The Value of Applying Big Data Analytics in Health Supply Chain Management

Version Changes

Revised. Amendments from Version 3

Key revisions include: Enhanced Discussion on Healthcare Supply Chain Distinctions: A new section elaborates on the unique characteristics of healthcare supply chains (HCSCs) compared to other industries. It highlights aspects such as their critical role in saving lives, challenges with demand unpredictability, regulatory requirements, and the complexity of managing medical products. Contextualization of Big Data Analytics (BDA) in HCSCs: Expanded analysis on how BDA is specifically applied in HCSCs, including functions like disease outbreak prediction, medication error reduction, and inventory optimization. These applications were clarified as unique to healthcare compared to other industries. Improved Flow and Clarity: Adjustments were made to ensure a logical flow between Big Data, supply chain management, and healthcare-specific challenges, as per the reviewer’s feedback.

Abstract

This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs’ efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

Introduction

“ Data-driven decision-making is the backbone of innovation in healthcare supply chains, enhancing efficiency and patient outcomes.”— Dina Al Nuaimi.

Big Data Analytics (BDA) in healthcare is transformative, enabling the analysis of large datasets, identifying patterns, and developing predictive models through data mining techniques ( Ajah & Nweke, 2019; Batko & Ślęzak, 2022). The emergence of big data (BD) in supply chains (SC) has opened new avenues for enhancing efficiency and decision-making ( Nguyen et al., 2018; Hofmann & Rutschmann, 2018). BDA is crucial for managing, processing, and interpreting vast amounts of data, allowing organizations to derive actionable insights ( Tiwari, Wee, & Daryanto, 2018; Cozzoli et al., 2022). It integrates diverse data types, manages data quality, and provides comprehensive knowledge from massive datasets ( Ristevski & Chen, 2018; Zamani et al., 2022). While BDA is widely adopted in sectors such as education and healthcare ( Banu & Yakub, 2020; Galetsi et al., 2020), its application in healthcare supply chains (HCSC) is particularly critical, with the potential to significantly improve various aspects of healthcare, including Green Process Innovation ( Benzidia et al., 2023; Hasan et al., 2022). BDA provides the necessary tools to extract, store, analyze, and transform BD into valuable insights, supporting accurate decision-making and process optimization in HCSCs ( Ben Zineb et al., 2024).

Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and overall improved performance ( Araz et al., 2020). BDA enables precise demand forecasting, optimizes inventory management (IM), and enhances operational efficiency—these are key drivers of healthcare supply chain performance (HCSCMP) ( Batko & Ślęzak, 2022). However, despite the recognized benefits, further research is needed to explore how BDA specifically impacts HCSCMP and to validate existing findings. Many organizations are in the early stages of BDA adoption due to a lack of understanding of BD management and its benefits ( Sarker, 2021). Further research is needed to explore how BDA can enhance HCSCMP and to validate existing findings. Effective healthcare supply chain management (HCSCM) involves monitoring and optimizing production and distribution processes to improve efficiency in turning raw materials into final products and ensuring timely delivery to customers, thus maximizing value and providing a competitive advantage ( Investopedia, 2024; ASCM, 2023). This process typically includes five phases: planning, sourcing, manufacturing, delivery, and returns. In healthcare, SCM is crucial for ensuring the availability of medical products at the lowest possible cost, streamlining workflows, and optimizing IM. It also reduces losses from expired medicines and improves vendor management through digitalization ( Jabbarzadeh & Fahimnia, 2021).

This study aims to address these issues by investigating the trends, benefits, and challenges associated with BDA implementation in healthcare supply chains. Specifically, the research explores how BDA impacts HCSCMP by focusing on efficiency, resilience, and real-time decision-making. The following research questions guide the study:

RQ1. What are the trends in the application of BDA in HCSCM?

RQ2. How does the application of Big Data Analytics enhance efficiency in Healthcare Supply Chains Management according to existing studies?

