Simulation-Based Models of Multi-Tier Financial Supply Chain Management Problem: Application in the Pharmacy Sector

Abstract

A crucial role in the continuation of economic activities is played by the financing of services and production in supply chains. A key element of optimizing the financial flow of these complex networks is to pay attention to the financial aspects of these complex networks since they are becoming more and more complex and expanding. This study aims to investigate the supply chain of a pharmaceutical company’s holding company and its subsidiaries while using internal resource valuation to develop a new strategy for financing the company’s operations. There is a process of money circulation through the chain, which consists of passing through two treasuries (primary and secondary), which provide liquidity to compensate the deficits of some institutions with the excess liquidity of other institutions. In this article, we present three simulation-based models based on a case study conducted at Shafa Darou Investment Company in Tehran-Iran, a leading pharmaceutical investment company in the country, to examine the impact of implementing this idea in the real world. Considering the study’s results, it has been shown that the supply chain as a whole has improved in terms of its working capital. Using a set of local treasuries is generally associated with reducing risks and a greater level of stability when relying on the excess liquidity of chain members provided that financial independence from external institutions, such as banks, is maintained. In addition, if the members’ excess liquidity is deposited in a set of local treasuries rather than a bank, the profit and internal financial flow within the chain will be circulated throughout the chain, and more added value will be generated.

1. Introduction

Supply chain finance manages the financial flow along the supply chain to manage the maximum flow and delivery of financial services [1,2]. Supply chain finance management plays a significant role in financial or operational measures and is attracting more and more attention from researchers and industry activists [3,4,5,6]. According to Hoffmann [7], supply chain finance can be viewed as an interdisciplinary branch of management that integrates the three disciplines of logistics management, supply chain management, and financial management. The ultimate goal of supply chain finance management is to ensure that financial flows are coordinated with the flow of goods and information in the supply chain and to improve cash flow from the point of view of supply chain management. There are several benefits associated with the application of the financial approach in supply chains, which are derived from the cooperation between the stakeholders of the supply chain and usually result in a reduction in the cost of debt, new opportunities to receive facilities, and a reduction in the working capital of the members.

The prediction of each member of the chain regarding the final demand determines the amount of their investment. Predictable changes in the company’s working capital level make it possible to plan for its surplus and deficit. When companies face a working capital deficit, they usually have two methods to cover it, depending on the amount of time and the number of resources they require, either by converting their assets to cash or by obtaining loans (generally banks and financial institutions). When it comes to state-of-the-art financing methods, it is evident that they are attracting increasingly more attention from various institutions as they develop. A summary of some new-emerging financing methods is shown in Figure 1.

The idea of this research is inspired by FTP, with match maturity, in banking system operations. In the banking system, a general categorization could be made concerning the activities of attracting deposits and lending money out as costly and profitable in terms of the bank’s business model. It is important to note that the branches of banks perform different activities pertaining to these activities. As an example, those branches that are located in residential areas tend to be more active when it comes to attracting deposits, while those that are located in business areas tend to be more active when it comes to providing loans. Consequently, interest rates are designated for financial flows among various branches and the bank treasury. Several similarities can also be drawn between supply chain management entities, which is presently one of the top-notch and trendy topics these days, and the network of bank branches. In conclusion, we can conclude that the financing concept mentioned above has the potential to be incorporated into a supply chain network in a very beneficial way. A crucial goal of this study is to provide a solution for maintaining the financial resources within the supply chain and improving the financial flow.

Having identified this problem, the purpose of this study is to investigate the policy or approach that would take advantage of the surplus resources of some members of the chain to cover the lack of liquidity in the chain of other members through the establishment of a local treasury. A simulation model of a drug supply chain, inspired by the findings of a real-world case study at Shafa Darou Investment Company, has been developed to investigate the effect of introducing a local treasury on the performance of the entire supply chain. In the continuation of this article, in Section 2, articles on supply chain finance are reviewed, and research gaps in this field are indicated. A description of the problem and its assumptions is provided in Section 3. Section 4 describes the components of the model, the case study, and the proposed approach in detail. Section 5 presents the principles and method of simulating three-level financing, and the results of their output and analysis are presented. Finally, Section 6 presents the conclusion.

2. Literature Review

Many articles and books have been published in supply chain finance, especially in recent years, and many branches of this field have been divaricated. As a result of this increase, there is a clear indication that researchers are paying attention to this area and that it is becoming more critical for industry managers. It must be noted, however, that few studies have applied a comprehensive analysis and research approach to the various aspects of this field in a categorical and thorough manner [8,9].

As the name implies, supply chain finance management refers to all the levels involved in moving money along the supply chain. To optimize these financial processes, supply chain management helps companies look at the whole chain from a systematic point of view. Earlier studies on supply chains tended to focus on aligning product, service, and information flows [10]. However, they neglected to take into account financial aspects. Because of this, the balance between the financial flow and other components of the supply chain has become increasingly important in recent cases. In the wake of the financial crisis, there has been an increase in interest in supply chain finance, with a significant decrease in bank loans, as the need for better management and optimization of working capital has become apparent [11].

However, limited articles [8,9] have comprehensively and categorically examined the various dimensions of this field.

According to Bozacott and Zhang [12], it was the first time researchers explored the impact of loans on production decision-making in the face of capital constraints by using the newsvendor concept for retailers and banks. The authors represented that manufacturers tend toward credit financing and resolving the budget deficit via retailers.

