Healthcare Supply Chain Management Research Articles

Synergizing Ai and ERP for Predictive Supply Chain Management and Quality Assurance in Healthcare

This article explores the transformative potential of integrating Artificial Intelligence (AI) with Enterprise Resource Planning (ERP) systems in healthcare, focusing on applications in predictive supply chain management and quality assurance. The healthcare industry faces increasing pressure to improve efficiency while maintaining high-quality care, and AI-ERP integration offers a compelling solution. By combining ERP's comprehensive data management capabilities with AI's predictive and analytical power, healthcare organizations can enhance operational efficiency, improve decision-making, and ultimately deliver better patient outcomes. The article examines current implementations, case studies, and future projections to provide a comprehensive overview of how this technological synergy can help healthcare providers navigate the complex challenges of modern healthcare delivery, from optimizing inventory levels to ensuring regulatory compliance and proactive risk management.

Blockchain in Healthcare: Overview of Applications, Challenges, and Future Directions

There are numerous healthcare issues which show the potential of blockchain technology such as transparency, interoperability and security of data. Through this study, it tries to offer a very deep explanation of where the Blockchain applications today stand for in the healthcare domain emphasising on possible benefits from the technology and some known challenges that are being faced while actuating those. We then use blockchain applications in the above-mentioned aspects to explain different aspects of use of Blockchain in healthcare like supply chain management, clinical trials, secure medical data exchange and patient empowerment. We also talk about Blockchain from implementation point of view – smart contracts, consensus methods and data privacy. In this publication, we determine whether deploying Blockchain in healthcare raises ethical and legal issues and how existing issues can be addressed. In conclusion, we describe future directions for research and opportunities to use Blockchain to improve healthcare delivery and outcomes.

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

The use of big data analytics (BDAs) allows for the collection, management, and analysis of large volumes of data, which helps in making real-time decisions. This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP). Through a systematic literature review, the research explores how BDA enhances efficiency within healthcare supply chains (HCSCs) and identifies the critical factors enabling successful BDA implementation. 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.

A home healthcare routing-scheduling optimization model considering time-balancing and outsourcing

Home care services have a significant role in lowering healthcare expenditure. Supply chain management in home healthcare (HHC) ensures efficient delivery of medical supplies and equipment to patients' homes, improving overall quality of care and patient outcomes. This study proposes a routing and scheduling optimization model for HHC by prioritizing patients, developing an effective delivery strategy, and considering home care logistics and services. The model primarily concerns reducing logistics activities’ overall expenses while considering patients’ priorities. A bi-objective optimization model for a multi-period HHC problem is developed by prioritizing patients with urgent critical needs. The best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are used to prioritize patients using a linear programming metric (Lp-metric). The BWM and TOPSIS have been uniquely used in this study for routing and scheduling in HHC. Eventually, the applicability of the proposed method is demonstrated through a real-life case study with a series of numerical examples and sensitivity analysis. For instance, by analyzing privilege, we see patients are carefully matched with caregivers possessing advanced skills, leading to increased patient satisfaction. Based on assigned routes, caregivers prioritize patients with higher weight and emergency conditions at the start of each path, followed by patients with less urgent conditions. This ensures that patients with more severe conditions are serviced first.

Enhancing healthcare supply chain management through artificial intelligence-driven group decision-making with Sugeno–Weber triangular norms in a dual hesitant q-rung orthopair fuzzy context

The healthcare industry faces numerous challenges in managing its supply chain efficiently, where critical decisions must be made promptly to ensure the availability of essential medical resources. This research introduces a novel artificial intelligence (AI) approach, utilizing the “Sugeno–Weber (SW) t-conorms and t-norms” (SWt-CNs&t-Ns) for decision-making in a Dual Hesitant q-Rung Orthopair Fuzzy (DHq-ROF) context. The SWt-CNs&t-Ns are chosen for their adaptability in data unification, serving as prominent operations for union and intersection processes. Developing a set of fundamental operations is imperative to effectively utilize SWt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF settings. Following the introduction of these processes, several aggregating operators have been provided. These operators include DHq-ROF SW weighted averaging, ordered weighted averaging, hybrid averaging, and their geometric counterparts utilizing DHq-ROF data. The SW triangular norm-based approach aggregates group preferences, facilitating a systematic decision-making process. Triangular norms ensure a realistic representation of interrelationships among decision criteria, leading to optimal healthcare supply chain management solutions. Furthermore, the SW triangular norm-based approach aggregates group preferences, enabling a systematic and comprehensive decision-making process. Choosing the best healthcare supply chain management solutions is easier when you use triangular norms because they give a more accurate picture of how the decision criteria affect each other. The effectiveness of the proposed AI framework is demonstrated through a series of experiments and case studies, showcasing its ability to enhance decision accuracy, reduce uncertainty, and improve overall supply chain performance. The research findings underscore the potential of AI-driven solutions to revolutionize healthcare supply chain management, ultimately leading to better resource allocation, cost efficiency, and improved patient care.

