resource utilizations and related variables for LCM in two levels: shop-floor and extended supply chain. Finally, the pilot implementations of LCM are discussed with two approaches: desktop or micro machines and devolved manufacturing. The paper is concluded with further discussions on the potential and application of LCM for manufacturing industry

    Operations research and computers

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    operational research

    Locating emergency services with priority rules: The priority queuing covering location problem

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    One of the assumptions of the Capacitated Facility Location Problem (CFLP) is that demand is known and fixed. Most often, this is not the case when managers take some strategic decisions such as locating facilities and assigning demand points to those facilities. In this paper we consider demand as stochastic and we model each of the facilities as an independent queue. Stochastic models of manufacturing systems and deterministic location models are put together in order to obtain a formula for the backlogging probability at a potential facility location. Several solution techniques have been proposed to solve the CFLP. One of the most recently proposed heuristics, a Reactive Greedy Adaptive Search Procedure, is implemented in order to solve the model formulated. We present some computational experiments in order to evaluate the heuristics’ performance and to illustrate the use of this new formulation for the CFLP. The paper finishes with a simple simulation exercise.Location, queuing, greedy heuristics, simulation

    Improving warehouse responsiveness by job priority management: a European distribution centre field study

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    Warehouses employ order cut-off times to ensure sufficient time for fulfilment. To satisfy increasing consumer’s expectations for higher order responsiveness, warehouses competitively postpone these cut-off times upholding the same pick-up time. This paper, therefore, aims to schedule jobs more efficiently to meet compressed response times. Secondly, this paper provides a data-driven decision-making methodology to guarantee the right implementation by the practitioners. Priority-based job scheduling using flow-shop models has been used mainly for manufacturing systems but can be ingeniously applied for warehouse job scheduling to accommodate tighter cut-off times. To assist warehouse managers in decision making for the practical value of these models, this study presents a computer simulation approach to decide which priority rule performs best under which circumstances. The application of stochastic simulation models for uncertain real-life operational environments contributes to the previous literature on deterministic models for theoretical environments. The performance of each rule is evaluated in terms of a joint cost criterion that integrates the objectives of low earliness, low tardiness, low labour idleness, and low work-in-process stocks. The simulation outcomes provide several findings about the strategic views for improving responsiveness. In particular, the critical ratio rule using the real-time queue status of jobs has the fastest flow-time and performs best for warehouse scenarios with expensive products and high labour costs. The case study limits the coverage of the findings, but it still closes the existent gap regarding data-driven decision-making methodology for practitioners of supply chains
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