1. Introduction

A sustainable pharmaceutical supply chain (PSC) aims to provide an effective end-to-end information and material flow system to attain a responsive, agile, accurate, and data-driven supply chain (SC). In the current competitive market, the pharmaceutical industry needs to evolve into a lean, focused organization with massive research and innovation activities to generate sustainable income streams from developing specialized goods and technologies [1]. The convergence of Industry 4.0 (I4.0) and healthcare has been influencing the big pharmaceutical industries for the past few years, resulting in ’Pharma 4.0’, resulting from adopting I4.0 concepts to the pharmaceutical and life sciences sectors [2]. The principal drivers of I4.0 in the convergence process include cloud computing, augmented reality (AR), virtual reality (VR), the Internet of Things (IoT), cyber-physical systems (CPS), big data analytics (BDA), cybersecurity, simulation, autonomous robots, etc., [3]. These emerging technologies are transitioning the pharmaceutical industry toward modern-day digital and technology-dependent manufacturing systems [4]. This change assists businesses in leveraging technology to reach their objectives by operating more efficiently at a lower cost but in a competitive and agile manner [5]. Even though I4.0 originated in manufacturing, it has significantly impacted strategic SC operations [6].

I4.0 technologies are attracting the pharmaceutical industry for several reasons. In the post-COVID-19 period, the need for achieving sustainability in the SC has intensified in various sectors [7]. Achieving sustainability will improve various aspects of PSC, such as customer satisfaction level, responsiveness, flexibility, communication, service fill rate, etc. Moreover, maintaining sustainable accessibility and affordability of indispensable medicines and treatments is critical. Therefore, disrupting the PSC might endanger human life, causing difficulties in healthcare operations and medication availability [8]. In such scenarios, emerging technologies of I4.0 can assist pharmaceutical industries in overcoming the issues hindering sustainability in this sector since adopting I4.0 facilitates businesses to attain the triple bottom line of sustainability, promoting economic progress, social well-being, and environmental conservation [9].

The instrumental role of I4.0 can enhance visibility and collaboration and build trust among all the SC stakeholders by changing the pharmaceutical business dynamics [10]. I4.0 offers a consolidated environment for PSC stakeholders for smooth information sharing, reducing the bullwhip effect in the SC, and increasing visibility to respond to disruptions [11]. Moreover, I4.0 enables long-term value creation and leads to a more agile, intelligent, and customized pharmaceutical industry, allowing pharmaceutical firms to gain a competitive edge [12]. Real-time monitoring and assessment of the whole SC, from raw materials to the finished product, are facilitated by the incorporation of the Internet of Things (IoT), artificial intelligence (AI), and blockchain, which offer data-driven views for formulating sustainable decisions [13]. To better understand the data provided, pharmaceutical businesses must integrate data-driven analytical tools like simulation, predictive analysis, data analytics, and optimization. These tools facilitate value creation for sustainable PSC management to obtain competitive advantages in sustainable practices [14]. Therefore, stakeholders must be aware of the latest technologies to ensure they can all function together to create a trustworthy data-driven value chain throughout the pharmaceutical industry [15]. Moreover, employees must be trained, and a culture transformation must occur to effectively adopt this awareness of the goals for an efficient transition to I4.0 [16].

Several studies have been conducted to integrate I4.0 into the pharmaceutical manufacturing industry. For instance, Arden et al. [17] initially identified various known or underlying risks related to specific new techniques and inconsistencies with current strategies for legislative compliance in order to create regulatory structures that promote I4.0 in the pharmaceutical business. However, the study hasn’t mentioned how the regulatory scheme incentivizes investment in the pharmaceutical industry. Chen et al. [18] provided a comprehensive assessment of the vital elements of Pharma 4.0 concerning the regulatory driving factors for smart manufacturing, difficulties, and potential future advances. However, no quantitative or qualitative analysis was used to prioritize or develop a relationship-building framework to evaluate those vital elements. Reinhardt et al. [19] recognized and discussed the challenges through a survey regarding the holistic integration of I4.0 into the organizational culture of the pharmaceutical business. However, this research has advised the regulatory body, experts, and policymakers to work together but has not provided any action plans or strategies from the decision-makers’ perspective to incorporate I4.0 into the PSC in emerging business circumstances. Therefore, it is apparent that there exists a significant research gap in the literature on the pharmaceutical industry, particularly from the perspective of an emerging country like Bangladesh, to carry out an in-depth analysis of the critical success factors (CSFs) that significantly influence the I4.0 deployment to develop a sustainable SC. This gap in the literature is worth exploring to facilitate sustainable development in the pharmaceutical industry. This research, thereby, aims to address this gap by answering the following significant research questions (RQs):

