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The purpose of this paper is to create a resilient supply chain (SC) plan to contain disruptions and risks in the overall operations of a business.

The study integrates resilience considerations in a business planning model that formulates resilience performance (RP) of SC functions in terms of flexibility, reliability, and similar system factors. It evaluates the RP of SC plans and determines their vulnerability considering required and planned resources. The model estimates the possible effects of disasters on vulnerable functions using a scenario-based analysis and plans containment options. It also includes decision options for deploying resources to achieve the expected levels of resilience by preventing potential vulnerabilities. The model takes optimum decision in a what-if approach by comparing performance of the existing business plan, with options for containing the vulnerabilities inherent in not considering potential risks when planning to fulfill market demand, and the performance of a resilient plan that includes decision options to prevent vulnerabilities where possible and mitigate them otherwise.

It is possible, for example, to evaluate RP of SC plans, identify vulnerable functions, and decide optimum option to create resilient business system.

The present study takes a generic approach and creates bases to explore its application in any industry-based case.

The research introduces formulations for RPs and vulnerability indices that can be included in a planning model to create a resilient SC.

In order to evaluate and predict a reliability function for certain continuous type tasks, a three‐state (discrete state‐time continuous) Markovian model is developed. Applications of the model are described through generated data on human errors.

The purpose of this paper is to examine and model the in-plant operational efficiency of tow trucks of an automobile manufacturing plant. Even though, tow trucks contribute toward the improvement of operational performance, little case-based evidence prevail in the literature. For this purpose, a case study has been conducted in an Indian automobile manufacturer to address the prevailing issues in material handling (MH).

Initially, this paper focuses on grouping of the sequence parts and finding the shortest path among the groups. To elucidate this, an analytical framework based on the distance and stuffing quantity is proposed. A fuzzy Dijkstra’s algorithm is used to solve the issues in grouping of the sequence parts and shortest path among the groups.

This study addressed the four aspects of MH: move cost, time, distance and material by integrating the function of grouping, finding the shortest path and communication with low cost devices. The result shows that logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the production line managers is key performance indicator.

The paper helps the automobile practitioners on increasing the efficiency of tow truck by systemizing the routes. Logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the line managers is key performance indicator.

In 1973, Goel and Wu presented an algorithm for the economic design of cusum charts to control the mean of a process with a normally distributed quality characteristic. They employed the two‐dimensional pattern search technique to determine the optimal values of the sample size, the sampling interval and the decision limit. Proposes a search algorithm required to use the one‐dimensional pattern search technique to get the optimal values of design parameters instead of the two‐dimensional pattern search technique. So, our search algorithm is not only accurate and efficient but also simpler to solve than that of Goel and Wu.

The purpose of this paper is to: develop an effective cellular manufacturing system (CMS) design methodology by simultaneously considering system costs and individual machine reliabilities; and propose a combinatorial search‐based solution procedure to solve large‐sized problems.

This paper presents a multi‐objective mixed integer‐programming model for the design of CMS with the objective of minimizing costs and maximizing system reliability. The approach optimizes inter‐cell material handling costs, the variable cost of machining operations, and the machine under‐utilization costs. It also maximizes the system reliability by selecting process routes for the part types with the highest system reliability for the machines along the routes. To solve the multi‐objective, multiple process plan model, a simulated annealing (SA)‐based algorithm is developed. The algorithm follows the basic steps of SA, but also incorporates the genetic algorithm (GA) operations of crossover and mutations to generate better neighboring solutions from the current good solutions.

The algorithm in the paper solves the multi‐objective CMS design model and generates near optimal solutions for medium to large‐sized problems within reasonable limits of CPU time.

In the paper the CMS design approach can be implemented to improve reliability performance of the CMS.

A new CMS design methodology in this paper, which minimizes system costs and maximizes machine‐related system reliability, is developed. The proposed algorithm, which combines the basic steps of SA and crossover and mutation operations of GA, will enable CMS designers and users to obtain near optimal solutions for practical‐sized problems within reasonable time limits.

This paper investigates the effect of non‐normality errors on the economic design of x‐control charts. The measurable quality characteristic of the product is assumed to be non‐normally distributed random variable. The process is subject to a single assignable cause with exponentially distributed occurrence time. This assignable cause shifts the process from in‐control state to out‐of‐control state. The economic design of x‐chart involves optimal determination of the design parameters so as to minimize the expected total cost. The optimal value of the design parameters are obtained using a computerized search technique. Consequently, the effect of non‐normality parameters and errors on the design parameters and on the loss‐cost function is explained through numerical examples.

The purpose of this paper is to systematically demonstrate the use of an effective multiple criteria decision-making technique, i.e. fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) in ranking the decision criteria of flexible manufacturing systems (FMS).

A questionnaire is specially designed and served to the industry experts to collect their opinion on several FMS decision criteria. Subsequently, fuzzy TOPSIS is used to prioritize the decision criteria.

Fuzzy TOPSIS multiple criteria decision-making technique is explained and applied to determine relative importance of the several decision criteria of FMS. This will help management of organizations in taking decision for implementing FMS in their organizations. From this study, it is found that customer satisfaction is the top most criterion among several other criteria for the successful implementation of FMS.

In situation like the one considered in this research, there are dependencies and interactions among the criteria and, therefore, other techniques such as fuzzy analytic network process would have been a better choice. Nevertheless, fuzzy TOPSIS also provides good result as it incorporates vagueness associated with the decision maker’s opinion pertaining to the several FMS decision criteria.

This paper presents a fuzzy TOSIS model to help managers understand the relative importance of the several FMS decision criteria so that they can use this information for successful implementation of this advanced manufacturing technology in their organizations.

The purpose of this paper is twofold: to investigate the antecedents of resilience and to highlight the importance of resilience in achieving reconfiguration in supply chains.

This paper draws on literature on supply chain resilience and collects data from 253 companies in Qatar to understand the influence of the antecedents of supply chain resilience and the impact of resilience on reconfiguration using partial least squares structural equation modeling.

The findings show that antecedents like risk management culture, agility and collaboration positively affect the supply chain resilience. Further, the study establishes that companies can leverage their supply chain resilience to reconfigure supply chain in case of disruptions.

This study is important for supply chain managers in Qatar, as the country faced major disruption of supply chains in wake of the blockade imposed by its neighbors with which it had the only land route and maximum trade. The findings from this study should aid mangers in developing resilient supply chains.

This paper highlights the role of supply chain resilience in achieving reconfiguration. Further, novelty of the work reported in this paper lies in its context where supply chains recently faced actual disruptions.

Develops the joint economic designs of • and S² control charts under four operating policies to monitor the process in a situation where the occurrence of the assignable cause follows a general distribution with an increasing hazard rate. The four operating policies can be chosen by quality controllers to cope with the specific process situation. Policy I and policy II assume that the process performs the preventive maintenance programme at equal and decreasing sampling time intervals, respectively. Policy III and policy IV in turn merely take samples using the non‐uniform and uniform sampling interval schemes without preventive maintenance. The derivation of the four models is not very difficult, so it can be used to derive another model. Offers numerical examples to compare the economic designs and the total expected costs per hour of the four models. Finds, from the computational results, policy II is the best for adoption in the design of • and S² control charts. The results also show that the proposed solution procedure is more accurate and better than Rahim et al.’s and Chung and Chen’s procedures. Concludes with remarks and some advantages of introducing the periodic preventive maintenance policy into a process.

Big data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.

The study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.

The results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.

The present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.