A Comparative Study of the Severity Ranking of Risks in the Indian Automobile Manufacturing Supply Chain Using PROMETHEE,VIKOR and TOPSIS

Abstract

The supply chains in automobile manufacturing face numerous risks, impacting organisational performance due to improvised responses and inadequate contingency plans. This study employs the PROMETHEE methodology to identify and rank critical risk factors (CRFs) in the Indian automotive manufacturing supply chain. Thirteen risks were evaluated across five industry criteria using entropy methodology to ensure a robust and objective assessment of each risk factor. Risks related to delays, management, and suppliers emerged as the most severe. A comparison with VIKOR and TOPSIS methods was conducted. Prioritising risk factors through this approach aids organisations in addressing threats effectively.

Executive Summary

Risks can arise at various stages of today’s complex, uncertain and lengthy automobile manufacturing supply chain. Improvised responses and a lack of contingency plans result in organizations underperforming. Despite awareness of numerous supply chain risks, there is no clear understanding of their adverse impacts. This study identifies and ranks the critical risk factors (CRFs) in the Indian automotive manufacturing supply chain. The primary objective is to provide an evident understanding of the severity of CRFs’ adverse impacts on various organizational concerns acknowledging the multifaceted and unpredictable nature of risks. An in-depth review of reputable international journals was conducted to identify the CRFs in the automobile supply chain. The preference ranking organization method for enrichment evaluation (PROMETHEE) was employed to obtain a complete ranking of risks identified via a literature review. The impact severity of 13 identified risks on five separate industry criteria was examined, with the weight of each industry criterion computed by entropy methodology. While various CRFs examined in this study exert a notable impact on the supply chains of the Indian automotive sector, risks linked to delays, management and suppliers emerged as primary risks with the highest adverse impacts. A comparison was made between results from the PROMETHEE method and the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) methods. A pragmatic approach to risk factor prioritization improves organizations’ responses to negative threats. The paper presents a thorough comparative analysis of risk prioritization by using three different methodologies to explore, assess and prioritize CRFs throughout the supply chain of Indian automobile manufacturing. A practical approach to prioritizing risk factors will assist organizational efforts in tackling the threats with the most severe adverse effects. This approach can significantly enhance the resilience and performance of the supply chain, offering valuable guidance for industry stakeholders.

Keywords

Risks Manufacturing PROMETHEE VIKOR TOPSIS

There is often intense competition among various manufacturing supply chains due to globalization and shifting market dynamics. A number of specific factors drive organizations to compete with national rivals and, simultaneously, the most influential companies worldwide. One major factor is the increasing expectations and demands of customers. With recent advances in technology and communication, customers are more aware of their options and have higher expectations for quality, speed and convenience. A second factor is the complexity of the products and services being offered. As industries become more specialized and sophisticated, it becomes increasingly challenging for enterprises to stay abreast of the latest technology and trends. Finally, limited financial, human and material resources add to the challenge of competing in the global marketplace. These factors together create a need for enterprises to continuously improve and innovate to stay competitive (Antony & Desai, 2009; Singh et al., 2018).

The manufacturing industry in India is increasingly becoming one of the country’s most important growth sectors. According to the India Brand Equity Foundation Report (2021), it is anticipated that the industry will continue to expand as a result of growth in market size and demand, as well as increases in local and worldwide investments, support from government authorities and resource potential. The manufacturing industry is considered the backbone of the Indian economy and has played a significant role in the country’s rapid economic growth. As shown in past research (Kaur & Mehta, 2019; Mehta & Rajan, 2017; SME Chamber of India, 2021), the manufacturing sector is the most important contributor to employment in India and is a crucial contributor to India’s gross domestic product (GDP).

The Indian auto industry, which encompasses the production and assembly of components and vehicles, is one of the country’s fastest-growing manufacturing industries (Agrawal et al., 2021; Gautam et al., 2018; Katsaliaki et al., 2021). The growth of India’s auto sector has been an important factor in the success of the country’s overall economic advancement (Press Trust of India, 2021), with approximately 49% of India’s manufacturing GDP and 7.5% of India’s total GDP attributable to automobile manufacturing. It is a major employer in India, with 32 million people working for automakers and their suppliers. India ranks as the world’s seventh-largest producer of commercial vehicles and the fourth-largest producer of passenger cars (Agrawal et al., 2021; Gautam et al., 2023; Kumar et al., 2020).

The negative impact that risks have on a variety of operational activities, whether directly or indirectly, can severely affect business performance (Can Saglam et al., 2020; Huma et al., 2020; Kumar et al., 2018; Thun & Hoenig, 2011). An interruption in one element of the supply chain can have numerous repercussions for the other interconnected parts (Rezaei Vandchali et al., 2020, 2021). Furthermore, because of the dynamic and complicated nature of manufacturing supply chain operations, manufacturing businesses are susceptible to a wide variety of risks (Alora & Barua, 2020; Engemann, 2019; Ghadir et al., 2022; Islam & Tedford, 2012b; Kumar Pradhan & Routroy, 2014; Pfohl et al., 2011; Prakash et al., 2017). Surging globalization increases supply chain length, and the inclusion of multiple players in the supply chain increases its complexity (Shenoi et al., 2018). In this complex environment, risks surface at various stages of the supply chain without warning. Several examples illustrate this phenomenon. In 2000, Ericsson lost $400 million due to a fire (Chopra & Sodhi, 2004). As a result of the 2011 tsunami and earthquake, Toyota lost $72 million in profits, with their share price dropping by up to 9.5% in the first few days and then by more than 17% over the following month, with overall car sales in Japan hitting a 34-year low (Pettit et al., 2013). Additionally, there have been losses of $2 billion for Boeing, $2.25 billion for Cisco, and $2.8 billion for Pfizer in recent years as a result of supply chain issues (de Oliveira et al., 2017). Ghadir et al. (2022) identified the top ten supply chain risks during the recent COVID-19 outbreak as insufficient consumer demand information, supply-market shortages, Bullwhip effect, loss of important providers/suppliers, problems with the transportation system, supplier-timely delivery, restriction by the states, temporary closure of supplier, shifts in consumer demand and single source of supply. These problems unquestionably place an entire supply chain in a fragile and unpredictable state, especially when occurring simultaneously. According to the PTI (2021) report, in addition to the structural slowdown, the COVID-19 epidemic significantly impacted the Indian automotive industry, setting it back by many years. Since the unpredictability of the business climate and the complexity of supply chains increase the likelihood of breakdowns, Ghadge et al. (2012) and Colicchia and Strozzi (2012) argue that risks can originate both within the company (operational) and in the external business environment (rupture). The authors Christopher and Lee (2004) and Rinaldi et al. (2022) state that this is why risk management is becoming an integral part of many supply chain management initiatives. The goals of risk management in this context are to minimize the likelihood of disruptions happening, lessen their effect on performance and get the supply chain back to normal as quickly as possible (Hendricks et al., 2009).

