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The purpose of this paper is to develop a combined intuitionistic fuzzy analytic hierarchy process (IFAHP) and fuzzy multi-objective optimization approach to select suppliers and allocate the orders to them in the bottled water production context.

First, the primary weights of criteria associated with the supplier selection problem are calculated using the IFAHP technique. Then a fuzzy multi-objective optimization model is developed to allocate the appropriate amount of orders to each supplier.

The proposed methodology has been successfully implemented in the case of an Iranian food company in its bottled water factory. Results demonstrate our model is capable of practically handling the uncertainty in DMs’ preference that leads to effective and efficient supplier selection and order allocation decisions.

The authors develop a novel hybrid decision-making tool to tackle the uncertainty in decision-makers’ opinions with a demonstrated applicability and some promising outcomes in efficiently allocating the order quantity to suppliers in the area of bottled water production.

The purpose of this study is to evaluate two supply chain resilience key elements of vulnerability and capability in the automotive industry.

We propose a fuzzy approach for statistical hypothesis testing and analyze two research hypotheses by synthesizing the results of a questionnaire given to 44 companies in the Iranian automotive industry.

The results indicate that the automotive industry in Iran should: (1) resist five elements of vulnerability, i.e. “external pressures,” “sensitivity,” “connectivity,” “supplier/customer disruptions,” and “resource limits,” and (2) embrace nine elements of capability, i.e. “flexibility in order fulfillment,” “capacity,” “efficiency,” “visibility,” “adaptability,” “recovery,” “dispersion,” “organization,” “market position” and “security” to achieve greater resiliency elasticity in the supply chain.

This is the first study on the supply chain resilience vulnerabilities and capabilities in the Iranian automotive industry.

The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand.

The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain.

The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent.

The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.

The purpose of this paper is to study, analyze and implement continuous improvement (CI) techniques in an interior design case company, which faces challenges in different departments that affect the case company performance.

The proposed methodology implemented in three departments of an interior design company in Gulf Cooperation Council (GCC). First, the authors analyzed and identified problems using Pareto chart and cause and effect diagram. After that, they improved identified problems using Kaizen, 5S, developed project selection form and modified organization chart. The result has been shown savings regarding money and time.

Successful implementation of the proposed methodology reduced project in pipeline time from 16 weeks to nine weeks, profit margin increased from 25 to 27 per cent, sales win ratio increase from 11 to 32 per cent, better project and financial forecasting and 92 per cent of tender submission deadline achievement. A habit of clean, tidy and organized workplace has been developed among workers.

Proposed solutions contributed significantly to saving time and effort spent to accomplish different tasks in the case company. The company approved the proposed solutions and implemented them, which show that these proposed solutions are feasible and practical. In addition to that, in literature, most of the CI applications are in the manufacturing or production sectors. This was the first study, which implemented CI techniques in an interior design company.

The oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry.

To address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers.

To exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust.

The proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry.

This study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications.

This study aims to evaluate the role of social media (SM) tools in polio prevention in an Indian context by using a hybrid Delphi-decision-making trial and evaluation laboratory (DEMATEL) approach.

A preliminary list of suitable evaluation criteria was derived from an extensive literature review. Ten experts were then contacted to collect data and finalize the most prominent criteria by using the Delphi method. To establish cause-effect relationships among the criteria, further data were collected from 21 experts. The DEMATEL method was applied to process and interpret the data collected.

The analysis grouped criteria into two sets, i.e. cause and effect. The results show that awareness of social cause and government utilization of resources fall into the cause group; these elements are critical as both directly affect the remaining criteria. These outcomes can help government and businesses to utilize SM for public health surveillance, e.g. to promote schemes/initiatives through sites concerning polio or related health issues.

The findings of this research are useful for governments and individual companies to conceive their marketing initiatives akin to polio prevention issues by using SM.

Despite the emergence of SM, there has been little discussion in existing literature on their role in polio prevention; however, measuring such role could be useful in practice to help decision-makers in exploiting the potential of SM in the health-care context. To fill this gap, this study aims to measure the role of SM in polio prevention in the Indian context and to create a cause-effect evaluation model. Using an integrated Delphi-DEMATEL framework for decision-making in the health-care context is another novelty of this study.

The purpose of this paper is to develop a multi-item economic production quantity (EPQ) strategy under grey environment and space constraint. Since the “demand” cannot be predicted with certainty, it is assumed that data behave under grey environment and compare the proposed inventory model with other studies using crisp or fuzzy environments.

This paper is to optimise the cycle time and total cost of the multi-item EPQ inventory model. For this purpose, the Lagrangian coefficient is used to solve the constrained optimisation problem. The grey relational analysis approach and grey data are applied in developing the EPQ inventory model.