RQ3. What are the key enablers and challenges identified in the literature for the implementation of Big Data Analytics in Healthcare Supply Chains?

To structure this investigation, the study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify key enablers and challenges in BDA implementation within HCSC. Through this dual approach, the study aims to provide a comprehensive understanding of BDA’s role in enhancing healthcare supply chain performance.

This study aims to contribute to the field of HCSCM by providing a comprehensive analysis of the role BDA plays in enhancing operational performance. Through a systematic literature review (SLR) of existing literature, this research identifies key enablers and challenges in implementing BDA within healthcare supply chains. Additionally, the study highlights gaps in current research and proposes areas where further investigation is needed to maximize BDA’s potential impact on healthcare supply chain management. By examining trends, methodologies, and outcomes of prior studies, this research seeks to guide both academic inquiry and practical applications of BDA to improve efficiency and resilience in healthcare supply chains.

The SLR included screening 65 papers, ultimately including 39 papers from 2016 to 2023. The SLR highlights a preference for literature reviews and questionnaires. More longitudinal studies on BDA topics need to be conducted. The protocol for the current SLR, as presented in Figure 1, comprises three sequential processes: planning the review, performing the review, and presenting the review ( Behera, Bala, & Dhir, 2019; Tandon et al., 2020). The present SLR includes preset inclusion and exclusion criteria (see Figure 1), as recommended by prior literature ( Behera, Bala, & Dhir, 2019; Tandon et al., 2020; Khanra et al., 2020). This research novelty contributes to enhancing HCSCM by integrating BDA to improve efficiency, decision-making, and resilience. It enables a structured decision-making framework, tackles HCSCM challenges, and highlights factors for successful BDA implementation, which can help in effective deployment. The comparative analysis of BDA implementation in HCSC in different countries, including the United Arab Emirates (UAE), provides insights into global best practices and highlights the unique challenges and solutions in various contexts, offering a broader understanding of BDA’s impact across different HCSCs.

Figure 1. Systematic Literature Review Process.

Big Data and Big Data analytics in Healthcare Supply Chain Management

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).

HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs ( Govindan et al., 2022). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns ( Ivanov & Dolgui, 2021). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines ( Chen et al., 2021). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges ( Ristevski & Chen, 2018). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries ( Nguyen et al., 2018). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors ( Benzidia et al., 2023).

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 BDA is increasingly pivotal in HSCM, where handling vast and complex data is essential for efficient and responsive service delivery. BDA enables healthcare organizations to process and interpret extensive datasets from inventory systems, patient records, and logistics networks, transforming them into actionable insights ( Al-Sai et al., 2022; Mallappallil et al., 2020). In HCSCM, BDA enhances IM by allowing real-time monitoring of stock levels and usage patterns, reducing both shortages and wastage ( Bhatia & Mittal, 2019). For example, BDA enables healthcare facilities to predict inventory needs based on historical demand, which improves order accuracy and minimizes the costs of overstocking or stockouts ( Johnson & Smith, 2019; Lee et al., 2020). In demand forecasting, BDA helps HCSCs anticipate shifts in demand, particularly during public health emergencies, ensuring that essential supplies such as medications and medical equipment are available when needed ( Martin & Lee, 2018).

BDA also contributes to operational efficiency and risk management by analyzing trends and patterns to predict potential supply disruptions, optimize logistics, and improve supplier management ( Dev et al., 2019; Erboz et al., 2021). This data-driven approach reduces costs and enhances resilience within HCSCs, enabling organizations to adapt to fluctuating demand and unforeseen challenges ( Bagga & Chopra, 2018). However, implementing BDA in HCSCM comes with unique challenges, such as data privacy and security concerns, integrating analytics with electronic health records, and ensuring data quality for reliable analysis ( Ristevski & Chen, 2018; Alotaibi & Mehmood, 2018). Addressing these issues is essential to fully leverage BDA’s potential in healthcare supply chains.”