Sustainability of healthcare supply chain management: a panoramic approach through SLR and bibliometric

PurposeThe healthcare sector increasingly recognizes how critical sustainable supply chains are to lowering environmental impact, boosting productivity and satisfying public expectations for morally and responsibly provided healthcare. Consequently, the current study aims to thoroughly evaluate the literature on the sustainability of healthcare supply chain management.Design/methodology/approachThe paper uses a systematic literature review (SLR) technique and bibliometric review to examine the benefactions of different authors, nations and organizations to healthcare sustainability through bibliometric and network analyses.FindingsThe study concludes that the healthcare industry may advance sustainability on all levels by incorporating technology into the fundamentals of sustainability. Patient care is given priority in this proposed approach, which can also help healthcare executives create strategies that support efficient healthcare supply chains.Research limitations/implicationsThe research study can serve as a basis for future investigations into additional healthcare management domains, where integrating a sustainable supply chain can yield superior and observable results and bridge deficiencies in management protocols.Originality/valueUsing bibliometric visualization, this study shows the relevance of sustainability in the healthcare supply chain. By identifying its advantages, present-day circumstances, applications and prospective future research fields, the study took up the review and relevance of sustainability in many parts of the healthcare industry.

An enhanced MADM with Lq* q-Rung orthopair multi-fuzzy soft set in healthcare supplier selection

The global healthcare systems have encountered unparalleled difficulties due to the COVID-19 pandemic, underscoring the crucial significance of effective management within healthcare supply chains. This research contributes to the field of healthcare supply chain management by presenting a robust MADM methodology called lattice ordered(Lq*) q-rung orthopair multi-fuzzy soft set(Lq* q-ROMFS-MADM) for supplier evaluation and ranking amidst the challenges posed by the COVID-19 pandemic. Taking inspiration from multi-fuzzy soft set and q-rung orthopair fuzzy set, the present research article proposes a novel framework known as Lq* q-rung orthopair multi-fuzzy soft set (Lq* qROMFSS), which incorporates lattice ordering in q-rung orthopair multi-fuzzy soft set. The effectiveness of the proposed model is confirmed through successful experimentation on various important operations, including union, intersection, complement, restricted union and intersection. Moreover, the verification of De Morgan’s laws for Lq* qROMFSS is carried out specifically for these operations mentioned above. To highlight the significance of the proposed Lq* qROMFSS, a multi-attribute decision-making (MADM) problem is presented, showcasing its application in the domain of healthcare supply chain management. Furthermore, a comparative analysis is conducted to elucidate the advantages of this model in comparison to existing models.

Assessing the Impact of Healthcare 4.0 Technologies on Healthcare Supply Chain Management: A Multi-Criteria Evaluation Framework

Background: Healthcare 4.0 has transformed supply chain management in the healthcare sector, but there is a lack of comprehensive frameworks to evaluate the impact of Healthcare 4.0 technologies on sector operations, particularly in developing countries. Methods: This study introduces a multi-criteria framework that synergically combines the techno-economic implications of Healthcare 4.0 technologies to improve healthcare supply chain management. The proposed approach innovatively integrates fuzzy VIKOR and Entropy methods to handle data vagueness and uncertainty, using data collected from healthcare supply chain specialists in Lagos, Nigeria. Results: The developed framework identifies the most and least critical technical and economic parameters for Healthcare 4.0 implementation in healthcare supply chain management. It also determines the suitability of different Healthcare 4.0 technologies for supply chain management in the healthcare sector. Conclusions: The main innovation of this study lies in the development of a comprehensive and context-specific framework for evaluating Healthcare 4.0 technologies in healthcare supply chains. The framework offers a new perspective on technology evaluation and provides practical insights for decision-makers. The findings contribute to advancing knowledge and practice in this field, promoting the proper adoption of Healthcare 4.0 technologies in healthcare, particularly in developing countries.

