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Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers

Author

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  • Lakshmy Subramanian

    (Research Department, Pamela Steele Associates, Oxford OX2 7HT, UK)

Abstract

Health supply chains aim to improve access to healthcare, and this can be attained only when health commodities appropriate to the health needs of the global population are developed, manufactured, and made available when and where needed. The weak links in the health supply chains are hindering the access of essential healthcare resulting in inefficient use of scarce resources and loss of lives. A chain is only as strong as its weakest link, and demand forecasting is one of the weakest links of health supply chains. Also, many of the existing bottlenecks in supply chains and health systems impede the accurate forecasting of demand, and without the ability to forecast demand with certainty, the stakeholders cannot plan and make commitments for the future. Forecasts are an important feeder for budgeting and logistics planning. Under this backdrop, the study examines how improved forecasting can lead to better short-term and long-term access to health commodities and outlines market-related risks. It explores further how incentives are misaligned creating an uneven distribution of risks, leading to the inability to match demand and supply. For this purpose, a systematic literature review was performed, analyzing 71 articles from a descriptive and content approach. Findings indicate the emerging trends in global health and the consequences of inaccurate demand forecasting for health supply chains. The content analysis identifies key factors that can pose a varying degree of risks for the health supply chain stakeholders. The study highlights how the key factors emerge as enablers and blockers, depending on the impact on the overall health supply chains. The study also provides recommendations for actions for reducing these risks. Consequently, limitations of this work are presented, and opportunities are identified for future lines of research. Finally, the conclusion confirms that by adopting a combination of approaches, stakeholders can ensure better information sharing, identify avenues of diversifying risks, and understand the implications.

Suggested Citation

  • Lakshmy Subramanian, 2021. "Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers," Logistics, MDPI, vol. 5(1), pages 1-21, February.
    • Handle: RePEc:gam:jlogis:v:5:y:2021:i:1:p:12-:d:507512
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    References listed on IDEAS

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    1. Wang, Xun & Disney, Stephen M., 2016. "The bullwhip effect: Progress, trends and directions," European Journal of Operational Research, Elsevier, vol. 250(3), pages 691-701.
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    Cited by:

    1. Kamila Parzonka & Costase Ndayishimiye & Alicja Domagała, 2023. "Methods and Tools Used to Estimate the Shortages of Medical Staff in European Countries—Scoping Review," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    2. Chayada Kanokphanvanich & Wanchai Rattanawong & Varin Vongmanee, 2023. "A New Model for a Sustainable Healthcare Supply Chain Prioritizes Patient Safety: Using the Fuzzy Delphi Method to Identify Healthcare Workers’ Perspectives," Sustainability, MDPI, vol. 15(9), pages 1-23, April.
    3. Arnesh Telukdarie & Logistic Makoni & R. Raghunatha Sarma & Megashnee Munsamy & Sunil Kumar, 2025. "System Models for Synchronous Strategies in Operational Healthcare Forecasting," IJERPH, MDPI, vol. 22(2), pages 1-28, February.
    4. Horst Treiblmaier & Patrick Mair, 2021. "Textual Data Science for Logistics and Supply Chain Management," Logistics, MDPI, vol. 5(3), pages 1-15, August.

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