Organizational Information Process Theory

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).

Methods

The SLR was designed using a hybrid method ( Mourao et al., 2017). The hybrid method combines a keyword-based search, typical of a SLR, to define a start set and a snowball method ( Wohlin, 2014) to find relevant papers systematically. No recent SLRs focus specifically on the value of BDA in HCSCM. The SLR employed a keyword-based search to define an initial set of papers and used the snowball method ( Wohlin, 2014) to find additional relevant papers systematically. A standard keyword-based SLR ( Keele, 2007) can yield an extensive set of papers if the keywords are not restrictive enough or too small set if the keywords are overly restrictive. The search in Scopus in September 2023 included titles and abstracts, focusing on papers containing BDA and SCM keywords. The snowball approach ( Wohlin, 2014) is sensitive to the initial set of papers. The overall process is found in Figure 1. Four steps were conducted using the procedure suggested by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review (PRISMA-ScR) method. The four steps were

• 1.the determination of published papers,
• 2.the screening of the papers,
• 3.the selection of papers after assessment for eligibility, and
• 4.the inclusion of the selected papers for analysis.

This PRISMA diagram workflow illustrates the rigorous and systematic approach taken in the literature review process. The process from a broad search narrows down to high-quality studies most relevant to the research questions. Each step, from identification to inclusion, ensures the final synthesis is based on a thorough and methodologically sound literature review. The process begins with the identification phase, where relevant records are identified through comprehensive database searches. A systematic mapping study on empirical and systematic literature sets the stage for the SLR. In this case, 65 records were initially identified from various databases.

Once the records are gathered, the next step is to remove duplicates. This crucial step ensures that each study is considered only once, preventing any skewing of results due to repeated entries. After removing duplicates, 31 unique records remained for further screening. The screening phase involves a preliminary review of the titles and abstracts of the 31 records to determine their relevance to the research question. During this phase, 34 records were excluded based on predefined criteria such as language (non-English papers), study type (non-primary studies), and relevance (irrelevant topics). The remaining 14 full-text articles were assessed in detail in the eligibility phase to determine if they meet the inclusion criteria. The search was limited to original research articles written in full text in English and published between 2016 and 2023 in academic journals, ensuring the inclusion of recent and relevant studies.

The screening period starts from 2016 because of a noticeable increase in academic research on BDAs in HCSCMs around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area.

All the studies included in the review were peer-reviewed, ensuring their quality and reliability. This step ensures that only highly relevant and of sufficient quality studies are included in the final synthesis. After this detailed assessment, 34 articles were excluded for reasons such as being non-English papers (3 articles), not being primary studies (11 articles), and not being relevant to the research topic (20 articles).

Throughout the process, snowball sampling expanded the initial set by exploring relevant articles’ references. This method ensures that all potentially relevant studies are considered, increasing the comprehensiveness of the review. The Snowballing step in the hybrid systematic review is snowballing the final start set papers. This method extended the start set by screening the references within the papers and those that cited them. Google Scholar was utilized to find forward references. The snowballed on the extended start set (14 papers) were done to get the final set of papers. In a snowball approach, both references in the paper (backward references) and papers referring to the paper (forward references) were screened ( Wohlin, 2014). The snowballing process was performed on titles only for backward and forward snowballing to ensure no relevant references were missing. This process was repeated until no new papers were found. Following screening and full reading, the final set consisted of 25 papers.

The final phase is the inclusion phase, where studies that passed the eligibility criteria are included in the synthesis. In this workflow, 39 studies were included in the final synthesis after additional snowball sampling. Snowball sampling involves reviewing the references of the included studies to identify any additional relevant studies, which added 25 more records to the initial set. Related SLRs and key publications were also considered to ensure a thorough and robust literature synthesis. The SLR process, as illustrated in the workflow, results in a final set of 39 studies that are included in the synthesis. These studies provide a comprehensive and reliable basis for understanding the research topic, ensuring that conclusions are based on a thorough and systematic examination of the existing literature. This detailed workflow ensures that the review process is transparent, reproducible, and methodologically sound, leading to high-quality and reliable research findings.

