2,926 research outputs found

    A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain

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    © 2019, Springer Science+Business Media, LLC, part of Springer Nature. This paper aims to develop a recovery planning approach in a three-tier manufacturing supply chain, which has a single supplier, manufacturer, and retailer under an imperfect production environment, in which we consider three types of sudden disturbances: demand fluctuation, and disruptions to production and raw material supply, which are not known in advance. Firstly, a mathematical model is developed for generating an ideal plan under imperfect production for a finite planning horizon while maximizing total profit, and then we re-formulate the model to generate the recovery plan after happening of each sudden disturbance. Considering the high commercial cost and computational intensity and complexity of this problem, we propose an efficient heuristic, to obtain a recovery plan, for each disturbance type, for a finite future period, after the occurrence of a disturbance. The heuristic solutions are compared with a standard solution technique for a considerable number of random test instances, which demonstrates the trustworthy performance of the developed heuristics. We also develop another heuristic for managing the combined effects of multiple sudden disturbances in a period. Finally, a simulation approach is proposed to investigate the effects of different types of disturbance events generated randomly. We present several numerical examples and random experiments to explicate the benefits of our developed approaches. Results reveal that in the event of sudden disturbances, the proposed mathematical and heuristic approaches are capable of generating recovery plans accurately and consistently

    Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers

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    © 2014 IEEE. In this paper, a supply disruption management model is introduced in a three-tier supply chain with multiple suppliers and retailers, where the system may face sudden disruption in its raw material supply. At first, we formulated a mathematical model for ideal conditions and then reformulated it to revise the supply, production and delivery plan after the occurrence of a disruption, for a future period, to recover from the disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We have also proposed an efficient heuristic to solve the model and the results have been compared, with another established solution approach, for a good number of randomly generated test problems. The comparison showed the consistent performance of our developed heuristic. This paper also presents some numerical examples to explain the usefulness of the proposed approach

    Increase supply chain resilience by applying early warning signals within big-data analysis

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    Purpose: The current environment of globally interconnected supply chains, the dynamics of changes and potential threats significantly reduce the time for a possible response. At the same time, there is a growing demand for information necessary to mitigate the consequences. To minimise the damage and increase the resilience of supply chains, it is necessary to identify sources of threats promptly, the extent of possible damage and the possibility of preventing or minimising their impact. The aim of the paper is to structure supply chain threats and search for appropriate datasets for prevention. Methodology: The paper analyses the state-of-the-art through a comprehensive literature review and demonstrates secondary data about how free-access business data can be used as Early Warning Signals to forecast supply chain disruptive events, with a particular focus on international maritime transportation. Results: It was confirmed that companies can access many open datasets, and collecting and aggregating these data can improve their preparedness for future disruptive events. As the most important issue, the authors defined the selection of proper datasets and interpreting results with foresight. Conclusions: Identifying and analysing the relevant Early Warning Signals by companies to prevent supply chain disruptions are essential for keeping their supply chains sustainable and their resilience on a sufficient level. It was proved that general business indicators (PMI delivery time, container capacity, inflation rate, etc.) can help to signal the increasing possibility of maritime traffic problems in ports and container unavailability as usual supply chain disruption types in recent years. Therefore, the companies in the supply chains need to find, collect and analyse the appropriate data, which are, in some cases, free and available. However, it is a substantial task for data analysts to identify the most relevant data and work out the analytical methodology which can be applied as Early Warning Systems (EWS)

    Managing disruptions in a refinery supply chain using agent-based technique

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    Master'sMASTER OF ENGINEERIN

    Development of a robust and resilient Supply Chain System for selected companies in Gauteng