Data-driven decision making: Informed healthcare supply chain management

With the evolution and incorporation of artificial intelligence (AI) and machine learning (ML) into so many business models and enterprises, data-driven decision making is assuming an ascendant role among organisational operations. While this work is not intended to negate experiential or intuitive decision making, leaders are encouraged to examine operational decision making through a lens of how experience can be supported by organisationally derived information yielding more effective operational decisions. While experiential learning is beneficial in determining how organisational decisions are being made, data presents why such decisions are made in the first place. More important still, what impact might decisions have on healthcare operations if grounded in ill-informed, ill-defined decision-making processes? Especially within healthcare supply chain operations, decision making should be a collaborative, multidiscipline, multistakeholder process that is best grounded in data-driven approaches. Healthcare supply chain data and analytics can and do provide a valuable framework within which operational decisions should be viewed. Potentially gleaned from this paper will be a perspective relatable to evidence-based analyses permitting the evolution of better-informed leadership decision criteria contributing to tangible organisational resilience, agility and flexibility.

Opportunities Presented by COVID-19 for Healthcare Green Supply Chain Management and Sustainability Performance: The Moderating Effect of Social Media Usage

This research explores the opportunities presented by COVID-19 for green supply chains' environmental practices and ecological sustainability performances in the healthcare sector. This study investigates the connections between uncertainty-fear of COVID-19, healthcare green supply chain management (GSCM), and the three pillars of a firm's sustainability performance (environmental, economic, and social). Moreover, this study examines the moderating role of social media usage (SMU) on the effect of uncertainty-fear of COVID-19 on healthcare-GSCM. When conducting the empirical part, this study uses the partial least squares structural equation modeling method based on a sample of 483 healthcare managers. The findings prove that the uncertainty-fear of COVID-19 has a beneficial impact on healthcare-GSCM. Besides, SMU moderates the relationship between uncertainty-fear of COVID-19 and healthcare-GSCM, indicating the importance of SMU in gathering information for the healthcare sector during COVID-19. Likewise, when interacting with healthcare firms' sustainability performances, healthcare-GSCM positively impacts environmental and social performances, though it has a negligible impact on economic performance. This study adds to the “social cognitive theory” by introducing the concept of uncertainty-fear of COVID-19. Furthermore, this research adds to the “resource-based view theory” and the “knowledge-based view theory” by exploring the SMU's role during the outbreak.

Healthcare cybersecurity and cybercrime supply chain risk management

Cybersecurity is paramount in today’s rapidly evolving healthcare industry, particularly as supply chain management and logistics undergo digital transformation. This study examines the substantial threat posed by cybercrime to patient safety, data security, and operational efficiency within healthcare logistics and supply chain management. These risks can significantly impact an organization’s reputation and financial stability, necessitating vigilant detection and mitigation efforts by healthcare companies. As the primary defence against online threats, cybersecurity plays a pivotal role in preventing data breaches, cyberattacks, and other malicious activities that could have devastating consequences for the healthcare sector. Its core objective is to ensure the availability, confidentiality, and integrity of data, systems, and resources within the realm of healthcare supply chain management and logistics. Patient data protection stands out as a critical aspect of cybersecurity in this context. Healthcare logistics and supply chain management systems frequently handle sensitive patient data, encompassing billing details and medical histories. The compromise of such data places patient trust and the organization’s regulatory compliance at risk, potentially leading to identity theft, fraudulent claims, and privacy breaches. Furthermore, safeguarding the security of medical equipment is of paramount importance. With the increasing connectivity of these devices through the Internet of Things (IoT), they become more vulnerable to cyberattacks. Apart from jeopardizing patient safety, a breach in medical device security raises questions about the authenticity and reliability of healthcare products and services. Another pressing issue that healthcare institutions must address is unauthorised access to their systems. Cybercriminals persistently seek entry points into these systems to exploit vulnerabilities for illicit or profitable purposes. Robust cybersecurity measures are essential to thwarting unauthorised access and ensuring that only authorised individuals can access and modify sensitive medical data. Maintaining the accuracy of patient records is crucial for efficient supply chain management and healthcare logistics. Cyberattacks that manipulate or corrupt patient records can lead to medical errors, endangering patient safety. Consequently, cybersecurity measures must include safeguards to preserve the integrity and accuracy of these records. Beyond these immediate concerns, cybersecurity is instrumental in preventing disruptions to healthcare operations and services. Downtime resulting from cyberattacks can be catastrophic, impeding patient care and undermining the overall effectiveness of supply chain management and healthcare logistics. Cybersecurity safeguards continuous healthcare services by guaranteeing the security and accessibility of data and systems. Furthermore, cybersecurity plays a pivotal role in protecting the integrity, trust, and reputation of healthcare organizations. A cyberattack or data breach can tarnish an organization’s reputation and erode patient confidence. Such damage can have enduring repercussions, affecting an organization’s ability to attract clients, partners, and investors.