1. RQ1: What significant CSFs influence I4.0 deployment in the pharmaceutical industry in an emerging economy?
2. RQ2: How can these CSFs be investigated hierarchically from a sustainable PSC perspective?
3. RQ3: What values do these CSFs provide to the pharmaceutical SC towards achieving the SDGs?
4. To address these RQs, the subsequent research objectives (ROs) need to be achieved:
5. RO1: To determine the significant CSFs of I4.0 implementation toward sustainable development in the PSC.
6. RO2: To analyze and prioritize the identified CSFs.
7. RO3: To equip SC managers, practitioners, and policymakers with important insights and knowledge that will help them effectively implement I4.0 throughout the PSC and meet SDGs.

A Bayesian Best Worst Method (BWM) framework has been proposed to accomplish the ROs of this study. The Bayesian BWM is a robust technique for generating multi-criteria decisions and determining criteria weight factors [20]. In many real-world multi-criteria evaluation problems, where numerous criteria have the same effect on decision-making, information gaps might exist after aggregating the individual compared pairings [21]. In such cases, the Bayesian BWM entails selecting one best and one worst criterion from a collection of observed criteria and overcoming the information gap problem by functioning as a group within the overall cluster. There are significant advantages to using Bayesian BWM for CSFs prioritization over other distinct pairwise comparison-based MCDM methods, like the Analytic Network Process (ANP) and the Analytic Hierarchical Process (AHP). Bayesian BWM generates a more reliable expert opinion matrix and requires fewer pairwise comparisons than AHP and ANP [22]. Again, while the conventional linear BWM depends on the opinion of an individual expert to establish the weights of various criteria [23], Bayesian BWM employs a statistical estimation-based method to allow collective or group decision-making without information loss [24]. Thus, Bayesian BWM has overcome the limitations of classic linear BWM by combining feedback data from multiple experts to provide a set of optimal group weights for more accurate evaluation [25]. Moreover, Bayesian BWM decreases the potential of making a mistake while comparing pairs of criteria, increasing the dependability of the findings [26].

2. Methodology

2.2 Data collection

The study proposed a Bayesian BWM approach to examine the CSFs to incorporate I4.0 in PSC while promoting sustainability. The data collection and survey have been conducted in two phases. We initially developed a listing of the sixteen most important CSFs for I4.0 deployment in the pharmaceutical business in emerging nations after conducting an extensive analysis of the related studies available in the Google Scholar and Scopus databases, published from 2018 to 2022. The key phrases applied to explore the CSFs from the literature included the following: "CSFs for the sustainable supply chain in the pharmaceutical industry" OR "CSFs for Industry 4.0 implementation in the pharmaceutical sector" OR "critical factors to integrating industry 4.0 in the sustainable supply chain" OR "factors to achieve sustainability with industry 4.0 implementation in the pharmaceutical supply chain", and so on. The sixteen initially identified CSFs were then sent to the participating experts as a Google Forms survey to authenticate and cluster the CSFs for subsequent assessment. Here, a non-random sampling method called purposive sampling was employed to pick industry practitioners [55, 56] to take part in the survey, which is critical for gathering enough responses in a specific category [57, 58]. Purposive sampling involves a professional appraisal of the sample’s suitability that makes a persuasive case that a particular set of respondents provides a more trustworthy foundation for experimental survey findings than other sampling methods [59, 60]. Experts were chosen based on at least seven years of relevant employment experience in the pharmaceutical business, healthcare, or academia, enough pharmaceutical operation and research knowledge, a deep understanding of I4.0 technologies, PSC, etc. Most of the industry experts were SC professionals, practitioners in R&D, and technological consultants. Table 1 summarizes the characteristics of participating experts. The chosen respondents are actively involved in the pharmaceutical sector in some way, with extensive knowledge and experience in SCM, planning, logistics, marketing, research and development, information and technology, procurement, distribution, administration, and other related sectors. These standard selection criteria have been preserved to ensure that the expert’s opinion is more relevant while avoiding significant subjective bias [61, 62].