Looking at the financial flows in supply chains, Pfohl et al. [13] have produced a review article on such flows in supply chains. According to this article, the analysis and optimization of capital costs have not been carried out clearly and systematically. An attempt has been made to fill this gap by developing a conceptual model and a mathematical formula for “supply chain finance”.

In an article, Gupta and Dutta [14] studied the flow of money in a supply chain from the view of supply chain components that receive money through downstream partners and pay to upstream partners; the goal is to schedule all payments within the limit of receiving money. A fine will be imposed if the payment is not made within a stipulated period. Any unused money in a certain period can be invested to earn profit. Srinivasa Raghavan and Kumar Mishra [15] considered a supply chain, including a manufacturer and a retailer, which are facing financial constraints, and none can produce at the optimal level. The study’s results demonstrated that a facility provider to the manufacturer might also be interested in covering the retailer’s deficit.

The authors Jing and Seidman [16] conducted a study where they compared bank facilities and intercompany facilities for a retailer with financial constraints. They showed that intercompany facilities are more effective in limiting price increases among chain companies when production costs are relatively low (Double marginalization) and the bank facilities work in opposite conditions.

Wu et al. [17] examined the situation where large manufacturing companies support their small supplier companies by using their resources to provide them with financial assistance.

Chen [18] examined a manufacturer and its distribution network, comparing banking loans and manufacturer-to-distributor loans and measuring the impact of production costs, internal capital, and demand changes on the firm and the bank. Kouvelis and Zhao [19] compared the short-term loan needs of suppliers and retailers. They examined factors such as banking loans, fixed and variable costs, and the risk of bankruptcy to conclude that the two-company solution is superior in increasing the profits of both companies involved.

Xiao [20] presented a model in which a supplier and a retailer with liquidity constraints are considered, and the retailer company cannot use bank facilities due to its low credit rating. As a result, the retailer company receives its bank facility in the form of a purchase credit from the supplier company. By presenting a model based on operational and financial planning aspects in a two-stage supply chain, Protopappa-Sieke [21] examine the operational and financial aspects of planning. A review of two working capital allocation scenarios is included in this study of a model. In the first scenario, supply chain members have their working capital separately. In the second scenario, working capital is allocated jointly so that a treasury of working capital is common to all supply chain members. According to the findings of this study, suppliers in the supply chain have several advantages due to sharing working capital.

Another study was conducted by Gelsomino et al. [8] to categorize the current studies dealing with supply chain finance in accordance with the main themes and methodologies. From a financial view, it focuses on short-term solutions provided by financial institutions and deals with accounts payable and receivable. From a supply chain view, it focuses on optimizing working capital in terms of accounts payable, receivable, and inventories.

Chakuu et al. [1] have conducted a study to review the literature on Supply Chain Finance (SCF), focusing on actors, tools, coordination, process performance, and their relationship.

Yang and Birge [22] developed a model to explore the role of purchase credit on shared risk in various situations, including retail prepayment, when the retailer has more financial power than the manufacturer or supplier. Several examples of emerging solutions have been used in practice, including providing various forms of guarantees by larger institutions to reduce the financing costs of smaller partners that require financing. In other words, this policy is a win-win for everyone in the chain. An in-depth review paper was published by Xu [9] that looked at the concept of supply chain finance and identified the most striking research gaps in the literature on this sub-branch. A study by Alavi et al. [23] was conducted to develop the supply chain network using commercial and bank credit and presented a model that can be applied in a real-life situation. This research team developed a robust probabilistic optimization model for the supply chain to estimate the financial resources for the supply chain based on financial and commercial credit conditions, which was developed to maximize the profit while simultaneously considering the uncertainty of the demand in the research. Huang et al. [24] considered two sources in a Buyer-Backed Purchase Order Financing (BPOF) financing mechanism (small institutions with limited capital and supporting institutions with stronger financing ability), with the approach of the resources supply and joint guarantee, which determined the conditions and features of the optimal strategy for the buyer to balance the risk and benefit of BPOF. According to Deng et al. [25], few studies have investigated the effectiveness of buyer financing in an assembly system with multiple suppliers. This research fills the literature gap by comparing buyer financing with bank financing in a supply chain with an assembler and several suppliers with a heterogeneous capital constraint component. Various equilibrium solutions are recommended for different financing plans (for example, buyer financing, bank financing, and no financing).

Cao et al. [26] present a BPOF contract assuming the presence of two institutions (one with limited capital and another with good capital) and the bureaucratic problems of obtaining loans from banks to fund production. Chain members offer credit guarantees to each other to receive loans.

Lee et al. [27] evaluated the optimal order quantities of retailers and the fair interest rates of banks in two guarantee models by examining two supply chain financial models and the process of receiving loans from banks. The result of the research provided a model with the purpose of financing in addition to guaranteeing the interest of the guarantor.