The results are compared with the analysis using crisp and fuzzy data. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The results of the study demonstrate that crisp data outperform the other two data in all scales problems in terms of cycle time and cost; grey data perform better in all scales problems than fuzzy data.

The contribution of this research is the use of grey data in developing the EPQ inventory model with space constraint.

Competition in dynamic markets requires operational excellence and effective operations strategy. Hence, organizations have to evaluate their operational performance in comparison to their rivals. The purpose of this paper is to present an operational performance evaluation framework for analyzing and ranking the organizations based on the effectiveness of their operations strategies.

In this study three uncertain data envelopment analysis (DEA) models consist of fuzzy DEA (FDEA), imprecise DEA (IDEA) and Grey DEA are applied to assess the performance of organizations in terms of the operations. The applicability of the proposed framework is demonstrated through a case study at Fars province cement manufacturers of Iran.

The ranking results assist the mangers of service and manufacturing enterprises in finding out the current positions of their companies and the effectiveness of their operations strategies in comparison to their rivals. Accordingly they can make proper strategic and operational decisions to improve the performance of the operations.

Based on the previous literature, this is the first study which uses FDEA and IDEA methods in an operational performance evaluation problem. Furthermore this study proposes a novel framework for evaluating and ranking the operational performance of firms from the perspective of operations strategy.

The hotel and tourism industry forms a crucial economic sector for all the economies around the world. However, it suffered the worst hit during the COVID-19 outbreak. Considering the hotel and tourism sector's critical situation, this manuscript aims to emphasise the importance of resilience in the hotel and tourism supply chain (HTSC) and explores the crucial barriers that tend to disturb the inculcation of stability in the hotel and tourism sector. The present research analyses the factors influencing the hotel and tourism sector's resilience and also takes into consideration the various critical success factors (CSFs) needed to build a resilient HTSC.

A two-phase research approach has been proposed and used in this study. In the first phase, eight CSFs and sixteen factors influencing the hotel and tourism sector's resilience during the COVID-19 pandemic were identified. The basis of the identification of the CSFs and factors was literature and inputs received from experts. In the second phase, the grey-Entropy-EDAS, a qualitative and quantitative analysis, was used to analyse the identified CSFs and factors to determine the priority of concern.

In this research, the most imperative facet influencing the hotel and tourism sector's resilience has been identified, and the findings will assist hotel and tourism sector in managing and mitigating the repercussions of the COVID-19 pandemic. The analysis of the results indicates that out of all the critical success factors, supply chain visibility is the most crucial aspect in building HTSC's resilience, whereas economic catastrophe is the most influential factor. Sensitivity analysis is also conducted to examine the priority ranking stability.

The results of this study can be used by the hotel supply chain managers and policymakers to plan for various challenges faced by them as they try to implement resilience-based strategies in their supply chain.

This research is unique as it analyses the general factors hindering the pathway of resilience in the hotel and tourism supply chain. This is also the first kind of study that has used grey-Entropy to analyse the critical success factors and grey-EDAS for analysing the impact of various factors influencing the hotel and tourism sector's resilience.

The purpose of this paper is to implement the strategies selection process in a proposed formulated mathematical framework to prioritize selected strategies with the interaction of other groups of strategies, known as the strategies interaction model (SIM).

SWOT analysis is a popular useful strategic planning tool, which analyzes organizations internal and external factors. The traditional SWOT procedure lists internal and external factors and derives four groups of strategies based on the organization’s strategic position. SWOT is easy to use as a business analyzing tool, while it is not competent enough for strategic formulation. With the emergence of the economy’s vicissitudes, undulations in the markets and multiple changes, and various variables in the industrial competitive environment, selection of the organization strategies confront uncertainty in decision making. The SIM framework presents a solution to select alternative strategies for organizations in unpredictable situations.

The findings show that SIM is a reliable approach to evaluate, select and rank organization’ strategies. SIM proposes alternative strategies due to the uncertainty of the organization’ environment with respect to the four strategic positions. The SIM’ proposed ranking process is in accordance with the highest impact of each strategy on each other. Furthermore, it possesses advantages of AHP, ANP and other applied multiple criteria decision-making (MCDM) techniques in SWOT analysis.

In this paper SIM is applied within a dairy company located in the north of Iran.

SIM has the advantages of the classic SWOT and fills the gaps of MCDM methods application in the SWOT analysis. Moreover, it provides a formulated algorithm for the organizations to face the uncertainty of the environment. SIM philosophy can be widely used in the decision and managerial implications.