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Table 1. A summary of the experts’ profile.

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The survey questionnaire provided to the experts to verify the primarily identified CSFs, is provided in Appendix A of the S1 File. The expert’s answers were received in a ’yes’ and ’no’ format regarding the validation of the relevance of the corresponding CSF. After getting the feedback, the ’yes’ response was considered ’1’, and the ’no’ was considered ’0’. The arithmetic mean was then calculated for each CSFs, and the CSFs feedback mean score greater than 0.7 were selected for further evaluation.

The experts eliminated two CSFs ("Organizational culture" and "Technical Capabilities") and added one additional driver ("Dedicated and robust Research and Development (R&D) team") by considering the relevance to the study. The finally selected list of sixteen CSFs is provided in Table 2. Table A1 of Appendix A in the S1 File briefly describes the finalized CSFs.

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Table 2. Finalized CSFs list to implement I4.0 toward developing sustainable PSC.

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Following their identification, the CSFs were validated and classified into three major clusters- organization and government, technology and innovation, and information and networks. Twenty-five experts were invited to participate in the CSF validation and clustering survey, among which 16 responded (64% response rate). Table 3 outlines the main clusters and their corresponding CSFs.

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Table 3. The major clusters and their corresponding CSFs.

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The experts were contacted again for the BWM survey in the second phase. The "Best-to-Others" and "Others-to-Worst" matrixes were generated from this survey data. Sixteen experts from the CSF validation phase were invited again to participate in the Bayesian BWM analysis survey [26, 77], among which 12 responded (75% response rate). Both surveys were conducted online using Google Forms for the convenience of the experts. The methodological framework of the study is represented in Fig 1.

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Fig 1. A proposed framework for the Bayesian BWM approach.

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2.3 Bayesian Best Worst method (BWM)

The Best Worst Method (BWM) is highly effective in making pairwise comparisons because it significantly minimizes the level of inconsistency present in the comparison data [78, 79]. Bayesian BWM is an upgraded form of the BWM method, a group decision-making technique for integrating the opinions of several experts in a probabilistic manner. The procedure is stochastic since pairwise comparisons are performed using the multinomial distribution, and global weights are computed using the Dirichlet distribution [80]. The salient advantage of Bayesian BWM is that it reduces the inconstancy in the comparison data more than the conventional BWM. In recent years, the Bayesian BWM approach has been applied in studies performed in a variety of sectors, as shown in Table B1 of Appendix B in supplementary materials.

Let, a group of criteria, G = {G₁, G₂, … … …, G_s} that are evaluated by several m specialists. The Bayesian BWM is implemented using the subsequent steps [81]:

1. Selection of best and worst criterion: The best and worst criteria from the criteria set G. Each expert, m, is required to choose only one from the best and only one from the worst criteria. The experts just decide the best and worst criterion instead of making any pairwise comparisons in this step. The best criterion has the most importance, while the worst criterion has the lowest importance, according to the group of specialists m. However, different experts’ choices could be distinct from each other’s.
2. Developing the best to others pairwise comparison: Expert m creates a pairwise comparison matrix of the best criteria in the set of G and other criteria. Here, every expert chooses a number between one and nine to express their priority for presenting their choice of the best criteria over other criteria in G, as determined in step 1. Nine means that is much more important than the other criteria, while one implies that the criteria are equally important. The collection of pairwise comparisons of expert m results in the vector "Best-to-Others," which is denoted by as follows: (1) where indicates the preferred criteria of expert m’s for the best criteria over g_j ∈ G.
   The "Best-to-Others" vectors for every expert in this study’s clusters and sub-clusters have been shown in Table C1-C4, Appendix C of S1 File.
3. Forming the others to worst pairwise comparison: Similarly, m experts create a pairwise comparison matrix among the worst and other criteria for the set of G. The collection of pairwise comparisons of expert m results in the vector "Others-to-Worst," which is represented by as follows. (2) Where, denotes the preference of an expert m for g_j ∈ G over the worst criteria .
   All experts’ "Others-to-Worst" vectors for every cluster and their corresponding sub-cluster in this research have been shown in Table D1-D4, Appendix D of S1 File.
4. Computation of aggregated weight: The aggregated weights of all m experts and the individual expert’s weight (y^m), are calculated with the following derived Bayesian BWM probabilistic technique, where m = 1, 2, ….. M; for each of the experts. (3) (4) (5) (6) (7)