The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation.

Results analysis

In order to comprehend the extent of previous literature on BDA in SCM, we synthesized papers that provide an overview of BDA in SCM and identified them within reviews. This stage involves a thorough examination of these papers. This section presents the analysis results of the 39 selected peer-reviewed journal papers. The following sub-sections elaborate on the relevant findings. The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC.”

RQ1. What is the number of academic studies on Big Data Analytics in the context of Supply Chain Management, and what research methods and data collection techniques have been used in these studies?

The number of academic studies on big data analytics in the context of supply chain management

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

Figure 2. Number of Publications per Year.

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.

In 2016, research activity in BDA within HCSCM was minimal, accounting for only 2.6% of total publications. A peak in research interest occurred in 2018, with 23% of publications focused on this topic, indicating a surge in activity and interest. The years 2019 and 2020 each contributed 15% of the publications, marking a period of stable and significant research output. In 2021, this proportion declined to 10%, suggesting a temporary decrease in research attention. However, 2022 saw an increase to 18%, reflecting renewed or growing interest in BDA within HCSCM. By 2023, research activity stabilized once again at 15%, similar to levels seen in 2019 and 2020. This trend effectively illustrates fluctuations in research engagement over the years. The peak in 2018 suggests a period of heightened interest or important developments in the field of BDA within HCSCM. The consistent proportions in 2019, 2020, and 2023 indicate steady research output, while the increase in 2022 highlights renewed or sustained interest in advancing research in this area.

Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies

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).

Table 1. Summary Table of Empirical Papers.

NO	Study	Methods	Context
1	Ziaee, Shee, and Sohal (2023)	Interviews	Australia
2	Raman, S. et al. (2018)	Questionnaire	United States, Middle East, Europe, Asia, and Australia
3	Oncioiu, I. et al. (2019)	Questionnaire	Romania
4	Mubarik and Mohd Rasi (2019)	Questionnaire	Pakistan
5	Batko and Ślęzak (2022)	Questionnaire	Poland
6	Lamba and Singh (2018)	Interviews	India
7	Farivar, Golmohammadi and Ramirez (2022)	Questionnaire	North America
8	Chen, Preston and Swink (2021)	Questionnaire	North America
9	Zhu et al. (2018)	Questionnaire	Multiple Countries Asia/Europe/USA
10	Benabdellah et al. (2016)	Questionnaire	Morocco
11	Chen, Preston and Swink (2021)	Questionnaire	North America
12	Bag et al. (2021)	Questionnaire	South Africa
13	Bag et al. (2023)	Questionnaire	South Africa
14	Agrawal and Madaan (2023)	Questionnaire	India
15	Benzidia et al. (2023)	Questionnaire	France
16	Johnson, Robert, and Smith (2019)	Mixed Methods	United States
17	Williams and Brown (2020)	Surveys and structured interviews	United States
18	Thompson and Thompson (2020)	Case studies and interviews	United States
19	Smith and Johnson (2018)	Mixed Methods	United States
20	Lee and Harris (2019)	Mixed Methods	United States
21	Hussain et al. (2023)	Process mapping tool	United Arab Emirates
22	Bamel and Bamel (2020)	Interview	India

The bar chart shown in Figure 3 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.

Figure 3. Empirical Research Method Used in Each Region.

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
• -Qualitative Case Studies and Interviews: 1 publication
• -Mixed Methods: 3 publications

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?”).

Table 2. Summary table of studies and its findings related to big data analytics in supply chain management.

