A taxonomy of critical factors towards sustainable operations and supply chain management 4.0 in developing countries

Abstract

Supply chain disruptions, intensified by black swan events such as the COVID-19 pandemic and the Russia-Ukraine war, have increased the interest in resilient supply chains, which can be achieved by adopting sustainable Industry 4.0 (I4.0) practices. However, the critical success factors (CSFs) for sustainable I4.0 in operations and supply chain management (S-OSCM4.0) are unclear, and there is a lack of a holistic and empirically validated taxonomy of CSFs from multiple stakeholders' perspectives to guide organizations in this transition. Moreover, developing countries face specific challenges that require prioritizing the proper set of CSFs for sustainable digitalization. Therefore, this paper aims to develop a CSFs-based taxonomy for S-OSCM4.0 to help organizations stay current in I4.0 adoption and integrate sustainability in OSCM. We first conducted a systematic literature review (SLR) of 131 papers using bibliometric and content analyses and synthesized the theoretical findings into an alpha taxonomy of CSFs following an inductive approach. Then, we employed a Delphi survey technique combining fuzzy logic to solicit experts' perceptions from a developing country to analyze and validate the taxonomy and determine the most pertinent CSFs, resulting in a beta taxonomy of CSFs for S-OSCM4.0. The developed taxonomy represents a pioneering managerial artefact that can guide sustainable development through an inclusive digital transformation with less environmental impact, contributing to decision-making in S-OSCM4.0, especially for operations in developing countries.

1 Introduction

1.1 Industry 4.0 and operations and supply chain management (OSCM) from a sustainability perspective

Supply chains have faced frequent disruptions in recent years, exacerbated by the COVID-19 pandemic and the Russia-Ukraine war (Micheli et al. 2021). These are examples of black swan events, which are unpredictable and have severe consequences for businesses and society. Therefore, supply chain resilience is essential, which entails agility and flexibility to adapt to crises (Van den Brink et al. 2022). One way to achieve supply chain resilience is through Industry 4.0 (I4.0), a concept that enables the application of digital technologies in operations and supply chain management (OSCM) (Caiado et al. 2021). OSCM integrates operations management (OM) and supply chain management (SCM) (Coughlan et al. 2016) and becomes more agile, efficient, and intelligent with I4.0 (de Paula Vidal et al. 2022). The non-understanding of the logic of OSCM puts many lives at risk, and digitalization in sustainability represents a reasonably open field (Kovács et al. 2020).

I4.0 enables the shift from mass production to mass customization through smart and flexible manufacturing (Koh et al. 2019). Another benefit of I4.0 is that it can foster sustainability in OSCM, which is increasingly demanded by various stakeholders in the manufacturing sector (Bonilla et al. 2018). According to Kovács et al. (2020), Sustainability in OSCM has become an increasingly relevant topic in empirical studies, capturing environmental and social impacts in the supply chain, which gives hope for the potential impact and opportunities offered by this research in developing economies and at the base of the pyramid. Sustainability encompasses environmental, social, and economic aspects that must be balanced in business decisions (Karmaker et al. 2023).

However, implementing sustainable practices in OSCM is challenging, especially for manufacturing companies. Thus, I4.0 methodologies can help these companies embed sustainability into their supply chains and improve global competitiveness (Yadav et al. 2020b). Sustainable I4.0 goes beyond environmental management and control initiatives and enhances supply chain productivity, resource efficiency, and process intelligence (Luthra and Mangla 2018). It also contributes to developing a circular economy, which aims to reduce waste and reuse resources (Trevisan and Formentini 2023) in a closed-loop system. Sustainable I4.0 can thus create value for both businesses and society by responsibly digitalizing OSCM processes (Srhir et al. 2023).

1.2 Research gaps and questions

Due to technological advances, OSCM is undergoing significant changes and improving operations and supply chains (Bueno et al. 2023). It is crucial to stay up-to-date with current research methods and theories, correlating with other areas of research (Melnyk et al. 2018). However, according to Koh et al. (2019) and Caiado et al. (2021), the literature is still lacking in studies that correlate I4.0 technological innovations with OSCM and even less research on what leads I4.0 towards sustainability (Stock et al. 2018). In addition, previous studies need to clearly show how I4.0 can contribute to sustainable OSCM (S-OSCM) (Bag et al. 2018).

Different critical factors should be identified and analyzed in this regard. Recent literature has dealt extensively with trends in smart manufacturing (Bag et al. 2018; de Sousa Jabbour et al. 2018a, b; Ghobakhloo 2020; Jabbour et al. 2020; Machado et al. 2020), but the future of the industry depends on some critical success factors (CSFs) that still need to be clarified (Panetto et al. 2019). CSFs are necessary to ensure an organization's or project's success and competitiveness (Lins et al. 2019). They are considered the main pillars that drive change (Julianelli et al. 2020) and can also optimize limited resources to maximize the company's results (de Sousa Jabbour et al. 2018a, b).

Therefore, there is a need to distinguish and examine the CSFs for integrating I4.0 into operations and supply chains (Bai and Sarkis 2023) to achieve S-OSCM, and limited discussion is still available regarding the definition of CSFs for applying I4.0 as a driving tool for adopting sustainable OSCM in developing economies (Luthra et al. 2020). While companies must recognize all the critical factors in I4.0 that can lead to smooth OSCM and achieve sustainability (Bag et al. 2018), it remains to be seen what the CSFs for the application of I4.0 in S-OSCM. Moreover, little is known about the current state of the art on digitally enabled sustainable OSCM (Kovács et al. 2020). Understanding the present knowledge scenario in this area is a relevant priority because I4.0 technologies can revolutionize OSCM (Jabbour et al. 2020).

The implementation of sustainable policies at all stages of OSCM, from raw material sourcing, and production, to distribution to the end customer, has been a growing object of study in academia (Beske et al. 2014) through the proposal of taxonomies and frameworks focused on S-OSCM (Julianelli et al. 2020; Bastas and Liyanage 2018). However, it is significant to note that the current era of the industry is rapidly shifting to digitalization; therefore, it has become difficult for organizations to adopt S-OSCM effectively using traditional and sustainable supply chain practices (Yadav et al. 2020a). In addition, the literature (Manavalan and Jayakrishna 2019) suggests that sustainability in OSCM leveraging the fourth industrial revolution has yet to be addressed and, therefore, is considered a gap. Studies in the literature addressing the use of digital technologies coming from I4.0 as a facilitating methodology in the application of sustainable OSCM is still incipient (Bag et al. 2018; Kamble et al. 2018; Yadav et al. 2020a, b). Therefore, considering the relevance of the subject and the existing gaps in the literature, this paper will answer the following research question:

RQ1: What is the state-of-the-art of CSFs for the S-OSCM4.0?

Defining the CSFs for S-OSCM4.0 is a complex task, mainly due to the changing environment that I4.0 and sustainability have faced; it is practically infeasible to prioritize all the possible enablers simultaneously (Kouhizadeh et al. 2021). Thus, a group decision-making (GDM) approach may be suitable to aid OSCM organizations (Kouhizadeh et al. 2021) towards sustainable I4.0 (Machado et al. 2021), as it can be used to design decision support tools (Quezada et al. 2017).

Taxonomies allow classifying objects and understanding and analyzing complex domains, simplifying complexity and highlighting the similarities and differences among objects (Bailey 1994). As a domain's vocabulary and as a collection of defined constructs, taxonomies can enhance an S-OSCM4.0's knowledge base and provide a foundation for future research approaches (Hevner et al. 2004). Although a well-designed method for developing taxonomies in OM could serve as a framework for creating new taxonomies that could organize complex areas and potentially generate new research directions, it has yet to receive much attention in the OM field.

