Blood donation drive management: a discrete event simulation-based approach for enhanced operational efficiency Open Access

The realm of blood banking operations and logistics holds a crucial position within healthcare systems globally, ensuring the effective management of a vital medical resource: blood. Despite advancements in medical technology and logistics, operational challenges persist in blood donation drives, a fundamental component of the blood supply chain. Among the foremost challenges is the optimization of staffing and resource allocation to enhance efficiency and increase donor throughput. Interviews with blood banking officers have illuminated specific areas of struggle, particularly in the strategic deployment of staff and resources under various operational scenarios. For instance, a standard blood donation camp might be staffed by 9 non-medical and 3 medical personnel, with uncertainties regarding the best allocation of these resources affecting overall camp operations, including the organization of resources to expedite donor processing times.

Driven by the ambition to tackle these real-world challenges through computer simulation, this study conducts a thorough investigation into the operational intricacies of blood donation drives. The aim of this research is to meticulously document and analyze the operational flow and critical metrics within the camps. Utilizing “Simio”, known for enhancing operational planning and efficiency through in-depth scenario analysis, this study aims to offer concrete staffing and resource allocation recommendations.

Central to this research is the initiative to define and implement a more efficient operational procedure, specifically designed to enhance donor experience and retention. This approach is expected to streamline operations significantly, thereby improving the donation process’s efficiency.

This paper is organized to detail the literature review, methodology, data, simulation modeling and results.

The optimization of blood donation/collection drives is critical for maintaining an effective and efficient blood supply chain. Discrete Event Simulation (DES) has been increasingly utilized to enhance various aspects of these operations. This literature review synthesizes findings from multiple studies, highlighting how different tools have been applied to improve blood collection processes.

Efficient scheduling and configuration of blood collection centers are vital for maintaining service quality. Doneda, Yalçındağ, Marques, and Lanzarone (2021) analyzed operations at a blood collection center using DES to evaluate different configurations and scheduling strategies, demonstrating that DES can serve as a decision support tool for optimizing cost and service quality for donors, workers, and managers. Similarly, Elalouf, Tsadikovich, and Hovav (2021) focused on vehicle routing for blood sample transportation, applying a simulation-based approach to optimize vehicle fleet size, ensuring resource utilization without compromising healthcare service quality.

In Indonesia, Mansur, Vanany, and Arvitrida (2023) used DES to model a blood bank system considering supply unpredictability. This study explored various control variables to improve service quality and efficiency, emphasizing the impact of regulatory environments on shortages and obsolescence. In France, Alfonso, Xie, Augusto, and Garraud (2011) employed Petri net models to simulate fixed-site and mobile blood collection processes, providing a comprehensive view of resource requirements and donor behaviors. These models have been instrumental in identifying optimal strategies for managing donor flows.

Efforts to enhance donor satisfaction through simulation-based strategies are evident in the work by the Australian Red Cross Lifeblood. Using a Monte-Carlo simulation-based algorithm, Wellalage, Fackrell, and Zhang (2023) minimized staff requirements while reducing donor waiting times, thereby improving donor satisfaction and operational efficiency. Williams, Harper, and Gartner (2020) published a comprehensive review of quantitative methods in blood collection, emphasizing the need for integrated staff and appointment scheduling.

Meneses, Santos, and Barbosa-Póvoa (2023) reviewed optimization techniques for blood supply chains, highlighting the necessity of integrated solutions for strategic and tactical decision-making levels. A multi-objective mixed-integer location-allocation model by Karadağ, Keskin, and Yiğit (2021) demonstrated significant improvements in operational efficiency through real-life case studies, reinforcing the importance of robust planning in blood collection drives. Comparisons of integrated inventory blood supply chains using simulation showed improvements in key performance indicators like shortages and outdated units (Arani, Liu, & Abdolmaleki, 2020; AlZu’bi, Aqel, & Mughaid, 2021). These findings suggest that DES can provide feasible solutions for blood supply chain operations.