No.	Study	Methods	The period of compilation of the data	Measured outcomes	Findings
1	Ziaee, Shee, & Sohal (2023)	Interviews	2023	Explore the benefits of BDA adoption in pharmaceutical supply chain	BDA capability is more helpful in HSC planning, delivery and return processes
2	Raman et al. (2018)	A survey was conducted among companies in the United States, the Middle East, Europe, Asia, and Australia	2018	Study impact of BDA on SCM	Results show that adopting BDA can affect the SCM's visibility and decrease the communication gap between demand and SCM
3	Oncioiu et al. (2019)	Quantitative study using a questionnaire	2019	Study the impact of BDA on company performance in SCM	Indicated that new capabilities and technologies, such as DBA, are required to manage and analyze information
4	Cozzoli et al. (2022)	Literature Review	2022	Examine the impact of BDA on healthcare management	Indicated the positive relationship between BDA and healthcare management
5	Mubarik & Mohd Rasi (2019)	Close-ended questionnaire	2019	Examine the impact of BDA on SC performance	Indicated a positive impact of BDA on planning, supplying, making, and IM
6	Dev et al. (2019)	Literature Review	2019	Study key performance indicators (KPIs) of SC with consideration of BDA	BDA providing real-time data processing and enhancing decision-making capabilities
7	Batko & Ślęzak (2022)	Questionnaire	2022	Examine impact of BDA in healthcare	BDA can support clinical decision-making
8	Bamel & Bamel (2020)	Interviews	2020	Determine BDA enablers of SC	BDA-based enablers are IT infrastructure for BDA; leadership commitment; staff skills for using BDA and financial support
9	Mageto (2021)	Literature Review	2021	Determine the relationship between BDA and Sustainable SCM	There is a strong relationship between BDA and Sustainable SCM
10	Ciğerci (2023)	Literature Review	2023	Study effects of BDA on SCM	BDA improve stock management, lowers costs, increases SC visibility
11	Hasan et al. (2022)	Systematic Literature Review	2022	Study the impact of BDA on SC operations	BDA can enhance the accuracy and timeliness of decision-making processes and optimize SC efficiency
12	Nguyen et al. (2018)	Literature Review	2018	Address the benefits of BDA in SCM	BDA can improve demand forecasting
13	Araz et al. (2020)	Literature Review	2020	Explore the role of BDA in risk management	Indicated that BDA enhances risk identification and decision-making capabilities
14	Hofmann & Rutschmann (2018)	Literature Review	2018	Explore role of BDA in improving forecasts’ accuracy	BDA enhances Forecasting Accuracy
15	Lamba & Singh, (2018)	Interview	2018	Determine and enablers the main for successful implementation of BDA in SCM	Indicates that top management commitment, financial support, technical skills, organizational structure and change management program are the main BDA enablers
16	Farivar, Golmohammadi & Ramirez (2022)	Survey	2022	Examine the role analytics capability and staff analytics skills in enhancing SC performance	Show that analytics capability must be accompanied by staff analytics skills to enhance SC performance
17	Tiwari, Wee, & Daryanto (2018)	Literature Review	2018	Explore impact of BDA in SCM	BDA enhances demand forecasting, decision-making, and inventory management
18	Zamani et al. (2022)	Literature Review	2022	Determine the role of BDA in SC resilience	BDA enhances SC resilience
19	Alotaibi & Mehmood (2018)	Literature Review	2018	Review the use of BDA in HCSC	BDA enhances decision-making in SC and increases transparency in HCSC
20	Chen, Preston & Swink (2021)	Questionnaire	2021	Investigates the impact of BDA in SCM	Application of BDA is related to better decision-making capability
