Step 1. Problem articulation
The main problem addressed in this research is focused on sustainable drug supply chain management within inpatient pharmacies. Specifically, the study aims to investigate the dynamic and complex interactions among various factors to determine the policies that can enhance profitability and reduce the amount of disposed medicines.
Step 2. Formulation of the dynamic hypothesis
This study formulated dynamic hypotheses based on the literature review and expert opinions. Seventeen challenges that can impact the efficiency and effectiveness of sustainable drug supply chain management in pharmacies were identified, and their relationships were examined using the fuzzy Delphi method. We defined the dynamic hypothesis as follows: Insufficient specialized human resources, poor responsiveness in emergencies, unnecessary prescription of drugs by residents, weaknesses in financial analysis, and expiration of certain medications are significant challenges in managing sustainable drug supply chains. Addressing and mitigating these challenges can improve financial issues and profitability for pharmacies.
Step 3. Formulation of a simulation model
At this research stage, two critical diagrams are designed: the cause and effect diagram and the stock and flow diagram. These diagrams serve as visual tools to represent and explain the relationships and dynamics within the system being studied.
Design a cause-and-effect diagram
We utilized the fuzzy Delphi method to identify the causal relationships among the 17 identified challenges. The results indicated that ten challenges were of the causal type, while seven were of the effect type, forming eight loops. For example, two loops, B1 and R1, are described. Figure1 illustrates the cause-and-effect diagram of sustainable drug supply chain management.
Causal diagram of sustainable drug supply chain management
In loop B1, poor emergency responsiveness leads to incorrect prioritization in drug procurement. Consequently, frequently used drugs that require immediate supply may be procured at the last moment. This, in turn, creates difficulties in managing these drugs and prevents a thorough analysis of drug consumption patterns in the pharmacy, hindering accurate prediction of future needs and causing problems in purchasing and maintaining appropriate drug inventory. Furthermore, this issue negatively impacts financial aspects such as liquidity, cost analysis, and pharmacy profitability. Since financial matters are among the most critical factors in attracting personnel to any organization, including pharmacies, it also hurts the sufficiency and recruitment of personnel.
As depicted in the R1 loop, the lack of a certified financial manager in the pharmacy results in inadequate liquidity management, which hampers the pharmacy’s cost-profitability analysis. Similarly, the insufficient analysis of the cost-benefit balance contributes to the decision not to employ a qualified financial manager.
Stock and flow diagram
This diagram illustrates the interrelationships among variables within a system and serves as a fundamental framework for constructing a quantitative model. Comprehending two fundamental concepts, namely stock and flow, is crucial when creating a Stock and Flow Diagram. Stock variables represent the state or condition of the system at a specific point in time. Even when considering time, these variables can still be defined. Examples of stock variables include the inventory of products or the number of personnel within a company. These variables provide a snapshot of the system’s status at a given moment and can change over time due to various flows occurring within the system.
In contrast, flow variables are contingent upon time and cannot be defined independently of it. They capture the rates of change or movement between stocks. Flow variables are influenced and modified by stock variables. For instance, when an order is dispatched, the stock of products is reduced. Similarly, the production of a product results in an increase in the stock of products. Other examples of flow variables encompass sales, employee turnover, or inflow and outflow rates of financial resources.
The construction of a Stock and Flow Diagram for a system involves utilizing various sources of information, including subject literature, expert opinions, and data obtained through sampling over time. These sources contribute to extracting the necessary variables and relationships within the system. Figure2 demonstrates the incorporation of quantitative formulas, qualitative relations, and numerical functions during the diagram’s construction.
Stock and flow diagram of sustainable drug supply chain management in inpatient pharmacies
The figure depicts a model with many variables characterized by complex and nonlinear relationships. Given the complexity and nonlinearity, it becomes challenging to model such dynamics using methods other than system dynamics. Figure2 exemplifies this situation, where Stock variables such as pharmacy profit, number of residents, total expiring medications, total disposed medicine, and inventory of high-consumption medicines are included. The Flow variables associated with these Stocks change based on input and output prices.
For instance, variables like the sales of low-consumption medicine before expiration and the profit from selling high-consumption medicine are considered input variables for the pharmacy profit Stock variable. On the other hand, variables such as the cost of disposed medicine, personnel wages, overhead costs of the pharmacy, and average personnel salaries are regarded as output variables influencing the pharmacy profit Stock variable.
