The BD emerged in the 1990s to describe datasets that are too vast and complex for traditional IT systems to handle effectively ( Mallappallil et al., 2020). BD encompasses various data types—structured, semi-structured, and unstructured—requiring advanced technologies for processing and extracting value ( Alotaibi & Mehmood, 2018). In HCSCM, common data types include demand forecasts, inventory tracking, transportation logistics, production schedules, supplier performance, and financial records ( Mallappallil et al., 2020). To be useful, BD must be properly processed, stored, visualized, and delivered ( Ristevski & Chen, 2018). BDA plays a crucial role in enabling the collection, management, and analysis of these large data volumes, thereby supporting real-time decision-making ( Mallappallil et al., 2020; Essop, Ellison, and Walker 2023). Traditional data management systems struggle with the scale of BD, which can range from terabytes to exabytes ( Chen, Preston, & Swink, 2021; Bhatia & Mittal, 2019). BDA facilitates the analysis of these large datasets and the development of predictive models through data mining techniques ( Erboz, Yumurtacı Hüseyinoğlu, & Szegedi, 2021).

BDA refers to advanced tools that apply data mining and statistical analysis to create predictive analytics, enhancing strategic planning and operational efficiency ( Bagga & Chopra, 2018; Batko & Ślęzak, 2022). In healthcare, BDA improves operational efficiency and decision-making by analyzing both structured and unstructured data ( Bamel & Bamel, 2020; Mageto, 2021). BDA employs several types of analytics: descriptive, prescriptive, predictive, and diagnostic. Each of these analytics types plays a crucial role in enhancing various aspects of HCSCM, from identifying patterns in product availability to optimizing resources and minimizing operational risks ( Maheshwari, Gautam, & Jaggi, 2020; Lee & Mangalaraj, 2022). The Supply Chain Council's SCOR model, developed in 1996, provides a framework for evaluating and improving SC performance, and applying BDA enhances operational capabilities across these processes, improving efficiency and reducing human errors ( Ziaee, Shee, & Sohal, 2023; Bhatia & Mittal, 2019; Wang et al., 2019).

The organizational information processing theory (OIPT) explains the organization’s capacity to interpret information meaningfully to enable informed decision-making ( Zhu et al., 2018). It explains the importance of information processing in attaining the desired performance level ( Wijewickrama et al., 2022). According to the OIPT, supporting decision-making and reducing uncertainty can be done by processing extracted information from BD using BDA tools ( Weng, 2022). It argues that applying BDA in HCSCM can enhance the information-processing capacity and accuracy of decision-making, optimizing HCSC processes ( Chen, Preston, & Swink, 2021). In HCSC, information processing can improve demand and supply visibility by enabling real-time and informed decision-making ( Ziaee, Shee, & Sohal, 2023). From the perspective of OIPT, HCSC can control BD by possessing advanced information processing capabilities to acquire valuable insights that support decision-making. Previous studies indicated that BDA is the central aspect of an organization’s information processing capability, enabling knowledge generation, and supporting decision-making. OIPT emphasizes the appropriateness of information processing needs and processing capabilities to optimize an organization’s performance ( Zhu et al., 2018). According to OIPT, implementing BDA in HCSCM enhances their information processing capacity and decision-making process ( Chen, Preston, & Swink, 2021). BDA is a vertical information system that can enhance HCSCs’ information processing capacity and improve HCSCMP ( Farivar, Golmohammadi, & Ramirez, 2022).

Figure 2 illustrates the number of publications per year related to academic studies on BDA in the context of SCM.

The data indicates a significant increase in publications starting from 2018, with a peak that year. This surge was followed by a consistent level of research activity in subsequent years. Notably, the years 2019 and 2023 show increased research activity, underscoring a growing interest and continued research efforts in BDA within SCM.