Zhang et al. [28] examined the data of 18,448 companies from eight industries in the U.S. They concluded that supply chain financing is effective but does not improve the overall corporate financial performance and warehousing management. In research carried out by the researchers, it was found that supply chain financing reduces the risk of bankruptcy in the central company, resulting in a stabilization of the supply chain. Razavian et al. [29], through a two-stage stochastic programming model, presented an integrated material-financial resilient supply chain model. They reviewed trade credit and bank credit strategies in their study. They concluded that a resilient strategy can improve the supply chain performance. Considering the vital role of small institutions in the supply chain and social stability, Wu [17] has investigated the problem of these institutions accessing traditional bank financing. Several credit institutions in the chain have developed platforms to coordinate banks with other credit institutions to provide purchase order financing to fund small institutions. Mitra et al. [30] analyzed the financial supply chain in a smart lending platform. Supplier and retailer relationship was scrutinized in their study. Najafi-Tavani et al. [31] studied the impact of financial supply chain management on supply risk. They concluded that the contingent influence of relationship quality and buyer relative power exists on the FSCM-supply risk link. Perez et al. [32] investigated a three-echelon chemical supply chain in which material, information, and financial flows are integrated. They stated that their model has more accuracy than others and compared each model in a stochastic discrete event simulation environment. Yang [33] presented a strategic model for managing a pharmaceutical supply chain in which environmental, social, and governance issues are considered. He also proposed financial obstacles for the presented supply chain. Badakhshan and Ball [34] developed a simulation optimization for integrating physical and financial flow in a supply chain while economic uncertainty exists.

According to the literature review, in previous studies, banks have played an influential role in providing credit through loans, which are considered an external organization for the chain, and because of this, there are problems such as the administrative bureaucracy of receiving loans and paying high interest. Also, the need to provide credit guarantees to receive a loan can be an obstacle for smaller institutions in the chain. In past studies, an attempt has been made to reduce such risk, and it has been effective to some extent by presenting some approaches, but it remains one of the important factors of the issue to be investigated and resolved. Therefore, according to the mentioned cases, providing working capital and its details for chain institutions is one of the basic and vital issues.

In this research, presenting a model with a different attitude under the title of the collection of treasuries (primary and secondary) has been attempted to provide a new approach to the financing of chain members. By presenting the primary treasury plan, it is possible to sell the current assets of institutions to finance them. The importation of members’ petty liquidity to finance other members and receive profit, in other words, to increase the cash flow in the chain, is managed through a platform as a secondary treasury.

The researchers in the field of supply chain finance have mainly focused on the relationship between two members of the chain (supplier and retailer) and the bank. According to the research and studies done by the authors, until now, the research results on integrating the financial credits of the supply chain using the local treasury and the financial process between all the influential members in a supply chain along with its simulation have not been published.

In this way (by using this model), accumulating liquidity from some institutions instead of depositing in banks, which serves as an external institution, will help meet the deficits of other institutions in the supply chain. Thus, it provides a static liquidity profit for institutions within the distribution chain and a credit deficit for institutions within the supply chain. Since the cumulative profit approach for the entire chain (holding model) and not just the profit of individual institutions is the preconceived goal in this research, It is the strength and innovation of the study that it seeks to reduce borrowing from banks and to establish financial independence for the chains as well as the provision of credit guarantees by circulating cash within the chains. As mentioned above, the model presented in this article has several significant advantages, such as effective risk reduction as well as the simplicity with which working capital is provided to members, reducing the administrative bureaucracy of providing the required credit, increasing the profit of members, and preventing the bankruptcy of small chain institutions, as well as having the capability of buying and selling current assets in a high or low amount.

3. Problem Statement

According to studies that have compared the conditions and outcomes of receiving loans and fellow supply chain member companies (trade credits), the costs of obtaining inter-company loans are generally lower for the recipient company, while the granting company may increase its sales by granting this type of credit [35]. As a result, it can be concluded that many companies struggling with budget deficits in their supply chains are looking to obtain a credit of this type. However, the amount and duration of trade credits may be limited due to financial requirements and limitations of the companies that grant them. In contrast, banks establish their credit limits based on the applicant company’s financial records, aiming to reduce the risk of a default on a loan. Companies often feel less pressure when obtaining credit from other supply chain members since bank loans carry higher costs for the recipients. Further, as the default risk increases, the stability of the entire supply chain will also decrease.

In this study, we investigate a three-level supply chain. The lowest level consists of customers (the starting point of the chain), sellers (pharmacies and hospitals), and distributors (intermediaries). Inventory, as one of the crucial aspects of supply chains, is also considered [36,37]. Customers are the ones who inject cash flow into the chain, and it is they who purchase their medicine from two kinds of establishments: pharmacies and hospitals. If an establishment receives an order for medication, it will use its available stock to supply as much of that medication as possible from its current inventory. If a product ordered is out of stock, it is recorded as a lost sale. As for pharmacies, they procure what they need through distributors to meet their specific needs.

Simply put, in the supply chain, the distributors (intermediaries) receive orders from pharmacies and immediately forward them to the manufacturers, forming the second supply chain level. Hospitals place their orders directly with manufacturers. The manufacturing process starts as soon as a manufacturer procures the raw materials they need from the supplier, which is the third and last level of the chain. In this study, we propose that a local treasury be established in which the surplus cash flow of some members is collected to help members experiencing a budget deficit. Whenever the local treasury has insufficient funds to finance the troubled members, a bank enters the supply chain to provide loan financing to them. While Figure 2 depicts a schematic of the proposed financial supply chain network, Figure 3 provides a graphic representation of the supply chain and its various levels.