Technological advancements, such as data driven platforms for blood donation operations, were proposed by Elmir, Hemmak, and Senouci (2023) to improve blood collection efficiency and management. These platforms facilitate immediate donation requirements, notifications and organized blood distribution. All helping to support the goal of improving operational practices. Additionally, Ghosh, Goto, Ghosh, and Sen (2023) applied lexicographic optimization and taxicab geometry-based paths to optimize the routes from blood donation camps to blood banks. This method helped reduce spoilage and enhance transportation efficiency, contributing to the literature of ever-improving blood collection logistics.

Addressing uncertainties in blood supply chains, Hosseini-Motlagh, Samani, and Cheraghi (2020) employed mixed possibilistic-stochastic flexible robust programming, showing that donor motivation and efficient network design are crucial for maintaining supply chain reliability. Integrated simulation-optimization models were used by Osorio, Brailsford, Smith, Forero-Matiz, and Camacho-Rodríguez (2016) to support production planning decisions in blood supply chains, showing improvements in key indicators like shortages and outdated units.

The importance of promoting blood collection during crises, such as the COVID-19 pandemic, is emphasized by Mouncif and Bellabdaoui (2022), offering insights into effective donor recruitment and management strategies. Addressing the supply-demand mismatch in blood transfusion services, Gong, Dai, Ma, and Li (2022) highlighted the need for an active volunteer incentivizing donation system as an effective blood collection strategy. Collectively, these studies contribute to highlight the need for sophisticated modeling techniques, effective donor recruitment strategies, and adaptive approaches to maintain a reliable blood supply, particularly during crises and supply-demand mismatches.

The reviewed literature highlights significant advancements in blood collection and supply chain management through various methodologies, particularly emphasizing the use of Simulation and other optimization techniques. These studies have demonstrated improvements in scheduling, resource allocation, donor flow management, and supply chain efficiency. However, some gaps remain in the practical implementation of these models, especially in the context of standardized management of blood donation camps and optimizing donor experience.

This work aims to address these gaps by:

1. Collecting empirical data through interviews with blood banking professionals to capture real-world operational challenges.

2. Developing a baseline simulation model that accurately represents current blood donation camp practices.

3. Proposing an optimized camp layout and process flow based on identified inefficiencies.

4. Quantifying and analyzing the operational improvements achieved through the enhanced model.

By bridging the gap between theoretical models and practical implementation, this paper contributes to the field by providing a concrete example of how simulation and optimization techniques can be applied to real-world blood donation scenarios. The findings from this study have the potential to inform practices in blood donation camp management, ultimately leading to more efficient blood collection processes and improved donor experiences.

This study combines quantitative data from observations of blood donation camps with qualitative insights taken from interviews with blood banking officers. This approach allows for a detailed understanding of all interactions and processes, facilitating the development of a simulation model to test various staffing configurations and optimize operational procedures.

Data were collected through a dual approach:

1. Interviews

Conversational interviews were conducted with two blood banking officers, selected based on their roles in managing regional blood bank operations. These interviews provided an in-depth understanding of the operational flow, data management practices, challenges, and logistical nuances of conducting blood donation drives.

1. Observations

Data were sourced from 58 observations at one blood donation camp, offering empirical insights into the operational dynamics and donor flow within the camp.

In this paper, “stations” refer to specific desks or points that donors visit sequentially during their stay at a blood donation camp. These include the registration desk, doctor’s desk, preliminary blood analysis desk, bagging desk, blood donation desk, refreshment area, and medical checkup and feedback desk. Processing times were recorded for each station to quantify the time donors spend at each point, alongside the number of personnel taking control of each station to facilitate simultaneous processing of multiple donors.

This section presents a quantitative analysis conducted using Python programming to process and analyze log files generated from a simulation. The simulation reflects operational activities, and the analysis focuses on calculating essential queuing metrics. These metrics include the average arrival rate (λ), service rate (μ), average inter-arrival time, service time, waiting time, and the estimated average queue length. These calculations are vital for assessing and improving the flow of donors through the camp.