In addition, most of the studies on sustainability and I4.0 are based on theoretical research and empirical studies, such as setting the CSFs that should be prioritized in developing nations settings are urgent (Kovács et al. 2020). After the coronavirus disease 2019 (COVID-19) pandemic period, the theme of sustainability 4.0 has gained even more notoriety and importance, beyond the need for a recognized taxonomy of CSFs that leverage the sustainable digital transformation (Torbacki 2021), considering the mutual influence of quantitative and qualitative elements (Chang et al. 2021). None of the studies in the literature established an empirically-validated taxonomy of CSFs to obtain the S-OSCM4.0.

Moreover, the disruptive solutions brought by I4.0 and sustainability usually mean organizational changes and new ways of handling processes that can be different in developed and developing countries (e.g. in technological growth and opportunities due to resource scarcity) (Raj et al. 2019) still necessary to have local, practical, multi-stakeholders, and holistic views (Gupta et al. 2021; Kazancoglu et al. 2021; Machado et al. 2021). In this vein, a GDM method can aid the development of a taxonomy to aid participants of S-OSCM 4.0 organizations to determine the best way to ensure the company’s sustainable digitalization aligned with the developing nations' pace (Bai et al. 2020; Luthra and Mangla 2018), supported by the best implementation strategy, through better decisions and efficient allocation of resources (Torbacki 2021). In this sense, through a study with professionals from a developing economy context such as Brazil, which is a good representative of developing economies (Machado et al. 2021), this study also seeks to answer the following question empirically:

RQ2: From the perspective of a developing country, what are the CSFs to integrate sustainability and I4.0 in OSCM?

1.3 Research aim and contributions

Within this context, this paper aims to build a CSFs-based taxonomy for S-OSCM4.0 for developing economies. To this end, this research first identified the CSFs to integrate sustainability and Industry 4.0 in OSCM to propose an alfa taxonomy of CSFs to S-OSCM4.0. Then, through an empirical study, this proposed taxonomy was assessed with key stakeholders from a developing country to refine and develop a beta taxonomy of CSFs to S-OSCM4.0. This managerial artefact could help organizations adopt the Triple Bottom Line concept, thus maintaining a balance between economic, environmental, and social justice outcomes, especially from a developing country perspective.

This paper has several contributions to the literature on I4.0 and sustainability in OSCM. First, it adopts a rigorous and well-defined approach based on a systematic literature review (SLR) to review, analyze, synthesize, and interpret the fragmented and diverse body of knowledge on this topic. Second, it provides a holistic and integrated view of how I4.0 digital technologies can enable and enhance sustainable OSCM practices, addressing the research-practice gaps identified by previous studies (e.g., de Sousa Jabbour et al. (2018a, b) and (Matos et al. 2020)). Third, it proposes novel taxonomies of CSFs for sustainable OSCM in I4.0 based on both theoretical and empirical evidence (Bashshur et al. 2011). The taxonomies cover multiple dimensions, including circular & sustainability, information & technology, and innovation. The literature on sustainable I4.0 adoption is scarce and mainly focuses on specific aspects of OSCM (e.g., manufacturing) or sustainability (e.g., environmental).

Moreover, few studies have empirically tested the proposed taxonomies in this context (Yadav et al. 2020b). Thus, this research fills an essential gap in the literature using the fuzzy group decision-making (GDM) approach to validate the taxonomies (Gupta et al. 2021) with a panel of experts from developing nations. Fourth, this research is timely and relevant in the post-pandemic era, where the value of sustainability and digitalization in OSCM has become more evident and urgent (Kovács et al. 2020). The proposed taxonomies can facilitate sustainable digital transformation by providing guidance and prioritization of CSFs for developing sustainable technological solutions in developing economies and at the base of the pyramid. This research also opens new avenues for future studies to apply other fuzzy GDM methods for S-OSCM4.0.

This paper is organized into five main sections. Section 1 introduces the context and motivation of this research, the research gaps and questions, and the research aim and contributions. Section 2 describes the research methodology that combines a systematic literature review and a fuzzy Delphi method to achieve a taxonomy of critical success factors for S-OSCM4.0. Section 3 presents the theoretical findings from the content analysis of the literature and proposes an alpha taxonomy of CSFs based on five dimensions. Section 4 reports the empirical findings from the fuzzy Delphi method with a panel of experts and develops a beta taxonomy of CSFs that contrasts with the alpha taxonomy and the existing literature. Section 5 concludes the paper with the main implications, limitations, and directions for future research.

2 Research methodology

This paper adopts a design science approach to develop a taxonomy to help researchers and practitioners understand and analyze a complex domain in information systems. This paper employs a multi-method approach (Bueno et al. 2023), which combines empirical and conceptual methods to create and validate the taxonomy dimensions and characteristics (Nickerson et al. 2013; Caiado et al. 2022). The multi-method approach allows the researcher to use multiple sources of evidence and perspectives to construct and refine the taxonomy (Lambert and Loiselle 2008; Caiado et al. 2021). The multi-method approach for taxonomy development consists of two stages, as shown in Fig. 1.

Fig. 1

Taxonomy development stages

The first stage is theoretical research, which covers a systematic review to propose an alpha taxonomy of CSFs to achieve sustainable digitalization based on S-OSCM4.0. The second stage is empirical and seeks the opinion of industry professionals to incorporate suggestions and evaluate the pertinence of these factors from a developing country perspective, identifying the necessary CSFs to develop a beta taxonomy (Dutra and Busch 2003). This is motivated by the fact that exploratory qualitative methods are used when it is desired to create a new area of knowledge by exploring the professionals' expertise (Tiwari and Khan 2020), as the one aimed in this research. In order to collect credible and factual empirical data through a specific approach in this study (Nyumba et al. 2018), the activities of this stage can be reached through the Delphi method (Murray et al. 1985; Fontoura et al. 2023). The sampled group must have characteristics of homogeneity and heterogeneity (Kitzinger 1994) and, therefore, be composed of industry professionals with different levels of experience in OSCM, sustainability and I4.0.

This beta taxonomy aims to classify different types of organizations and industries from developing countries regarding CSFs to sustainable Industry 4.0, with the meta-characteristic of creating value via successful integration between I4.0 and sustainability. The ending conditions for the beta taxonomy development were established according to objective and subjective approaches. The objective ending conditions were: mutually exclusive and collectively exhaustive features; no new dimension in the last iteration, and no other related process needed to be examined. The subjective ending conditions were: (i) concise; (ii) robust; (iii) comprehensive; (iv) extensible; and (v) explanatory (Nickerson et al. 2013). The final artefact is presented in Subsection 4.3.

2.1 Systematic literature review

This research adopts a systematic review as the theoretical data collection method. An SLR is a type of research synthesis that follows a structured and pre-defined process to identify, appraise, and synthesize the existing literature on a given topic and to generate new frameworks and perspectives based on the evidence (Munn et al. 2018). An SLR can be conducted for both new and well-established research areas. However, it requires ascertaining the need for such a review by checking the existence of a literature review (LR) on the topic of interest (Tranfield et al. 2003).

We follow the step-by-step approach proposed by Thomé et al. (2016), consistent with Tranfield et al. (2003) and other established guidelines for SLR. The SLR focuses on identifying research results and secondary review methods to identify the dimensions in an integrative synthesis of the existing literature reviews on I4.0, sustainability, and OSCM. We chose the integrative review as the research synthesis method because it is a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives are generated (Torraco 2005). We adopted a neutral perspective, aiming at an exhaustive review with clear criteria for selecting and excluding conceptually and methodologically organized articles. This approach provides detailed guidelines for all phases, from planning to reporting the SLR, and is structured in eight main steps, as shown in Fig. 2.

Fig. 2

Source: Adapted from Thomé et al. (2016)

Steps for systematic reviews in OM.

The first step of the systematic review is to formulate the problem, the research questions (RQs), and the aims of this review, which are delineated in Sect. 1 of this paper. The second step is to conduct the literature search, which comprises selecting the databases and identifying the critical keywords to ensure a comprehensive and unbiased review. Before conducting the SLR, we also performed a scoping review to identify the nature of a broad field of evidence and to ensure that our RQs are answerable by available and relevant evidence (Munn et al. 2018). The scoping review helped to develop and confirm a priori inclusion criteria (search keywords) for the SLR. The keywords are based on previous works in the literature, such as Kamble et al. (2018), Luthra and Mangla (2018), Yadav et al. (2020a), Bag et al. (2021), Machado et al. (2021), and Caiado et al. (2022).