Once the causal relationships within the system have been analyzed and understood, the next step is to test the complete system dynamics model. The testing process should be conducted systematically and purposefully. By conducting careful and deliberate simulation experiments, valuable insights can be gained into the real problem and the functioning of the systems involved in the problem situation. This process aligns with the fundamental objective of system dynamics modeling, which aims to understand the behavior of the actual system.
Testing the system dynamics model involves running various simulation experiments and observing the model’s behavior. These experiments help analyze the system’s dynamics, identify patterns, and assess the model’s performance. A deeper understanding of the system can be obtained by examining the behavior of the Stocks and Flows within the model.
Figure3 illustrates the behavior of selected simulation factors in the Stock and Flow Diagram. This diagram visually represents the interactions and dynamics among these factors.
Simulated behaviour of some variables over a period of 24 months
Variable - total disposed medicines
According to the simulation diagram and the relationships identified, the variables of total expiring medicines, the rate of disposed medicines, and the exchange rate between pharmacies influence one another. Based on this information, it can be inferred that the trend of disposed medicines over the next 24 months will be upward, indicating an increasing wastage rate.
Moreover, the rate of increase in disposed medicines is expected to be even higher during the second 12-month period compared to the first 12 months. This suggests that the growth in the total wastage of medicines will follow an exponential pattern. Consequently, pharmacies will face rising costs and reduced profits due to increased wastage of medicines.
Additionally, the simulation results indicate that the overall trend of expiring medicine will exhibit an upward trajectory over the next 24 months. However, it is noted that the trend remains stable during the initial months, specifically the first two months.
Variable - level of total expiring medicines
The delivery rate of expiring medicines to other pharmacies can significantly influence the behavior of the variable related to medicine disposal/expiration. If the delivery rate is low or inefficient, it can contribute to the accumulation of expiring medicines within pharmacies. This, in turn, can lead to a higher rate of medicine disposal or expiration, increasing the total waste of medications.
Variable - cost of disposed medicines
As per the flow diagram in the research, the variable of medicine disposal rate, along with the average selling price of low-consumption medicines, influences the behavior of the variable associated with pharmacies’ profits. The disposal rate of medicine refers to the rate at which pharmacies discard or waste medicines.
The flow diagram suggests that this variable directly impacts pharmacies’ profits. When the disposal rate of medicine increases, the costs associated with wasted medicine also increase. These costs can include disposal fees, write-offs, or any other costs incurred due to the disposal of medicines.
The simulation results indicate that the cost trend of disposed medicine is expected to increase over the next 24 months. This suggests a rising trend in the costs associated with wasted medicine. Furthermore, the rate of increase in the second 12 months is higher than in the first 12 months, emphasizing the need for pharmacies to pay close attention to this trend.
Variable – sales of low-consumption medicine before expiration
This variable exerts a direct influence on the profitability of pharmacies. Its behavior is shaped by several factors, including response speed (the timely processing and delivery of medicines, as well as providing accurate advice and addressing customer queries), the rate of distributing expiring medicine to other pharmacies, the profit rate associated with low-consumption medicine, and the average selling price of such medicines.
The results indicate that the overall trend of sales for low-consumption medicines before expiration is projected to increase over the next 24 months gradually. However, it is noteworthy that there is a notable surge in sales during the initial two months, followed by a subsequent period of slower growth.
Variable – pharmacy profitability
The most crucial variable in the model, directly reflecting a pharmacy’s performance, is its profitability. This variable’s behavior is directly influenced by several factors, including profit derived from the sales of low-consumption medicines before their expiration, profit from the sale of high-consumption medicines, the cost associated with disposed medicines, staff salaries, and other operational costs incurred by the pharmacy.
The results indicate that the overall trend in pharmacy profitability will increase over the next 24 months. Notably, the rate of increase during the first 12 months is higher. This can be attributed to factors such as the rising trend in total wasted medicines and the associated costs of disposing of these medicines.
Variable – buying high-consumption drugs
One of the other variables in the model that affects the profitability of pharmacies is “Buying high-consumption drugs.” The behavior of this variable is influenced by the variables of response speed, the share of buying high-consumption drugs from the monthly purchase, time to receive the order, and monthly purchase of medicine. Based on the simulation graph in Fig.3, the overall trend of “Buying high-consumption drugs” over the next 24 months fluctuates sinusoidally with increases. It decreases proportionally to the volume of orders for this type of drug.
Variable – selling high-consumption drugs
Among other variables in the model that impact profitability, the variable of high-consumption drug sales plays a significant role. The variables of inventory of high-consumption drugs and demand for high-consumption drugs influence the behavior of this variable. As depicted in the simulation graph in Fig.3, the overall trend of high-consumption drug sales over the next 24 months follows a sinusoidal pattern, increasing and decreasing monthly, similar to the purchasing levels.