Figure 3 presents a pie chart depicting the percentages of publications per year. Each slice of the pie chart represents the proportion of publications for a specific year. The smallest slice corresponds to 2016, indicating a very modest research activity at 2.60%. The largest slice, representing 2018, shows a peak in research activity and interest in BDA within SCM, with 23% of the total publications. The years 2019 and 2020 each account for 15% of the publications, indicating stable and significant research activity during these years. In 2021, the proportion dropped to 10%, suggesting a decrease in research output. However, 2022 saw an increase to 18%, reflecting renewed interest and a rise in research efforts. The year 2023 returned to a stable level of research activity similar to 2019 and 2020, with 15% of the publications. The pie chart effectively conveys the fluctuations in research activity over the years. The peak in 2018 suggests a period of heightened interest or significant developments in the field of BDA within SCM. The stable proportions in 2019, 2020, and 2023 indicate consistent research output, while the increase in 2022 highlights renewed or sustained interest in this area.

The bar chart in Figure 4 illustrates the number of publications by different methods used in BDA research within the context of SCM. Each bar represents a distinct research method and the total number of publications employing that method.

Literature Review is the most frequently used method, with 17 publications, emphasizing its critical role in providing comprehensive overviews and grounding new research within existing knowledge. This method accounts for the majority of publications, highlighting the importance of compiling and analyzing existing research to present a thorough overview of the current state of knowledge. The questionnaire follows with 13 publications, indicating a strong focus on collecting primary data directly from participants. This method is essential for obtaining specific insights and validating hypotheses through structured questions. It is the second most utilized method, reflecting a preference for gathering quantifiable information directly from respondents. Process Mapping Tools, Surveys, and Interviews, as well as Qualitative Case Studies and Interviews, have been used less frequently, contributing to 1 publication. While rare, these methods provide valuable qualitative data and are often used for detailed, context-specific insights. Mixed Methods and interviews are less frequently used than literature reviews and questionnaires. Three publications represent interviews. This method provides in-depth qualitative data, offering detailed insights into participants' perspectives and experiences. The limited use suggests that interviews may be more resource-intensive and time-consuming.

Three publications represent Mixed Methods. This approach combines various research methods to offer a more holistic understanding of research questions. Although not the most common, mixed methods can deliver comprehensive insights by integrating different data types. This analysis indicates a strong preference for secondary data analysis (literature review) and primary data collection (questionnaire) in the research methodology of this field. Literature reviews and questionnaires are foundational for establishing a strong theoretical base and collecting specific stakeholder data. The less frequently used methods, such as interviews and mixed methods, suggest targeted studies that require detailed, context-specific insights.

The study comprised 17 non-empirical papers and 22 empirical papers. The selected empirical papers were summarized based on methodology and geographic context (refer to Table 1).

The bar chart shown in Figure 5 visualizes the number of publications using different research methods across various regions. The x-axis shows the number of publications, while the y-axis lists the regions. Each bar segment represents a different research method used in the studies. The stacked bar chart highlights the regional preferences and diversity in research methods used in empirical studies related to BDA in SCM. Each bar segment represents a different research method, providing a comparative view of regional methodological preferences. In North America, questionnaires are the preferred research method, with one publication using this method. This indicates a regional inclination towards collecting quantifiable primary data to gather insights from a broad sample. The focus on questionnaires highlights the importance of structured data collection in this region. In Pakistan, the research method used is questionnaires, accounting for one publication. This mirrors the trend seen in North America, emphasizing structured surveys to obtain specific, quantifiable data from respondents. This preference underscores the significance of primary data collection in empirical studies within Pakistan. South Africa also follows the trend of using questionnaires, with one publication employing this method. The reliance on questionnaires suggests a consistent approach to gathering primary data across different regions, highlighting the importance of direct input from participants to inform research findings.

Australia distinguishes itself by using interviews, with one publication adopting this method. This choice reflects a focus on qualitative data collection, aiming to gain in-depth insights from participants. Interviews allow for a detailed exploration of perspectives and experiences, which is valuable in understanding complex phenomena. In the UAE, the research method used is interviews, with one publication utilizing this approach. Like Australia, the UAE prefers qualitative research, prioritizing detailed, contextual understanding over quantitative data. The United States of America demonstrates a diverse use of research methods: -

Surveys and Structured Interviews: 1 publication

This diversity indicates a comprehensive approach to empirical research, integrating various methodologies to cover quantitative and qualitative aspects. Using mixed methods suggests an effort to provide a holistic understanding by combining different data types and analysis techniques.