The databases used in this research are Scopus and Web of Science (WoS) since they are complementary and cover most of the publications in the area of operations management (Mongeon and Paul-Hus 2016; Machado et al. 2021). They differ mainly in Natural Science and Engineering and Arts and Humanities, with a higher proportion of WoS-exclusive journals. The search keywords were derived from the concepts of I4.0, sustainability and OSCM and are shown in the review protocol (see Table 1).

Table 1 Review protocol—SLR

The database search was targeted at titles, abstracts, and keywords, with the filter of articles published by 19th May 2022. The exclusion criteria used (step 3) were: a) articles not published in peer-reviewed journals; b) articles written in a language other than English c) documents not available online; d) not fitting the scope of the research, which is about the integration between I4.0 and sustainability in OSCM. This subjective evaluation counted on the participation of 3 researchers seeking to provide further reliability to the research, as Thomé et al. (2016) recommended. As in Cunha et al. (2021), a trained team was constituted and involved in all the significant steps (e.g. data collection, analysis, synthesis, validation) of the process to ensure transparency, and the criteria of Methodi Ordinatio, which considers impact factor, year of publication and number of citations, was used to ensure the relevance and quality of the base used in the present study. The trained team comprised five researchers with PhD degrees in engineering, production, logistics, and sustainability. They all have more than ten years of experience in sustainability and more than seven years of experience in Industry 4.0, have an h-index (Scopus) more significant than ten and have published several papers in high-impact journals.

The final step in the article inclusion/exclusion process was to perform the snowballing search strategy in which the references of the articles found from the keywords (backward) and the articles that cited them (forward) are analyzed (Thomé et al. 2016). Figure 3 describes the process of SLR following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) protocol (Moher et al. 2009).

Fig. 3

Source: Adapted from Moher et al. (2009)

Flowchart of the research methodology.

Finally, after the article selection and exclusion process, 131 papers remained for analysis. The following research steps were supported by a matrix prepared in Microsoft Excel, with the rows representing the papers and the columns the information analyzed in categories. This matrix was used for data analysis, synthesis, and interpretation (steps 5 and 6).

The results were first analyzed using a bibliometric analysis, which considered the chronological distribution of the selected articles by the distribution of studies by year, principal authors, journals, and the thematic map. After bibliometric analysis, the content of each article was critically analyzed according to Mayring (2004), and the results were synthesized in a taxonomy (Torraco 2005), whose objective was to analyze the state-of-the-art of the main CSFs and their relationship to the implementation of S-OSCM4.0 (Bashshur et al. 2011). Thus, an alpha taxonomy following a deductive approach (derived from theory) (Bailey 1994) was proposed, which went through the steps of proposing, checking, and revising categories and data, involving the authors of this paper (Eisenhardt 1989; Mayring 2004). As in Caiado et al. (2022), to structure the alpha taxonomy, sets of concepts were heuristically grouped until an internal homogeneity and generality to obtain a classification of the categories, which usually contain several exclusive components. Then, the authors seek to understand the effects of specific compounds that constitute the various categories and identify exclusive sets of CSFs, structuring them at levels, dimensions (e.g., circular and sustainability), and the specific components of each of these dimensions that are combined to form a multidimensional taxonomy.

Finally, the reporting of the results (step 7) through the alfa taxonomy is covered in Sect. 3. The updating of this review (step 8) is suggested for future research.

2.2 Fuzzy delphi method

Then, RQ2 aims to investigate the CSFs to integrate sustainability and I4.0 in OSCM from the perspective of a developing country. After developing an alpha taxonomy a priori, based on the literature review, it was presented to critical stakeholders for refinement (Cunha et al. 2021). Beta taxonomy was built a posteriori, based on empirical discussions with the research team, which represents a configuration that meets the five attributes, following Nickerson et al. (2013); and combining the two levels of the taxonomy (dimensions and components/subcomponents) (Bashshur et al. 2011) to form a multidimensional taxonomy of S-OSCM4.0, which also considered the UN SDGs to operationalize sustainable development in OSCM. For beta taxonomy development, we followed an iterative method considering three rounds, two for Delphi until we achieved consensus regarding the CSFs in each dimension, and one final round of discussion to refine and propose a taxonomy-based framework, considering the 2030 Agenda.

This study conducted a Fuzzy Delphi Method (FDM) to group decisions to solve the fuzziness of shared understanding of expert opinions (Murray et al. 1985). FDM combines the Delphi method and fuzzy theory, which mainly focuses on uncertainties and linguistic variables to model the experience and judgment of a group of participants (Tsai et al. 2020). As in previous studies (Hsu et al. 2010), this research applied the triangular membership functions and the fuzzy theory to solving the group decision (Fontoura et al. 2023), as triangular fuzzy numbers are a common and effective way of modelling the imprecision and uncertainty in the scales used by experts (Cunha et al. 2021). According to Tsai et al. (2020), FDM is a simple and systematic method that is less complicated and less time-consuming (fewer rounds than those used in the Delphi method), in which the number of samples required (usually between 10 and 15) increases the reliability. Thus, in this study, an FDM with 11 practitioners determines the most pertinent CSFs to be considered to achieve S-OSCM4.0 in developing countries.

The process of FDM followed eight steps, which were adapted from Hsu et al. (2010) and Chang et al. (2011), as shown in Fig. 4.

Fig. 4

Source: Adapted from Hsu et al. (2010) and Chang et al. (2011)

Steps for the FDM.

• 1. Collect opinions of the decision group. In this step, k experts are invited to determine the importance of the evaluation criteria (score of each alternate factor’s significance) concerning various criteria, using the semantic description method, to allow the respondents to express their assessments and subjective judgments entirely through linguistic variables (Table 2). We chose a 7-point Likert scale because it is widely used in fuzzy set theory and provides more accurate results for respondent behaviour compared to the 5-point scale, as argued by Finstad (2010).

  Table 2 Linguistic variables for the importance weight of criteria

• 2. Calculate the aggregation weight of experts. The degrees of importance are defined according to the years of experience reported by each expert in three subjects (Sustainability, Industry 4.0, and OSCM). Then, the aggregation weight is calculated, by getting the sum of the percentage for each expert in each subject over the total years of experience (Sánchez-Lezama and Cavazos-Arroyo 2014), following the scale of Table 3.

  Table 3 The scale of the level of experience proposed

• 3. Set up fuzzy triangular numbers. Calculate the evaluation value of the triangular fuzzy number of each alternate factor given by experts, and find out the significance triangular fuzzy number of the alternate factor. Let fuzzy numbers ${\widetilde{r}}_{ij}^{k}$ be the importance of alternative I concerning criteria $j$ and ${\widetilde{w}}_{ij}^{k}$ be the $j$th criteria weight of the $k$th expert for $i=1, ..., m j=1, \dots , n, k=1, \dots , K.$

  $${\text{And}}\;{\widetilde{r}}_{ij}^{k}= \frac{1}{K}\left[{\widetilde{r}}_{ij}^{1} \oplus {\widetilde{r}}_{ij}^{2} \oplus \cdots \oplus {\widetilde{r}}_{ij}^{k}\right] {\widetilde{w}}_{ij}^{k}= \frac{1}{K}\left[{\widetilde{w}}_{ij}^{1} \oplus {\widetilde{w}}_{ij}^{2} \oplus \cdots \oplus {\widetilde{w}}_{ij}^{k}\right]$$