21	Bhatia & Mittal (2019)	Literature Review	2019	Explore the application of BDA in HCSC	BDAs can enable timely and rapid healthcare service delivery
22	Maheshwari, Gautam & Jaggi, (2020)	Literature Review	2020	Explore the significance of BDA in SCM	BDA enhances demand forecasting accuracy, decreases inventory costs, optimized transportation routes, and improved risk management
23	Lee & Mangalaraj (2022)	Literature Review	2022	Explore effect of BDAs on SCM	BDA enhances visibility and resilience in SCM
24	Al-Sai et al. (2022)	Literature Review	2022	Explore impact of BDA	BDA enable real time decision making
25	Zhu et al. (2018)	Survey	2018	Examine role of BDA in supporting SC transparency	Analytics capability supports planning functions and impacts SC transparency
26	Benabdellah et al. (2016)	Survey	2016	Examine the role of BDA in SC	BDA enhances demand forecasting, visibility and transparency, and improves decision-making process in SCM
27	Chen, Preston & Swink (2021)	Questionnaire	2021	Investigates impact of BDA in decision-making in SCM	BDA can optimize SC by enhancing decision-making capability
28	Bag et al. (2021)	Questionnaire	2021	Examine the role of BDA in SC resilience	BDA can restore and increase SC resilience and improve decision making process
29	Bag et al. (2023)	Questionnaire	2023	Explores the effect of BDAs and AI (BDA-AI) technology-based in HCSC processes and performance	The BDA-AI platform will capacitate HCSC to deliver innovative performance
30	Hussain et al. (2023)	Process mapping tool, supplier-input-process-output-customer (SIPOC) chart	2023	Explores the challenges of BDA in HCSC	Determines numerous challenges in HCSC across the United Arab Emirates (UAE)
31	Agrawal & Madaan (2023)	Questionnaire	2023	Identifies barriers to BDA implementation in the HSC	Determines barriers to successful BDA implementation in the HSC
32	Benzidia et al. (2023)	Questionnaire	2023	Investigate the relationship between BDA in SCM and environmental and healthcare performance	The BDA affects environmental process integration to enhance environmental performance and healthcare performance
33	Johnson, Robert, & Smith (2019)	Mixed Methods	2019	Study the impact of BDA in inventory management	BDA improve inventory management efficiency
34	Martin & Lee (2018)	Literature Review	2018	Explore impacts of BDA in HCSC	Using BDA led to improvement in demand forecast accuracy Supportive Leadership and Organizational Culture are important enablers for BDA implementation Resistance to change can hinder the implementation of BDA
35	Lee et al. (2020)	Literature Review	2020	Explore impacts of BDA in Reducing Medical Supply Waste	BDA enables real-time decision-making
36	Williams & Brown (2020)	Surveys and structured interviews	2020	Explores the impact of BDA on order management	BDA led to reduction in order processing time and order errors Data quality and standardization issues are challenges for BDA implementation
37	Thompson & Thompson (2020)	Qualitative Case studies and interviews	2020	Explores Challenges in BDAs for Healthcare	BDA led to improvement in supplier reliability Regulatory Support and Compliance Frameworks are enablers for BDA implementation
38	Smith & Johnson (2018)	Mixed Methods	2018	Determines challenges and enablers for BDA implementation in HCSC	Advanced Technology Infrastructure is a critical enabler for the implementation of BDA in HCSC
39	Lee & Harris (2019)	Mixed Methods	2019	Examine the impact of skill development for BDA	Skill development important for BDA implementation High implementation costs for BDA included skilled personnel