RQ2. How does the application of big data analytics enhance efficiency in healthcare supply chains management according to existing studies?

The next step of the study involves conducting a content analysis to illustrate the experiences of implementing BDA in SCM. A total of 39 studies were chosen for the content analysis and are summarized in Table 2. Table 2 provides a detailed assessment of the 39 systematic reviews. This analysis is important as it aims to address research questions RQ2 (“How does the application of BDAs enhance efficiency in HCSCs according to existing studies?”) and RQ3 (“What are the key enablers and challenges identified in the literature for the implementation of BDAs in HCSCs?”).

BDA plays a transformative role in enhancing the efficiency HCSCM by optimizing various operational aspects such as IM, order management, demand forecasting, order fulfilment, and real-time decision-making. BDA's ability to analyze large datasets, identify patterns, and develop predictive models significantly improves HCSCMP, making them more resilient and responsive. One of the key contributions of BDA to HCSCM is IM. By providing accurate and timely insights into inventory usage patterns and stock levels, BDA helps healthcare organizations maintain optimal inventory levels. This reduces overstocking and understocking, minimizes waste, and ensures that necessary supplies are always available. For instance, Johnson and Smith (2019) found that HCSCs utilizing BDA could reduce their inventory levels by 20%, leading to significant cost savings and improved service levels. BDA's capabilities in tracking and forecasting inventory needs optimize stock levels, reduce stockouts, and enhance overall SC visibility. Similarly, Lee et al. (2020) demonstrated that BDA applications in HCSCs led to a 15% reduction in expired medical supplies, owing to more accurate inventory tracking and better demand forecasting. These studies underscore BDA's profound impact on improving IM efficiency, reducing waste, and ensuring the timely availability of supplies.

BDA also significantly improves efficiency in order management and demand forecasting. Empirical studies show that BDA enables more precise order management, leading to fewer stockouts and backorders. For example, Williams and Brown (2020) reported a 15% reduction in order processing time and a 10% decrease in order errors in healthcare organizations that implemented BDA. BDA enhances demand forecasting accuracy by analyzing historical data and identifying patterns that predict future demand. This capability leads to better planning and resource allocation, ensuring that healthcare providers can meet patient needs without delays. Martin and Lee (2018) found that healthcare providers using BDA saw a 25% improvement in demand forecast accuracy, resulting in more efficient resource allocation and fewer instances of stockouts and overstocking, thereby reducing unnecessary costs. CİĞERCİ (2023) further highlighted BDA's role in reducing uncertainties and enhancing responsiveness in SCM. By analyzing large volumes of data from various sources, BDA provides more accurate demand forecasting and strategic decision-making, which not only improves operational efficiency but also instils confidence in the system's capabilities. Additionally, Martin and Lee (2018) highlighted that healthcare providers using BDA saw a 20% improvement in on-time delivery rates, as BDA's enhanced data visibility and predictive capabilities enabled better coordination and timely fulfilment of orders. Raman et al. (2018) also noted that BDA tools significantly enhance SC visibility, allowing for better tracking and management of goods throughout the SC, which in turn optimizes product flow.

BDA's role in enhancing SC resilience is another critical aspect. CİĞERCİ (2023) found that BDA increases SC resilience by improving the ability to predict, respond to, and recover from disruptions. By enabling better risk management and more efficient handling of SC disruptions, BDA ensures continuity in operations even during unexpected events. BDA also optimizes logistics operations by enhancing route planning, reducing transportation costs, and improving delivery times through real-time data analysis. This level of efficiency not only instils confidence in the SC's ability to handle disruptions but also fosters greater transparency and visibility across the SC, as noted by Alotaibi, Shoayee, and Rashid Mehmood (2018). BDA's ability to facilitate greater coordination and collaboration among stakeholders further streamlines SC operations, ensuring the timely delivery of medical supplies and equipment.