Where Dir is a Dirichlet distribution, multinomial is a multinomial distribution, and gamma (0.1, 0.1) represents gamma distribution with form parameters of 0.1. The probabilistic model is specified by Eq (7) without a closed-form solution.

Once the issue has been resolved using the Bayesian BWM, S samples are generated using the posterior distributions of the aggregated weight (y*). Depending on the aggregated weight, it is possible to assess the importance of each criterion and the extent to which one criterion is preferred over another when considering all experts’ opinions.

2.3.1 Group decision-making.

In this study, the expert’s opinions are processed using a group decision-making method (Munim et al., 2022 [81]). This group decision-making method utilizes a joint probability distribution approach. Here, the m^th decision makers (DMs), m = 1, ….., M, evaluate the set of criteria G = {G₁, G₂, … … …, G_s} by providing a vector and . Let the total of all vectors in the base be indicated as superscript ^1:M turning the best criteria as and worst criteria . The overall optimal weight is y^agg. The y^agg estimation is related to some secondary variables. The optimal weight of m DMs is computed by y^m, m = 1, ….., M. Thus, the Bayesian-BWM model computes y^agg and y^1:M simultaneously before doing any statistical calculation. Given the provided data, it is necessary to write the joint probability distribution of all random variables. While doing group decision-making in BWM, and are given, both y^agg and y^1:M need to be calculated accordingly. Therefore, the joint probability can be formulated as (8)

The individual variable probability can be computed using the probability rule by following Eq 9 when Eq 8 calculates the probability.

(9)

Here, u and v are two arbitrary random variables.

3. Results

This section presents the rankings of CSFs obtained using the Bayesian BWM approach. For every cluster, a weighted directional graph was generated with the vertices representing CSFs and corresponding weight, which was determined by the average of the aggregated weight (y*). Every line denotes M is more important than N, owing a confidence level of ’p’. In essence, the graph’s overall structure encompasses the hierarchy of a set of criteria in terms of credal ranking, with each line symbolizing a unique credal ordering.

Fig 2 shows the main three clusters with their credal ranking of technology and innovation-related cluster (0.3660), organization and government-related cluster (0.3227), and information and network-related cluster (0.3113). The technology and innovation-related cluster is the most significant as it is comparatively weighted higher than the information and network-related and technology and innovation-related clusters. Therefore, management must pay more attention to the CSFs relating to the technology and innovation-related cluster. However, organization and government-related and information and network-related clusters weigh close to each other. The technology and innovation-related cluster was revealed to be more important than the organization and government-related cluster (with a 0.66 confidence score) and the information and network-related cluster (with 0.71 confidence). Likewise, organization and government-related cluster was more implemented than information and network related, with a 0.55 confidence score.

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Fig 2. Ranking of main clusters.

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Fig 3 illustrates the local credal hierarchy of CSFs within the organization and government-related cluster. The dedicated and robust Research and Development (R&D) team (0.2324) is found to be the most significant CSF, followed by auspicious government policies and legislations (0.2109), existent sustainable organizational strategies (0.1974), proper support from top management (0.1901), and Establishment of a robust technical support team (0.1692) respectively. Among the CSFs of the organizational and governmental-related cluster, dedicated and robust Research and Development (R&D) team weighed higher. Therefore, a dedicated and robust Research and Development (R&D) team is required to set the strategies, action plan, and policies to adopt I4.0 for sustainability in PSC. Dedicated and robust Research and Development (R&D) is involved in all the organizational and governmental operation phases to achieve sustainability in the PSC. The pharmaceutical business may contribute to ensuring that necessary pharmaceuticals and treatments are accessible to people worldwide by investing in R&D, addressing probable risks, and supporting responsible and ethical activity in the SC. Moreover, government policy and regulations may be quite effective at encouraging and fostering sustainable practices in the pharmaceutical sector, eventually resulting in a more environmentally friendly and resilient SC.

