SPAR-4 and sustainable operations: A meta-analytic breakthrough

2.1 Analytics

In current dynamic times, it is difficult to ignore the revolution set by both sustainability and business analytics. Business analytics is fundamentally about using data to create value. Data is no longer referred to as the “sludge of the information age,” but rather as “the new oil” (Acito & Khatri, 2014; Kumar et al., 2021a). Aligning strategy and desired behaviors with operations management, together with analytical activities and capabilities, is necessary to extract value from the data. According to a Boston Consulting Group (BCG) report on Industry 4.0, there are nine technologies that will be significant in bringing about transformation in the industrial environment: Data and Analytics, robotics, simulation, integration, IoT, cybersecurity, cloud, augmented reality, and additive manufacturing (Rüßmann et al., 2015).

The ability of a company to extract data, analyze it for business insights, and gain an edge over its rivals could determine its competitive edge (Oh et al., 2012; Wong, 2012). However, a clear operational requirement, fact-based decision-making culture, robust data infrastructure, necessary analytic tools, and qualified analytical staff within a suitable organizational structure are essential for business success (Watson, 2013; Kumar et al., 2015; Bangwal et al., 2022b).

Based on the extent to which analytics is utilized in a business, it can be categorized into four types: (1) exploratory analytics, which detects relationships among parameters and analyzes them for insights and discovery; (2) descriptive analytics, which considers historical information in an easily comprehensible manner and arranges the acquired information and presents it as visual aids such as graphs, charts, maps, etc. for data implications; (3) predictive analytics, which extrapolates what is anticipated to occur in the future by using technologies such as machine learning, neural networks, and time series analysis; and (4) prescriptive analytics, which directs decision-makers towards accomplishing an objective through optimization of solutions to existing challenges (Beer, 2018; Rajaraman, 2016; Delen & Demirkan, 2013; Bose, 2009).

Different kinds of capabilities—decision, analytical, and information—require a multitude of technologies (Cao et al., 2019; Ferraris et al., 2019). The term ‘decision capabilities’ describes instruments, including dashboards and reports, that facilitate the distribution of information needed to make decisions (Kowalczyk and Buxmann, 2014; Kumar & Kansara, 2018). These tools facilitate the communication necessary for decision making, in addition to the visual representation of insights (Kumar et al., 2014; Wang and Byrd, 2017). A portfolio of tools and techniques for analysis is referred to as an analytical capability (Sazu and Jahan, 2022). Descriptive, diagnostic, predictive, and prescriptive analytics are supported by these technologies, which also include traditional ad hoc queries, inferential statistics, predictive analytics, simulation, and optimization. Lastly, technologies that aid in the description, organization, integration, and sharing of data assets are referred to as information capabilities.

2.2 Analytics and Operations Management

In the last few years, research in operations management has observed a shift primarily owing to the expansion of data availability. Richer and more exhaustive data are becoming accessible (Mišić and Perakis, 2020) in different types of industries, including manufacturing, healthcare, and retail. Hazen et al. (2018) also proposed that research in the field of operations can be instrumental in solving analytics problems in the domain of operations and supply chain management. There have been significant contributions in various domains directly or indirectly associated with data analytics and thus it becomes imperative to also focus on ‘data integration’ in operational decision-making (Kumar et al., 2013; Feng & Shanthikumar, 2022; Gupta et al., 2024).

Considering the current business scenario to handle uncertainty in decision-making, firms must be adaptable and nimble. Operations have recently begun, including socially and ecologically responsible objectives, along with traditional performance objectives. Fortune 500 companies use green washing to leverage sustainable operations and strategies as a strategic advantage. Significant research is required to understand how social and environmental factors are integrated into various aspects of operational management (Gunasekaran & Subramanian, 2018). Araz et al. (2020) also suggest analytics for operational risk management. Managers and organizations need to use operations management-based decision tools to solve data-driven problems for effective decision-making and thus highlight the interdependence between analytics and operations management (Dutta et al., 2017; Kashav et al., 2022). It has also been argued that operational risk management is critical for any organization; not only are analytical tools evolving faster, but their effective application is crucial for the success of the organization (Araz et al., 2020; Arora et al., 2023).