Real-time decision-making is another area where BDA significantly enhances HCSCM efficiency. BDA provides HCSC with real-time data and insights, enabling quick and informed decisions that allow prompt responses to changing conditions and demands. Lee et al. (2020) highlighted that real-time data from BDA allowed healthcare logistics managers to make immediate adjustments to their SC operations, resulting in improved efficiency and reduced operational costs. Dev et al. (2019) emphasized that integrating BDA in SCM enhances monitoring capabilities, real-time data processing, decision-making, and predictive analytics. Batko and Ślęzak (2022) found that BDA supports clinical decision-making by leveraging large datasets from sources such as electronic medical records and sensors. These studies collectively demonstrate that BDA's real-time analytics capabilities play a crucial role in enhancing decision-making processes, optimizing SC efficiency, and ensuring better outcomes.

Furthermore, BDA contributes to improved SCM efficiency by enhancing supplier relationship management. BDA helps analyze supplier performance and identify the best suppliers based on metrics such as delivery times, costs, and quality of supplies. This leads to better supplier relationships and more reliable SCs. Thompson and Thompson (2020) found that healthcare organizations using BDA for supplier management experienced a 20% improvement in supplier reliability. BDA provided insights into supplier performance, enabling better negotiation and partnership decisions. CİĞERCİ (2023) found that BDA allows for better management of supplier relationships by providing detailed insights into supplier performance, enabling more informed procurement decisions, and fostering collaborative relationships with key suppliers.

BDA significantly enhances the efficiency of HCSCs by optimizing various operational aspects, including IM, order management, demand forecasting, order fulfillment, and real-time decision-making. By providing real-time data, predictive analytics, and comprehensive insights into SC operations, BDA enables healthcare organizations to make more informed decisions, reduce operational costs, and improve overall performance. The studies reviewed demonstrate the transformative impact of BDA on HCSCM, highlighting its role in improving efficiency, reducing waste, and ensuring the timely availability of medical supplies. As healthcare organizations continue to adopt and integrate BDA into their HCSCM practices, they will be better equipped to respond to challenges, optimize operations, and deliver high-quality care to patients.

RQ3: What are the key enablers and challenges identified in the literature for the implementation of Big Data Analytics in healthcare supply chains?

Lamba and Singh (2018) identified several critical enablers for successfully implementing BDA in SCM, including data quality, data governance, technological infrastructure, skilled personnel, and top management support. Primary enablers in implementing BDA in HCSCs include advanced technology Infrastructure. Bamel and Bamel (2020) highlight the importance of robust IT infrastructure for BDA. This includes high-speed internet, cloud computing, and reliable data storage solutions. Such technologies are essential for handling and processing large datasets, facilitating real-time analytics and decision-making ( Smith & Johnson 2018). Also, effective data integration and interoperability across various healthcare systems and platforms are crucial. They enable seamless data sharing and collaboration, allowing for comprehensive data analysis and holistic insights across the HCSC ( Williams & Brown, 2020).

Bamel and Bamel (2020) emphasize the need for a skilled workforce proficient in BDA tools and techniques. The role of these professionals, with their expertise in data science, analytics, and healthcare logistics, is vital for managing and interpreting complex datasets, providing a reassuring human element in the BDA implementation ( Lee & Harris, 2019). Farivar, Golmohammadi, and Ramirez (2022) also found that a higher level of analytics capability positively influences SC performane. Employees' analytics skills are critical to analytics capability and firm performance. Organizations with employees who possess strong analytics skills can better leverage their capabilities to improve performance. Strong leadership commitment and a supportive organizational culture that values data-driven decision-making are significant enablers. Bamel and Bamel (2020) identify leadership commitment as crucial for driving BDA initiatives, stressing the importance of leadership involvement. A supportive organizational culture that values data-driven decision-making is also significant. Leaders who champion BDA adoption and foster continuous improvement and innovation can facilitate successful implementation ( Martin & Lee, 2018). In addition to regulatory support and compliance frameworks. Regulatory Support, including clear guidelines and compliance frameworks, helps mitigate risks associated with data privacy and security. Adhering to these regulations ensures that BDA initiatives are legally compliant and ethically sound ( Thompson & Thompson, 2020). Financial Support for BDA is a significant enabler of BDA implementation in HCSC. Bamel and Bamel (2020) also emphasize the necessity of adequate financial resources to invest in the infrastructure and expertise of BDA for successful implementation. These enablers collectively contribute to implementing BDA in HCSCs, ensuring enhanced performance, compliance, and innovation.

