Modelling an End to End Supply Chain system Using Simulation

Within the current uncertain environment industries are predominantly faced with various challenges resulting in greater need for skilled management and adequate technique as well as tools to manage Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable modelling framework that would allow firms to analyse their operational performance over time (Mackulak and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed towards identifying the scope of the SC and the key processes performed between players. Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation and provide directions for future research by reviewing existing Operations Research/Operations Management (OR/OM) literature. Structural and operational complexities as well as different business processes within various industries are often limiting factors during modelling efforts. Successively, this calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and techniques could be captured and supported by the powerful capabilities of simulation. Research Approach: Following Mitroff’s (1974) scientific inquiry model and Sargent (2011) this research will adopt simulation methodology and focus on systematic literature review in order to establish generic OR processes and differentiate them from those which are specific to certain industries. The aim of the research is provide a clear and informed overview of the existing literature in the area of supply chain simulation. Therefore through a profound examination of the selected studies a conceptual model will be design based on the selection of the most commonly used SC Processes and simulation techniques used within those processes. The description of individual elements that make up SC processes (Hermann and Pundoor 2006) will be defined using building blocks, which are also known as Process Categories. Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through means of systematic literature review. Practitioners have adopted the term E2E SC while this is not extensively featured within academic literature. The existing SC studies lack generality in regards to capturing the entire SC within one methodological framework, which this study aims to address. Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and holistic assessment of an E2E SC, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution. Thus by providing significant advances in understanding of the theory, methods used and applicability of supply chain simulation, this paper will further develop a body of knowledge within this subject area. Practical Impact: The paper will empower practitioners’ knowledge and understanding of the supply chain processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of specific data required for the simulation in accordance to the individual needs of the industry

A methodological framework for the analysis of agent-based supply chain planning simulations

Agent-based simulation is considered a promising approach for supply chain (SC) planning, configuration and design. Although there have been many important advances on how to specify, design, and implement agent-based simulation, the concerned literature does not properly addresses the analysis phase. In this early phase, SC stakeholders decide what kind of simulation experiments should be performed and their requirements, which considerably influence the whole development process and the resulting simulation environment. This work proposes an agent-based simulation framework for modeling SC systems in the analysis phase. In addition, it proposes a formal method for converting the analysis model into specification and design models. The proposed framework is being validated by means of an agent-based simulation platform developed in the context of the lumber industry.

Effective use of product quality information in meat processing

This paper presents a case study on use of advanced product quality information in meat processing. To serve segmented customer demand meat processors consider use of innovative sensor technology to sort meat products to customer orders. To assess the use of this sensor technology a discrete-event simulation model is built. Various scenarios were defined for processing strategy (buffered or non-buffered), the number of end product groups to sort to and the availability of product quality information. The performance of these scenarios is measured w.r.t. order compliance, labor consumption and throughput-time. Our results reveal that the current processing and product sorting strategy is in-effective for sorting to a large number of end product groups. Furthermore, the current availability of product quality information is insufficient to ensure high levels of order compliance for advanced product quality products

Comparing conventional and distributed approaches to simulation in complex supply-chain health systems

Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete-event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today's powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models. To investigate this claim, this paper presents experiences in implementing a simulation model with a 'conventional' approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period. However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations

Ensuring the visibility and traceability of items through logistics chain of automotive industry based on AutoEPCNet Usage

Traceability in logistics is the capability of the participants to trace the products throughout the supply chain by means of either the product and/or container identifiers in a forward and/or backward direction. In today's competitive economic environment, traceability is a key concept related to all products and all types of supply chains. The goal of this paper is to describe development of application that enables to create and share information about the physical movement and status of products as they travel throughout the supply chain. The main purpose of this paper is to describe the development of RFID based track and trace system for ensuring the visibility and traceability of items in logistics chain especially in automotive industry. The proposed solution is based on EPCglobal Network Architecture

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

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

Facilitating the analysis of a UK national blood service supply chain using distributed simulation