  (1)

  where the operation laws for two triangular fuzzy numbers ${\widetilde{m}}_{j}= \left({m}_{1}, {m}_{2}, {m}_{3}\right)$ and ${\widetilde{n}}_{j}= \left({n}_{1}, {n}_{2}, {n}_{3}\right)$ are as follows:

  $$\begin{array}{l}\widetilde{m}\oplus\widetilde{n}= \left({m}_{1}+{n}_{1},{m}_{2}+{n}_{2},{m}_{3}+{n}_{3} \right),\\\widetilde{m}\otimes\widetilde{n}= \left({m}_{1}{n}_{1}, {m}_{2}{n}_{2}, {m}_{3}{n}_{3}\right),\\a\otimes\widetilde{m}= \left({am}_{1},{am}_{2},{am}_{3}\right),\;a>0\end{array}$$

  (2)

• 4. Use the vertex method. For each expert, use the vertex method to compute the distance between the average ${\widetilde{r}}_{ij}$ and ${\widetilde{r}}_{ij}^{k}$ and the distance between the average ${\widetilde{w}}_{j}$ and ${\widetilde{w}}_{ij}^{k}$, $k=1, \dots , K.$ This method computes the distance between two fuzzy numbers ${\widetilde{m}}_{j}= \left({m}_{1}, {m}_{2}, {m}_{3}\right)$ and ${\widetilde{n}}_{j}= \left({n}_{1}, {n}_{2}, {n}_{3}\right)$ as follows:

  $$d\left(\widetilde{m},\widetilde{n}\right)= \sqrt{\frac{1}{3}\left[{\left({m}_{1}-{n}_{1}\right)}^{2}+{\left({m}_{2}-{n}_{2}\right)}^{2}+{\left({m}_{3}-{n}_{3}\right)}^{2}\right]}$$

  (3)

• 5. Consensus analysis. According to Cheng and Lin (2002), if the distance between the average and the expert’s evaluation data is less than the threshold value of 0.2, then all experts are considered to have achieved a consensus. Furthermore, among those m x n ratings of alternatives and n criteria weights, if the percentage of agreement (achieving a group consensus) is greater than 75% (Chang et al. 2011), then go to the following step; otherwise, the second round of Delphi is required.

• 6. Aggregation. Aggregate the fuzzy evaluations by

  $$\widetilde{A}=\left[\begin{array}{c}{\widetilde{A}}_{1}\\ \begin{array}{c}{\widetilde{A}}_{2}\\ \vdots \end{array}\\ {\widetilde{A}}_{m}\end{array}\right]{\text{where}}\;{\widetilde{A}}_{i}={\widetilde{r}}_{i1}\otimes {\widetilde{w}}_{1}\oplus {\widetilde{r}}_{i2}\otimes {\widetilde{w}}_{2}\oplus \cdots \oplus {\widetilde{r}}_{in}\otimes {\widetilde{w}}_{n}i=1,\cdots , m$$

  (4)

• 7. Defuzzification. In this step, a simple centre of gravity method was used to defuzzify the fuzzy weight ${\widetilde{A}}_{i}= \left({a}_{i1}, {a}_{i2}, {a}_{i3}\right)$ for each assessing variable (alternate option) to a definite value ${S}_{j}$; the following are obtained:

  $${S}_{j}= \frac{1}{3}\left({a}_{i1}+{a}_{i2}+{a}_{i3}\right), i=1,\cdots , m$$

  (5)

• 8. Screen evaluation indexes: Finally, proper factors can be screened out from numerous factors by setting the threshold $\alpha$. In this study, the threshold was the arithmetic mean of the ${S}_{j}$ of the category of factors. This step can improve the efficiency and quality of questionnaires through more objective evaluation factors that could be screened through the statistical results (Tsai et al. 2020). This process reduced subjectivity in determining the threshold value while keeping the experts’ input on the significance of each category (Priyadarshini et al. 2022). The relatively significant CSFs in each category were thus selected. The principle of screening is as follows:

  If ${S}_{j }\ge \alpha$, then-No. $j$ is the evaluation index (retain the variable)

  If ${S}_{j }< \alpha$, then delete No. $j$ factor (remove the variable)

3 Theoretical findings and taxonomy proposal

This section presents the alpha taxonomy of CSFs. The bibliometric analyses are shown in Appendix B.

3.1 Content analysis results

The CSFs were grouped according to the content analysis results (please see Subsection 2.1) into Circular & Sustainability, Information & Technology, Innovation, People & Culture, and Supply Chain Organization & Processes. Appendix A of the Supplementary File lists papers dealing with each critical factor. Figure 5 shows the result of the taxonomy developed for CSFs, which is discussed next.

Fig. 5

Alpha taxonomy of CSFs to S-OSCM4.0

3.1.1 Circular & sustainability

To create a genuinely sustainable OSCM and reap all the benefits of sustainability, companies must introduce change and innovation beyond the current technical systems by adopting a sustainable philosophy that corresponds to their objectives (May et al. 2016). Awareness of sustainability concepts among customers will increase their adoption rate (Yadav et al. 2020b). In addition, adopting industrial ecology initiatives helps implement circular economy practices for better sustainability (Yadav et al. 2020a). This philosophy involves several stages of innovation and corporate culture and must encompass management and employees, to provide a vision and guidance for the direction of companies (May et al. 2016). In a complementary way, the focus should be on renewable natural resources as alternatives to non-renewable ones and replace the “weak sustainability” model with the rational exploitation of natural resources (Olah et al. 2020). If good use is made of I4.0, it can be well integrated with the SDGs, resulting in efficiency and the effective use of non-renewable and renewable resources (Bonilla et al. 2018).

Interdisciplinary and holistic integration is also necessary for a perfect understanding of S-OSCM4.0. The study of sustainability must be approached from a holistic perspective, considering emerging areas, such as the circular economy (Martín-Gómez et al. 2019), as well as the combination of two or more study areas that aim to integrate your insights to build an understanding more comprehensive (interdisciplinarity) and address more complex issues (Dragone et al. 2020). Another relevant factor is the potential for sharing, also called the sharing economy, made possible by digital platforms that allow a straightforward combination of supply and demand (Pham et al. 2019). Sustainable business models (e.g., Airbnb and Uber) are successful because they share value and cost assets (Brenner 2018). In addition, a life cycle thinking approach is required, which means that for each product, all operations (design, production, transport, use, and end of life) and material and immaterial inputs and outputs related to its realization are considered (Garcia-Muiña et al. 2018). Thus, thinking about the life cycle of a product or service means thinking about all stages of your life “from the cradle to the grave” (Julianelli et al. 2020). Adopting this thinking offers a systemic view of the production processes, monitoring the consumption of resources, the production of waste and scrap, and emissions into the atmosphere at each stage of the process (Garcia-Muiña et al. 2018).

Another current trend is the sustainable OSCM approach based on implementing organizational strategies through circular processes (Daú et al. 2019). De Sousa Jabbour et al. (2018a, b) state that decision-making concerning sustainable operations management implies a connection between the circular economy (CE) approach and the principles of I4.0. The implementation of circular business models with I4.0 technologies allows the development of local business networks that contribute to generating local jobs (Mattos Nascimento et al. 2019). The use of sustainable manufacturing techniques, such as closed-cycle supply chains (Panetto et al. 2019) and their integration with I4.0 to create a sustainable supply chain seeks to produce a model for disseminating sustainable practices through social responsibility (Daú et al. 2019). Circular business models using I4.0 technologies can promote a culture of reuse and recycling and motivate the development of techniques for collecting and processing urban waste (Mattos Nascimento et al. 2019). For example, developing advanced production capabilities using 10R-based manufacturing approaches (refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, reuse, recycle and recover options) can provide opportunities for cleaner production in the CE-based business (Bag et al. 2021).

3.1.2 Information & technology

Bag et al. (2021) state that companies with a high degree of implementation of I4.0 led to a positive development of sustainable practices (e.g., 10R) that positively influenced sustainable development results. It is observed that the development of infrastructure for I4.0 readiness is considered an essential factor for organizations to improve sustainable development results and achieve their objectives in OSCM (Bag et al. 2021). In addition, adopting smart factory components, considered an indicator based on I4.0, ensures overall sustainability, including economic, environmental, and social concerns (Yadav et al. 2020b). Yadav et al. (2020a) also state that using intelligent factory components will increase the possibility of the success of S-OSCM. Networked equipment and computers require horizontal and vertical information connectivity in OSCM (Kusiak 2019).