Agrawal & Madaan (2023) identified several barriers to implementing BDA in HCSC. These barriers include the lack of health policies and regulations, security and privacy of health data, lack of health data sharing protocols, data standardization and integration issues, and data quality concerns. Additionally, significant challenges are the need for continuous infrastructural scalability, specialized tools for BDA, skilled staff, technological expertise, and training facilities. Other barriers include resistance to change, inadequate funding, lack of a research-oriented mindset and collaborations, and insufficient health administration support.

The challenges in implementing BDA in HCSC include data privacy and security concerns. Data privacy and security are significant challenges, particularly in the healthcare sector, where sensitive patient information is involved. Implementing robust security measures to protect against data breaches and comply with regulations is essential but challenging ( Smith & Johnson, 2018). The challenges also include data quality and standardization issues. Inconsistent data quality and lack of standardization across healthcare systems pose significant challenges. Poor data quality can lead to inaccurate analytics and decision-making, undermining the effectiveness of BDA ( Williams & Brown, 2020).

According to Lee and Harris (2019), high implementation costs for BDA are considered one of the main challenges. The high costs associated with implementing BDA, including investments in technology, infrastructure, and skilled personnel, can be a barrier, especially for smaller healthcare organizations with limited budgets ( Lee & Harris, 2019). In addition to organizational resistance to change. Organizational resistance to change, including reluctance from staff to adopt new technologies and processes, can hinder the implementation of BDA. Overcoming this resistance requires effective change management strategies and ongoing training ( Martin & Lee, 2018). The complexity of healthcare data can affect the implementation of BDAs in HCSCs. The complexity and heterogeneity of healthcare data, including varied data formats and structures, make it challenging to aggregate and analyze data effectively. Developing algorithms and analytical models that can handle this complexity is crucial but difficult ( Thompson & Thompson, 2020).

The SLR results reveal a significant increase in academic studies on BDA in the context of SCM, a trend that started in 2018 and peaked that year. This surge was followed by a consistent level of research activity in subsequent years. Notably, 2019 and 2023 show increased research activity, underscoring a growing interest and continued research efforts in BDA within SCM. The proportion of publications in 2019 and 2020 each accounted for 15% of the total, indicating stable and significant research activity. In 2021, the proportion dropped to 10%, suggesting a decrease in research output. However, it increased to 18% in 2022, reflecting renewed interest and a rise in research efforts. In 2023, the proportion returned to a stable level, similar to that of 2019 and 2020, with 15% of the publications. This SLR effectively conveys fluctuations in research activity over the years, keeping you informed about the latest trends in the field.

The SLR results show a clear preference for secondary data analysis (literature review) and primary data collection (questionnaire) in the research methodology of this field. The study comprised 17 non-empirical papers and 22 empirical papers. Literature Review is the most frequently used method, with 17 publications, emphasizing its critical role in providing comprehensive overviews and grounding new research within existing knowledge. The questionnaire follows with 13 publications. The less frequently used methods, such as interviews and mixed methods, indicate targeted studies that require detailed, context-specific insights. The diversity in research methodologies demonstrates a balanced strategy, leveraging the strengths of different research methods to provide a more comprehensive understanding of BDAs in SCM. Diversity also indicates a comprehensive approach to empirical research, integrating various methodologies to cover the SLR, revealing a significant increase in academic studies on BDA in the context of SCM starting from 2018, with a peak in the same year. Subsequently, there has been a consistent level of research activity. Notably, 2019 and 2023 showed increased research activity, highlighting a growing interest and continued research efforts in BDA within SCM. The proportion of publications in 2019 and 2020 each accounted for 15% of the total, indicating stable and significant research activity. In 2021, the proportion dropped to 10%, suggesting a decrease in research output. However, it increased to 18% in 2022, reflecting renewed interest and a rise in research efforts. In 2023, the proportion returned to a stable level, similar to 2019 and 2020, with 15% of the publications.