The underlying assumptions of the problem are as follows:

• The supply chain’s member companies are coordinated and integrated.
• The repayment period and structure of the inter-company credits are fixed and clear.
• Companies can use the income they collect by selling goods and/or services on the market again as working capital.
• The production or service delivery speed in the supply chain is fixed, and no fluctuations occur.
• From the beginning of the production or modeling period, the bank provides the entire working capital deficit of the member companies without any restrictions.
• The interest on deposits or loans is calculated daily.
• Sales are made daily. Each sales order is the sum of all the orders on that day.

4. Model Description

In addition to a thorough analysis of the various financing approaches available to supply chain members, which are presented in Section 1, three main categories are followed in this investigation. The first approach (External Financing—EF), and perhaps the most common approach across various industries, is that every company independently requests and receives loans from banks or other financial institutions. As for the second approach (Internal Financing I—IF-I), in this case, the influential members forming a supply chain, together with each other, determine how to create a primary treasury in which the members’ checks are exchanged. In the case of an institution that cannot meet its liquidity deficit through the sale and redemption of checks, obtaining credit from a bank becomes necessary to meet that deficit. In the third approach (Internal Financing II—IF-II), which is the focus of the present research, the chain members establish a secondary treasury in addition to the aforementioned primary treasury. By coordinating information, the members share their cash surplus and deficit in this treasury and make a profit in return. In this approach, each institution first provides its liquidity deficit by selling checks and, if necessary, obtaining loans from the local treasury. Bank loans are a possible solution if the primary and secondary treasuries cannot meet the need for extra liquidity that troubled companies need. Hence, securing bank credit through loans to cover the debt is the only choice.

4.1. Primary Treasury

Establishing a primary treasury is an approach for dealing with the negative liquidity level that precedes obtaining banking loans. This approach, an innovation in the proposed model, involves purchasing and selling non-cash assets, including checks. Each asset is subject to value amortization over time, and checks are no exception since they lose value as they approach their maturation date due to a mathematical function called the yield curve (Figure 4). Efficiency curves are examined to obtain the criterion and shape of the function. Various models are used to describe the yield curve in different periods. The yield curve can be modeled based on the interest rate and period and features like the futures rate, the discount rate, and the zero coupons. The Nelson-Siegel (N.S.) model is a parametric model consisting of three factors: surface, S, and arc [38]. The NS model has undergone several changes over the years. For instance, Svensson [39] added a second arc to the original model and developed a four-factor variation called the Svensson (S.V.) model.

$${\mathrm{f}}_{\mathrm{i}}\left(\mathsf{\lambda };\dot{\mathsf{\beta }},\mathsf{\tau }\right)={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\exp \left(-\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{{\mathsf{\tau }}_1}\right)+{\mathsf{\beta }}_2\left[\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{{\mathsf{\tau }}_1}\exp \left(-\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{{\mathsf{\tau }}_1}\right)\right]+{\mathsf{\beta }}_3\left[\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{{\mathsf{\tau }}_2}\exp \left(-\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{{\mathsf{\tau }}_2}\right)\right];\mathrm{i}=1,\dots ,\mathrm{p}$$

(1)

where ${\mathrm{f}}_{\mathrm{i}}$ is a function of the futures rate of the bonds, $\mathrm{i}$ is the number of bonds, and ${\mathsf{\lambda }}_{\mathrm{i}}$ is the time left until the bonds mature. $\dot{\mathsf{\beta }}={\left({\mathsf{\beta }}_0,{\mathsf{\beta }}_1,{\mathsf{\beta }}_2,{\mathsf{\beta }}_3\right)}^{\mathrm{T}}$ is a vector of linear parameters and $\mathsf{\tau }={\left({\mathsf{\tau }}_1,{\mathsf{\tau }}_2\right)}^{\mathrm{T}}$ stands for overdue parameters. Based on the description given for the discount rate of the value of checks, we have the following function:

$$\begin{gathered} {\mathrm{f}}_{\mathrm{i}}\left(\mathsf{\lambda };\dot{\mathsf{\beta }},\mathsf{\tau }\right)=0.4+\left(-0.4\right)\times \exp \left(-\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{8.9}\right)+\left(-0.14\right)\times \left[\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{8.9}\times \exp \left(-\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{8.9}\right)\right] \\ +\left(-0.009\right)\times \left[\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{2}\times \exp \left(-\frac{{\mathsf{\lambda }}_{\mathrm{i}}}{2}\right)\right] \end{gathered}$$

(2)

4.2. Secondary Treasury

This research has also led to the establishment of a secondary treasury as a second innovation that can be attributed to the study. As part of this approach, the petty cash of institutions will be used to compensate for the lack of working capital by providing loans to overcome the lack of working capital. In addition, by accumulating petty cash, institutions can buy checks with a high amount in the primary treasury. Commonly, checks with high amounts cannot be traded by a single institution. In this approach, companies that need extra liquidity refer to the secondary treasury to obtain cash raised by other companies. By depositing small amounts of money from their accounts into the secondary treasury, other companies can make a profit equal to the amortized rate of the check in the primary treasury for the money deposited into the secondary treasury.