Data-centred solutions (e.g., BDA-centric solutions) can help companies better implement sustainable supply chain practices and can help organizations gain sustainable competitive advantage through opportunities related to business intelligence, value creation, and business decisions (Raut et al. 2019). Data-based analyses can be used to optimize the use of resources or balance the perspectives of TBL, which is necessary for industrial symbiosis in an eco-industrial park (Tseng et al. 2018). However, it is essential to be aware that S-OSCM4.0 can be facilitated by developing other organizational capabilities (Jabbour et al. 2020). For example, it becomes vital for organizations to adopt and establish processes using BDA resources to achieve sustainable performance in OSCM (Bag et al. 2020). Management must consider data analysis a key element in establishing stakeholder cooperation and collaboration (Olah et al. 2020). It is necessary to build a truly data-driven culture within companies and among members of the supply chain (Jabbour et al. 2020). Therefore, another relevant factor is a consistent data flow; according to Bonilla et al. (2018), increased integration through the data flow promotes a more flexible structure and data exchange between all elements. Data flow processes represent a hierarchical structure in which data collection, integration, and analysis are connected (Kristoffersen et al. 2020). Managing the exponentially growing data is vital to support new requirements for day-to-day operations and requires access to reliable data (Ghobakhloo 2020).

Modular development allows the development of products based on established modules and is also considered an essential concept for sustainability (Gu et al. 2018). Configuring technology with a modular design can manage the complexity of digital systems, reduce entrapment to specific technologies, make it possible to deliver each solution step by step, as the training is in progress, can help with acceptance, and offers opportunities for continuous innovation (Sjödin et al. 2018). In addition, scalability is crucial to reducing cost and improving performance, being a critical factor in making the I4.0 system commendable and brilliant (Manavalan and Jayakrishna 2019). Thus, modular and scalable business models can be used to obtain financial gains in S-OSCM.

Full transparency, verifiability, greater confidence, and information security are also needed (Kouhizadeh et al. 2021). Information transparency is the key to any social responsibility reporting initiative that is considered a lever to improve internal and external transparency (May et al. 2016). Digital technologies such as Blockchain can track sustainability-related indicators (e.g., child labour or the source of resources) and make them available to stakeholders and decision-makers (Stock et al. 2018). In addition, high investments in data security specialists are expected, which is essential to protect intellectual property and prevent the loss of competitive advantages (Birkel et al. 2019). Blockchain technologies are used to distribute data and increase security in OSCM (Stock et al. 2018).

3.1.3 Innovation

Braccini and Margherita (2018) state that the transition to I4.0 was driven by a purposeful internal innovation process that proved to be sustainable according to sustainable principles that guided it. Companies with greater innovation capacity can leverage green product development to drive more excellent performance (Bag et al. 2020). Thus, the company's internal potential is most strongly influenced by the potential and commitment of its employees (Stachová et al. 2019).

In addition, open innovation with sustainability in manufacturing systems is significant and represents a growing issue (Shim et al. 2018). Open innovation is a process of interaction between the company and its environment to reach a broader spectrum of knowledge, skills, ideas, and solutions (Negny et al. 2017). Open innovation allows companies to identify and explore new technological capabilities developed inside and outside the company's boundaries (Brenner 2018), despite requiring investments in internal resources (Stachová et al. 2019).

Change management practices are essential in promoting the transition process towards completing eco-factories (May et al. 2016). Readiness for change can affect how working procedures will be achieved, and new work skills will be required (de Sousa Jabbour et al. 2018a, b). I4.0 requires adopting a new organizational structure, systems, and policies. Therefore, to achieve S-OSCM4.0, companies must manage changes strategically and proactively deal with workers' attitudes and resistance (Bag et al. 2018).

The dynamic capacity view (DCV) is an extension of the Resource-based theory (Gupta et al. 2020) that theorizes that a firm earns income by leveraging its unique resources, which in turn gives rise to the analysis of learning and knowledge management as the means to create new resources (Brenner 2018). Dynamic capabilities play a critical role in a company's sustainability in a complex and volatile environment (Bag et al. 2018). Dynamic innovation will play an essential role as it allows the development of new organizations and business models, leading to a greater degree of participation by representatives (Munoz-La Rivera et al. 2020). Brenner (2018) states that essentially, resources/skills and dynamic capabilities must be established internally and cannot be acquired externally, and as a result, detecting, taking advantage, and transforming (continuous renewal) are attributes that allow companies to (co) evolve the business environment.

The adoption of innovative business models is a facilitator for sustainable I4.0 through the inclusion of sustainability in business models or the development of purely digital business models (digitization, internet, and networking technology) (Strandhagen et al. 2017). Innovations of the new business model (e.g., Crowd-Sourced Innovation, Manufacturing as a Service, and Product-as-a-Service) can offer significant economic and social sustainability opportunities (Ghobakhloo 2020). New and sustainable business models must guarantee fair business benefits among all stakeholders in the value chain, and they must facilitate innovation, product development, financing, reliability, risk, intellectual property, and the protection of know-how in a network environment (Prause 2015). In addition, service design (solutions) is considered sustainable, and successful service design solutions must be connected to an intelligent business model (Prause 2015).

3.1.4 People & culture

Knowledge sharing and effective communication are fundamental aspects of industrial work and are critical in S-OSCM4.0. Information technology can support the generation of meta-knowledge from those who know what and can generate benefits such as improving team performance (Kaasinen et al. 2019). Information sharing will be the basis for the application of I4.0 technologies in the conduct of environmentally sustainable manufacturing decisions (de Sousa Jabbour et al. 2018a, b). Digital technologies such as virtual reality (VR) or augmented reality (AR) are valuable tools to support participatory design in which, through the sharing of knowledge, one learns from the other, supporting common understanding and collaboration (Kaasinen et al. 2019). In addition, increased productivity and reduced costs can be fully achieved by modern communication technologies (for example, mobile internet and industrial internet) (Ren et al. 2019).

Effective communication is crucial in developing a collaborative workplace and can improve a company's performance level and the relationship between different companies in a supply chain (de Sousa Jabbour et al. 2018a, b). The communication efficiency associated with transparency, surveillance, and control generates advantages for S-OSCM 4.0, such as minimizing downtime, waste, defect, and risk in the processes (Ghobakhloo 2020).

According to Stock and Seliger (2016), it is necessary to increase extrinsic motivation by implementing individual incentive schemes based on performance feedback mechanisms within the product's life cycle. Coordination incentive schemes (such as repurchase, quantity discounts, revenue sharing, price discounts, portfolio contracts, and combined mechanisms) suggest decentralized coordination that has been considered the basic principle in I4.0, such as the cyber-physical production systems (CPPS) (Ma et al. 2020). The incentive to offer users must be accurately calculated with compelling business models and sustainability consequences (Esmaeilian et al. 2020).

In addition, employee empowerment positively affects companies' sustainable performance (de Sousa Jabbour et al. 2018a, b). The paradigm is changing to adjust the systems to the human operator, with empowerment supported by adaptive human-automation interaction solutions that improve workflow and, thus, job satisfaction (Kaasinen et al. 2019). Empowerment is exemplified through a work environment based on managerial practices that allow employees to develop autonomy and responsibility to be innovative and thus develop proactive behaviour (de Sousa Jabbour et al. 2018a, b). Employees can also benefit from personal health technologies (personal monitoring devices and applications, such as wearable motion trackers, heart rate monitors, and health-related mobile apps) to gain strength-building feedback on their well-being concerning different jobs (Kaasinen et al. 2019). An open-minded culture is also vital and must be considered when adopting new organizational tools and techniques for sustainable management and transitioning to I4.0 and environmentally sustainable manufacturing (de Sousa Jabbour et al. 2018a, b). Likewise, experimentation can help develop and update ideas for future development and cooperation, and companies must be open to establishing niches as protected spaces for experimenting with new and extraordinary ideas (Hahn 2020).