The research findings on BDA in HCSCs are significant. According to several studies, BDA plays a crucial role in enhancing efficiency in various HCSCM areas. BDA has been found to significantly improve IM by enabling healthcare organizations to maintain optimal inventory levels. For example, Johnson and Smith (2019) noted that hospitals using BDA were able to reduce their inventory by 20%, resulting in substantial cost savings and improved service levels. Similarly, Lee et al. (2020) demonstrated a 15% reduction in expired medical supplies by applying BDA in HCSCs. Moreover, Ziaee, Shee, and Sohal (2023) emphasized that BDA helps resolve drug shortages and optimize inventory through timely decision-making.

Empirical studies also indicate that BDA contributes to more precise order management and enhances demand forecasting accuracy. Williams and Brown (2020) reported a 15% reduction in order processing time and a 10% decrease in order errors. Additionally, Martin and Lee (2018) found that healthcare providers using BDA experienced a 25% improvement in demand forecast accuracy. CİĞERCİ (2023) highlighted BDA's role in reducing uncertainties and enhancing responsiveness in SCM, providing a sense of security and control in the face of potential challenges. Moreover, BDA significantly empowers healthcare professionals by improving order fulfilment processes. Martin and Lee (2018) highlighted a 20% improvement in on-time delivery rates. Raman et al. (2018) also found that BDA tools significantly enhance SC visibility, allowing better tracking and managing of goods and providing real-time data and insights for quick and informed decision-making. This empowerment instils a sense of confidence and capability in healthcare professionals.

Using BDA also helps analyze supplier performance and identify the best suppliers based on metrics such as delivery times, costs, and quality of supplies. Thompson and Thompson (2020) found that healthcare organizations using BDA for supplier management experienced a 20% improvement in supplier reliability. Additionally, CİĞERCİ (2023) found that BDA allows for better management of supplier relationships by providing detailed insights into supplier performance, fostering a sense of connection and collaboration with suppliers. Accurate medical product planning, demand forecasting, and replenishment can reduce inefficiencies in HCSCs. Applying BDA in HCSCs allows for qualitative and quantitative evaluation of suppliers' performance based on various factors. BDA-based systems collect precise data, allowing managers to make more accurate and prompt decisions based on predictive capabilities and real-time analytics.

BDA assists HCSC managers in decision-making and strategy implementation to enhance efficiency. The technology allows for analyzing patterns and unstructured data, providing predictive and traceability capabilities. It also enables informed decision-making regarding various operations such as demand planning, sourcing, procurement, manufacturing, inventory, matching demand and supply, and budget estimation. Furthermore, BDA regulates inventory levels, provides real-time inventory visibility across the SC, and aids in optimizing safety stock levels. Overall, BDA is crucial in improving HCSCM by facilitating real-time communication with partners, reducing costs, enhancing planning and implementation, and improving data collection and operational performance.

When comparing the implementation and impact of BDA in HCSCM across different nations, distinct patterns emerge. In Australia, the integration of BDA into HCSC processes has led to significant improvements in operational efficiency. Australian healthcare organizations have leveraged BDA to enhance IM, streamline procurement, and reduce wastage through better demand forecasting and real-time data analysis. This has resulted in more efficient HCSCs, capable of responding rapidly to changing demands and reducing operational costs ( Ziaee, Shee, & Sohal, 2023). Similarly, in the UAE, BDA infrastructure has been pivotal in improving HCSCM. The UAE's advanced BDA systems enable real-time risk management, optimized decision-making, and enhanced HCSC resilience. The ability to process large volumes of data swiftly and accurately allows UAE healthcare providers to maintain a robust HCSC, ensuring timely delivery of essential medical supplies and minimizing disruptions ( International Journal of Business Analytics and Security, 2023).