In an attempt to investigate blood unit ordering policies, researchers have created a discrete-event model of the UK National Blood Service (NBS) supply chain in the Southampton area of the UK. The model has been created using Simul8, a commercial-off-the-shelf discrete-event simulation package (CSP). However, as more hospitals were added to the model, it was discovered that the length of time needed to perform a single simulation severely increased. It has been claimed that distributed simulation, a technique that uses the resources of many computers to execute a simulation model, can reduce simulation runtime. Further, an emerging standardized approach exists that supports distributed simulation with CSPs. These CSP Interoperability (CSPI) standards are compatible with the IEEE 1516 standard The High Level Architecture, the defacto interoperability standard for distributed simulation. To investigate if distributed simulation can reduce the execution time of NBS supply chain simulation, this paper presents experiences of creating a distributed version of the CSP Simul8 according to the CSPI/HLA standards. It shows that the distributed version of the simulation does indeed run faster when the model reaches a certain size. Further, we argue that understanding the relationship of model features is key to performance. This is illustrated by experimentation with two different protocols implementations (using Time Advance Request (TAR) and Next Event Request (NER)). Our contribution is therefore the demonstration that distributed simulation is a useful technique in the timely execution of supply chains of this type and that careful analysis of model features can further increase performance

Offshoring Decision based on a framework for risk identification

[EN] Offshoring has been a growing practice in the last decade. This involves transferring or sharing management control of a business process (BP) to a supplier in a different country. Offshoring implicates information exchange, coordination and trust between the overseas supplier and the company that means to assume risk. In this paper categories and types of risk have been hierarchically classified using a new approach with the aim to propose a multilevel reference model for Supply Chain Risk evaluation. This classification has been used to analysis the offshoring decision taking into account not only operational and financial risks but other aspects as strategic, compliance, reputation and environmental. The proper risk identification can help to take the correct decision whether or not to bet on offshoring or maintain all the processes in the country of origin.Franconetti Rodríguez, P.; Ortiz Bas, Á. (2013). Offshoring Decision based on a framework for risk identification. IFIP Advances in Information and Communication Technology. 408:540-547. doi:10.1007/978-3-642-40543-3_57S540547408Aron, R., Singh, J.V.: Getting Offshoring Right. Harvard Bus. Rev. 83, 135–155 (2005)Contractor, F.J., Kumar, V., Sumit, V., Kundu, K., Pedersen, J.: Reconceptualizing the Firm in a World of Outsourcing and Offshoring: The Organizational and Geographical Relocation of High-Value Company Functions. J. Manage. Stud. 47, 1417–1433 (2010)Holweg, M., Reichhart, A., Hong, E.: On risk and cost in global sourcing, Int. J. Int. J. Prod. Econ. 131, 333–341 (2011)Kleindorfer, P.R., Saad, G.H.: Managing Disruption Risks in Supply Chains. Prod. Oper. Manag. 14, 53–68 (2005)Neiger, D., Rotaru, K., Churilov, L.: Supply chain risk identification with value-focused process engineering. J. Oper. Manag. 27, 154–168 (2009)Kumar, S., Kwong, A., Misra, C.: Risk mitigation in offshoring of business operations. J. Manufac. Tech. Manag. 20, 442–459 (2009)Bandaly, D., Satir, A., Kahyaoglu, Y., Shanker, L.: Supply chain risk management –I: Conceptualization, framework and planning process. Risk Management 14, 249–271 (2012)Klimov, R., Merkuryev, Y.: Simulation model for supply chain reliability evaluation. Balt. J. Sust. 14, 300–311 (2008)Chopra, S., Sodhi, M.S.: Managing Risk To Avoid Supply-Chain Breakdown. MIT Sloan management review 53 (2004)Blackhurst, J.V., Scheibe, K.P., Johnson, D.J.: Supplier risk assessment and monitoring for the automotive industry. Int. J. Phys. Distrib. 38, 143–165 (2008)Tang, O., Musa, S.N.: Identifying risk issues and research advancements in supply chain risk management, Int. J. Production Economics 133, 25–34 (2011)Christopher, M., Mena, C., Khan, O.: Approaches to managing global sourcing risk. Supply Chain Manag 16, 67–81 (2011)Olson, D.L., Wu, D.: Risk Management models for supply chain: a scenario analysis of outsourcing to China. Supply Chain Manag 16, 401–408 (2011)Supply Chain Council, Inc. SCOR: The Supply Chain Reference ISBN 0615202594Lambert, D.: Supply Chain Management: Processes, Partnerships, Performance, 3rd edn.Kern, D., Moser, R., Hartmann, E.: Supply risk management: model development and empirical analysis. Int. J. Phys. Distrib. 42, 60–82 (2008)Saaty, T.L.: The analytic hierarchy and analytic network measurement processes: Applications to decisions under Risk. Eur. J. Pure. Appl. Math., 122–196 (2008)Lockamy III, A., McCormack, K.: Analysing risks in supply networks to facilitate outsourcing decisions. Int. J. Prod. Res. 48(2), 593–611 (2010