An integrated G-MACONT approach for healthcare supplier selection

PurposeSupplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.Design/methodology/approachTo deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.FindingsWhile assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.Originality/valueThis integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence.

Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection. Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment. The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.

A literature survey on healthcare supply chain management

Supply Chain Management (SCM) is a practice that has rapidly spread across industries. SCM may boost output while simultaneously satisfying customers. Despite SCM's recognition as a key factor in enhancing healthcare efficiency, widespread adoption remains in its infancy. Hospitals, a crucial element of the healthcare supply chain (HSC), have failed to fulfill the primary goals of lowering costs and providing high-quality treatment due to their inadequate knowledge of supply chain management (SCM). This research was conducted to fill in the blanks in the current HSC literature. Achieving the healthcare supply chain's goal of reducing costs will be greatly aided by the thorough literature study completed for this report. This review of healthcare supply chain management can quantify the benefits of supply chain initiatives and identify opportunities for improvement. Healthcare institutions can make informed decisions on optimizing their supply chains by understanding customer and supplier needs. This includes making strategic decisions on how to improve inventory management, streamline processes and reduce costs. The focus of this study is on the relationship between supply chain practices, the efficiency of supply chain performance, and the financial outcomes for healthcare organizations. By highlighting certain key research issues that are shared by supply chain management and healthcare management, this article contributes to the literature in both areas.

Examining the influence of industry 4.0 in healthcare supply chain performance: Moderating role of environmental dynamism

Healthcare supply chain management (HCSCM) is a network of different systems, tools, and processes. This system works in a seamless manner to ensure healthcare related materials such as medicines, hospital supplies, and so on. These materials are manufactured in the firms, distributed through agents, and eventually reach patients on time. During COVID-19, HCSCM was severely impacted in many countries. Many patients had to face severe difficulties during the pandemic. But post pandemic and after the adoption of several modern technologies like Industry 4.0, the performance of HCSCM was more reliable. Not many studies are available which have examined the influence of Industry 4.0 in HCSCM during such a crisis. In such context, the study aims at examining the influence of Industry 4.0 in HCSCM with moderating role of environmental dynamism. A conceptual model is proposed with the support of theories and literature. This was later validated by PLS-SEM approach considering 312 respondents from people of HCSCM system. The study ascertained that Industry 4.0 has greatly improved the performance of HCSCM system after COVID-19 crisis. In addition, this study highlighted that environmental dynamism has a significant moderating role on the linkage between adoption of industry 4.0 technology with HCSCM.

Optimizing Hospital Services: Implementing Healthcare Supply Chain Management through MERN Stack Web Application

This paper offers a new approach to hospital service optimization that seamlessly incorporates concepts from healthcare supply chain management. Using the advantages of MERN stack technology, we are developing a dynamic web service that connects hospitals in real time and expedites patient transfers. By addressing the pressing need for efficient supply chain management in the healthcare sector and by offering features like virtual consultations and organ transplant search capabilities, this system enhances patient care and accessibility. By putting this into practice, we intend to improve and elevate the standard for hospital treatment. This paper offers a new approach to hospital service optimization that seamlessly incorporates concepts from healthcare supply chain management. Keywords- Supply Chain Management, Healthcare Service Delivery, MERN stack, Real-time data processing, Patient Transfers, Predictive Analytics, Patient-Centric Care, Efficiency, Seamless Coordination, Fast Connectivity