Additionally, a specific metric for the time donors spend in the system was directly calculated using the simulation tool. This measurement is critical for understanding the efficiency of the camp’s operations and identifying opportunities for optimization. The mathematical notations and techniques used in the analysis are as follows:

$D$ represents the initial dataset containing records of operational activities within a donor camp, loaded from a simulation log (CSV) file. Each record in $D$ comprises the following attributes:

1. $Rid$⁠: Resource identifier

2. R: Resource

3. $Oid$⁠: Owner identifier (donor)

4. $O$⁠: Owner (donor) name

5. $T$⁠: Task identifier

6. $Sstart$⁠: Start time of the activity

7. $Send$⁠: End time of the activity

8. Auxiliary Attributes: $Aavg$⁠, $Amin,Amax,RL,NL$

The first operation, $O1$⁠, involves the removal of irrelevant columns from $D$⁠, resulting in a cleaned dataset $D′$⁠. This step focuses on retaining only the attributes essential for subsequent analysis:

Operation $O2$ sorts $D′$ by the “Owner” (⁠$O$⁠) in ascending order based on numerical values extracted from the “Owner” attribute, producing D″:

The transformation operation, $O3$⁠, constructs a new dataset $Dtrans$ by reorganizing $D″$ such that each “Owner” is associated with a unique set of “Resource” start and end times. This operation is performed to facilitate the calculation of durations spent at each station by the donors:

Operation $O4$ calculates the duration $Dij$ each donor spends at various stations within the camp, with each duration rounded to two decimal places. This step is crucial for analyzing the flow and identifying bottlenecks within the camp operations:

Operation $O5$ calculates the average service time spent by donors at each station, excluding the initial “Owner” stage. This operation updates the dataset with an analysis of service durations, providing insights into the efficiency of each station.

Let $S$ represent the set of all stations (excluding “Owner”) and let $Di$ represent the duration spent by all donors at station $i$⁠. The average service time $Asi$ at each station $i$ is calculated as follows:

where $N$ is the number of donors processed at station $i$⁠, and $Dij$ is the duration spent by donor $j$ at station $i$⁠. The resulting average service times are stored in a dictionary, with station names as keys and their respective average times as values.

$O6$ involves embedding predefined average walking times between stations directly into the dataset $(Dfinal)$⁠, resulting in an enhanced dataset $Dwalking_averagetime$⁠. This operation introduces new columns that represent the average time donors spend walking from one station to the next:

These average walking times between stations are taken from the simulation results.

This is achieved by first calculating the total time spent between stations (the difference between the start time at the subsequent station and the end time at the preceding station). Then, the predefined average walking time is subtracted from this total time, yielding the waiting time. The dataset is thus updated to $Dwait$⁠, incorporating these calculated waiting times:

The formula to calculate the waiting time between two stations for a donor is given by:

where $Wij$ is the waiting time between station $i$ and station $j$⁠, $Tstartj$ is the start time at station $j$⁠, $Tendi$ is the end of the station $i$⁠, and $Waverageij$ is the predefined average walking time between the two stations.

Given $Dwait$⁠, the dataset with calculated waiting times, to ensure that all waiting times are non-negative, reflecting the premise that waiting cannot be negative in a real-world context:

where:

1. $Wi$ represents the original waiting time between stations $i$ and $i+1$⁠,

2. $Wi′$ represents the adjusted waiting time, ensuring that $Wi′≥0$⁠.

Following the adjustment of waiting times, the average waiting time $Wi̅$ for each transition $i$ is calculated as:

where $N$ is the number of donors processed at station $i$⁠. The resulting average wait times are stored in a dictionary, with station names as keys and their respective average times as values.