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Fig 3. Ranking of organization and government-related CSFs.

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Fig 4 represents the local credal hierarchy of the CSFs within the technology and innovation-related cluster. Sufficient investment in technological advancement (0.2170) is found to be the most significant CSF, followed by sourcing and application of smart and advanced equipment (0.2033), training programs for employee skill advancement (0.1731), incorporation of emerging sustainable technologies (0.1593), increased automation in material handling and inventory management (0.1474), and decentralized management and flexible operations (0.0998) respectively. For implementing I4.0 technologies, the pharmaceutical industry first needs sufficient investment in technological advancement in the emerging economy context. Once the pharmaceutical industry can get enough investment, they can go for various technologies and innovations. Therefore, sufficient investment in technological advancement is fundamental for achieving technological and innovation-related advancement. To promote innovation, growth, sustainability, and positive change and to maintain competitiveness in their SC operations, businesses must invest in technological advancement.

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Fig 4. Ranking of technology and innovation-related CSFs.

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Fig 5 depicts the local credal hierarchy of the CSFs regarding the information and network-related cluster. Maintaining proper collaborative communication among supply chain entities (0.2435) is identified to be the top three CSFs in information and network-related cluster, followed by Digitalized product monitoring and traceability (0.2123) and Establishment of the computer-integrated cloud manufacturing system (0.1967). Maintaining proper collaborative communication among supply chain entities (0.2435) weighs higher than all the CSFs in information and network-related cluster. I4.0 significantly emphasizes the fusion of digital and physical systems, necessitating intense coordination and communication amongst many SC entities. Moreover, maintaining proper collaborative communication among SC entities creates a competitive advantage by enhancing customer service quality, profit generation, resource utilization, and cost reduction by promoting communication among SC partners to increase sustainability.

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Fig 5. Ranking of information and network-related CSFs.

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Fig 6 illustrates the global rankings of all CSFs. The global hierarchy of all CSFs is calculated by multiplying weights at the cluster level with the local weights of every CSF. The study outcome indicates that sufficient investment in technological advancement (0.0794) is the most significant CSF, and decentralized management and flexible operations (0.0365) is the least significant CSF. The confidence score matrices for all clusters are provided in Tables E1-E4, Appendix E of the S1 File.

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Fig 6. Global ranking of all significant CSFs.

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4. Discussion

Fig 6 shows the proposed structured framework with Bayesian BWM is used to rank the CSFs for implementing I4.0 in the PSC Sustainability context of an emerging economy. “Sufficient investment in technological advancement (T1)” was ranked top and determined to have the most influential role in implementing I4.0 in the pharmaceutical industry toward sustainable SC development because sufficient investment enables stakeholders to increase tangible and intangible assets to strengthen the pharmaceutical industry’s infrastructures of I4.0 to remain competitive in the rapidly evolving digital economy. Moreover, the technological investment will assist PSC in applying sustainable development tools to achieve technological changes such as recycling, waste minimization, substituting non-sustainable materials, changing production processes, pollution control, and more efficient resource utilization. Therefore, pharmaceutical industries should emphasize acquiring substantial investment for technological advancements to ensure growth and facilitate the holistic transformation of the SC towards technical sustainability. Arden et al. [17] also discovered the relevance of investment for the technological advancement of I4.0 in the pharmaceutical industry on SC performance, resilience, agility, flexibility, and visibility.

“Digitalized product monitoring and traceability (I5)” is found to be the second most important CSF in implementing I4.0 in PSC sustainability. The pharmaceutical industry deals with various reagents, solvents, buffers, leaves, fungi, and other materials that produce various vital products: medicine, antibiotics, vitamins, critical drugs, and vaccines. In such settings, dealing with various medications in their short life cycle becomes difficult without effective technology management. I4.0 implementation involves adopting various recent digital technologies, such as blockchain, IoT, CPS, and robotics, which facilitate the monitoring and traceability of pharmaceutical products so that they are notcompromised during transportation and storage. Throughout the production and SC cycle, digital transformation of product monitoring and traceability can reshape whole sectors, redefine established business models, and improve consumer experiences, allowing industries to be sustainable in the long term. Previously, Leal et al. [82] also found the importance of adigitalized product monitoring system in overcoming PSC hurdles and advised using advanced quantitative analysis.