4.3. Objective Functions

All three financing approaches use an objective function to calculate the time value of the working capital in the supply chain. The value of each objective function is obtained by calculating the time value of total liquidity, cleared checks at maturity, checks traded in the primary treasury, and the profit from sharing money in the secondary treasury. The instalments of the received credit are also considered. The parameters used in the objective functions of the model are introduced in

Table 1. Parameters related to the model’s objective function.

Parameter	Description
$L{q}_j^i$	Level of liquidity of institution i on day j
$Trad{e}_j^i$	Checks of institution i received on day j and on time
$Trad{e}_j^{\ast i}$	Checks of institution i received on day j at the primary treasury
$Pe{y}_j^i$	Installments of institution i on day j

below.

The three objective functions of the three approaches are presented in Equations (3) and (4) as follows:

The objective function of the first approach (EF):

$${\mathrm{Z}}_1^{\mathrm{S}.\mathrm{C}.}={\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(L{q}_j^i\right)+{\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(Trad{e}_j^i\right)-{\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(Pe{y}_j^i\right)$$

(3)

The objective function of the second (IF-I) and third (IF-II) approaches:

$${\mathrm{Z}}_2^{\mathrm{S}.\mathrm{C}.}={\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(L{q}_j^i\right)+{\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(Trad{e}_j^i\right)+{\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(Trad{e}_j^{\ast i}\right)-{\displaystyle \sum }_{i=1}^I{\displaystyle \sum }_{j=1}^{J}FV\left(Pe{y}_j^i\right)$$

(4)

There is one vital point to be noted in this subsection. It is a fact that the objective functions of IF-I and IF-II differ in terms of the number of checks received from the primary treasury, which is a significant point. In IF-II, institutions that have shared their cash in the secondary treasury receive checks in exchange for the interest on their deposits. The checks can then be deposited in the primary treasury and sold with a specific profit determined by the yield curve function.

Generally, to compare the three approaches to financing in the supply chain, the problem is modeled through simulation under the same conditions, and then the results are evaluated. First, a conceptual model of institutions, events, and processes is constructed as part of the first stage of the simulation process. In the case of this research, the method of model validation used for model validation is the flowchart approval by experts, which is a widely-used method performed before the simulation. The proposed model is fully introduced in the following subsections, with each module described separately.

4.4. Components of the Drug Supply Chain

In this subsection, various components of the considered drug supply chain are scrutinized. The mentioned components are as follows: price of medicine, demand, drug inventory, liquidity, drug orders, costs, and financial exchanges.

The raw materials required to produce the drugs are procured and delivered only if the order has already been placed. For raw materials to be ordered, they must be requested by either hospitals or distributors and then delivered immediately to the supplier by the manufacturers. The pharmacies order drugs every 14 days, and the hospitals place orders every seven days. Two types of drugs are considered in this study, and there are two ordering procedures: pharmacies place orders through distributors, while hospitals contact the manufacturers directly to place their orders.

Legal authorities, such as the Ministry of Health and Medical Education, must approve drug prices. A certain margin may be added to fees for each member of the chain. Every item is subjected to a monthly inflation rate, which affects the price of the item. Every drug needs a subset of raw materials for its manufacture, and the value of those raw materials equals 95% of the final drug’s price when approved.

To determine the demand for the simulated year, pharmacies and hospitals use data from recent years to calculate the anticipated demand. The input data used for determining the demand for two types of drugs was real-world data collected over the past five years by hospitals and pharmacies, upon which quarterly estimates were determined for 2022 and 2023. Seasonally, the winter season has a peak demand, while the following seasons gradually decline.

There are two institutions involved in the supply chain study in this research. Both hospitals and pharmacies have warehouses and store their inventory. There is an assumption that each drug has a fixed order period of seven days for hospitals and fourteen days for pharmacies. Sometimes, the inventory runs out, and, in some cases, the surplus inventory is transferred to the next period. To begin the simulation, a random number is assigned to each institution to establish the primary inventory stock level for each drug.

In this study, liquidity is defined as the amount of cash that circulates within the supply chain. According to the parameter definition, this parameter is updated based on various factors, such as the fulfillment of orders, payment of instalments, settlement of checks, and other intra-chain financial transactions. All five nodes have liquidity. There is a lower order volume in pharmacies than in hospitals; consequently, there is a smaller inflow of liquidity to pharmacies than in hospitals.

Each pharmacy and hospital places its orders periodically, based on the previous year’s data, with pharmacies having longer order periods due to a lower level of demand. The pharmacy and the hospital consider a percentage of the estimated demand between two consecutive order periods as the current order quantity. If the actual demand exceeds the estimated demand, the institution records a lost sale. Distributors receive a limited number of orders from the pharmacy. It is the same logic that is applied to manufacturers as well.

Each member of the supply chain incurs two types of costs: fixed and variable. The fixed costs are calculated over the course of 30 days. Hence, 10 to 30% of the total order value between two consecutive periods is considered to be the fixed cost. To calculate the variable cost for each institution, a percentage of each order placed by the same institution is deducted from the liquidity level.

All orders from distributors, manufacturers, and raw material suppliers are recorded through checks. It is important to note that the check settlement period varies from institution to institution. Specifically, this period is 5 to 30 days for pharmacies, 30 to 60 days for hospitals and distributors, and 90 to 180 days for manufacturers and suppliers. Each day, the liquidity level of the institutions is checked at the end of the business day. The most appropriate approach is adopted to receive the required credit if necessary. The repayment period is fixed at 180 days and paid through a profit and loss sharing mechanism known as Mudarabah (Trustee finance; a passive partnership contract). The interest rate paid on banking loans is 24% per annum. In contrast, the interest rate paid on secondary treasury loans is determined according to the repayment period that the parties have agreed upon between themselves. The following section also provides an in-depth description of the real-life case study (Shafa Darou Investment Company) on which this study is based.