Industries may fail to realize their full potential when dealing with known and unknown risks when they cannot fully comprehend the severity and interdependence of various significant risk factors (Chopra & Sodhi, 2004; Daultani et al., 2019). An inadequate understanding of the importance and implications of critical risk factors (CRFs) can result in gaps in risk mitigation techniques, leading to underperformance and falling short of expectations.

Businesses across industries are responding to these threats by investing heavily in the improvement of operational excellence and strategic management approaches in an effort to maintain their competitive advantage and protect their market dominance (Chiarini & Kumar, 2021; Gólcher-Barguil et al., 2019). Each organization has a unique approach to handling risks, which largely depends on how well-prepared organizations are with proactive and reactive risk mitigation planning (Chopra & Sodhi, 2004). By adopting proactive risk management strategies and leveraging the latest insights on CRFs and their impact severity, the manufacturing sector can enhance its ability to protect its companies and increase their value. An understanding of CRFs can also serve as a vital input in developing proactive risk management approaches that establish robust procedures and ultimately improve the overall situation.

However, despite the evolution of risk management techniques worldwide, only major and financially well-established organizations are capable of effectively adopting them and reaping the benefits of implementing risk management strategies (Ferreira de Araújo Lima et al., 2020). Studies on risk management in the Indian manufacturing industry are currently limited (Babu et al., 2020; Surange & Bokade, 2022). To effectively manage risks in the supply chain, companies must be able to respond to both internal and external disruptions promptly and efficiently. This requires a robust supply chain risk management (SCRM) system that can prevent potential negative impacts. Previous studies have identified a wide range of risks that businesses must consider to minimize the adverse effects on their supply chain. However, due to limited time and resources, it can be challenging for companies to respond to all identified risks. To manage potential disruptions in their supply chain effectively, businesses should prioritize their risk management efforts by focusing on mitigating CRFs that significantly impact various business functions.

The focus of this study is to identify CRFs in the Indian automotive supply chain and rank them according to their severity. The preference ranking organization method for enrichment evaluation (PROMETHEE) is employed to obtain a ranking of identified risks. PROMETHEE–II was selected as it provides a complete ranking. The research study involves qualified industry practitioners with extensive experience. Additionally, the study compares the results obtained by applying the PROMETHEE method with those of the VIseKriterijumska Optimizacija I Kompromisno Resenje method (VIKOR) (Cheraghalipour et al., 2018; Opricovic, 1998) and the technique for order of preference by similarity to ideal solution method (TOPSIS) (Dandage et al., 2018; Hwang et al., 1993). Programming software was used to obtain ranking by VIKOR and TOPSIS. Software is beneficial because it ensures accurate calculations, a streamlined interface and quick, low-cost implementation with rapid data processing (Ahmad & Qahmash, 2021; da Cruz et al., 2022).

The primary objective of this study is to devise a decision-making strategy that prioritizes CRFs in the Indian automotive sector based on their adverse effects while considering the most critical criteria (constructs). The proposed research approach aims to assist managers and decision-makers in their risk mitigation efforts by streamlining the decision-making process through the prioritization of CRFs.

The paper is structured as follows. First, there is an extensive review of the existing research in the literature review. In the subsequent section, the methodology employed in this study is described. Following this, the results and discussion are presented. Finally, the conclusion summarizes the paper and explains practical implications for managers.

Literature review

To enhance decision-making, decision-makers must have a comprehensive understanding of the CRFs in their supply chain. Therefore, an extensive review of the literature related to the supply chain of manufacturing industries in India was conducted to gain insights into this area. Primary research (using the survey method) and secondary research (literature review and expert opinion) were used to accomplish this goal. Many risks contribute to SCRM and are referred to as risk factors in the scientific literature. An effort has been made to locate the risk factors described in the published research using several keywords. For this reason, only peer-reviewed journals were taken into consideration. After reviewing the relevant literature and consulting with industry experts, the CRFs identified as most relevant for this study are as follows.

Catastrophes: Over the past decade, natural disasters and their aftermath have led to an increased focus on supply chain risks. The global COVID-19 pandemic forced manufacturing firms into a complete lockdown, causing production stoppages and supply and demand disruptions (Mohammed et al., 2021). Consequently, leaders and experts sought long-term solutions and alternatives, and manufacturing organizations were forced to implement best practices to ensure smooth operations. The automobile sector, in particular, experienced significant disruptions due to the COVID-19 pandemic (Eldem et al., 2022). In an example of disruption due to a catastrophe provoked by humans, as a consequence of the terrorist attacks that took place in the United States on 11 September 2001, Ford and Toyota were compelled to cease production at their manufacturing plants in the United States due to significant delays in the delivery of parts originating from other countries (Sheffi, 2001; Thun & Hoenig, 2011). These examples highlight how interdependence and integration within the supply chain can lead to new vulnerabilities.

Human error: Routine and day-to-day difficulties can also harm supply chains. Human errors can range from minor to catastrophic and are unfortunately all too common in the workplace. Errors made during manual assembly activities, for example, can significantly impact the final product’s quality (Torres et al., 2021). Geopolitical issues, corruption, system deterioration, employee unions and strikes and regional political instability are all risks related to human errors cited in various studies.

ICT systems risks: Various problems can arise in technology, including hardware malfunctions and compromised internal software. If issues prevent backups from being created or restored, data loss can occur in both software and hardware. Researchers have identified several risks, such as complex networks, denial-of-service attacks, virus intrusion and violations of software defence (Gautam et al., 2018; Thun & Hoenig, 2011).

Risks linked to the competition: Competitive risk can arise from threats by rival companies. In a competitive market, businesses often vie for the same clients and distributors, leading to attempts to prevent others from gaining access to their territory. Companies must manage such risks to maintain a sustained competitive advantage (Huang, 2023).

Risks linked to the primary commodities: If certain raw materials become scarce, it can cause a fluctuation in demand for finished products, potentially affecting various supply chains. This could cause broad financial damage across industries. According to Cimprich et al. (2022), over the next few decades, raw material criticality will continue to be a source of growing concern.