Another critical factor in this digital age is education and training. Learning factories seek an action-oriented approach, with participants acquiring skills in a technological learning environment and thus integrating different teaching methods to bring the teaching/learning processes of real industrial problems (Kaasinen et al. 2019). Adopting I4.0 technologies will require adequate training and skill development for OSCM employees and partners and environmental training is also necessary to enable employees to adopt more advanced sustainable practices (de Sousa Jabbour et al. 2018a, b).

The digital age will require the development of talents, skills, and experience in addition to traditional technical skills, such as new technical, analytical, and interdisciplinary leadership, to deal with intelligence for decision-making in a connected world in real time (Dragone et al. 2020). Decision-making managers must have specific soft and technical skills; therefore, a human resources program is crucial in recruiting, educating, and maintaining management improvement (Ozkan-Ozen et al. 2020). Necessary soft skills include continuous learning, innovative and critical analytical thinking, and technical skill requirements include experience in programming, BDA, robotics, and maintenance of intelligent systems (Bag et al. 2020). Additionally, according to Muñoz-La Rivera et al. (2020), some innovative characteristics of the new professional are: adapter, searcher for multiple alternatives, experimenter, knowledge integrator, curious to do and learn, communicator, collaborative and integrative, creative, leader and team manager and focused on the user.

Adopting I4.0 principles to boost sustainability performance may require a transformational leadership style capable of inspiring followers to neglect their interests in favour of the organization's good (de Sousa Jabbour et al. 2018a, b). As a result, management's leadership style can influence the implementation of emerging trends such as S-OSCM4.0. In addition, top management commitment is responsible for providing organizational opportunities to integrate I4.0 technologies and environmentally sustainable manufacturing into existing production systems (de Sousa Jabbour et al. 2018a, b), so top management's commitment also plays a role vital to the current sustainable digital revolution in OSCM.

3.1.5 Supply chain organization & processes

Pham et al. (2019) I4.0 starts with customer requirements and integrates different systems, such as connected technologies. There must be customer and supplier integration for S-OSCM4.0. Companies should extend their digital technologies to supply chain processes and establish digital supply chain platforms with upstream suppliers, downstream customers, and other partners to facilitate supply chain relationships and integrate the decision-making process to achieve sustainability (Li et al. 2020).

A sustainable approach offers solutions (given by product integration with ancillary services) capable of satisfying customer needs but using fewer resources and with less environmental and socio-economic impact (Garcia-Muiña et al. 2018). Using digital technologies (e.g., BDA), customers can actively engage in green purchasing practices, cleaner production, eco-labelling, and eco-design feedback, improving customer satisfaction (Raut et al. 2019).

In the same way, suppliers can be considered the main input parameter for the effective execution of Sustainable OSCM. Therefore, suppliers must commit to sustainability parameters (Yadav et al. 2020b), for example, by implementing sustainable purchasing practices. The decreasing computing and communications costs have facilitated collaboration with suppliers and other participants in the market ecosystem, increasing the feasibility of close cooperation with customers or suppliers, using co-creation and open innovation approaches to create value (Brenner 2018). Organizations must collaborate with suppliers for technological integration, environmental criteria, and environmental auditing (Raut et al. 2019). Thus, it is critical for S-OSCM4.0 to maintain constant control of supplier-related activities to establish an uninterrupted sustainable supply chain system (Yadav et al. 2020a), as the supplier's active involvement improves data sharing and encourages companies to achieve more meaningful sustainability goals (Raut et al. 2019).

Integrating new unconventional partners such as Research Institutes & Universities, startups with sustainability solutions, environmental NGOs, or investors can also contribute to S-OSCM4.0 (Hahn 2020). Stakeholders (public and private, industrial and academic leaders) must work together to ensure that I4.0 sustainability opportunities are distributed to communities and worldwide in the most equal and fair way possible (Ghobakhloo 2020). In addition, by creating clear pollution control guidelines (Luthra et al. 2020), government support offers several sustainability support policies for organizations to develop a general sustainable environment (Yadav et al. 2020b). In addition, institutional and government pressure can positively stimulate workforce skills development (Bag et al. 2020).

Another critical factor is the establishment of collaborative networks. It is equally important to properly plan and cooperate with external stakeholders (Bag et al. 2020). Creating an open development platform involving key industries can allow collaboration, including developing data-based models (collaborative manufacturing networks) (Kusiak 2018). Digital infrastructures (of digitally enabled technology) that incorporate supply chain partners can detect and monitor changes in the external environment more efficiently, thus making it a necessary choice for companies to achieve success in sustainable OSCM (Li et al. 2020).

The structure of an organization is a critical factor in implementing digital technologies successfully. Its redesign must accompany a change in jobs and tasks and, therefore, a practical change and work structure (Murmura and Bravi 2017). A fundamental system rethinking is necessary to ensure that technology focuses on doing the right things and just doing something the right way (Esmaeilian et al. 2020). Thus, sustainable design strategies are necessary for a flexible organizational structure (Bag et al. 2020) (Machado et al. 2020). Stock et al. (2018) claim that decentralized corporate systems facilitate and promote partnerships between companies, and a reduction in the total amount of waste is expected, e.g., inefficient planning and resulting waste can be minimized. For example, the decentralized organization allows it to implement concepts such as Water 4.0, which aims at more efficient and flexible water management through digitization (Ozkan-Ozen et al. 2020).

Due to the current turbulent situation in the market and its demand, the organization's operations must be agile (Raut et al. 2019) and reliable through risk management systems to minimize the loss of time due to equipment interruptions and injuries (Pinzone et al. 2020), and have a greater capacity for updating, standardization, and adaptability of systems (Mattos Nascimento et al. 2019). In a complementary way, integrating I4.0 technologies with environmentally sustainable decisions would be implemented through improvement projects (de Sousa Jabbour et al. 2018a, b). According to Yadav et al. (2020a), adopting advanced quality improvement techniques is essential. It is crucial to continuously monitor the implementation of these practices to ensure that these systems achieve the desired sustainable benefits, for example, energy conservation (Leng et al. 2020). In this sense, tangible and quantitative terms are needed to build and increase reliability among the various stakeholders (Ozkan-Ozen et al. 2020). Adopting sustainability performance metrics (Caiado et al. 2017) ensures the tracking of activities and the alignment of sustainability (Yadav et al. 2020b).

Regarding strategy, it is critical to allocate the budget intelligently to different sections of the organization. Smart budget allocation using digital technologies (e.g., Internet of Things—IoT) makes it possible to follow global progress. It helps distribute the available financial resources effectively among the entire organizational structure, allowing sustainability (Yadav et al. 2020b). According to Ivascu (2020), despite sustainable development, which is a voluntary approach, improving organizational conditions, thus contributing to its competitiveness, interested parties are only interested in this concept as long as they obtain better financial results (increased profit). Finally, De Sousa Jabbour et al. (2018a, b) state that organizations can only be competitive with total alignment with information technology, and the appropriate selection of I4.0 technologies to assist in environmentally sustainable decisions can allow for better strategic alignment.

4 Empirical findings and discussion

This section first provides information on the experts involved in the FMD, and next, the empirical findings and the Beta taxonomy of CSFs to S-OSCM4.0 from the developing country perspective.

4.1 Information on the experts

All Delphi experts involved in the research have experience in OSCM, I4.0, and sustainability. Table 4 indicates that four of the eleven experts have more significant weight as they either have more than ten years of experience in OSCM or sustainability or more than five years of experience in Industry 4.0, which is a more recent topic and has little more than a decade. All experts also belong to managerial positions, and it is worth noting that each one is from a different company, with the frequency being from the following sectors: technology (2); ICT management (2); finance (2); healthcare (1); government (1); hospital (1); education (1), advertising (1).