A Review on the Healthcare Supply Chain Management in Transforming Hospital Services

The aim of this paper is to present a novel idea to revolutionize hospital services by integrating healthcare supply chain management principles. By using a web application which enables hospitals to establish a real-time connection and streamline patient transfers. Many sectors focus on supply chain management optimization and qualifications to construct a better network of suppliers for their end consumers. Especially in healthcare sector, not only for pharmaceutical products but also for hospital materials, supply chain management gets importance for service quality and patient satisfaction. Thus, there are many studies about supply chain management in healthcare to emphasize its importance. In this study it is aim to put forward more review of existing solution on healthcare management system and patient transfer. Keywords- Supply Chain Management, Healthcare Service Delivery, MERN stack web application, Real-time data processing, Patient Transfers, Predictive Analytics, Patient-Centric Care, Efficiency, Seamless Coordination, Fast Connectivity

Strategizing the online healthcare supply chain management in rural areas: A study of critical success factors

AbstractOnline healthcare supply chain management (OHCSCM) in rural areas is a matter of concern and is susceptible to policy changes, especially after the alarms raised by the COVID‐19 pandemic. This study aims to develop an approach towards examining the state and building a framework for examining the OHCSCM in rural areas. The study develops a framework by integrating various theories and methodologies like stakeholders' theory, critical success factor (CSF) theory, triple bottom line theory, fuzzy set theory, evidential reasoning approach, and scenario analysis for identifying the dynamism among the CSFs. The developed framework helps prioritize the CSFs based on environmental, economic, and social perspectives. Further, the model helps create scenarios for decision‐makers to improve the understanding of the CSFs influencing OHCSCM and achieve sustainability. The study reveals that infrastructure development, security and privacy of data, and use of predictive tools are the top factors that need immediate attention. By recognizing the interdependencies and interactions between these factors, managers can develop a more comprehensive and integrated approach to OHCSCM in rural areas. This understanding enables them to identify potential bottlenecks, streamline processes, and optimize resource allocation, ultimately saving valuable time and resources for achieving sustainability in the online healthcare supply chain in rural areas.

Overview of Cyclic Bottleneck Problem

Abstract: In this paper, we are going to overview a newvariant of the classical assignment problem referred to as the cyclic bottleneck assignment problem (CBAP). This problem can arise in the healthcare industry or supply chain management. The problem can arise in surgical room scheduling and can be described as follows. Each of m successive time units can be allocated to any of the m surgeonsavailable. The number of patients to be scheduled for surgery on a given unit and the length of stay of the patient in the recovery unit are dependent on the type of surgery. The problem is to assign one surgeon to each OR time so as to minimize the maximum number of patients that will be expected to occupy the recovery unit in the long run. In this paper we have compared various ways through which the CBAP has been solved , that is Cohort Intelligence[4] , Tabu Search[2] and Iterated Local Search

Effective use of artificial intelligence in healthcare supply chain resilience using fuzzy decision-making model

AbstractAI technologies are absolutely changing the rules of the game all around the world. However, the diffusion rate of AI is widely ranging across countries. This study aims to fulfill a research gap regarding multidimensional comprehensive studies which could provide academic information to the policy makers, technology producers, adopters of technology and the workforce. Friction against the use of new technologies has been existing since the beginning of industrial revolution. This study examines the possible factors behind the friction in AI adoption process. The subject of the course in this study is the supply chain resilience which is a keystone in healthcare sector especially after the recent pandemics. Studies promise the efficiency improvements and cost reductions in healthcare when AI technologies are implemented in supply chain management of the industry. This paper proposes a fuzzy Aczel–Alsina-based decision-making model to analyze the factors that enhance the diffusion of AI technologies in healthcare supply chain management. The model is tested for the case of Turkish healthcare industry. Fuzzy decision-making model is used to solve the complexities in unveiling success factors in the implementation and diffusion phases. Results show that among many other factors tested, technology intensity, trialability and government support and policies are the most important AI success factors. The results are discussed to reveal potential policy recommendations.