Risks linked with suppliers: Achieving supply chain objectives relies heavily on the performance of suppliers. Among the various external risks that contemporary supply chains face, those posed by suppliers are the most frequent (Hosseini & Khaled, 2019). With a growing emphasis on supplier relationship management and development, businesses must implement robust processes for analysing and selecting suppliers (Mohammed et al., 2021). In the automotive manufacturing industry, which is characterized by intense competition and a volatile environment, companies cannot be successful without conducting a comprehensive identification and evaluation of suppliers (Raut et al., 2012).

Risks linked to delay: Dias et al. (2021) and Junaid et al. (2020) have highlighted that the most sensitive risks in automotive supply chains include forecasting errors, supplier communication breakdowns, supplier quality issues and supplier delivery delays, with delivery delays being the most detrimental for the studied automotive supply chain.

Risks linked to demand in the market: Although most publications concentrate on supply-side issues, fluctuations in demand also frequently cause disruptions. In contrast to supply-side risks, which primarily affect downstream supply chains, demand-side risks primarily affect upstream supply chains and, thus, the entire supply chain system (Zhao et al., 2020).

Risks linked with economic factors: A company’s business environment comprises various elements, each of which can affect the company’s operational and financial success. Therefore, the decisions that companies make about their businesses are dependent on a wide variety of circumstances. Managers and investors continue to place a stronger emphasis on the importance of financial and operational risks. There is a correlation between the incidence of various risks and inconsistent performance from suppliers, as well as financial losses. Profitability and effective supply chain coordination are negatively impacted when there is insufficient cash flow in closed-loop supply chains (Ethirajan et al., 2021).

Risks linked to management: A lack of initiative on the part of managers might prevent their companies from reaching their goals. Many studies have identified a lack of support from top management as a barrier to the adoption of improvement initiatives (Hariyani et al., 2022; Parmar & Desai, 2021; Ullah et al., 2021). Risk cannot be eliminated entirely, as evidenced by both theory and practice, but it may be reduced over the long term with the help of engaged and committed senior leadership. Top management plays a vital role in monitoring internal processes, identifying the impact of external factors and making prompt and effective decisions to mitigate potential risks.

Risks linked to tools and techniques: Failure to adapt to advances in technology, relying on outdated tools and processes, a lack of effective change management and the absence of staff education programmes can result in business loss. Inadequate tools and procedures can lead to decreased productivity, compromised product quality and damaged brand reputation. For example, Sundarani and Qureshi (2017) have identified several challenges that must be overcome to implement a flexible manufacturing system that can enhance production efficiency, emphasizing the importance of embracing new techniques.

Risks linked to workers/employees: These risks are associated with employee incompetence or misunderstanding, underestimating staff capabilities, employee overconfidence and unethical acts, all of which may contribute to a decrease in the quality of both the product and the process. Hess and Cottrell (2016) suggested principles for managing company risks caused by unethical and fraudulent acts. The significance of evaluating and enhancing the level of competence of human resources and the relationship between these two factors with increased productivity was demonstrated by Otto et al. (2008). Keith Denton (1995) developed a team management approach that emphasized the significance of competence. This strategy was intended to bring about continual progress.

Risks linked to negative effects of product/processes on the external environment: Companies or industries that demonstrate a complete disregard for the environment in which they operate may provoke adverse events, and these negative occurrences can have direct and indirect repercussions on a company’s operations, employees and income. Although manufacturing is responsible for the performance of every economy, it also contributes to environmental degradation and social problems (Bonvoisin et al., 2017). This has led to the widespread belief that factories are to blame for a variety of international social and environmental issues (Nartey & van der Poll, 2021).

Overall, the preceding breakdown of key risks demonstrates their complexity and breadth, and how it is crucial for organizations to comprehend the CRFs related to the supply chain. Table 1 sums up the risk factors extracted from the literature, complete with source information.

Table 1:

List of CRFs.

Code	Risk Variables	Short Explanation About Triggering Elements	Sources
Rsk1	Natural mishaps	Downfall, tornado, earthquakes, extreme temperature, infectious diseases etc.	Dias et al., 2020; Gautam et al., 2018; Ghadir et al., 2022; Mohammed et al., 2021; Thun & Hoenig, 2011
Rsk2	Human blunders	Geopolitical issues, corruption, system deterioration, employee unions and walkouts, unstable regional political scenario.	Gautam et al., 2018; Mohammed et al., 2021; Thun & Hoenig, 2011
Rsk3	ICT systems	Complex networks, denial-of-service attacks, virus encroachment, software defence violation.	Gautam et al., 2018; Thun & Hoenig, 2011
Rsk4	Risks linked to competition	Joint ventures of elite industries, new business gambit.	Kidane & Sharma, 2016; Mehrjerdi & Dehghanbaghi, 2013; Silva et al., 2013; Simons, 1999
Rsk5	Risks linked to primary commodity	Commodity dearth, deficient commodity, price alterations.	Gautam et al., 2018; Nunes & Bennett, 2008; Prakash et al., 2018; Thun & Hoenig, 2011
Rsk6	Risks linked with suppliers	Transactional association, poor process accuracy, suboptimal supplier selection, supplier debts and limited responsiveness.	Alikhani et al., 2019; Babu et al., 2020; Chan et al., 2008; Deep et al., 2019; Gautam et al., 2018; Mohammed et al., 2021; Pitchaiah et al., 2020; Prakash et al., 2018; Thun & Hoenig, 2011
Rsk7	Risks linked to delay	Logistical obstacles, unforeseeable circumstances, conflicts among parties, lengthy and complexity associated with paperwork, critical component failure, late response to approval requests etc.	Gautam et al., 2018; Thun & Hoenig, 2011
Rsk8	Risks linked to demand in the market	Bullwhip phenomenon, prediction inaccuracy, marketplace volatility.	Gautam et al., 2018; Kumar et al., 2020; Prakash et al., 2018; Thun & Hoenig, 2011
Rsk9	Risks linked with economic factors	Alteration in tax legislation, economic recessions, payment processing troubles and payments that are late or below standards set.	Babu et al., 2020; Batkovskiy et al., 2015; Dandage et al., 2019; Deep et al., 2019; Dey & Ogunlana, 2004; Islam & Tedford, 2012b; Prakash et al., 2018
Rsk10	Risks linked to management	Inability to resolve opinion differences, operational and management strategy mismatches, lack of conviction from the top tier authorities, reasoning and conclusion delays, lack of transparency and governance problems.	Abdolshah & Moradi, 2013; Batkovskiy et al., 2015; Cao & Hoffman, 2011; Czuchry & Yasin, 2003; Dandage et al., 2018b; Gautam et al., 2018; Islam & Tedford, 2012a; Keil et al., 1998; Maya, 2016; Ojiako et al., 2008
Rsk11	Risks linked to tools and techniques	Lack of ability to adjust to evolution, adherence to the attempted methods, and low motivation to use modern techniques.	Dandage et al., 2019; Gautam et al., 2018; Shevtshenko & Mahmood, 2015
Rsk12	Risks linked to workers/employees	Ineptness, a lack of job expertise, poor training and lack of required attitude, malpractices etc.	Gautam et al., 2018; Keil et al., 1998; Prakash et al., 2018; Shevtshenko & Mahmood, 2015; Shin et al., 2018
Rsk13	Risks linked to negative effects of products/processes on the external environment	Tainting, industrial wastes, unconcerned approach towards surroundings/environment.	Nartey & van der Poll, 2021; Nunes & Bennett, 2008.