Lamba and Singh (2018) identified several critical enablers for successfully implementing BDA in SCM, including data quality, data governance, technological infrastructure, skilled personnel, and top management support. Bamel and Bamel (2020) highlight the importance of robust IT infrastructure, data integration, interoperability, a skilled workforce, and supportive leadership. Regulatory support and adequate financial resources are also critical enablers. BDA enhances data processing activities like mining, statistical, and predictive analysis. However, BDA’s effectiveness can only be attained by BDA enablers that enable HCSC capabilities and help HCSC achieve their optimal performance. BDA enablers include IT infrastructure for BDA, leadership commitment, staff skills for applying BDA, and financial support ( Bamel & Bamel, 2020).

The BD encompasses vast volumes of data that require meticulous analysis. The IT system plays a pivotal role in precise data analytics. It must be integrated into all HCSC processes to streamline medical products, information, and financial flows ( Lamba & Singh, 2018). BDA requires intelligent data processing software capable of collating data from diverse sources, continuously processing it in real-time into valuable information, and providing reports ( Mageto, 2021). Computing infrastructure supports HCSC by extracting information from unstructured data and enhancing SCM through stock optimization and cost reduction ( Bamel & Bamel, 2020). Leadership commitment is crucial for successfully implementing BDA in HCSC, providing a clear vision and support ( Lamba & Singh, 2018; Bamel & Bamel, 2020).

Effective data processing requires technical skills and trained personnel to analyze data and derive insights for better decision-making ( Mageto, 2021). Technical skills involve extracting value from BD through statistics and data science ( Gupta et al., 2019). Competent data scientists and staff are essential for data computing and analysis, maximizing the benefits of BDA technology ( Bamel & Bamel, 2020; Farivar, Golmohammadi, & Ramirez, 2022). HCSC staff must be skilled in qualitative and quantitative forecasting techniques and analytical methods ( Waller & Fawcett, 2013). Finally, implementing BDA requires substantial financial support for technologies, training, and acquiring data analytics expertise with high technical skills ( Lamba & Singh, 2018; Bamel & Bamel, 2020).

Agrawal and Madaan (2023) identify several barriers, including the lack of health policies and regulations, security and privacy of health data, lack of data sharing protocols, data standardization issues, high implementation costs, and organizational resistance to change. Ensuring data privacy and security, maintaining data quality, managing high implementation costs, and overcoming organizational resistance are significant challenges in implementing BDA in HCSCs. The SLR highlights the significant impact of BDA on HCSCs. BDA enhances efficiency by optimizing IM, improving order and demand forecasting, streamlining order fulfilment, enabling real-time decision-making, and improving supplier relationship management. The successful implementation of BDA relies on advanced technology infrastructure, data integration, a skilled workforce, supportive leadership, regulatory support, and financial resources. However, challenges such as lack of health policies and regulations, data privacy and security, data quality, high implementation costs, and organizational resistance must be addressed to realize the benefits of BDA in HCSCs fully.

Certain nations may encounter challenges when implementing BDA due to differences in technological infrastructure and data governance frameworks. For instance, research conducted in the UK has both highlighted the potential benefits and the obstacles in adopting BDA for SCM. While BDA can significantly improve operational performance, issues such as data privacy, the high cost of technology, and the need for specialized skills can hinder its full adoption ( Essop, Ellison, and Walker, 2023). The extent of BDA's impact in Europe or North America largely depends on the existing technological infrastructure, regulatory environment, and organizational readiness. In some cases, such as in the UAE and Australia, the benefits are more evident due to robust infrastructure and supportive governance. However, in other regions, these factors may limit the effectiveness of BDA ( Ziaee, Shee, & Sohal, 2023).