88,601 research outputs found
A simulation model for lean, agile, resilient and green supply chain management: practices and interoperability assessment
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Engenharia e GestĂŁo IndustrialIn todayâs global market, the environment of unpredictable events has imposed a competitiveness improvement that requires a greater coordination and collaboration among Supply Chain (SC) entities, i.e., an effective Supply Chain Management (SCM). In this context, Lean, Agile, Resilient and Green (LARG) strategies emerged as a response. However, interoperability issues are always presents in operations among SC entities. From the Information Technology (IT) perspective, among all the multi-decisional techniques supporting a logistics network, simulation appears as an essential tool that allow the quantitative evaluation of benefits and issues deriving from a co-operative environment.
The present work provides a SC simulation model for analysing the effect of the interoperability degree of LARG practices in the SC performance, through Key Performance Indicators (KPIâs) such as cost, lead time and service level. The creation of two scenarios with a different point of view about the LARG practices allowed to analyse which one contributes to the best SC performance. Since some of the inputs were assumed, it was made a sensitivity analysis to validate the output of the simulation model. Based on the creation of six types of math expressions, it was possible to establish the connection between the effect of the interoperability degree of LARG practices and the SC performance. This analysis was applied on a case study that was conducted at some entities of a Portuguese automotive SC. The software used to develop the simulation model is Arena, which is considered a user-friendly and dynamic tool.
It was concluded that SCM, interoperability and simulation subjects must be applied together to help organisations to achieve overall competitiveness, focusing their strategies on a co-operative environment
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
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Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
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A survey of simulation techniques in commerce and defence
Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches
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Multiobjective optimization as a decision aid for managing build-to-order supply chains
This paper provides an overview of multiobjective optimization (MOO) as a decision aid in
build-to-order supply chains (BTO-SC). The main features of BTO-SCs are discussed along
with capabilities of MOO to enhance decision making at different points along the chain.
Key decision points across a typical BTO-SC are identified and potential applications of
MOO are discussed. A sample application is presented and future avenues for further research
highlighted
Business Process Innovation using the Process Innovation Laboratory
Most organizations today are required not only to establish effective business processes but they are required to accommodate for changing business conditions at an increasing rate. Many business processes extend beyond the boundary of the enterprise into the supply chain and the information infrastructure therefore is critical. Today nearly every business relies on their Enterprise System (ES) for process integration and the future generations of enterprise systems will increasingly be driven by business process models. Consequently process modeling and improvement will become vital for business process innovation (BPI) in future organizations. There is a significant body of knowledge on various aspect of process innovation, e.g. on conceptual modeling, business processes, supply chains and enterprise systems. Still an overall comprehensive and consistent theoretical framework with guidelines for practical applications has not been identified. The aim of this paper is to establish a conceptual framework for business process innovation in the supply chain based on advanced enterprise systems. The main approach to business process innovation in this context is to create a new methodology for exploring process models and patterns of applications. The paper thus presents a new concept for business process innovation called the process innovation laboratory a.k.a. the Ă-Lab. The Ă-Lab is a comprehensive framework for BPI using advanced enterprise systems. The Ă-Lab is a collaborative workspace for experimenting with process models and an explorative approach to study integrated modeling in a controlled environment. The Ă-Lab facilitates innovation by using an integrated action learning approach to process modeling including contemporary technological, organizational and business perspectivesNo; keywords
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