According to Dandage et al. (2018), the effective management of risks is crucial to successfully completing any project without compromising the four major constraints of scope, cost, schedule and quality. However, Reiff et al. (2021) point out that in order to stay competitive, it is necessary to strike a delicate balance between manufacturing costs, delivery time and product quality. Previous researchers have revealed the multiple elements to be considered as performance indicators specifically in the context of the manufacturing sector. These include quality, safety, cost, management, financial performance/perspectives, schedule, brand name, lead time, productivity, reliability, supplier issues, manufacturing processes, employee perspectives and the company’s future perspectives (da Cruz et al., 2022; Susilawati, 2021; Tokola et al., 2016). These metrics are all essential for achieving sustainable production.

Any risk that occurs can affect various aspects of manufacturing, so it is important to examine how risk factors impact industrial criteria. To ensure the smooth functioning of the supply chain, it is important for businesses to manage and mitigate potential disruptions that can arise from various CRFs. These factors can severely impact the performance of different business functions such as production, logistics and customer service. Therefore, businesses must prioritize their risk management efforts by focusing on mitigating CRFs that have a significant impact on these functions. By doing so, businesses can effectively allocate their resources and develop robust risk management strategies to minimize the adverse effects of potential disruptions and maintain their competitive edge in the market.

In previous studies on SCRM, authors have explored different approaches to protecting supply chains from disruptions. These include the proactive approach, which involves creating defences ahead of time, and the reactive approach, which consists of adjusting supply chain processes and structures after disruptions occur.

Methodology

A literature review, brainstorming sessions with automobile industry professionals, the nominal group approach and idea engineering, were all used to determine the most influential CRFs (Singh & Gupta, 2020). These factors relate to the organization’s high-level objectives, its day-to-day operations and key functional areas. During the process of selecting essential risk factors, several one-on-one and group discussions with industry professionals were conducted. However, since the criteria for selecting risk factors may vary from one industry to another, the selected experts were asked to validate the risk factors through a pilot survey. The group’s consensus was then documented.

The risks aggregated in Table 1 were verified by consulting nine experts (see Table 2) from India’s top manufacturing companies. During the pilot study, the respondents were given a list of 23 risk factors derived from the literature and asked to identify the risks they felt were critical to the manufacturing industry. A Google form was used to distribute and gather responses to the survey. The responses from nine industry professionals were analysed for the pilot study. Based on the analysis, 13 common CRFs were selected for further investigation in this study. According to the evaluation of specialists, the selected risks are relevant and significant to the current condition of the Indian automobile sector.

Table 2:

Industry Expert’s Details.

Industry Expert (IE)	Designation	Total Experience In Years	Type of Industry
1	Senior Engineer	5.5	Automotive manufacturing
2	Senior Engineer	7.4	Automotive manufacturing
3	Engineer (Quality Assurance)	5.6	Radiators manufacturing
4	Deputy Manager (Engineering)	13.4	Electronic clusters and sensors manufacturing
5	Sr. Design Engineer	9.2	Automotive manufacturing
6	Manufacturing Plant Head	11.4	Manufacturing of thermal systems
7	Manager (Operations)	9.3	Packaging manufacturing
8	Design Engineer	6.3	Hydraulic power unit manufacturing
9	Head (Fabrication unit)	11.4	Hydraulic power unit manufacturing

Several approaches may be used to determine the relative importance of different risk categories. Researchers and professionals in various fields have shown great interest in Multi-Criteria Decision-Making (MCDM) techniques (Raut et al., 2019, Raut et al., 2021a, b; Singh et al., 2016, 2017, 2020; Singh & Gurtu, 2021). The term MCDM methods refers to examining several competing criteria in a subject while applying quantitative evaluations to alternatives. MCDM enables decision-makers to perform assessments and make decisions regarding various aspects by merging the approaches of several other academic subjects, including mathematics, management, the social sciences and economics. Additionally, MCDM techniques are effective for exploring, rating and ranking various decision-making tasks (Chowdhury & Paul, 2020; da Cruz et al., 2022; Kharat et al., 2016; Singh & Agrawal, 2018). Unlike statistical theory, MCDM does not require a large sample size in order to function properly. Rezaei et al. (2018) observed that the MCDM procedure only requires 4–10 specialists at the most in order to gather credible information. It is essential, of course, to make sure that the individuals being questioned are extremely knowledgeable in their fields and highly professional (Gul et al., 2021). Different MCDM techniques are known to have varying performances and features (Zamani-Sabzi et al., 2016). Therefore, selecting a specific approach from the numerous MCDM methodologies is not simple. According to Ghaleb et al. (2020), to increase selection efficiency and effectiveness, it is necessary to compare several MCDM strategies. Three MCDM techniques, namely PROMETHEE, VIKOR and TOPSIS, were selected for this study based on their extensive use, ease of implementation and excellent performance in previous research (da Cunha et al., 2022; Singh & Gupta, 2019, 2020; Surange & Bokade, 2022; Taghavifard & Majidian, 2022).

After reviewing all the criteria derived from the literature, specialists who were consulted (see Table 2) concluded that it would be beneficial to limit the number of criteria to simplify the decision-making process. As a result, the specialists agreed on five priority criteria (constructs) for evaluating the impact of CRFs. These selected criteria are discussed below. The negative impact intensity of 13 identified CRFs on five separate industry concerns was examined during the decision table formulation, namely, commercial considerations, production activities, business reputation, planning and quality of the process/end item.