4.5. Case Study Description

The Shafa Darou Investment Company is a privately owned joint stock company founded in 2002. Currently, the company’s main activity is overseeing and managing seven pharmaceutical companies operating in various fields of pharmaceutical production through investments in pharmaceutical companies, constituting its principal activity.

As a means of enhancing economic efficiency and maximizing capital efficiency, the company strives to centralize planning and use capital to achieve opportunities in the pharmaceutical industry and market of the country and to increase economic performance. To supervise the financial activities of the Shafa Darou Securities Investment Company (CJC), which the Securities and Exchange Commission regulates under the auspices of the said organization, the company has been put under financial supervision. Seven Shafa Darou Investment Corporation (JSC) subsidiaries are involved in the country’s pharmaceutical industry as part of the company. Figure 5 depicts the subsidiary companies of the Shafa Darou Investment Co.

According to Figure 5, Shafa Darou Investment Company has a wide range of subsidiaries actively involved in various sectors of the Iranian pharmaceutical industry. The subsidiaries mentioned above are involved in various activities, ranging from the production of drugs to the transport of goods, as outlined in Figure 6. Shafa Darou Investment Company and its subsidiary companies’ activities are scrutinized to be incorporated into the proposed models of the study. Accordingly, every element of this investigation has been shaped based on the analysis of information, the implementation of the study, and the collection of related data from Shafa Darou Investment Company, which confirms the credibility of the findings of this investigation.

This is an indication that can be deduced from Figure 6 that Shafa Darou’s investment company owns the entire pharmaceutical supply chain entity chain. Razi, for example, is viewed as a major distributor company in the field (the company owns various distributors under the name Razi), while other subsidiary companies function as suppliers and producers (It is clear that some companies act as a supplier for others. For example, Jaberabnehayan is a supplier to Dana (for producing antibiotics), an aspect considered in this study. To avoid misunderstandings and clarify, the rest of the paper will be represented in general form (which means integrating general terms instead of mentioning specific companies).

5. Simulation and Computational Results

Simulation is incorporated in various areas of research [41,42,43]. In this section, the simulation model is designed and constructed based on the intended flowchart. The pseudocode describing this process is presented in Algorithm 1. In this research, a three-step code is executed. As a first step, the initial data and database must be prepared so that they will be ready to be executed by the simulator engine. After this, the simulation model and the calculations associated with all the financial transactions and chain flow processes are performed through the simulation program for each member at each unit, and the system information will be updated. In the final stage, in the third and final step, the graphs and other information are received as outputs. The proposed three-level supply chain model, stemming from Shafa Darou Investment Company and its subsidiary organizations, is simulated and analyzed with six pharmacies and three hospitals at the lowest level, two manufacturers and three distributors at the middle level, and one supplier for the two types of drugs at the highest level. With regard to the Shafa Darou Investment Company case study, two drugs, one of which is a high-consumption drug and the other of which is a low-consumption drug, are selected, and associated data is collected. Amoxicillin capsules and Pantoprazole tablets are chosen as high-demand and low-demand products, which are being produced in the holding, respectively.

Algorithm 1 Simulated pseudocode

Start For each day of the simulation interval    Update the new liquidity level for each institution    Check and payment of the checks with the current settlement date    Check and pay in instalments with the current date    For each hospital and pharmacy       Supply demand and updated inventory and liquidity       Register required orders, deduct variable costs, and update liquidity    For each institution       Calculate the percentage of monthly orders and deduct fixed costs and liquidity updates    For each institution of the institutions with a negative liquidity level (primary treasury)       For each check of the checks with settlement time priority         Calculate the transaction value of the check based on the settlement date         Update the liquidity level of the seller and the buyer         Check the level of liquidity (to break the cycle)    For each institution of the institutions with a negative liquidity level (settlement treasury)       For each of the voluntary institutions to lend         Calculation of the amount to be borrowed by the institution         Borrower and lender liquidity level update         Calculate the value of a check and issuance, including the profit of the settlement period         Check the level of liquidity (to break the cycle)    For each institution of the institutions with a negative liquidity level (bank)       Calculate the required liquidity, create loans and installments       Update the borrower’s liquidity level    For each institution of the institutions with a positive level of liquidity       Calculate daily earnings for existing liquidity and update liquidity

In this section, the simulation results are presented. The supply chain simulation model is coded in the Python v3.8 environment, and the calculations are performed using a personal computer with 6 GB of RAM and an Intel^® Core™ i7 processor under the Linux v20.04 operating system. The simulation of each day is performed in 0.97 s on average. Communicating with the database in the coding environment was done using an ORM called SQLAlchemy (Version 1.4.32), while the two libraries Bokeh and PrettyTable were used to draw diagrams and record logs, respectively.