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    Abstract: These days, in the extremely competitive nature of business, nearly every big business has to reap the benefits of investing in improvements of its supply chain. The beginning of the upgrades is considered together with the examination concerning profits and most organisations have addressed measures that a supply chain execution and monitor changes in order to drive the benefits of their business. While execution estimation is basic, most organisations either measure excessively or pay little attention to supply chain. Different weaknesses may incorporate; an excessive number of measurements, disconnected measurements, clashing measurements, obsolete measurements, temperamental information, and absence of possession, among others. Some organisations measure incorrect variables in their pursuit of their objectives. This is detrimental to the realisation of these objectives and this affects the organisation. Framework estimations lead to improved framework. "Estimation is the initial step that prompts control and in the long run to progress. In the event that you can't gauge something, you can't get it. On the off chance that you can't get it, you can't control it. On the off chance that you can't control it, you can't improve it" (Harrington, 2012)...M.Ing. (Quality and Operations Management

    A quantitative model for disruption mitigation in a supply chain

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    © 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches

    After sales supply chain risk management.

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    Lean supply chains with cost optimized production and logistics processes in the automotive industry have become a benchmark for other industries. Short delivery times, low inventories and high availability are parameters which assume a robust supply chain. In industrial practice we see, however, that in the After Sales business particularly related to the supply of automotive spare parts, that there are always unforeseen delays in delivery. In order to avoid service level losses on the focal firm level due to missing parts it is necessary to understand the risk structure on the supplier side. For this reason, a risk model for the After Sales inbound SC is developed through this work. Based on an extensive analysis of delivery data a central risk size was derived. Comprehensively researched SC risks are supplemented by After Sales specific risks derived through an empirical supplier survey. A reference network, which is methodologically based on the Bayesian theorem, to control the dynamic relationships was developed. The developed risk model allows for the identification of proactive and reactive measures by top-down and bottom-up analyzes to make lean supply chains for after sales requirements in the best cases robust and resilient. A big advantage of the developed model is not only the ability to quantify the cause and effect of supply chain risks but also to describe the constantly changing risk environment of the supply chain through continuous belief updates within the model. The risk analysis in the developed model potentially reduces the delivery delay of spare parts by 65 percent and diminishes the buffer stock value by 50 percent. To achieve such improvements in the real world organizations must be able to implement measures in explicit SC risk clusters for sustainable supply chain performance and inventory management. Improvements in the internal supplier processes, due to risks like prioritized series supply, or inappropriate after sales supply strategies are necessary. Utilizing the developed After Sales Risk Management Model (ASRIM) organizations will be able to implement proactive risk mitigation strategies, facilitating agile SC performance, while simultaneously reducing buffer stocks

    A novel classification of supply chain risks: scale development and validation

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    Purpose: Supply chain has become an essential element for any organization but risks are the major obstacles in achieving the performance even it can disrupt not only the organization but a whole system. Thus, it is compulsory to manage the risks efficiently and effectively. Risk cannot be managed until properly identified, there are numerous studies on risk identification, after comprehensive literature, it has been revealed that the study that identifies overall supply chain risk is scaring. The manufacturing sector of any country is considered as the backbone of any economy, in Malaysia it is the second largest sector in economic contribution and highest in productivity level. The aim of this study is to provide a reliable tool to assess the overall supply chain risks of Malaysian manufacturing through a systematic process. Design/methodology/approach: A detail literature review has been done for categorization of overall supply chain risk sources. Then an instrument has been developed from a pool of items. The questionnaire was purified through pretesting, pilot testing (by the exploratory view) and reliability and validity tests. The data were collected by email from Federation of Malaysian Malaysia (FMM-2017) through systemic probability sampling. Total 132 final responses have been considered for exploratory factor analysis through SPSS 23. Findings: The finding of this study revealed that overall supply chain risks can be categories into seven constructs that are supply side risks, process side risks, demand side risks, logistic side risks, collaboration side risks and environment side risks and the final questionnaire is consisting of 57 items. Research limitations/implications: This study covered tier 1 members of the supply chain. Secondly, the supply chain of manufacturing organizations only has been considered. Practical implications: This study will help the managers to understand what kind of risk sources they can face and which type of risks under these risk sources they should consider while decision making. This study will update the managers about the identification of risks and their potential negative effects. Originality/value: This article will justify the need for Malaysian manufacturing by providing a validated and reliable instrument for the identification and assessment of their risks under major supply chain risk sources.Peer Reviewe
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