Commercial considerations: Profitability is the most-used metric for measuring business performance (Susilawati, 2021). Therefore, assessing the adverse impact of risk factors on commercial considerations is of utmost importance.

Production activities: Efficient use of resources is critical for seamless and uninterrupted production activities. However, any adverse circumstance can cause delays and unexpected events, leading to production interruptions. Hence, it is essential to investigate the impact of the severity of risk factors on production activities. Ensuring the efficiency of production activities is crucial to optimize resource utilization and minimize disruptions. Therefore, monitoring the functioning of production activities becomes a key performance indicator (KPI) for evaluating the effectiveness of manufacturing processes (Tokola et al., 2016).

Business reputation: Many studies have revealed the connection between industry reputation, economic impacts and industrial importance (Loureiro et al., 2017; Nadanyiova et al., 2019). Therefore, studying the effects of risk factors on business reputation is crucial.

Planning: Production planning enables firms to lay out the full sequence of events. There is risk related to unanticipated occurrences that disrupt routine performance planning. Effectively preparing for unexpected events, such as a scarcity of materials, rush orders, defective machines and so on, is critical to ensuring business continuity. It requires an in-depth understanding of the effects of various risk factors on organizational planning. To remain viable in the context of customization, a rising number of global competitors and fluctuating markets, a thorough, adaptable, and user-friendly process-planning framework is essential (Reiff et al., 2021).

Quality of the process/end item: Losses may occur as a result of product/process quality that falls short of organizational quality targets (Teli et al., 2013). Hence, the adverse impacts of risk factors on quality aspects must be examined.

Ranking of risk factors using PROMETHEE

To arrange the identified critical risks according to their adverse impact intensity, the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE–II), was employed (Potdar & Rane, 2018; Rao & Patel, 2010; Venkatesan & Kumanan, 2012). The step-by-step approach to this method is described below.

Step 1. Industrial Practitioners’ Profile and Creation of Decision Table

Seven highly qualified and experienced industry experts were approached, and their input was obtained to create a decision table. Table 2 (Sr. Nos 1–7) presents the profile of industrial practitioners, and Figure 1 illustrates the overall process adopted for ranking. As can be seen, CRF analysis is comprised of three distinct stages. At the highest level, the goal of the problem is outlined, and the criteria, along with the risk factors, are discussed sequentially.

Figure 1.

Source: Zubayer et al. (2019).

Table 3 aggregates the average input from industrial practitioners for each alternative (i.e. risks concerning each criterion considered, as explained earlier in the Literature Review section). The input was gathered using a Likert scale from 1 (very low adverse impact) to 7 (very high adverse impact).

Table 3:

Aggregation of Average Input Obtained from Industrial Practitioners.

$R F_{i}$ / $C_{j}$	Crt 1	Crt 2	Crt 3	Crt 4	Crt 5
Rsk1	6.1429	5.2857	3.0000	5.7143	4.2857
Rsk 2	5.7143	5.1429	3.8571	5.2857	4.2857
Rsk 3	5.0000	5.7143	4.7143	5.5714	4.2857
Rsk 4	4.7143	3.7143	5.2857	4.5714	4.8571
Rsk 5	5.5714	5.1429	4.1429	5.4286	5.0000
Rsk 6	5.0000	4.7143	4.8571	5.8571	5.7143
Rsk 7	5.7143	5.2857	6.0000	6.0000	5.1429
Rsk 8	5.7143	5.0000	4.1429	4.0000	3.8571
Rsk 9	5.8571	4.5714	4.8571	4.4286	3.2857
Rsk 10	5.8571	5.2857	5.8571	5.1429	5.0000
Rsk 11	5.0000	5.0000	4.8571	5.4286	5.2857
Rsk 12	4.2857	5.0000	5.1429	5.0000	5.1429
Rsk 13	4.4286	4.5714	5.2857	4.4286	4.7143

Step 2. Assignment of Preference Function to Each Criterion

The preference for the usual function can be determined by calculating the difference between the values of a specific criterion ( $C_{i}$ ) for two alternatives ( $a_{1}$ and $a_{2}$ ). The preference function ( $P_{i}$ ) then converts this evaluation difference into a preference degree ranging from 0 to 1. This allows for a quantifiable measure of preference between the two alternatives (see Figure 2).

P (d) = \{\begin{matrix} 0 d \leq 0 \\ 1 d > 0 \end{matrix}

Figure 2:

Preference Function Assignment.

Let $P_{c} (a, b)$ = $F_{c} [d_{c} (a, b)] \forall a, b \in A, 0 \leq P_{c} (a, b) \leq 1$ be the preference function corresponding to criterion ‘C’, where $F_{c}$ is a non-decreasing function of the calculated deviation $d_{c} (a, b)$ between the alternatives belonging to the set of alternatives A, $a, b \in A$ (see Tables 4 –8).

Table 4:

Preference Function for Criterion 1 (Crt 1).

Crt1	Rsk 1	Rsk 2	Rsk 3	Rsk 4	Rsk 5	Rsk 6	Rsk 7	Rsk 8	Rsk 9	Rsk 10	Rsk 11	Rsk 12	Rsk 13
Rsk1	-	1	1	1	1	1	1	1	1	1	1	1	1
Rsk 2	0	-	1	1	1	1	0	0	0	0	1	1	1
Rsk 3	0	0	-	1	0	0	0	0	0	0	0	1	1
Rsk 4	0	0	0	-	0	0	0	0	0	0	0	1	1
Rsk 5	0	0	1	1	-	1	0	0	0	0	1	1	1
Rsk 6	0	0	0	1	0	-	0	0	0	0	0	1	1
Rsk 7	0	0	1	1	1	1	-	0	0	0	1	1	1
Rsk 8	0	0	1	1	1	1	0	-	0	0	1	1	1
Rsk 9	0	1	1	1	1	1	1	1	-	0	1	1	1
Rsk 10	0	1	1	1	1	1	1	1	0	-	1	1	1
Rsk 11	0	0	0	1	0	0	0	0	0	0	-	1	1
Rsk 12	0	0	0	0	0	0	0	0	0	0	0	-	0
Rsk 13	0	0	0	0	0	0	0	0	0	0	0	1	-

Table 5:

Preference Function for Criterion 2 (Crt 2).