2.3 Sustainability and operations

Lack of resources to meet the demands of production presses the need for the advancement of sustainable operations (Beltrán-Esteve and Picazo-Tadeo, 2017; Rajani et al., 2022). In the Brundtland Report (1987), the United Nations defined sustainable development as “meeting the needs of the present without compromising the ability of future generations to meet their own needs.” Therefore, every stakeholder must act responsibly to ensure the creation of policies and practices that support an economic model that prioritizes the production of environmentally clean, socially conscious, and financially successful goods (Dey et al., 2011).

Operations and supply chains are vulnerable to hazards such as inadequate resource planning, unavailability of suppliers, underutilization of plants, and labor shortages, which can lead to delays and interruptions (Vidal et al., 2024). Incorporating sustainability into operations will benefit not only manufacturers but also all stakeholders, resulting in reduced losses and wastage, enhanced performance, ultimately leading to enhanced profitability, coordination, and communication, and promoting the apportioning of resources as well as competencies across partners (Kumar et al., 2012; Arora et al., 2023).

Some initiatives to incorporate business sustainability into operations include cleaner production (Ikram et al., 2021; Zeng et al., 2010; Fijał, 2007), green lean, green lean six sigma, green manufacturing (Kumar, 2020; Gholami et al., 2021; Garza-Reyes, 2015; Chugani et al., 2017), green supply chain management (Tseng et al., 2019; Green, Zelbst, Meacham & Bhadauria, 2012) and circular economy (Morseletto, 2020; Barreiro-Gen & Lozano, 2020; Geissdoerfer et al., 2017; Bangwal et al., 2022a). Stakeholders must now develop a greater understanding and innovativeness to comprehend and incorporate sustainability into their business operations.

Kosa and Dhliwayo (2024) in their study emphasized on the term “"digitalization of social innovation” which clearly highlights the incorporation of technology in operations for enhanced sustainability. When applied to operations, sustainability has significant benefits for performance, including reduced expenses, improved product quality and delivery, increased volume flexibility, and overall flexibility (Magon et al., 2018). Green (2012), Liu and Huang (2012), and Yang (2010) emphasized the promotion of sustainability, as it also results in gaining competitive advantage. Collaboration between participants can enhance supply chain efficiency and contribute to environmental enhancement (Huang and Yao, 2021).

3.1 Phase I - SLR (systematic literature review) using SPAR-4

Thomé et al. (2016) proposed a step-by-step approach to a systematic literature review. A systematic approach, while ensuring that no significant step is missed, also helps with easy comprehension and application. In this study, SPAR 4 (Paul et al., 2021; Kumar et al., 2015) was applied as a six-step systematic literature review process to identify influential contributions, current research interests, and present recommendations for future research in the domain under study ( Figure 1).

According to the SPAR-4 tool, the SLR can be divided into three main stages:

Phase I: Assemble Two sub-stages comprising this stage as well: (i) Identification: The researcher chooses the field in which to carry out the investigation. Research questions were developed once the literature was reviewed. The types of articles to be extracted are also indicated. (ii) Acquisition: Understanding and application of the extraction period, search criteria, and extraction source. The researcher will extract the n’ number of publications after acquisition.

Phase II: Arrange There are two substages in Stage II: (i) Organization: The methodology for organizing the data analysis is described here. (ii) Purification: It is crucial to eliminate cases that do not add value to this study. This involves a purification procedure, following which the final count of the included articles will be decided.

Phase III: Assess There are two sub-stages inside this stage as well: (i) Evaluation: the agenda proposal process and the analysis of literature data are highlighted here. (ii) Reporting the analysis using tables, figures, and other formats. Restrictions and available sources of assistance are also noted.

3.2 Phase II - Meta-analysis using Bibliometrics

3.2.1 Keywords

Operations management and analytics were the two main keywords selected for data extraction and to ensure that the domain was well covered. The search string used for this purpose was (“operations management” OR “operation management”) AND ((“data analytics” OR “analytics”) OR (“sustainability” OR “sustainable operations” OR “ESG”)).

3.2.2 Initial data gathering

For the purpose of data acquisition, the Scopus database was considered, as it possesses a huge collection of data with numerous titles and is most popularly referred (Yong-Hak, 2013; Kumar et al., 2014). The keywords were searched in “title, abstract, keywords” of the scopus database in Aug2024. The period considered for the search was 2000-2024. Only the English language was considered a popularly understood and read language. Initially 1072 articles were exported in the form. csv. However, after refining it there were 857 articles included in the final analysis. Books, book chapters, conference proceedings, unpublished articles, and magazine articles are excluded from the filtering process.