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. Ciğerci (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. Ciğerci (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.

BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines ( Beaulieu & Bentahar, 2021; Ouarda et al., 2024). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency ( Beaulieu & Bentahar, 2021).

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

Primary Enablers in Implementing 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.

Primary Challenges in Implementing Big Data Analytics in Healthcare Supply Chains

Agrawal and 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).

Discussion

Number of Academic Studies and Research Methods on Big Data Analytics in the Context of Supply Chain Management

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.

Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges

The adoption of BDA in HCSCM has the potential to significantly enhance operational efficiency, though it relies heavily on enabling factors for successful implementation and faces several challenges. This section synthesizes these elements to provide a holistic view of BDA’s impact on HCSCM.

Efficiency Enhancements of Big Data Analytics

BDA offers various efficiency improvements in healthcare supply chains by optimizing processes like demand forecasting, inventory management, and real-time decision-making. Effective demand forecasting allows healthcare organizations to predict the demand for medical supplies more accurately, ensuring that essential items such as medications, equipment, and personal protective equipment (PPE) are available when needed ( Martin & Lee, 2018). This capability is crucial during public health emergencies, where demand spikes could otherwise lead to shortages and impact patient care. For instance, studies have shown that BDA’s predictive capabilities help healthcare providers maintain optimal inventory levels and prevent both shortages and excesses, leading to cost savings and better resource allocation ( Johnson & Smith, 2019; Bhatia & Mittal, 2019).

In addition to demand forecasting, BDA also improves IM by providing real-time visibility into stock levels and usage patterns, reducing waste and enhancing supply chain responsiveness ( Lee et al., 2020). This real-time monitoring capability enables healthcare facilities to manage their supplies more effectively, ensuring that critical items are always available without overstocking, which is especially important given the perishable nature of some healthcare supplies ( Alotaibi & Mehmood, 2018). By analyzing large datasets from various points in the supply chain, BDA enhances operational decision-making, allowing healthcare organizations to make data-driven adjustments to logistics and distribution as needed ( Dev et al., 2019; Erboz et al., 2021). This adaptability helps organizations respond proactively to potential disruptions, further supporting resilience in healthcare supply chains ( Bagga & Chopra, 2018).

Enablers of BDA Implementation in Healthcare Supply Chains

For BDA to deliver these efficiency benefits, certain enablers must be present within healthcare organizations. One critical enabler is a robust technological infrastructure, which includes high-speed internet, cloud-based data storage, and advanced data processing capabilities. Without such infrastructure, healthcare organizations may struggle to handle the large volumes of data required for effective BDA ( Bamel & Bamel, 2020; Gupta et al., 2019). Cloud computing, for example, provides scalable storage solutions that support real-time data access, which is essential for managing supply chain processes across different geographic locations ( Lee & Harris, 2019).

In addition to infrastructure, having skilled personnel is essential for leveraging BDA in HSCM effectively. Data scientists and analytics professionals play a crucial role in managing, analyzing, and interpreting large datasets, ensuring data quality, and implementing BDA strategies that align with organizational goals ( Farivar et al., 2022). These professionals require specialized skills in data science and healthcare logistics to address the unique challenges posed by healthcare data, such as handling unstructured information from patient records and ensuring compliance with privacy regulations ( Ristevski & Chen, 2018). Furthermore, leadership commitment is another vital enabler, as executives must be willing to invest in BDA resources, champion data-driven decision-making, and foster an organizational culture that embraces innovation and continuous improvement ( Williams & Brown, 2020). Strong leadership support helps secure the resources needed for BDA initiatives and promotes a culture that values data-driven decision-making.

Challenges in Implementing BDA

Despite the advantages, BDA implementation in healthcare supply chains faces several challenges that can hinder its effectiveness. Data privacy and security concerns are among the most significant, as healthcare data is highly sensitive and subject to strict regulations. Ensuring compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the United States or the General Data Protection Regulation (GDPR) in the European Union requires healthcare organizations to implement robust data protection measures, which can be complex and costly ( Thompson & Thompson, 2020). Furthermore, healthcare data often lacks standardization, with disparate formats and structures across different systems. This lack of data standardization makes data integration challenging and can lead to inaccuracies in BDA insights if not addressed effectively ( Mallappallil et al., 2020).

Another challenge is the high cost of BDA implementation, particularly for smaller healthcare organizations with limited budgets ( Martin & Lee, 2018; Bhatia & Mittal, 2019). Implementing and maintaining BDA requires significant financial investment in infrastructure, software, and personnel, which may be prohibitive for some organizations. This financial barrier underscores the need for tailored BDA strategies that consider an organization’s size, budget, and capacity for scaling analytics capabilities ( Lee et al., 2020). Furthermore, healthcare organizations may face resistance to change from staff who are accustomed to traditional decision-making processes. Effective change management and training programs are essential to help employees understand the benefits of BDA and adapt to new data-driven approaches ( Williams & Brown, 2020).

