Raising quality and safety of platelet transfusion services in a patient-based integrated supply chain under uncertainty

Blood supply chain network design: a systematic review of literature and implications for future research

Purpose

The purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.

Design/methodology/approach

The study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.

Findings

The results of the study present the researchers’ tendencies and preferences when designing their BSC network models.

Research limitations/implications

The study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.

Originality/value

The study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

Robust possibilistic programming to design a closed-loop blood supply chain network considering service-level maximization and lateral resupply

Reconfiguring the structure of the supply chain network is one of the most strategic and vital decisions in designing a supply chain network. In this study, a Closed-Loop Blood Supply Chain Network (CLBSCN) considering blood group compatibility, ABO-Rh(D), and blood product shelf life has been studied to determine the best strategic and tactical decisions simultaneously considering lateral resupply/transshipment and service-level maximization. Several vital parameters, including supply and demand, are considered fuzzy numbers to approximate reality due to the nature of the world. Furthermore, two crucial factors include ABO-Rh(D) and blood product shelf life considered, while the concept of lateral resupply governs the interconnections of hospitals' excess blood units. We propose a fuzzy multi-objective Mixed-Integer Non-Linear Programming (MINLP) model to consider two critical objective functions: minimizing the total costs of the network and maximizing the minimum service level to the patients at each Hospital. The fuzzy multi-objective MINLP model is converted to a deterministic multi-objective model using the equivalent auxiliary crisp model to deal with uncertainty. Then, by utilizing two interactive fuzzy solution approaches, the results have been compared based on a real case study to suggest the best solution for the proposed model. Also, we conduct sensitivity analysis on essential parameters such as demand, supply, and capacity to understand how these parameter variations impact two proposed objective functions. Then, the proposed model is tested on a real case study for model validation. The results confirmed that considering the lateral resupply could significantly save the costs of the designed network by a total of $343,000. Interestingly, maximizing the minimum service level at hospitals increased the service level from 58% to 68%.

Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages

As supplying adequate blood in multiple countries has failed due to the Covid-19 pandemic, the importance of redesigning a sensible protective-resilience blood supply chain is underscored. The outbreak-as an extensive disruption-has caused a delay in ordering and delivering blood and its by-products, which leads to severe social and financial loss to healthcare organizations. This paper presents a robust multi-phase optimization approach to model a blood supply network ensuring blood is collected efficiently. We evaluate the effectiveness of the model using real-world data from two mechanisms. Firstly, a Geographic Information System (GIS)-based method is presented to find potential alternative locations for blood donation centers to maximize availability, accessibility, and proximity to blood donors. Then, a protective mathematical model is developed with the incorporation of (a) blood perishability, (b) efficient collation centers, (c) multiple-source of suppliers, (d) back-up centers, (e) capacity limitation, and (f) uncertain demand. Emergency back-up for laboratory centers to supplement and offset the processing plants against the possible disorders is applied in a two-stage stochastic robust optimization model to maximize the level of hospitals' coverage. The results highlight the fraction cost of considering back-up facilities in the total costs and provide more resilient decisions with lower risks by examining resource limitations.

A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty

This paper presents a multi-product multi-period stochastic program for an integrated blood supply chain that considers red blood cells and platelets while accounting for multi-product interactions, demand uncertainty, blood age information, blood type substitution, and three types of patients. The aim is to minimize the total cost incurred during the collection, production, inventory, and distribution echelons under centralized control. The supply chains for red blood cells and platelets intertwine at the collection and production echelons as collected whole blood can be separated into red blood cells and platelets at the same time. By adapting to a real-world blood supply chain with one blood center, three collection facilities, and five hospitals, we found a cost advantage of the multi-product model over an uncoordinated model where the red blood cell and platelet supply chains are considered separately. Further sensitivity analyses indicated that the cost savings of the multi-product model mainly come from variations in the number of whole blood donors. These findings suggest that healthcare managers are able to see tremendous improvement in cost efficiency by considering red blood cell and platelet supply chains as a whole, especially with more whole blood donations and a higher percentage of whole blood derived platelets pooled for transfusion.