3.2.3 Integration of SPAR-4 LR protocol and Bibliometric perspective

The integration of SPAR-4 and bibliometric analysis is presented in Table 1, highlighting the stages, criteria, and related actions.

4.1 Publication trend analysis

The growth and progress of analytics and sustainability in operations management are demonstrated by the total number of publications and citations. A total of 857 cold chain papers gathered between 2000 and Aug 20, 2024 contained 33257 citations. The research fields’ publication and citation trends are displayed in Table 2 ( Figure 2 & Figure 3).

Figure 2. Year - wise number of publications.

Figure 3. Yearwise number of citations.

Figure 2 shows that the number of publications in the domain showed an increasing trend. Until 2012, the growth was not significant, but in 2012, there was a steady rise, and a steep rise further in 2020 and 2022. It is expected to increase further in the coming years with the growing utility of analytics and environmental concerns.

Figure 3 clearly shows a sturdy upsurge in citations, reflecting a steep increase in 2017 and 2019. This is an indication of the increasing interest of researchers in the area of analytics and sustainability, as applicable in the area of operation management. Therefore, the domain under study is gaining scholarly attention and influence, which is indicative of the expanding significance of analytics and sustainability in the operational management domain.

As shown in Table 3, ‘Production and Operations Management’ and ‘Journal of Cleaner Production’ have the highest citation counts, reflecting their significance in this domain.

As indicated by the table above, although there are few publications, the increasing number of citations reflects growing interest in the area.

The United States leads in citations for research on analytics and operations management, as illustrated in Figure 6 Other notable contributors include the UK and Brazil, reflecting global research trends.

As indicated by Table 4 and Figures 4 and 5, the USA holds the first rank in both the number of publications and citations. While India is at 4th place where the number of publications is considered, there are few citations (2634). One can interpret that compared to the number of publications, the number of citations is significant. This reflects the pressing need to conduct more research in the area, as not many studies have been conducted; however, there is a growing interest as well as the need for further investigation in the realm of analytics in operations.

Figure 4. Source map (citations) [Overlay visualization].

Figure 5. Contribution of top 5 countries (based on publications).

Figure 6. Contribution of top 5 countries (based on citations).

The top contributing authors in the domain are presented in Figure 7, with Kleindorfer et al. making the most significant impact in terms of citations.

Figure 7. Top 5 contributing authors (citations).

Table 5 highlights the most cited authors, with Kleindorfer et al. leading the field in sustainable operations management.

Figure 8 depicts the co-occurrence of keywords in the reviewed literature, with ‘sustainability’ and ‘operations management’ emerging as the most connected terms.

Figure 8. Keyword Co-occurrences (All keywords).

The network of author-specific keywords is shown in Figure 9, revealing the prominence of terms such as ‘big data,’ ‘machine learning,’ and ‘supply chain’ in recent research.

Figure 9. Keyword co-occurrences (author keywords).

The most frequently used keywords, such as ‘sustainability’ and ‘operations management,’ are summarized in Table 6.

The co-citation network illustrated in Figure 10 highlights the influential authors in the field, with Sarkis J. and Gunasekaran A. showing strong connections.

Figure 10. Co-citation authors network.

The co-citation analysis, as detailed in Table 7, reveals Sarkis J. as the most frequently cited author, indicating his influence in this field.

5.1 Implications for Policy makers, decision-makers and strategists

Businesses must implement a framework of strategic flexibility to enable them to compete in the current business environment and prepare for a contentious global arena. To deal with these uncertainties, organizations should embrace flexibility and utilize the benefits offered by analytics to predict future outcomes, thereby reducing the extent of uncertainty. The authors therefore recommend that businesses capitalize on the advantages offered by analytics and integrate them into their operations. Analytics can significantly improve visibility, transparency, and performance in operations and supply chains (Conboy et al., 2020; Zhu et al., 2018; Papadopoulos et al., 2017; Kumar et al., 2021b). Hence, firms can enhance their operational management-based sustainable business performance. Therefore, it is suggested that firms formulate policies and procedures for the enhanced integration of analytics and sustainability practices into their operations.