Step 4. Validating the model
To ensure the validity of the model’s performance, several tests were conducted.
- a)
Structure Verification Test.
This study presents a sustainable drug supply chain model in hospital pharmacies affiliated with Iran’s Medical Sciences Universities. The model has been developed at a macro level based on a natural system, and it incorporates a comprehensive visual representation of the system and its workflow. The model’s structure is consistent with the existing knowledge of the actual system and aims to simulate the most relevant aspects of the actual system. Expert opinions were also sought to ensure the model’s relevance and alignment with the real-world system.
- b)
Parameter Verification Test.
Given that the variables employed in the model are based on extensive background research, the parameter values were derived from documentation obtained from 17 hospital pharmacies affiliated with Iran University of Medical Sciences. Additionally, expert interviews were conducted to ensure the accuracy and alignment of these parameter values with real-world conditions. Thus, it can be asserted that the selected parameter values reflect actual values prevailing in the studied context.
- c)
Boundary – Adequacy (Structure) Test.
In order to conduct this test, the behavior of critical variables in the model, namely the resident pharmacy rate, the number of residents, and the graduation rate, was evaluated under the assumption of approaching a minimal value (zero). Figure4 presents the outcomes obtained from applying these zero-limit conditions. The simulated behavior of the variables demonstrates that they exhibit rational and acceptable patterns. Specifically, when one variable experiences a significant decrease, its dependent variables also decrease as anticipated.
Graph of resident pharmacy rate, number of residents and graduation rate variables in normal and boundary conditions
Step 5. Policy design and evaluation
A vital advantage of the system dynamics approach is its ability to accommodate various scenarios and facilitate result comparisons. Multiple future scenarios can be developed once a reliable model with an enhanced policy structure is established. At this stage, diverse management options can be explored by manipulating systemic and policy parameters. The impact of these variations on the dynamic behavior of the model can be observed, generating a range of future scenarios.
Different decisions can be made within each scenario, considering the specific problem and its associated results. This allows for the evaluation of different policies and their effectiveness. By comparing these scenarios, the relative merits and drawbacks of different policies can be assessed, aiding decision-making processes. This approach provides a valuable tool for exploring alternative pathways and identifying the most favorable strategies for addressing complex problems.
Scenario 1: increasing the competence of human resources
In this scenario, the workforce competence variable was doubled, and its impact on the behavior of pharmacy profit can be observed in Fig.5. The figure demonstrates that with an increase in workforce adequacy (doubled in this case), the profit of pharmacies also exhibits a steady upward trend. This observation suggests that the adequacy of human resources, influenced by factors such as employee training and experience, plays a significant role in determining the profitability of pharmacies.
Changes in the main variables of the research under the four research scenarios
Scenario 2: increase the response speed
In this scenario, the response rate is doubled, as reflected in the depicted profit amount. Initially, with the increased response speed in the first few months, the profit amount is lower compared to the baseline case. This could be attributed to reduced accuracy and decreased staff concentration during the adjustment period. However, the profit gradually increases at a steep rate afterward.
Therefore, increasing the response speed by augmenting the number of staff and involving pharmacists in the prescribing team can significantly impact pharmacy profits. Additionally, the results indicate that as the response speed increases (doubled in this scenario), the expiration rate of medicines decreases. This suggests that a faster response rate, influenced by factors such as staffing levels and the involvement of pharmacists in the prescribing process, can effectively reduce the rate of medicine expiration.
Scenario 3: using a pharmacist in the prescribing team
In this scenario, the number of pharmacists involved in the prescribing team was reduced by half. The outcome of this reduction is a decrease in profit. Hence, it can be concluded that utilizing a pharmacist team in the prescription process significantly impacts the profitability of pharmacies. The involvement of pharmacists in the prescribing team enhances response speed, enabling the sale of low-consumption medicines before their expiration dates. Consequently, this contributes to an increase in pharmacy profits.
Scenario 4: simultaneous increase of human resource competence, response speed and use of pharmacist in the prescription team
Figure5 presents the outcomes of augmenting workforce adequacy, response speed, and the integration of pharmacists in the prescribing team, as well as the combined implementation of all three scenarios on pharmacy profits. The figure demonstrates a consistent upward trend in profits as workforce adequacy, response speed, and pharmacist involvement in the prescribing team increase.