5.1. Liquidity

As shown in Figure 7, the liquidity of the supply chain is illustrated, using the case of one of the distributors as an example, under each of the three financing approaches that have been considered in this study. As seen in Figure 7, the liquidity of EF reaches zero in some cases; the same situation can also be seen in the IF-I. It is, therefore, fair to say that some institutions have an accumulation of liquidity, while in other institutions, there is a deficit. However, when it comes to the IF-II, there is a greater level of uniformity and leveling and, therefore, a lower level of risk. Throughout the chain liquidity is spread throughout the entire chain. In the figures below, the EF is labeled as Bank-Only, the second as Trade-Curve, and the third as Petty-Cash.

5.2. Loans

In this subsection, the subject of loans is examined in terms of the total number and amount of loans received by the institutions and the total interest paid by the members of the chain.

As shown in Figure 8, in IF-I, compared with EF, there is a decrease in the number of loans the supply chain members receive from the bank. The IF-II, which involves establishing a secondary treasury, results in an even more significant decrease. Since the amount of petty liquidity available to each institution is used to replenish the secondary treasury, external institutions (such as banks) access to the liquidity is blocked. Instead, it is made available to the member institutions in the supply chain. Moreover, this trend also solves the problem of ordering, including the delays in lost orders and leveling the liquidity.

Furthermore, because the petty cash in the secondary treasury is traded at a rate equivalent to the amortized rate of the check in the primary treasury, the owner of the funds makes much more profit compared to the case where the institution deposits its petty chases in a bank account (It should be noted that this rule is subject to the banking strategies of Iran). Figure 9 shows the value and the number of loans received from the bank by the supply chain members. According to the chart, and as can be seen from Figure 9, when it comes to the financial flow of the bank, the red line represents the amount of the loan obtained, while the blue line represents the payment situation of the installments by the institutions, as indicated in Figure 9.

It is important to note that in IF-I and IF-II, in addition to reducing the number of loans obtained compared with the first method, the maximum number of loans obtained is also reduced. For example, the loan ceiling in the IF-I is $100,000, while the one in IF-I and IF-II, under the same conditions, drops to $70,000 and $2000, respectively. Based on this and our analysis of the number of loans, we have determined that IF-I and IF-II have superior effectiveness compared to the first. According to Figure 10, the sum of the interest paid by the chain members on the loans they received from the bank can be seen in Figure 10.

At this stage, we perform an analysis similar to the one in the subsection on the number and value of received loans. It can be seen that the interest rates of the loans have taken a considerable downward trend compared to those of EF in the IF-I and IF-II. It is significant to note that at the end of EF, the interest rate rates reach $12,000, whereas, in IF-I and IF-II, they reach $10,000 and $140, respectively. Although IF-I is more effective than EF, it is the IF-II that is far superior to both the IF-I and EF

5.3. Objective Functions

Assuming that the relation provided in the Section 4 of this paper can be used to compute the objective function, Figure 11 illustrates the cash flow of the objective function as a result of each of the three approaches considered in this study.

It should be noted that, with respect to EF, since the supply chain members do not deposit their revenues into the treasury, their liquidity level remains unchanged and is only subject to interest rates charged by the bank. In addition, the money deposited into the bank to compensate for the lack of liquidity needed by some fellow companies is considered unlimited. Consequently, the liquidity in the EF remains stagnant and, to a large extent, available to the companies. It is due to this that the objective function’s value increases. Contrary to this, when introducing cash flow into the two treasuries in IF-I and IF-II, the bank’s role diminishes due to the introduction of cash flow.

Consequently, the value of the objective function decreases. As a result, a significant portion of the working capital needed by a supply chain is provided by the bank in EF. In contrast, in IF-I and IF-II, the working capital is provided by the internal resources of the supply chain members.

5.4. Primary Treasury

Figure 12 shows a detailed list of the checks exchanged in the primary treasury in IF-I and IF-II. Due to the fact that the secondary treasury is producing checks, the number of trades has increased, and there are more checks with lower amounts that exist (as a result of transferring petty checks from the primary treasury to the secondary treasury).

The value of the checks exchanged in the primary treasury in IF-I and IF-II is presented in Figure 13. A similar analysis can be carried out for this cash flow, as it would be for the number of checks exchanged in the primary treasury.

5.5. Difference in the Number of Transactions in the Primary Treasury under IF-I and IF-II

It can be seen in Figure 14 that there is a difference in the number of transactions completed in the primary treasury in the presence of the secondary treasury and its absence. The blue bar represents the positive difference between the number of transactions in each mode. In contrast, the red bar represents the negative difference between the number of transactions in each mode. As can be observed, the number of transactions is higher when the secondary treasury is active.

In IF-II, there are a total of 456 positive differences compared to IF-I, in which 221 positive differences were recorded compared to IF-II.

This difference in value follows the same logic as in Figure 15.

In this subsection, the values of the objective functions in the three approaches are discussed in detail. The financial value of the settled checks in IF-I and IF-II, with a daily interest rate of 0.1%, is moved to the end of the simulation time. The same process is repeated for IF-II, in which the daily interest rate is 0.07%. The values of the objective functions under the three approaches are presented in

Table 2. Objective function values under the three approaches over a two-year simulated period.

