Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR

Green, resilient, agile, and sustainable fresh food supply chain enablers: evidence from India

The existing research on fresh food supply chains (FFSC) sustainability consisting of fur fundamental pillars, namely green (G), resilient (R), agile (A), and sustainability (S) (hereafter GRAS), is explored sparsely and needs thorough investigation. Further, conceptualization and mutual interactions among GRAS enablers that can help perpetuate sustainable supply chains (SSC) still need to be addressed. This study proposes a methodological framework to evaluate the SCS from the perspective of GRAS enablers with an application for the Indian FFSC. A mixed-method sequential approach was used with interviews followed by integrated fuzzy interpretive structural modelling-decision-making trial and evaluation laboratory (FISM-DEMATEL) techniques. The study recognizes twenty supply chain sustainability (SCS) enablers through an extensive literature review and discussions with the expert group. The research discloses that the firms' 'organization culture' acts as the most powerful driver in achieving sustainability in FFSC, followed by the firms' 'environmental certification program' and 'financial strength.' This investigation helps the managers/policymakers of the Indian FFSC to ascertain and comprehend the most significant SCS enablers to achieve sustainability in the supply chain (SC). The causation of SCS enablers supports the managers in systematically focusing on the most significant enablers and working towards their successful implementation. According to our knowledge, this is the first scholarly work that establishes hierarchies and interrelationships among GRAS enablers, thereby providing a holistic picture to decision-makers while adapting such practices.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10479-023-05176-x.

Control measure prioritization in Fine - Kinney-based risk assessment: a Bayesian BWM-Fuzzy VIKOR combined approach in an oil station

The Fine - Kinney is a risk assessment method widely used in many industries due to its ease of use and quantitative risk evaluation. As in other methods, it is a method that recommends taking a series of control measures for operational safety. However, it is not always possible to implement control measures based on the determined priorities of the risks. It is considered that determining the priorities of these measures depends on many criteria such as applicability, functionality, performance, and integrity. Therefore, this study has studied the prioritization of control measures in Fine - Kinney-based risk assessment. The criteria affecting the prioritization of control measures are hierarchically structured, and the importance weights of the criteria are determined by the Bayesian Best-Worst Method (BBWM). The priorities of control measures were determined with the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) method. The proposed model has been applied to the risk assessment process in a petrol station's liquid fuel tank area. According to the results obtained with BBWM, the most important criterion affecting the prioritization of control measures is the applicability criterion. It has an importance weight of about 42%. It is followed by performance with 31%, functionality with 18%, and integrity with 10%, respectively. FVIKOR results show that the "Periodic control of the ventilation device" measure is the top priority for Fine - Kinney risk assessment. "The absence of any ducts or sewer pits that may cause gas accumulation in the tank area and near the dispenser; Yellow line marking of entry and exit and vehicle roads; Placing of speed limit warning signs" has been determined as a secondary priority. On conclusion, this proposed model is expected to bring a new perspective to the work of occupational health and safety analysts, since the priority suggested by Fine - Kinney risk analysis methods is not always in the same order as the one in the stage of taking action, and the source, budget, and cost/benefit ratio of the measure affect this situation in practice.

Towards measuring SMEs performance amid the COVID-19 outbreak: exploring the impact of integrated supply chain drivers

Purpose

This study aims to explore supply chain disruptions during the COVID-19 pandemic in the small and medium enterprise (SME) sector in Oman. This study analyzes the impact on selected supply chain drivers – facilities, inventory, transportation and sourcing. It further intends to explore whether the supply chain challenges faced by the SME sector in Oman impact their overall performance.

Design/methodology/approach

This study follows the quantitative technique of structural equation modeling to examine the proposed hypotheses. Data were collected electronically from SME managers/owners/entrepreneurs. All items were adopted and measured using a five-point Likert scale. One hundred and four complete and usable responses were received and considered.

Findings

The data was analyzed using SPSS and PLS statistical software. The model has been supported empirically, and the results showed a significant relationship between supply chain drivers and SMEs’ overall performance in Oman, except for supply chain inventory. The results have demonstrated that the COVID-19 pandemic has affected the SMEs’ supply chain drivers in Oman and, consequently, their overall performance.