Given $S={s1,s2,…,sn}$

For each station $siϵS$⁠, let $Tstartsi$ $={t1si,t2si…,tnsi}$ represent the ordered set of start times for all donors at station $si$⁠, where $n$ is the number of donors processed at $si$⁠.

The average interarrival time $T̅intersi$ for station $si$ is then calculated by averaging all $tjsi$⁠, excluding the first since it cannot be calculated due to lack of a preceding arrival:

Operation $O9$ provides a systematic approach for quantifying and analyzing donor flow through the camp.

For each station $si$ with an average interarrival times $T̅intersi$ greater than zero, the average arrival rate $λsi$ is defined as:

For each station $si$ with an average service time $Asi$ greater than zero, the average arrival rate $usi$ is defined as:

Given the dataset $Dfinal$⁠, defining:

1. $S={s1,s2,…,sn}$ as the set of stations.

2. $Tsi={t1,t2,…,t2}$ as the set of start times for activities at station $si$⁠.

3. $Δt:$ Desired time interval for aggregation.

For each start time $tj$ in $Tsi$⁠, after converting $tj$ into a datetime object and segmenting the value of $Δt$ as desired and assigning $tj$ to the corresponding interval $Ik$⁠. Calculation of count for activities $Csi(Ik)$ that begin within interval $Ik$ for each station $si$ is given by:

The validation of the simulation model and its recommendations have been conducted through real-world application, with the participating blood bank implementing the suggested staffing configurations and reporting back on the observed impacts on operational efficiency and donor processing times.

This methodology ensures a rigorous and comprehensive approach to identifying and addressing inefficiencies within blood donation camp operations. By combining qualitative insights with quantitative modeling, the study offered actionable recommendations to improve the managerial efficiency and effectiveness of blood donation drives.

This section details the comprehensive insights and data points provided by blood banking officers regarding the operations of blood donation camps. Figure 1 illustrates the donor-station flow diagram along with donor processing capacities, offering a visual representation of the structured operations described in this section. These insights form the empirical foundation upon which the study’s simulations, analytics and prescriptive recommendations are built.

1. Registration

   • Duration: 3–10 minutes (median 5 minutes).

   • Process: Donors register and complete an 8-page questionnaire aimed at efficiency, including repeat donor information and health screening details.

2. Doctor’s Desk

   • Duration: 4–5 minutes (median 5 minutes).

   • Screening: Health checks are conducted against exclusion criteria guidelines to ensure donor eligibility.

3. Preliminary Blood Group and Hemoglobin Checking Desk

   • Duration: 2–4 minutes (median 3 minutes).

   • Process: Blood group analysis and hemoglobin level checks are performed to confirm blood type and ensure hemoglobin levels meet the 12.5g/dL minimum.

4. Bagging Desk

   • Duration: 3–4 minutes (median 3 minutes).

   • Process: Donors receive a 4-compartment blood collection bag, with one compartment containing an anticoagulation solution to preserve blood viability.

5. Blood Donation Station

   • Duration: 4–10 minutes (median 8 minutes).

   • Setup: Equipped with foldable beds and managed by a technician, where women donate 350ml and men typically donate 450ml of blood, adjusted based on the donor’s weight.

6. Refreshment Area

   • Duration: 5–10 minutes.

   • Purpose: Provides sugary snacks and beverages to replenish blood sugar levels and aid recovery, with a brief medical evaluation if necessary.

7. Medical Check and Feedback

   • Duration: 5–10 minutes (median 10 minutes).

   • Final Steps: A medical check is conducted, feedback forms are filled, and donors receive an appreciation certificate and a leave form if applicable.