Table 4 Score of experience of experts

As shown in Table 4, first, the degree of importance of the specialists was calculated based on the average of the experience scores. Then the aggregate weight was calculated to normalize each specialist's weight. This calculation was based on Sánchez-Lezama and Cavazos-Arroyo (2014). Then, the group consensus was estimated based on the frequency of agreement, and the distance between two fuzzy numbers was calculated by measuring the deviation between the mean fuzzy assessment data and the experts' assessment data (Vertex method).

4.2 Empirical findings

Tables 5 and Appendix C display the results obtained from the FMD. Six CSFs present a level of agreement below 75%, which were: "Data-centered solutions", "Modular design", "Information transparency", "Open innovation", "Change management", and "Governmental and institutional pressures". Based on Table 5, it is observed that 15 of the 34 CSFs (approximately 44%) presented a distance above 0.2 and therefore were considered acceptable, among them: "sharing economy", "circular processes", "customer and supplier integration", "Support of unconventional partners", and "adoption of advanced quality improvement techniques". It can be seen from Table 5 that the factors "Data-centered solutions", "Modular design", "Information transparency", "Open innovation", "Change management", and "Governmental and institutional pressures" were eliminated by the two FDM conditions (vertex method and percentage of agreement). During the assessment, some respondents also pointed out specific factors that could be part of more general ones, such as the “open innovation” that was removed but could be part of the "innovative business models" that were retained. Based on the previously proposed taxonomy of CSFs, the 34 factors were clustered into five categories: "Circular and sustainability" (CS), "Information and technology" (IT), "Innovation"(I), "People and culture" (PC), and "Supply chain organization and processes" (SOP). Those CSFs which showed an actual score higher than the threshold value of the category ${(S}_{j}>\alpha )$ after defuzzification were retained, and the remainder discarded, as follows:

• 1. CS: Of the seven variables, two were retained, "Focus on renewable natural resources”, having the highest score, with 0.0797, followed by “Sustainable philosophy”, with a score of 0.0776.

• 2. IT: Of the six variables retained, "Data security" had the highest score, with 0.0861, followed by “Consistent data flow”, with a score of 0.0708.

• 3. I: Of the six variables retained, "Internal innovation process" had the highest score, with 0.0772, followed by “Innovative business models”, with a score of 0.0768.

• 4. SOP: Of the eight variables retained, "Effective communication" had the highest score, with 0.0822, followed by "Top management commitment", with a score of 0.0820.

• 5. PC: Of the seven variables retained, "Strategic alignment had the highest score, with 0.0802, followed by “Collaborative networks” with a score of 0.0730.

Table 5 Expert consensus about CSFs to S-OSCM 4.0

The ten relatively essential factors resulting from the examination process are shown in Fig. 7. The selected variables from each latent variable (category) are given in Appendix C. FDM helps solve the uncertainty of an accurate expert distinction in examining the key CSFs during the procedure, ensuring a better quality of analysis.

The taxonomy development process is illustrated in Appendix D, showing the results from SLR, from the consensus of experts, and from the FDM about CSFs to S-OSCM 4.0. Figure 6 presents the Line plots of group Fuzzy Delphi in this context. The graphs are divided into (a) circular and sustainability; (b) information and technology; (c) innovation; (d) people and culture; (e) supply chain organization and processes. These categories are evaluated for a multidimensional evaluation of the CSFs to S-OSCM 4.0, considering that each category has defined its threshold according to the seminal papers in the literature and also to evaluate the relationship between Alpha and Beta taxonomies. The Beta taxonomy is from the accepted CSFs, with a threshold above the corresponding category and expert consensus.

Fig. 6

Line plots of group Fuzzy Delphi opinion scores (y-axis, Sj) for each latent variable (x-axis)

The results of the experts' evaluation are presented on the y-axis with opinion scores evaluated (y-axis, Sj) and on the x-axis for each latent variable (x-axis), considering this relationship between opinion scores (Sj) and latent variables (CSFs) presented in detail also through a matrix in Appendix C of this work, it is demonstrated in a Beta version consolidated both by the literature and by specialists in OSCM, I4.0 and sustainability so that sustainable organizations can review their guidelines and clear goals in all organization levels. In this way, the Beta taxonomy of CSFs of S-OSCM4.0 (Fig. 7) can be used at the strategic level, derived through working groups at the tactical level and detailed in practical actions at the operational level. The clarity of the methodological procedure allows adaptation to each management board of a sustainable company.

Fig. 7

Beta taxonomy of CSFs of S-OSCM4.0

In the circular and sustainability category, the CSF focus on renewable natural resources (CS2) was highly rated as the most important, priority and urgent, followed by sustainable philosophy (CS1), which was also accepted, with consolidated values above the threshold. Notably, interdisciplinary and holistic integration (CS3) and I4.0 readiness (CS7) achieved consolidated values close to the threshold but below. However, sharing economy (CS4) and life cycle thinking (CS5) received a deficient score that it can be verified that they obtained little perception of priority, also highlighting that the CSF circular processes (CS6) were below the threshold and in a way intermediate, denoting a point of attention in this analysis of the circular and sustainability category.

In the information and technology category, CSF data security was highly rated as urgent since data security is a crucial decision factor for more significant investment in this category due to the digital transformation. Then, the CSF consistent data Flow was accepted above the threshold by the expert group evaluation.

In the innovation category, a positive assessment was obtained and above the threshold of the CSFs internal innovation process (I1) and innovative business model (I5), denoting a solid prioritization of continuous improvement of internal processes for innovation management. However, the open innovation factor (I2) obtained a deficient score, denoting an aversion to this factor and change management (I3) which also received low adherence and importance by specialists.

Regarding people and culture, effective communication (PC2) and top management commitment (PC8) were better evaluated as fundamental CSFs for S-OSCM 4.0. It is worth noting that the category supply chain organization and processes, collaborative networks (SOP4) and strategic alignment (SOP7) were very well evaluated for resilience and agility.

4.3 Taxonomy of CSFs to S-OSCM4.0

This subsection presents the Beta taxonomy (Fig. 7) and how it relates to integrating sustainability and I4.0 in OSCM. This study empirically-validate a novel managerial artefact for S-OSCM4.0, emphasizing the significance of adopting a sustainable philosophy (or culture) that aligns with the company's objectives. Embracing sustainability concepts enhances customer adoption rates, and integrating industrial ecology initiatives fosters CE practices. CE-I4.0 nexus can help companies achieve operational excellence, making their OSCM more sustainable and resilient (Behl et al. 2023). The culture of sustainability requires innovation at various stages, involving all levels of the organization, to steer operations towards sustainability while making the best use of renewable natural resources. Collaborating with unconventional partners, such as research institutes, startups, NGOs, and investors, can foster a sustainable digital ecosystem. Embracing innovative business models, enabled by I4.0 technologies, opens avenues for economic and social sustainability. Moreover, information technology is pivotal in S-OSCM4.0, enabling data-driven decision-making and supply chain integration. Transparency, verifiability, and information security are vital for sustainable practices. Sustainable business practices should extend to suppliers and customers, necessitating collaborative networks and technological integration throughout the supply chain. People and culture also form the cornerstone of sustainable digital transformation. Finally, top management commitment and effective communication drive the successful implementation of I4.0 principles. To achieve successful integration of technology and sustainability, managers must implement organizational changes (e.g. agile methodologies) to break down silos and advance long-term value creation for stakeholders (Abiodun et al. 2023). In summary, this research provides a comprehensive taxonomy and CSFs for S-OSCM4.0, serving as a valuable managerial artefact.

This CSFs-based taxonomy has a holistic and interdisciplinary view. The CSFs (described in Table 6) should be appropriately managed to use the I4.0 technologies to build sustainable technology solutions, fully integrating sustainability and I4.0 in five focal areas of OSCM to obtain the SD benefits of this integration in line with SDGs. These five categories of critical factors are essential predictors of S-OSCM4.0 maturity, with a better understanding and performance of these CSFs in an organization, relevant factors can be manipulated to improve S-OSCM4.0 integration. Although the CSFs play a pivotal role in potentializing the S-OSCM4.0, it is expected that the CSFs be fully integrated into OSCM to ease the I4.0 and sustainability adoption in an integrated way; and the digital technologies must also be combined under socio-environmental demands and not only with an economic focus. It offers practical guidelines for practitioners, policymakers, and researchers, particularly in developing countries, where such initiatives may be scarce and challenging. The proposed taxonomy acts as a benchmark instrument to adopt and advance S-OSCM4.0, facilitating decision-making and strategic alignment for sustainable digitalization in organizations.