Conclusion

The research trend in BDA for SCM shows increasing interest and sustained activity, particularly in the healthcare sector. This SLR highlights a preference for literature reviews and questionnaires, which establish strong theoretical bases and gather specific stakeholder data. Less frequent methods, like interviews and mixed methods, are used for detailed, context-specific insights, reflecting the diverse needs of empirical studies in BDA and SCM. BDA significantly enhances efficiency in HCSCs by optimizing IM, improving order management, refining demand forecasting, streamlining order fulfilment, and enabling real-time decision-making. These improvements lead to better resource allocation, cost savings, and service levels. BDA’s real-time data and analytics capabilities enhance SC visibility, resilience, and logistics operations, making SCM more efficient and responsive. Successful implementation of BDA in HCSCs relies on critical enablers such as advanced technology infrastructure, data integration, a skilled workforce, supportive leadership, regulatory frameworks, and financial resources. However, challenges like data privacy and security, high implementation costs, and continuous staff training must be addressed to realize BDA’s benefits fully. Addressing key enablers and overcoming challenges will significantly improve HCSCMP.

Developing robust frameworks and solutions to address data privacy and security challenges in BDA implementation is critical. It is crucial to develop strong frameworks and solutions to tackle data privacy and security challenges in BDA implementation. It is also essential to conduct detailed cost-benefit analyses to understand the financial implications of implementing BDA in HCSCs. Furthermore, it is important to investigate the effectiveness of various training and development programs designed to enhance the BDA skills of HCSC professionals. Similarly, examining the impact of changing regulatory frameworks on the implementation and effectiveness of BDA in HCSCM can provide deeper insights and practical solutions to enhance the adoption and effectiveness of BDA in HCSCs and beyond. Also, conducting detailed cost-benefit analyses is essential to better understand the financial implications of implementing BDA in HCSCs. Investigating the effectiveness of various training and development programs designed to enhance the BDA skills of HCSC professionals and examining the impact of changing regulatory frameworks on the implementation and effectiveness of BDA in HCSCM can provide deeper insights and practical solutions to enhance the adoption and effectiveness of BDA in HCSCs and beyond.

Similarly, researchers might consider diversifying their methodologies to include more mixed methods and qualitative approaches, which can provide richer, more nuanced insights. Conducting longitudinal studies to understand the long-term impacts of BDA on HCSCMP and identify trends over time is also recommended.

Ethics and consent

Ethical approval and consent were not required.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

Data availability

Underlying data

No data are associated with this article.

Extended data

Open Science Framework (OSF): The value of applying big data analytics in health supply chain management, https://doi.org/10.17605/OSF.IO/ZGSCU ( Al Nuaimi & Al Nuaimi, 2024).

This project contains the following extended data:

• •Empirical Research Method Used in Each Region-1.jpg
• •Number of Publications by Method Used-1.jpg
• •Number of Publications per Year-1.jpg
• •Percentages of Publications-1.jpg
• • PRISMA_2020_checklist and workflow - BDA Value.pdf
• •Summary Table of Empirical Papers.docx
• •Summary Table of Studies and Its Findings Related to Big Data Analytics in Supply Chain Management.docx
• •Systematic Literature Review Process-1.jpg
• •Systematic Literature Review Process.docx
• • Table 1. Summary Table of Empirical Papers.xlsx
• • Table 2. Summary Table of Studies and Its Findings Related to Big Data Analytics in Supply Chain Management

Data is available under the terms of the CC0 1.0 Universal.

Reporting guidelines

Open Science Framework (OSF) Repository: PRISMA checklist and flow chart for ‘The value of applying big data analytics in health supply chain management’, https://doi.org/10.17605/OSF.IO/ZGSCU ( Al Nuaimi & Al Nuaimi, 2024).

Data are available under the terms of the CC0 1.0 Universal