Blood donation camps operate with dedicated personnel and a well-defined process flow. Each camp, equipped with four blood donation beds runs for eight hours (usually from 9:00 AM to 5:00 PM). The camp manages to serve at least fifty donors daily with the support of a structured staffing system:

1. Registration Desk: Managed by 1 Public Relations officer.

2. Doctor’s Desk, Medical Check and Feedback: Each operated by 1 doctor.

3. Preliminary Blood Group and Hemoglobin Checking Desk: Handled by 1 technician.

4. Bagging Desk: Staffed by 2 attendants.

5. Blood Donation Station: Operated by a team of 4, including 1 pricking professional, 1 nurse, and 2 attendants.

6. Refreshment Area: Overseen by 1 attendant.

In addition to the ten core staff members assigned to specific stations, five more ‘floating’ staff members facilitate overall camp operations.

This section outlines the simulation model developed to optimize the blood donation process, focusing on two main entities: donors and blood, designated as “ModelEntity”. The model simulates the donor journey from entry to donation, and the generation of blood as a separate entity.

Simulation Entities:

1. “Source”: Titled as “Donor_Source” introduces donors into the simulation, mimicking real-world arrival rates.

2. “Connector”: Used as a direct, zero travel distance connection for the donor to be introduced to the simulated blood donation camp.

3. “Server”: Representing each station at the camp except the blood donation station. Allowing modeling the specific capacity (i.e. number of ‘ModelEntity”) that can be processed concurrently and to set the processing time per entity as required.

4. “Seperator”: Representing the blood donation station, allowing creation of a blood entity for each donor entity upon a designated processing time as required.

5. “TimePath”: Used as a pathway between stations where the travel time is specified as required.

6. “Sink”: Titled as “Blood Exit” and “Exit” to facilitate destroying entities that have finished processing in the model.

Table 1 illustrates the configuration for each entity as used in the simulation.

The donor source configuration was based on data from a blood drive provided by blood banking officers. Interarrival times were calculated from this data and fitted to this statistical distribution. The rest of the data was derived from interviews with blood banking officers, incorporating their operational insights. Observations were analyzed and fitted to this statistical distribution.

The simulated blood donation camp serviced donors from 9:00 AM to 4:59 PM (7.98 hours). The simulated donor time at the camp was found to be 1.79 hours (107.4 minutes) on average.

Table 2 illustrates the calculated metrics derived from the mathematical analysis and techniques detailed earlier, summarizing the observed outcomes.

Average Interarrival Times: The consistent interarrival times noted here across stations suggest a steady donor flow.

Average Service Times: The average service times across the blood donation camp’s stations reflect operational efficiency and highlight improvement areas. Shorter service times, like at the Preliminary Blood Group and Hemoglobin Checking Desk (2.91 minutes), demonstrate swift donor processing, improving satisfaction. Longer service times at the Blood Donation Station (7.43 minutes) and Medical Check and Feedback station (8.44 minutes) indicate areas where future optimizations could enhance efficiency and the donor experience.

Average Waiting Times: Low waiting times between initial stations denote a streamlined process, but the elevated waiting time at the Medical Check and Feedback station, averaging 45.23 minutes, highlighted a serious area for operational improvement.

The camp’s flow efficiency was analyzed using average arrival (λ) and service rates (μ) per hour at each station. Arrival rates gauge how many donors reach a station hourly, while service rates show how many donors are processed. Figure 2 illustrates these rates to identify any stations, like the Medical Check and Feedback, where service rates are lagging behind other stations. This visual comparison will help confirm areas needing resource adjustments for better flow.

The queue lengths in Table 3 were estimated using (Little’s Law):

where:

$L$ is the long-term average number of items (donors, in this context) in a queuing system.

$λ$ is the long-term average arrival rate of items into the system.

$W$ is the average time an item spends in the system.

Upon presenting these results to the blood bank management, the findings were corroborated by the feedback from donors who have experienced longer waits at the Medical Check and Feedback station. The simulation has provided quantitative evidence that substantiates the anecdotal complaints, highlighting a simulated queue length at the Medical Check and Feedback station nearly six times longer than at any other station. This data-driven insight offered a solid foundation for targeted improvements to significantly reduce wait times and enhance the donor experience at this critical juncture of the donation process.

