Modelling hospital inventory management using interpretive structural modelling approach

2019

JM2

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Purpose Delivering vaccines to the children who need them requires a supply chain that is efficient and effective. In most of the developing countries, however, the unknown and unresolved supply chain issues are causing inefficiencies in distributing vaccines. There is, therefore, a great need in such countries to recognize the issues that cause delays in vaccine delivery. With this purpose, the present study aims to identify and analyze the key issues in the supply chain of basic vaccines required to immunize children in developing countries. Design/methodology/approach Based on a field survey of three states of India, in-depth review of relevant literature and experts’ opinions, 25 key issues were recognized as factors of the vaccine supply chain (VSC) and categorized into five main domains. Using integrated interpretive structural modeling and fuzzy analytic network process approaches, the issues have been prioritized to determine their relative importance in the VSC. In addition, a sensitivity analysis has been performed to investigate the priority stability of the issues. Findings The results of the analysis show that among the five domains of VSC issues, the economic domain with a weight of 0.4262 is the most important domain, followed by the management (0.2672), operational (0.2222), environmental (0.0532) and social (0.0312). Research limitations/implications This study focuses on the prioritization of VSC issues; therefore, the results of the present study can provide direction to the decision-makers of immunization programs of developing countries in driving their efforts and resources on eliminating the most important obstacles to design successful vaccination programs. Originality/value To the authors’ knowledge, this paper is first to provide a direction to the decision-makers in identifying and managing important issues through the use of an analytical approach.

Section: Interpretive Structural Modelingmentioning

confidence: 99%

“…The main steps involved in the ISM methodology are (Kumar and Kumar, 2015): ISM approach starts by identifying the variables or factors considered for the problem.…”

Section: Interpretive Structural Modelingmentioning

confidence: 99%

Prioritizing the vaccine supply chain issues of developing countries using an integrated ISM-fuzzy ANP framework

Chandra

2019

JM2

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“…The availability of staff in rural areas is affected by various policies that include incentives, regulatory barriers, and funding sources (Figueroa, 2016). An interpretive structural model was developed for identifying the factors, such as lead time and patient arrival rate influence the entire inventory management, related to medicine stock management at a hospital (Kumar and Kumar, 2015). The lead time throughout the supply chain can increase because of numerous reasons.…”

Section: Literature Reviewmentioning

confidence: 99%

Managing the essential medicines stock at rural healthcare systems in India

2018

IJHCQA

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Purpose The purpose of this paper is to eliminate the medicine stock-out problem by building an optimum medicine stock in rural healthcare centers in India. Design/methodology/approach Data associated with inflow and outflow of a specific medicine (folic acid tablets) arer collected from all consecutive supply chain stages during the survey. While conducting the survey, it is found that several medicines are out of stock owing to uncertain lead time and demand. Integrating with quantity discount and min–max (s, S) inventory policy, two models are developed using system dynamics: one is Model 1 with constant lead time and uncertain demand, and the other is Model 2 with both uncertain lead time and demand. Findings Both models are simulated for a period of one year on Stella 9.1 platform. The results are compared with actual data, and the comparison shows significant improvement of the medicine stock at all downstream stages, while maintaining a certain safety stock. Further, Model 2 suggests a larger stock than Model 1 at each point of time. Practical implications Despite numerous issues, the stocks of medicine in rural healthcare systems can be improved as suggested by the models. The models depict the behavior of inventory stock at each stage of the supply chain and act as a function of time that could be used in the form of a prediction tool for the policymakers. Originality/value This paper is one of the first papers that had developed the model of the medicine supply chain in rural parts of a developing country. It provides a generic framework for the stock assessment and improvement throughout the supply chain.

“…Furthermore, the effect of medical tourism has been discussed and it has been concluded that the factors such as medicine insurance coverage, international healthcare collaboration and efficient information system are dependent factors, whereas the factors such as research in medicine and pharmaceutical science, medical tourism market, transplantation law, top management commitment, national healthcare policy, competent medical and paramedical staffs are found as the independent enablers (Ranjan Debata et al , 2013). An interpretive structural model (ISM) was developed to identify the factors related to medicine stock management at a hospital, such as lead time and patient arrival rate that influence the entire inventory management and supply chain system (Kumar and Kumar, 2015). Also, other healthcare aspects investigated so far provide a strong base to the body of knowledge.…”

Section: Literature Surveymentioning

confidence: 99%

India’s rural healthcare systems: structural modeling

2018

IJHCQA

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Purpose The purpose of this paper is to identify factors related to rural healthcare services and establish a hierarchical model for the effective rural healthcare management in India. Design/methodology/approach A questionnaire survey identified and correlated numerous factors related to the Uttarakhand rural healthcare systems. Experts opinion were translated into a reachability matrix and an interpretive structural model. A fuzzy matriced impacts croises-multiplication applique and classment (FMICMAC) analysis arranged the factors as hierarchical stages using their driving power. Findings The interpretive structural and FMICMAC hierarchical models suggest four key driving factors: diseases, climatic conditions, population growth and political pressure. Practical implications Despite numerous issues, rural healthcare services can be improved by considering key driving factors that could be used as a prediction tool for policy makers. Originality/value Results demonstrate that population control, coordinating services with local bodies and rural health center annual maintenance can be game changers toward better healthcare services.