Crt2	Rsk 1	Rsk 2	Rsk 3	Rsk 4	Rsk 5	Rsk 6	Rsk 7	Rsk 8	Rsk 9	Rsk 10	Rsk 11	Rsk 12	Rsk 13
Rsk1	-	1	0	1	1	1	0	1	1	0	1	1	1
Rsk 2	0	-	0	1	0	1	0	1	1	0	1	1	1
Rsk 3	1	1	-	1	1	1	1	1	1	1	1	1	1
Rsk 4	0	0	0	-	0	0	0	0	0	0	0	0	0
Rsk 5	0	0	0	1	-	1	0	1	1	0	1	1	1
Rsk 6	0	0	0	1	0	-	0	0	1	0	0	0	1
Rsk 7	0	1	0	1	1	1	-	1	1	0	1	1	1
Rsk 8	0	0	0	1	0	1	0	-	1	0	0	0	1
Rsk 9	0	0	0	1	0	0	0	0	-	0	0	0	0
Rsk 10	0	1	0	1	1	1	0	1	1	-	1	1	1
Rsk 11	0	0	0	1	0	1	0	0	1	0	-	0	1
Rsk 12	0	0	0	1	0	1	0	0	1	0	0	-	1
Rsk 13	0	0	0	1	0	0	0	0	0	0	0	0	-

Table 6:

Preference Function for Criterion 3 (Crt 3).

Crt3	Rsk 1	Rsk 2	Rsk 3	Rsk 4	Rsk 5	Rsk 6	Rsk 7	Rsk 8	Rsk 9	Rsk 10	Rsk 11	Rsk 12	Rsk 13
Rsk1	-	0	0	0	0	0	0	0	0	0	0	0	0
Rsk 2	1	-	0	0	0	0	0	0	0	0	0	0	0
Rsk 3	1	1	-	0	1	0	0	1	0	0	0	0	0
Rsk 4	1	1	1	-	1	1	0	1	1	0	1	1	0
Rsk 5	1	1	0	0	-	0	0	0	0	0	0	0	0
Rsk 6	1	1	1	0	1	-	0	1	0	0	0	0	0
Rsk 7	1	1	1	1	1	1	-	1	1	1	1	1	1
Rsk 8	1	1	0	0	0	0	0	-	0	0	0	0	0
Rsk 9	1	1	1	0	1	0	0	1	-	0	0	0	0
Rsk 10	1	1	1	1	1	1	0	1	1	-	1	1	1
Rsk 11	1	1	1	0	1	0	0	1	0	0	-	0	0
Rsk 12	1	1	1	0	1	1	0	1	1	0	1	-	0
Rsk 13	1	1	1	0	1	1	0	1	1	0	1	1	-

Table 7:

Preference Function for Criterion 4 (Crt 4).

Crt4	Rsk 1	Rsk 2	Rsk 3	Rsk 4	Rsk 5	Rsk 6	Rsk 7	Rsk 8	Rsk 9	Rsk 10	Rsk 11	Rsk 12	Rsk 13
Rsk1	-	1	1	1	1	0	0	1	1	1	1	1	1
Rsk 2	0	-	0	1	0	0	0	1	1	1	0	1	1
Rsk 3	0	1	-	1	1	0	0	1	1	1	1	1	1
Rsk 4	0	0	0	-	0	0	0	1	1	0	0	0	1
Rsk 5	0	1	0	1	-	0	0	1	1	1	0	1	1
Rsk 6	1	1	1	1	1	-	0	1	1	1	1	1	1
Rsk 7	1	1	1	1	1	1	-	1	1	1	1	1	1
Rsk 8	0	0	0	0	0	0	0	-	0	0	0	0	0
Rsk 9	0	0	0	0	0	0	0	1	-	0	0	0	0
Rsk 10	0	0	0	1	0	0	0	1	1	-	0	1	1
Rsk 11	0	1	0	1	0	0	0	1	1	1	-	1	1
Rsk 12	0	0	0	1	0	0	0	1	1	0	0	-	1
Rsk 13	0	0	0	0	0	0	0	1	0	0	0	0	-

Table 8:

Preference Function for Criterion 5 (Crt 5).

Crt5	Rsk 1	Rsk 2	Rsk 3	Rsk 4	Rsk 5	Rsk 6	Rsk 7	Rsk 8	Rsk 9	Rsk 10	Rsk 11	Rsk 12	Rsk 13
Rsk1	-	0	0	0	0	0	0	1	1	0	0	0	0
Rsk 2	0	-	0	0	0	0	0	1	1	0	0	0	0
Rsk 3	0	0	-	0	0	0	0	1	1	0	0	0	0
Rsk 4	1	1	1	-	0	0	0	1	1	0	0	0	1
Rsk 5	1	1	1	1	-	0	0	1	1	0	0	0	1
Rsk 6	1	1	1	1	1	-	1	1	1	1	1	1	1
Rsk 7	1	1	1	1	1	0	-	1	1	1	0	0	1
Rsk 8	0	0	0	0	0	0	0	-	1	0	0	0	0
Rsk 9	0	0	0	0	0	0	0	0	-	0	0	0	0
Rsk 10	1	1	1	1	0	0	0	1	1	-	0	0	1
Rsk 11	1	1	1	1	1	0	1	1	1	1	-	1	1
Rsk 12	1	1	1	1	1	0	0	1	1	1	0	-	1
Rsk 13	1	1	1	0	0	0	0	1	1	0	0	0	-

Step 3. Calculation of Weight for Each Criterion Using the Entropy Method

Weight computation of each criterion is performed as follows (Dehdasht et al., 2020; Mavi et al., 2016; Shannon, 2001).

Normalization of Table 3 is performed using the equation $N_{i j} = \frac{x_{i j}}{\sum_{i = 1}^{A} x_{i j}}$ as shown in Table 9.

Table 9:

The Normalization of Average Ratings.

$R s k_{i}$ / $C r t_{j}$	Crt1	Crt2	Crt3	Crt4	Crt5
Rsk1	0.089027	0.082040	0.048387	0.085470	0.070423
Rsk 2	0.082816	0.079823	0.062212	0.079060	0.070423
Rsk 3	0.072464	0.088692	0.076037	0.083333	0.070423
Rsk 4	0.068323	0.057650	0.085253	0.068376	0.079812
Rsk 5	0.080745	0.079823	0.066820	0.081197	0.082160
Rsk 6	0.072464	0.073171	0.078341	0.087607	0.093897
Rsk 7	0.082816	0.082040	0.096774	0.089744	0.084507
Rsk 8	0.082816	0.077605	0.066820	0.059829	0.063380
Rsk 9	0.084886	0.070953	0.078341	0.066239	0.053991
Rsk 10	0.084886	0.082040	0.094470	0.076923	0.082160
Rsk 11	0.072464	0.077605	0.078341	0.081197	0.086854
Rsk 12	0.062112	0.077605	0.082949	0.074786	0.084507
Rsk 13	0.064182	0.070953	0.085253	0.066239	0.077465

Criteria weight is calculated using equations as presented in Table 10.