“Dedicated and robust Research and Development (R&D) team (O5)” is prioritized as the third most important CSF because it not only improves the health of the population but also reduces operating costs in the healthcare sector by providing technical expertise, guidance, and support in technology installation and configuration. Dedicated R&D explores various advanced technological options for I4.0 adoption to continue meeting pharmaceutical business needs and contributes to operational and SC sustainability. For example, efficient allocation and utilization of funds, tracking market intelligence, developing new ways to produce certain medicines to cut down production costs, upgrading lab and computing facilities with emerging technologies, and so on, can provide long-term sustainability through the SC. Previously, Sood et al. [83] also suggested that a dedicated, committed, and strong R&D team may play a vital role in PSC for producing excess revenue during the product life cycle and improving overall operational excellence by ensuring that technology performance is at its best.

“Sourcing and application of smart and advanced equipment (T3)” is ranked as the fourth most crucial factor. In the current highly competitive market, firms must focus increasingly on new sources of competitive application of data-driven, intelligent, and advanced equipment for promoting sustainability and gaining an edge over existing competitors. In PSC, application and sourcing have replaced human visual inspection of packaging, caps, and vials; predictive equipment maintenance has reduced disturbances, hazards, and production downtime. Thus, intelligent, advanced equipment can increase production output, raise manufacturing safety, improve quality, increase agility, offer flexibility, and minimize waste, which results in a hyper-connected, well-controlled, digitized ecosystem in the pharmaceutical value chain. Saha et al. [73] have also emphasized the application of intelligent and advanced equipment for overcoming pharmaceutical logistics and manufacturing barriers.

Government policies and legislation also play a significant role in implementing I4.0 in the pharmaceutical industry in the emerging economy. The government can support I.40 implementation by making policies that support and help firms respond to the call for global technological changes and adopt new emerging technologies to improve systems efficiently and flexibly. Fiscal policies and regulations can transform the manufacturing systems of traditional factories into technologically advanced futuristic manufacturing facilities with enhanced efficiency, accountability, and social responsibility to achieve sustainability. As a result, “Auspicious government policies and legislation (O3)” ranked as the 5^th most significant CSF in this research. Sharma et al. [84] have also identified government legislation, policies, and regulatory bodies as important contributors that can facilitate the successful adoption of I4.0.

“Maintain proper collaborative communication among supply chain (I4)” is the sixth most important CSF, which can boost organizational performance through customer service enhancement, improved resource utilization, and cost reduction. It significantly affects operational performance and responsiveness in every phase of the PSC, which can significantly reduce the bullwhip effect. Collaborative communication encourages enhanced openness and information sharing, which promote efficient decision-making and improve operational performance. Improved information sharing contributes to increased interconnection, leading to higher responsiveness in removing learning barriers and promoting greater understanding and communication among SC partners to increase sustainability [85].

Among all the CSFs, “advanced database management systems (I2)” and “decentralized management and flexible operations (T6)” had the most negligible impact. However, these CSFs are still important since they can assist pharmaceutical industry managers in improving their information processing, data management, and operational performance to facilitate long-term industrial sustainability [86].