Table 6 CSFs to S-OSCM4.0 in developing countries

To guide an organization on the journey to S-OSCM4.0, managers could implement this taxonomy in three steps: (i) manage and integrate the CSFs into OSCM; (ii) build sustainable technological solutions combining multiple I4.0 technologies; and (iii) use CSFs and solutions to obtain SD in OSCM, as portrayed in Table 7.

Table 7 Example of journey towards S-OSCM 4.0

As presented in Table 7, the identified CSFs could have a multifaceted impact on various SDGs. CSFs like "Sustainable Philosophy" and "Focus on Renewable Natural Resources" could directly contribute to SDGs #9, #11, #12, and #17 by fostering sustainable practices, responsible resource management, and infrastructure development. For instance, sustainable sourcing practices contribute to responsible production and align with SDG #12. On the other side, SDGs #7 and #13 could benefit indirectly from CSFs that promote sustainable practices, energy efficiency, and climate action. For instance, energy-efficient manufacturing practices align with SDG #7 by reducing energy consumption. While indirectly influenced, CSFs could also contribute to foundational SDGs (SDGs #1 to #6) by fostering sustainability, innovation, and responsible resource management within OSCM. For example, sustainable practices in supply chains can improve the livelihoods of workers, contributing to SDG #1 (No poverty) and SDG #8 (Decent work and economic growth). In conclusion, the taxonomy's impact on these dimensions demonstrates its potential to significantly contribute to the Agenda 2030 and its associated SDGs by fostering sustainability, innovation, and responsible practices within OSCM. This holistic approach underscores the research's relevance in advancing sustainable development on a global scale.

5 Conclusions and implications

This research aims to develop a pioneering CSFs-based taxonomy to implement S-OSCM4.0, a novel and emerging area that integrates sustainability and Industry 4.0 in OSCM. The proposed taxonomy was built initially by investigating the current state-of-the-art research on S-OSCM4.0 by performing an SLR on selected publications through an appropriate and rigorous review methodology. The study analyzed 131 articles for this purpose. These SLR results indicate that S-OSCM4.0 is an emerging area with increased publications over the past few years. The taxonomy was refined to incorporate a developing country perspective by applying an FDM with 11 industry experts.

5.1 The novelty of the research

The novelty and contribution of this research lie in being the first to develop a CSFs-based taxonomy to implement S-OSCM4.0. This cutting-edge area has received increasing attention from academia and industry. Moreover, this research adopts a rigorous and comprehensive methodology combining an SLR and an FDM to construct and validate the taxonomy from theoretical and practical perspectives. Furthermore, this research provides a valuable managerial artefact that can guide practitioners, policymakers, and researchers in adopting and advancing S-OSCM4.0, especially in developing countries where such initiatives are scarce and challenging. Like other taxonomies, the proposal in this research is not intended to be a finished product; instead, it can offer subject matter experts in various aspects for their evaluation, testing, review, and verification (Bashshur et al. 2011).

In contrast to purely theoretical research-based approaches that employed a scoping review and frameworks like ERIC (Expert Recommendations for Implementing Change) such as Sacca et al. (2022), or statistical inference-based techniques like exploratory factor analysis, which can be utilized for factor ranking (Simpeh et al. 2023), or the use of Structural Equation Modeling, which can also be employed for validating relationships between factors, our study adopted a qualitative-quantitative approach for taxonomy development that accounts for uncertainty through fuzzy logic, grounded in Design Science Research (DSR). This choice of methodology brings several advantages to the study. Firstly, the use of DSR allows the development of practical and contextually relevant taxonomies, addressing the specific needs of a developing country. Secondly, the incorporation of fuzzy Delphi in our methodology facilitates the aggregation of diverse expert opinions, enabling a more comprehensive understanding of the subject matter. Furthermore, the fuzzy logic aspect acknowledges and embraces uncertainty, recognizing that real-world situations often involve imprecise or incomplete information, making our taxonomies more adaptable and robust. This approach, therefore, not only provides a holistic perspective but also enhances the practical applicability and resilience of the resulting taxonomies in addressing complex issues in the developing country context.

This research has important implications for both theory, practice and society in the domain of OSCM, involving sustainable OSCM and I4.0 (Kovács et al. 2020).

5.2 Theoretical implications

This research contributes to the literature on S-OSCM4.0 by providing a clear definition and conceptualization of this emerging area, which currently needs to be improved in the existing studies. This research also contributes to the literature on taxonomies by proposing a novel CSFs-based taxonomy to implement S-OSCM4.0 based on a rigorous and comprehensive methodology combining an SLR and an FDM. This research identifies 10 CSFs for S-OSCM4.0 derived from theoretical and practical sources, which can serve as a basis for further exploration and validation of these factors in different contexts. This research also provides a classification scheme for S-OSCM4.0 based on four dimensions: strategic orientation, operational orientation, technological orientation, and environmental orientation, which can help to clarify the scope and content of this area.

5.3 Managerial insights

This research provides a valuable managerial artefact that can guide practitioners, policymakers, and researchers in adopting and advancing S-OSCM4.0, especially in developing countries where such initiatives are scarce and challenging. The proposed taxonomy can be a benchmark instrument for professionals and companies to adopt S-OSCM4.0 through its CSFs, allowing it to be replicated and implemented. The proposed taxonomy can also act as a set of guidelines to be considered for decision-making involving S-OSCM4.0, serving as a guide for implementing sustainable digitalization in developing countries. The leaders will also acknowledge the critical role of CSFs in integrating I4.0 and sustainability in organizations.

5.4 Society insights

The research and taxonomy proposed holds significant implications for society. Firstly, in the realm of education, this research can serve as valuable content for teaching and academic programs, equipping future professionals with the knowledge and tools to address sustainability challenges while integrating I4.0 technologies. Secondly, in influencing public policy, the findings provide insights that can inform policymakers and government bodies on strategies for fostering sustainable practices in industries. Furthermore, from a research perspective, this work contributes to the body of knowledge by offering a comprehensive framework for the alignment of CSFs and the SDGs. Ultimately, the impact upon society extends to influencing public attitudes towards sustainable practices, contributing to a more responsible and efficient use of resources, and ultimately enhancing the overall quality of life by aligning economic growth with environmental and social well-being, in harmony with the SDGs.

5.5 Limitations and future research directions

Some limitations of this research lead can open avenues for future research. First, the selection criterion of considering only articles published in peer-reviewed journals may generate an additional bias since publications in these journals may favour only studies with positive results and may not contemplate the findings of the so-called "grey literature". Thus, other systematic reviews may go beyond these limitations to broaden the research findings. Moreover, the taxonomy can be presented to different cultures, nations, and continents beyond the ones obtained with the industry experts for FDM, which further highlights cultural aspects and implementation challenges that could be considered when adopting the ideas of this work.

The proposed taxonomy may also be used to conduct further studies that explore new measurement constructs for S-OSCM4.0, considering CSFs. More empirical studies may be performed to analyze the proposed relationships using structural equation modelling (Bag et al. 2020) or the application of Multi-Criteria Decision-Making approaches for computing the intensity of CSFs in OSCM (Yadav et al. 2020b). Finally, future research studies may also investigate the influence of various CSFs on the level of S-OSCM4.0 integration that the organizations achieve. This research work will help to identify the maturity of S-OSCM4.0. They conducted in-depth case studies to understand the 'soft side' of integrating I4.0 and sustainability in OSCM by qualitatively exploring CSFs to S-OSCM4.0. This understanding should prove valuable to promote and encourage the adoption of the various results from quantitative studies (de Sousa Jabbour et al. 2018a, b).