Table 10:

Computation of the Degree of Deviation and Entropy Weight for Each Criterion.

Description	Equations	Crt1	Crt2	Crt3	Crt4	Crt5
Entropy for each index	$E n t r o p y_{j} = - \frac{1}{\ln A} \sum_{i = 1}^{A} N_{i j} \ln N_{i j}$	0.997661	0.998172	0.994395	0.997332	0.996324
Criteria degree of deviation of essential information	$D j = 1 - E n t r o p y_{j}$	0.002339	0.001828	0.005605	0.002668	0.003676
Computation of the entropy weight for criteria	$w e i g h t_{j} = \frac{D j}{\sum_{j = 1}^{n} D j}$	0.145116	0.113436	0.347782	0.165553	0.228113

Step 4. Calculation of Aggregated Preference Indices

Aggregated preference indices for criteria are calculated using the equation:

Π (a, b) = \sum_{c = 1}^{c} P_{c} (a, b) w_{c},

where $w_{c}$ denotes the weights associated with criteria C and 0 ≤ $Π (a, b)$ ≤ 1; $Π (a, b)$ ; represents the decision-maker’s preference intensity of alternative a over b, considering all the criteria simultaneously. These results are presented in Table 11.

Table 11:

Aggregated Preference Indices for Criteria.

Aggregate	Rsk 1	Rsk 2	Rsk 3	Rsk 4	Rsk 5	Rsk 6	Rsk 7	Rsk 8	Rsk 9	Rsk 10	Rsk 11	Rsk 12	Rsk 13
Rsk1	0	0.4241	0.3107	0.4241	0.4241	0.2586	0.1451	0.6522	0.6522	0.3107	0.4241	0.4241	0.4241
Rsk 2	0.3478	0	0.1451	0.4241	0.1451	0.2586	0	0.5071	0.5071	0.1656	0.2586	0.4241	0.4241
Rsk 3	0.4612	0.6268	0	0.4241	0.6268	0.1134	0.1134	0.8549	0.5071	0.279	0.279	0.4241	0.4241
Rsk 4	0.5759	0.5759	0.5759	0	0.3478	0.3478	0	0.7414	0.7414	0	0.3478	0.4929	0.5388
Rsk 5	0.5759	0.7414	0.3732	0.6522	0	0.2586	0	0.5071	0.5071	0.1656	0.2586	0.4241	0.6522
Rsk 6	0.7414	0.7414	0.7414	0.6522	0.7414	0	0.2281	0.7414	0.5071	0.3937	0.3937	0.5388	0.6522
Rsk 7	0.7414	0.8549	0.8866	1	1	0.7719	0	0.8549	0.8549	0.7414	0.7719	0.7719	1
Rsk 8	0.3478	0.3478	0.1451	0.2586	0.1451	0.2586	0	0	0.3415	0	0.1451	0.1451	0.2586
Rsk 9	0.3478	0.4929	0.4929	0.2586	0.4929	0.1451	0.1451	0.6585	0	0	0.1451	0.1451	0.1451
Rsk 10	0.5759	0.8344	0.721	1	0.6063	0.6063	0.1451	1	0.8549	0	0.6063	0.7719	1
Rsk 11	0.5759	0.7414	0.5759	0.6522	0.5759	0.1134	0.2281	0.7414	0.5071	0.3937	0	0.5388	0.6522
Rsk 12	0.5759	0.5759	0.5759	0.5071	0.5759	0.4612	0	0.7414	0.8549	0.2281	0.3478	0	0.5071
Rsk 13	0.5759	0.5759	0.5759	0.1134	0.3478	0.3478	0	0.7414	0.5759	0	0.3478	0.4929	0

Step 5. Calculation of Net Flow and Ranking of Risk Factors.

Leaving flow

φ^{+} (a) = \sum_{x \in A} Π_{x a}

Entering flow

φ^{-} (a) = \sum_{x \in A} Π_{a x}

Net flow

φ (a) = φ^{+} (a) - φ^{-} (a)

See Table 12 for the results of the net flow calculations.

Table 12:

Net Flow Calculation and Ranking of Risks.

	Leaving Flow $φ^{+} (a) = \sum_{x \in A} Π_{x a}$	Entering Flow $φ^{-} (a) = \sum_{x \in A} Π_{a x}$	Net Flow $φ (a) = φ^{+} (a) - φ^{-} (a)$	Rank
Rsk1	4.87406904	6.442833	-1.56876	9
Rsk 2	3.60718818	7.532918	-3.92573	11
Rsk 3	5.13390877	6.119633	-0.98572	7
Rsk 4	5.28560878	6.366609	-1.081	8
Rsk 5	5.11597448	6.029141	-0.91317	6
Rsk 6	7.07300905	3.941195	3.131814	3
Rsk 7	10.2497733	1.00501	9.244763	1
Rsk 8	2.39323111	8.741884	-6.34865	13
Rsk 9	3.46905666	7.411275	-3.94222	12
Rsk 10	8.72224067	2.677659	6.044582	2
Rsk 11	6.29611923	4.325661	1.970459	4
Rsk 12	5.95123164	5.593783	0.357448	5
Rsk 13	4.69470923	6.67852	-1.98381	10

Ranking of Risk Factors Using VIKOR

This approach emphasizes prioritizing and selecting from a group of options before examining a solution. In doing so, it seeks to retain the possible benefit for the majority while causing the individual the least regret (Varshney et al., 2021). The ranking was computed using the VIKOR method in programming software based on input from industry experts (see Appendix I).

Ranking of Risk Factors Using TOPSIS

The fundamental tenet of TOPSIS is simple. Its roots lie in the idea of a dispersed ideal point, from which the compromise solution is closest (Shih et al., 2007). In addition, Hwang and Yoon (1981) and Hwang et al. (1993) indicated that options should be ranked according to how close they are to the positive ideal solution (PIS) and how distant they are from the negative ideal solution (NIS). In TOPSIS, the distances to PIS and NIS are considered simultaneously, and a preference order is rated according to the relative proximity of these two distance measures. The input from industry experts was used to compute the ranking using the TOPSIS method in programming software (refer to Appendix II). The higher the relative proximity to the ideal solution, the greater the adverse impact of that risk category on the criteria considered.