The subsequent subsections detail the recommended changes and their outcomes following implementation by the blood bank. The section elaborates on the specific adjustments proposed to address the identified bottlenecks, particularly at the Medical Check and Feedback station, and provides an analysis of their effectiveness in reducing wait times, improving flow, and enhancing the overall donor experience based on post-implementation data.

This section documents the targeted operational improvements suggested after an extensive consultation with blood bank officers, focusing on streamlining processes and eliminating redundant steps to enhance efficiency and donor satisfaction.

Operational Overlap:

An identified overlap between Station 2 (Doctor’s Desk) and Station 7 (Medical Check and Feedback) involved redundant health screenings and questionnaire assessments. Both stations required donor’s health information to be assessed, including the 8-page questionnaire and family medical history, leading to unnecessary repetition and prolonged wait times.

Recommendations:

1. Streamlining Health Checks: Consolidating the initial health check and medical questionnaire assessment into Station 2 (Doctor’s Desk) was recommended. By conducting a comprehensive health assessment at this early stage, including the medical checkup traditionally performed at Station 7, the process was effectively frontloaded. This adjustment reduced the workload and time required at Station 7, which was transformed into a Wellness Station, manned by a nurse for final checks, feedback collection, and certificate issuance.

2. Digitizing the Questionnaire: To further reduce registration times, the 8-page paper questionnaire at the Registration Desk was replaced with a digital version accessible via a QR code prior to the event. This pre-camp submission allowed donor information to be pre-loaded onto the registration desk PC, enabling quicker verification by doctors and reducing the registration process to 1–3 minutes (down from an average of 5 minutes). Moreover, the digital format allowed doctors to pre-screen donors for eligibility, potentially advising ineligible individuals not to attend, thereby streamlining the process even further.

3. Appointment Scheduling: The introduction of a “1 donor every 5 minutes” donor scheduling system aimed to manage donor flow more effectively, reducing congestion and wait times at critical stations.

4. Optimizing Resource Allocation: Simulation data indicated that during the camp’s operation, only three out of the four beds at the Blood Donation Station were utilized. Based on this finding, reducing the number of beds from four to three is recommended. This reallocation of resources is expected to cut unnecessary costs associated with idle capacity.

Outcomes:

These recommendations, upon implementation, resulted in significant operational improvements:

1. Doctor’s Desk: Service time was reduced to 2–5 minutes, with an average of 4 minutes.

2. Wellness Station (formerly Medical Check and Feedback): The restructuring led to a service time of 2–4 minutes, with an average of 3 minutes, reflecting the streamlined process.

3. Registration Desk: The shift to digital pre-registration shortened the service time to 1–3 minutes, with an average of 3 minutes.

Beyond enhancing the donor experience by minimizing the total time spent at the camp, these process optimizations yielded significant economic benefits. Most notably, by integrating the health checkup into the Doctor’s Desk and reassigning the responsibilities at the Medical Check and Feedback station, the camp was able to operate with one less doctor. This change not only maintained the quality of donor care but also resulted in considerable cost savings, demonstrating the substantial financial impact of the proposed enhancements. Figure 3 highlights the revised donor-station flow diagram, reflecting the process optimizations and updated donor processing capacities.

Table 4 illustrates the changes made to the simulation configuration to reflect the proposed operational improvements. Except for these adjustments, all other aspects of the simulation remained unchanged.

The simulated blood donation camp operated from 9:00 AM to 1:51 PM (4.85 hours), marking a 39.2% improvement in operational efficiency compared to the existing framework. Furthermore, the average time donors spent at the camp was reduced to 0.79 hours (47.4 minutes), reflecting a 56.4% enhancement in donor experience efficiency over the observed setup.

These findings aligned with observations from blood bank officers following the implementation of the proposed changes. The last donor left the camp at 2:09 PM—slightly later than the simulation predicted, but still allowing the camp to conclude all operations by 2:30 PM. This represented a significant improvement from the previous 5:30 PM closing time, highlighting the practical effectiveness of the adjustments in optimizing camp operations.