A novel capacity sharing mechanism to collaborative activities in the blood collection process during the COVID-19 outbreak

Because of government intervention, such as quarantine and cancellation of public events at the peak of the COVID-19 outbreak and donors’ health scare of exposure to the virus in medical centers, the number of blood donors has considerably decreased. In some countries, the rate of blood donation has reached lower than 30%. Accordingly, in this study, to fill the lack of blood product during COVID-19, especially at the outbreak’s peak, we propose a novel mechanism by providing a two-stage optimization tool for coordinating activities to mitigate the shortage in this urgent situation. In the first stage, a blood collection plan considering disruption risk in supply to minimize the unmet demand will be solved. Afterward, in the second stage, the collected units will be shared between regions by applying the capacity sharing concept to avoid the blood shortage in health centers. Moreover, to tackle the uncertainty and disruption risk, a novel stochastic model combining the mixed uncertainty approach is tailored. A rolling horizon planning method is implemented under an iterative procedure to provide and share the limited blood resources to solve the proposed model. A real-world case study of Iran is investigated to examine the applicability and performance of the proposed model; it should be noted that the designed mechanism is not confined just to this case. Obtained computational results indicate the applicability of the model, the superior performance of the capacity sharing concept, and the effectiveness of the designed mechanism for mitigating the shortage and wastage during the COVID-19 outbreak.

Re-design of a blood supply chain organization with mobile units

This research analyses the re-organization of a blood supply chain organization. Blood supply chain network design is a hard problem. Uncertainties of the blood supply and demand, perishability of blood over time and compatibility of blood types are some factors that make the problem difficult. This paper presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem. Unlike many studies on blood supply chain design in the literature, supply chain network consisting of mobile and permanent units is planned together effectively with our mixed-integer programming model. Multi-objective structure of the model minimizes distances between the blood supply chain elements and the length of the mobile unit routes. The objectives are prioritized by experts using the Analytical Hierarchical Process. Finally, the model is implemented on a real life case study using real data from the Eastern Anatolia region of Turkey for various supply demand scenarios. The solutions offered by the model are compared with the current situation in the region. It is shown that the proposed model gives at least %25 more effective solutions. Moreover, sensitivity analysis on the budget constraint is conducted, and robustness of the model is empirically illustrated.

A two-stage stochastic model for daily reserve in inventory management of Rh-negative red blood cells

Rh-negative rare blood inventory protection plays an important role in emergency blood protection. Normally, hospitals typically hold a fixed amount of daily reserve in response to emergency needs, but the measure can increase the unnecessary cost of repeated freezing and thawing. In order to save manpower, protect blood resources and reduce costs, a two-stage stochastic model is proposed to determine the optimal daily reserve of Rh-negative red blood cells, taking into account the uncertainty of demand. First, the model focuses on minimizing operational cost, shortage cost and damage caused by blood substitution. Then, the proposed model generates a series of discrete scenarios to solve the uncertainty of demand and predict the demand. In addition, a case study is presented to prove the validity of the proposed model with real data. Sensitivity analysis is also established to observe the effect of parameter changes on the results. Finally, the results show that the proposed model can effectively reduce the cost and current waste.

A reactive phase against disruptions for designing a proactive platelet supply network

Previous studies in blood supply chain network design often follow a commonly used approach in protecting the chain against disruptions, considering the effects of disruptions on the designing phase. However, in many real-world situations, disruptions cannot be adequately measured in advance. Moreover, using disruptions in the designing phase through the common two-stage stochastic programming models impose high costs on the network, since they cannot be updated based on unpredicted disruptions. This paper proposes an updatable two-phase approach which deals with disruptions in the operational phase, not in the strategic design phase. In the first step, called the proactive phase, a nominal platelet supply chain network is designed under operational uncertainty, using the whole-blood collection method. In the event of disruptions, the second step, called the reactive phase, is applied, and the tailored network is updated based on the realized data, using apheresis as the collection mechanism. The operational risks are captured using a fuzzy programming approach in the model. Based on the real data from Fars province of Iran, we compare the performance of the two-phase approach with the commonly used approaches in the literature, resulting in more flexible decisions, and consequently, less conservatism degree rather than the existing approaches.