Authors :
Sudeep S. K.; Harshitha H. M.; Akshith R.; Vinay Dravid; Pranav L. R.
Volume/Issue :
Volume 11 - 2026, Issue 8 - August
Google Scholar :
https://tinyurl.com/ymrhesjx
Scribd :
https://tinyurl.com/3nzhunbr
DOI :
https://doi.org/10.38124/ijisrt/26aug718
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Perishable healthcare products such as vaccines, blood products, biologics and temperature sensitive medicines
incur high supply chain costs under limited shelf life due to uncertain demand with stringent storage requirements during
transport. However, a type of approach is to analyze and enhance the operation via simulation-based methods without
affecting the real-world systems. This paper presents an overview of studies that implemented simulation techniques in the
healthcare supply chains for perishable products linking inventory optimization, cold-chain logistics, waste elimination and
service-level improvement. The study uses discrete event simulation which models the movement, storage and consumption
of perishable health care products through many supply chain stages which include suppliers, distribution centers, hospitals,
and pharmacies. We look at key variables which include demand variability, lead times, spoilage rates, and storage capacity
to see how the system performs in different operational settings. What we found isthat which which optimized replenishment
policies, dynamic routing, and real time monitoring greatly reduce product waste at the same time that we maintain product
availability and patient safety. Results present that health care organizations which implement predictive analyticsin to their
simulation modelssee great benefit in terms of improved decision making which in turn they use to predict shortfalls, reduce
waste at point of expiry, and better respond in emergency or pandemic situations. Also we see in our study that which digital
technologies play a key role in this we look at IoT enabled cold chain monitoring and data driven inventory management.
References :
- Alidoost, M., et al. (2026). Simulation in Healthcare Supply Chains of Perishable Products
- Lowalekar, H., & Ravi, V. (2017). Inventory management of blood supply chain based on simulation.
- Evans, J. A., et al. (2019). Agent-based modeling of healthcare supply chains.
- Buschiazzo, V., et al. (2020). Modelling medical supply chain management with system dynamics.
- Ejohwomu, O., et al. (2021). Hybrid simulation method for optimisation of blood supply chain.
- Roy, D., et al. (2021). Applications of simulation in healthcare supply chain and logistics.
- Oliveira, M. D., et al. (2016). Supply Chain Modelling and Simulation
- Chilmon, B., & Tipi, N. (2020). A review of supply chain simulation models: I.
Perishable healthcare products such as vaccines, blood products, biologics and temperature sensitive medicines
incur high supply chain costs under limited shelf life due to uncertain demand with stringent storage requirements during
transport. However, a type of approach is to analyze and enhance the operation via simulation-based methods without
affecting the real-world systems. This paper presents an overview of studies that implemented simulation techniques in the
healthcare supply chains for perishable products linking inventory optimization, cold-chain logistics, waste elimination and
service-level improvement. The study uses discrete event simulation which models the movement, storage and consumption
of perishable health care products through many supply chain stages which include suppliers, distribution centers, hospitals,
and pharmacies. We look at key variables which include demand variability, lead times, spoilage rates, and storage capacity
to see how the system performs in different operational settings. What we found isthat which which optimized replenishment
policies, dynamic routing, and real time monitoring greatly reduce product waste at the same time that we maintain product
availability and patient safety. Results present that health care organizations which implement predictive analyticsin to their
simulation modelssee great benefit in terms of improved decision making which in turn they use to predict shortfalls, reduce
waste at point of expiry, and better respond in emergency or pandemic situations. Also we see in our study that which digital
technologies play a key role in this we look at IoT enabled cold chain monitoring and data driven inventory management.