Results and discussion

There are a number of risks linked with automobile manufacturing in India’s industrial sector which have an immediate impact on the effectiveness of the supply chain at a national level. The process of SCRM involves three steps, namely identification, assessment and mitigation, and the cooperation and coordination of the supply chain partners are crucial during these stages (da Cunha et al., 2022).

A robust risk management system is crucial for managing day-to-day operations and enhancing overall industrial efficiency, giving organizations a competitive edge. Managers should be knowledgeable about CRFs and the severity of their impact on various industrial key indicators. They should strive to have clear plans to minimize the negative impact of these risks while successfully implementing risk management plans. Therefore, to effectively apply SCRM, supply chain managers must first identify key supply chain risks in their firm’s context (Marcelino-Sádaba et al., 2014) and comprehend the severity of their impact among those risks.

This study consulted a team of decision-makers and specialists to collect input data. The industrial specialists consulted are senior officials from top-ranked businesses in Maharashtra, India, and each has extensive domain expertise. The impact severity of risk factors was assessed using five criteria. Shannon’s entropy was employed to quantify each criterion’s weight as it improves decision-making reliability and accuracy without requiring extensive computation. The weight of the information criterion increases with decreasing entropy of the assessed information criterion (Chen, 2020; Dehdasht et al., 2020). The computed entropy was used to determine the weight of each criterion in the study. For Crt3, the computed entropy was lowest, at 0.994395, resulting in a weight of 0.347782, which is the highest among all other criteria. Conversely, Crt2 had the highest computed entropy value at 0.998172, resulting in the lowest weight of 0.113436 being assigned to it. The comparison of CRF prioritization obtained by using PROMETHEE, VIKOR and TOPSIS methods is presented in Table 13.

Table 13.

Comparison of Ranking Results Obtained.

Rank	PROMETHEE	VIKOR	TOPSIS
1	Risks linked to delay	Risks linked to delay	Risks linked to delay
2	Risks linked to management	Risks linked to management	Risks linked to management
3	Risks linked with suppliers	Risks linked with suppliers	Risks linked with suppliers
4	Risks linked to tools and techniques	Risks linked to tools and techniques	Risks linked to workers/employees
5	Risks linked to workers/employees	ICT systems	Risks linked to tools and techniques
6	Risks linked to primary commodity	Risks linked to workers/employees	Risks linked to competition
7	ICT systems	Risks linked to competition	Risks linked to negative effects of product/processes on the external environment
8	Risks linked to competition	Risks linked to negative effects of product/processes on the external environment	ICT systems
9	Natural mishaps	Risks linked to primary commodity	Risks linked to primary commodity
10	Risks linked to negative effects of product/processes on the external environment	Human blunders	Risks linked with economic factors
11	Human blunders	Risks linked with economic factors	Human blunders
12	Risks linked with economic factors	Risks linked to demand in market	Risks linked to demand in market
13	Risks linked to demand in market	Natural mishaps	Natural mishaps

The availability of various MCDM approaches makes it crucial to compare and evaluate their individual performances and characteristics to identify the most appropriate approach for a given situation. Comparing different MCDM procedures can enhance the selection process’s efficiency and efficacy (Ghaleb et al., 2020; Jain et al., 2018). Through a systematic approach and analysis, this study has identified that delay, management and supplier risks are the top three risks faced by the Indian automotive supply chain. These findings can be valuable for automotive industry management to prioritize the CRFs and implement mitigation measures accordingly.

Conclusion

For supply chain managers, controlling risk in a highly volatile business environment is a complex undertaking. Numerous studies have shown that the manufacturing sector needs to be continually enhanced to improve its management of unforeseen events and remain viable in a globally competitive economy. Many manufacturing companies in developing countries do not currently manage various risk factors effectively compared to the proactive and reactive risk mitigation approaches required by international benchmarks. As a result, all manufacturing organizations are endeavouring to include risk management in their long-term business goals. Researchers are developing risk management measures to help supply chains survive disruptions. Many industries are using resilience- and responsiveness-based approaches to create robust supply chains.

This study has systematically compared the prioritization of critical risks that predominantly exist in the Indian automotive supply chain. This systematic approach will provide industry professionals with a better understanding of their complex environment, helping industrial managers prioritize the strategic factors identified in the study. These efforts can serve as input for better decision-making, leading to improved supply chain performance.

The study’s results reveal that risks linked to delay hold up further processing of parts and end up disrupting the supply chain flow. Unsupportive and uninvolved management practices hinder the organization from achieving timely results and success. Risks associated with suppliers are particularly critical in automotive manufacturing, as thousands of parts are assembled before the final vehicle is ready. Any defects from the suppliers can travel through the entire supply chain and affect the final assembly, resulting in various risks to the end-user and the brand itself. Hence, the findings imply that organizations should focus on these risks, assess them and create mitigation plans based on their individual circumstances.

The MCDM approaches discussed in this paper are considered standard but remain relevant and commonly used. However, there may be other MCDM tools that can be explored to validate the results obtained in this study. Moreover, other available methods for identifying and ranking risk factors and understanding the interdependencies and causal linkages among them may lead to additional insights in the future. Possible follow-up research could compare our findings to data collected from other industries or geographical regions with varied manufacturing sectors.

APPENDIX I: RANKING OF RISK FACTORS USING VIKOR IN R

Input

Output

APPENDIX II: RANKING OF RISK FACTORS USING TOPSIS IN R

Input

Output

Footnotes

DECLARATION OF CONFLICTING INTERESTS

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

FUNDING

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID IDs

Vinod Surange

Sanjay Bokade

Vinod G. Surange is an Assistant Professor at the Symbiosis Institute of Business Management, Nagpur, Constituent of Symbiosis International, Deemed University, Pune, India. He has two years of industrial experience and thirteen years of academic tenure. He holds a PhD in mechanical engineering from the University of Mumbai and has authored sixteen papers in esteemed international journals and conferences. His areas of academic and research interest span supply chain management, quality and reliability engineering, as well as production and operations management.

e-mail: vinod.surange@gmail.com

Sanjay U. Bokade is the Principal at Rajiv Gandhi Institute of Technology (RGIT), Mumbai, India. He has more than 28 years of academic experience. He serves as a professor in mechanical engineering at RGIT, Mumbai, actively participating in the University of Mumbai’s Board of Studies and supervising PhD candidates. With over 25 publications and extensive expertise in production planning, quality control, logistics, and supply chain management, he is highly regarded in academia and industry.

e-mail: sanjaybokade@gmail.com

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