Table 5 illustrates the metrics that were calculated and observed from the simulation.

Following the implementation of strategic changes, including the “1 donor every 5 minutes” policy, the blood donation camp witnessed substantial improvements in operational metrics. Key changes included a reduction in average interarrival times at all stations to approximately 5 minutes, a significant improvement from previous times that hovered around 7 minutes. This resulted in a more streamlined donor flow and scheduling efficiency.

At the Registration Desk, service times were drastically reduced from 5.81 minutes to 1.74 minutes, a 70% decrease, achieved through digitizing the pre-registration process. Similarly, at the Medical Check and Feedback station, which had been a major bottleneck, service times were cut from 8.44 minutes to 3.52 minutes, representing a 58% reduction. Waiting times at this station saw a dramatic decline from 45.23 minutes to just 0.60 minutes, an astounding 99% decrease.

Figure 4 illustrates average arrival (λ) and service rates (μ) per hour, showcasing improved flow efficiency under the proposed framework.

Significant improvements were observed across all stations. For instance, the Registration Desk saw arrival rates increase from 8.36 to 12.00 per hour (a 43.54% increase) and service rates jump from 10.33 to 34.48 per hour (a 233.79% increase), greatly boosting efficiency in donor processing. The Doctor’s Desk experienced an increase in arrival rates from 8.42 to 12.00 per hour (42.52% increase) and service rates from 12.82 to 17.05 per hour (32.99% increase). The Wellness Station (previously the Medical Check and Feedback station), saw arrival rates surge from 7.03 to 12.00 per hour (70.7% increase) and service rates escalate from 7.11 to 19.61 per hour (175.8% increase), effectively alleviating previous bottlenecks. Other flow improvements were also noted at other stations, such as the Blood Donation Station, where arrival rates went from 8.36 to 11.95 per hour (42.94% increase) and service rates slightly rose from 8.08 to 8.45 per hour (4.57% increase). These adjustments not only increased the camp’s capacity to handle more donors but also significantly streamlined operations, leading to enhanced flow efficiency and improved donor experiences.

Table 6 illustrates the average walking times between stations and the estimated queue lengths, providing a quantitative overview of the improvements achieved through process enhancements. Despite a very slight increase in average walking time across various stations within the blood donation camp, there was a noticeable overall improvement in queue lengths, particularly at critical stages. The average queue length saw a marked reduction, especially noteworthy at the transition from the Refreshment Area to the Wellness Check, where it dramatically decreased from 6.29 to 0.73. This significant improvement at a traditionally congested point highlighted the effectiveness of the strategic enhancements implemented, demonstrating a focused effort to alleviate bottlenecks and streamline the donation process, thereby improving the overall efficiency and donor experience.

This study has demonstrated significant efficiency improvements in blood donation camp operations by streamlining processes and drastically reducing time and resource use. Enhanced further by observed donor appreciation initiatives, these improvements not only promise broader application prospects for elevating donor retention rates but also support the adaptability of the framework for both stationary and mobile blood donation camps. Real-world implementations confirm the practical effectiveness of the given recommendations. This success, aligned with the subsequent flow analyses, showcases the methodology’s effectiveness in live settings and highlights potential for global scalability. Figure 5 illustrates the donor flow analysis under the observed framework, identifying baseline efficiency and key areas for improvement.

The encouraging results from the newly implemented framework, evidenced by the accompanying flow analysis graph (Figure 6), suggest a scalable model for enhancing blood donation operations worldwide by highlighting the enhanced efficiency and scalability achieved under the proposed framework.

Future research should investigate the universal applicability of such enhancements and their impact on donor retention, with a particular focus on varying cultural and operational settings. The use of discrete event simulation in this context illustrates its potential for broader application in healthcare operations, promising advances in end user-centered care and resource management.