Many studies have been performed in recent days on I4.0 implementation in various industrial sectors. However, our study varies from previous studies on I4.0 deployment in various industries from diverse economic circumstances, and the study’s findings also differ in many ways from the previous studies. For instance, El Baz et al. [87] investigated how sustainability drivers can be included in adopting Industry 4.0 technologies. They concluded that management support is a key driver, whereas economic advantages are the preferred sustainability externalities. Mahdiraji et al. [88] evaluated the possible interventions for the unsettling of I4.0 technologies in the circular supply chains of the pharmaceutical sector. They discovered that digital technology, environmental rules, subsidies, and rewards would help them transition to a lean, resilient, agile, and sustainable supply chain. Liu et al. [21] identified and rated the obstacles to deploying blockchain technology for SC sustainability. They found inter-organizational, intra-organizational, technical, and external barriers to be the most crucial factor in blockchain technology adaption. Again, Moeuf et al. [89] highlighted crucial success elements of I4.0 in SMEs and found a lack of experience, short-term strategy attitude, and lack of training to be the most crucial factor in implementing I4.0. However, the findings of these studies are not 100 percent applicable to PSC. On the other hand, our study analyzes the CSFs to implement I4.0 in the pharmaceutical industry while facilitating the achievement of the three bottom lines of sustainability, which no other studies have done yet. The study findings suggest Sufficient investment in technological advancement (T1), Digitalized product monitoring and traceability (I5), and Dedicated and robust Research and Development (R&D) teams (O5) to be the three most important CSFs to implement I4.0. These findings are quite unique and hence can provide unique perspectives to the managers and policymakers of the pharmaceutical industry to implement I4.0 and promote sustainability.

5. Conclusions

The pharmaceutical sector is currently undergoing significant changes, driven by the need to innovate and reduce costs while maintaining compliance with regulatory requirements. Thus, I4.0 has become an increasingly important tool for pharmaceutical companies to streamline their SCs and improve their operations. The PSC has been exposed as a vulnerable sector by the recent COVID-19 pandemic, which has prompted a push for improved resilience, and spurred the extensive application of predictive analytics, digital inventory management, and blockchain technologies. Therefore, the emergence of I4.0 is crucial in promoting sustainability in the pharmaceutical industry by facilitating improved efficiency, transparency, and collaboration throughout the SC, leading to reduced waste, minimized environmental impact, and responsible production and sourcing practices. However, the drivers or CSFs of I4.0 implementation in the pharmaceutical industry have not yet been explored in an emerging economy context, exposing a research gap.

This research proposed a Bayesian BWM methodology to investigate and assess CSFs. First, the substantial CSFs were identified through a comprehensive literature search, which the area experts later validated. Then, using the experts’ feedback, Bayesian BWM, a novel group decision-making approach, was utilized to prioritize the identified CSFs systematically. The findings indicate that "Sufficient investment in technological advancement (T1)", " Digitalized product monitoring and traceability (I5)", and " Dedicated and robust Research and Development (R&D) team (O5)" are the top three ranked CSFs for I4.0 deployment in the pharmaceutical industry.

The study’s contribution can be assessed from various perspectives. The theoretical significance of this study lies in integrating the Bayes theorem with the BWM technique to analyze the CSFs for I4.0 implementations in the pharmaceutical industry, which has never been attempted before. Moreover, this approach can be replicated and adopted in other sectors beyond the pharmaceutical industry. The study’s findings will assist the managers and practitioners in efficiently transforming their production process into a computer-integrated manufacturing system to improve decision-making, productivity, and efficiency by focusing on the evaluated CSFs. Thus, it will facilitate the development of innovative ways to streamline manufacturing processes and enhance operational sustainability.

Like other studies, this research also comprises a few limitations that may be addressed in future research initiatives. The research concentrates particularly on the context of emerging economies, which may limit the applicability of the study’s findings globally. Therefore, future research needs to investigate other economic perspectives to provide a more comprehensive outlook. Moreover, the framework presented in this study was developed entirely based on data collected from industry experts working in the pharmaceutical industry. Nevertheless, some judgmental bias may have crept into the results, leading to outcomes that do not precisely reflect reality. One potential way to reduce the impact of opinion bias in future similar studies would be to improve expert diversity by expanding the pool of participant experts from various relevant sectors. Again, this research focused merely on the 16 most significant CSFs. New significant CSFs may emerge in the future, and this research may need to be extended to encompass these newly identified factors. Additionally, this study sought to prioritize CSFs only. To delve deeper, the interrelationships between these CSFs should be explored as well. A fuzzy or grey-based total interpretive structural modeling (TISM) approach can be integrated with future research for this purpose.

Supporting information

Acknowledgments

The authors would like to thank the experts who took their valuable time to provide feedback for this study.