Practical implications

The results of this research can drive the development and implementation of a supply chain management strategy. This research will help policymakers induce the performance of SMEs affected by the COVID-19 pandemic. It would further enhance strategic sourcing and supplier performance considering the developed practices associated with the resource-based view.

Originality/value

The originality of the current study lies in its ability to empirically test two models within the Omani SMEs context while considering the supply chain drivers as a single variable or dividing it into four separate independent variables. This study would provide a preview for scholars for such empirical investigation and serve as a reference for policymakers and practitioners to maintain a management system of crises that may protect the SME supply chain drivers.

Supply chain sustainability risk assessment model using integration of the preference selection index (PSI) and the Shannon entropy

Purpose

This study proposes a new model for assessing supply chain sustainability risk integrating subjectivity and objectivity of decision-maker. Research has shown the vacancy of study in dealing with the above issue. To fill this research gap, a new decision support model considering the subjectivity and objectivity of decision-makers in assigning the weight of the supply chain risk reprioritization criteria is presented and demonstrated using a case example.

Design/methodology/approach

This study adopts a new decision support model for assessing supply chain sustainability risk based on additional failure mode and effect analysis (FMEA) parameters and its integration with preference selection index (PSI) methodology and the Shannon entropy. A case example of the supply chain small and medium enterprise (SME) producing handy crafts has been used in this study.

Findings

The result of the study reveals critical sustainability risk dimensions and their risk elements demanding management attention to support realization to a more sustainable business operation.

Research limitations/implications

The use of a single case study is often associated as a limitation in the research studies, and this study is based on findings from SMEs in the handy craft sector in a developing country. Nonetheless, future studies may focus on replicating this study using more samples. This preliminary study provides academics and practitioners with an exemplar of supply chain sustainability risk assessment from the SME in a developing country.

Practical implications

The result of this study is beneficial for practitioners, particularly owner-managers of SMEs who can use this study as guidance on how to identify and select the critical sustainability risks and plan mitigating strategies accordingly.

Originality/value

Scientific effort on appraising criticality of supply chain sustainability risks considering subjectivity and objectivity of decision-maker simultaneously is missing in earlier studies. To the best of the author’s knowledge, this is the first paper applying the PSI and Shannon entropy method and using it for evaluating the impact of supply chain risk based on five sustainability pillars. The findings and suggestions for future research initiatives will provide new insights for scholars and practitioners in managing SME supply chain sustainability risks.

Resilience-Enhancing Solution to Mitigate Risk for Sustainable Supply Chain—An Empirical Study of Elevator Manufacturing

As the complexity of supply chains increases, the enhancement of resilience for mitigating sustainable disruption risks in supply chains is an important issue. Quality function deployment (QFD) has been successfully applied in many domains to solve multicriteria decision-making (MCDM) problems. However, research on developing two houses of quality to connect sustainable supply chain disruption risks, resilience capacities, and resilience-enhancing features in elevator manufacturing supply chains by using the MCDM approach is lacking. This study aims to develop a framework for exploring useful decision-making by integrating the MCDM approach and QFD. By applying the framework, supply chain resilience can be improved by identifying the major sustainable risks and the key resilience to mitigate these risks. Important managerial insights and practical implications are obtained from the framework implementation in a case study of the elevator manufacturing industry. To strengthen resilience and thus mitigate key risks, the most urgent tasks are to connect the working site and the backstage to enhance product development and design and to share real-time job information. When these features are strengthened, agility, capacity, and visibility can be improved. Finally, unexpected events lead to changes in supplier delivery dates, and factors such as typhoon and lack of critical capacities/skilled employees with the greatest impact can be alleviated. This framework will provide an effective and pragmatic approach for constructing sustainable supply chain risk resilience in the elevator manufacturing industry.

Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management

Purpose

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.

Design/methodology/approach

The proposed “Worst Expected Best” method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.

Findings

Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.

Originality/value

This paper introduces a new “Worst Expected Best” method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.