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

© 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

© 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

A quantitative model for disruption mitigation in a supply chain

© 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

Modeling Overstock

Two main problems have been emerging in supply chain management: the increasing pressure to reduce working capital and the growing variety of products. Most of the popular indicators have been developed based on a controlled environment. A new indicator is now proposed, based on the uncertainty of the demand, the flexibility of the supply chains, the evolution of the products lifecycle and the fulfillment of a required service level. The model to support the indicator will be developed within the real options approach.overstock, stock management, real options

An Optimized Resource Allocation Approach to Identify and Mitigate Supply Chain Risks using Fault Tree Analysis

Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains

Closed Loop Supply Chain (CLSC): economics, modelling, management and control

This article summarizes the papers published in the special issue entitled “Closed Loop Supply Chain (CLSC): Economics, Modelling, Management and Control” in the International Journal of Production Economics. A total of 24 papers, covering an extensive range of topics in the Closed Loop Supply Chain research area, have been included in this special issue. This special issue received a wide and diverse geographical contribution with authors from 16 countries located in 4 continents including America, Asia, Europe, and Africa. Initially, the special issue received 71 research paper submissions and the final selection of 24 papers, which were recommended by at least two reviewers, provide a basis for new research directions in the domain of reverse logistics and Closed Loop Supply Chain management