Row	Institution	Value of EF	Value of IF-I	Value of IF-II	Currency
1	Hospitals	80,593,243.5841	67,456,567.1353	51,671,458.7813	$
2	Pharmacies	14,300,515.2017	13,549,280.9753	6,170,619.4800	$
3	Distributors	24,304,438.0524	27,979,851.0076	27,487,469.3819	$
4	Manufacturers	543,804,924.1188	394,959,731.2830	404,115,441.6589	$
5	Suppliers	1,641,329,860.7227	1,541,182,534.2126	1,339,453,700.1232	$
Total		2,304,332,981.6797	2,045,127,964.6138	1,828,898,689.4252	$

. In EF, the transfer value is lower than in IF-I and IF-II because, in EF, there are a more significant number of loans obtained from the bank (i.e., the bank receives a larger amount of interest), and there are no primary and secondary treasuries. It is important to remember that in the IF-I, there is no secondary treasury. Suppose the companies do not succeed in overcoming their lack of liquidity even after selling their checks in the primary treasury. In that case, they must obtain loans from a bank to be able to meet the remaining needs. In light of the fact that this solution implies that the companies are required to pay interest to the bank, the values of objective functions increase compared to EF; however, they remain lower compared to IF-II.

In IF-II, which considers the secondary treasury, the companies’ petty liquidity compensates for the insufficient liquidity of other institutions that have been unable to fully meet their needs by selling checks in the primary treasury. Therefore, the companies’ need to obtain loans from banks decreases significantly. Another noteworthy point is that companies that have placed their petty liquidity in the secondary treasury earn interest at a rate equivalent to the reduction rate obtained from the Yield Curve function, which is higher than the interest on bank deposits. Companies are motivated to invest in secondary treasuries due to these advantages, which can be seen as a motivating factor for making investments. Furthermore, as no money leaves the supply chain with this solution, it increases the value of the objective function within the chain and a more significant overall profit for the chain as a whole.

Due to the absence of any internal treasury system in EF, and since there are no check transactions for pharmacies or hospitals, only the loan installments are being paid in EF. Therefore, the value of the objective function for both groups shows a negative value as a consequence. Due to the low liquidity level of pharmacies, they cannot participate expansively in IF-I and are adversely affected by the disbursement of loans. Alternatively, hospitals have more income because of their considerable demand, so they are better positioned to contribute to the treasury. Therefore, a positive value is associated with hospitals’ objective function. With the addition of the secondary treasury and the ability to borrow from others rather than banks, pharmacies can participate in joint initiatives in a much greater capacity under IF-II. This results in the positive value of the pharmacy’s objective function in IF-II.

5.6. Validation and Verification Analysis

In this subsection, we investigate the validation and verification analysis for the three mentioned financing approaches (EF, IF-I, and IF-II) through the statistical hypothesis test.

The data that can be extracted from the model’s output for running the statistical hypothesis test are the number and the amount of the total daily loans granted from outside the chain (

Table 3. Statistical hypothesis test results.

Financing Approach	Loan Number	Total NPV
EF	t-student formula value: 5.376225741442173 p-value: 9.984552337788502 × 10−8	1,663,881.044 vs. 802,704.7271
	IF-I is the better approach
IF-I	t-student formula value: 2.2215422293002183 p-value: 0.264158827293769	802,704.7271 vs. 2302
	IF-II is the better approach
IF-II	t-student formula value: 3.1299386643276113 p-value: 0.19052081933416223	1,663,881.044 vs. 2302
	IF-II is the better approach

). Another reason for using these items is the existence of analytical diagrams of the output of this model. It should be noted that the Net Present Value (NPV) on the zero day of the simulation is 1 January 2022. If we consider the number of loans received as the average, we can use the t-test to compare both pairs of scenarios:

$$\{\begin{array}{cc}{H}_0& \mathrm{There}\mathrm{is}\mathrm{no}\mathrm{significant}\mathrm{difference}\mathrm{in}\mathrm{the}\mathrm{mean}\mathrm{loan}\mathrm{number}\mathrm{between}\mathrm{the}\mathrm{two}\mathrm{scenarios}\\ H_1& \mathrm{There}\mathrm{is}\mathrm{a}\mathrm{significant}\mathrm{difference}\mathrm{in}\mathrm{the}\mathrm{mean}\mathrm{loan}\mathrm{between}\mathrm{the}\mathrm{two}\mathrm{scenarios} \end{array}$$

Calculate the test statistic (t-student) using the formula:

$$t=\frac{{\overline{X}}_1-{\overline{X}}_2}{\sqrt{\frac{s_1^2}{n_1}+\frac{s_2^2}{n_2}}}$$

(5)

where:

• ${\overline{X}}_1,{\overline{X}}_2$ are the sample means of the number of loans for EF and IF-I, respectively.
• $s_1^2,s_2^2$ are the sample standard deviations of NPV for EF and IF-I, respectively.
• $n_1,n_2$ are the sample sizes for EF and IF-I, respectively.

If the p-value is less than your chosen significance level (e.g., 0.05), you can reject the null hypothesis.

$$t_{s_1,s_2}=\frac{{\overline{X}}_{s_1}-{\overline{X}}_{s_2}}{\sqrt{\frac{s_{s_1}^2}{n_{s_1}}+\frac{s_{s_2}^2}{n_{s_2}}}}$$

(6)

In addition to the hypothesis test, the previously depicted diagrams and the NPV confirm the test output.

5.7. Outcomes and Managerial Insights

In conclusion, the following outcomes were derived from the study and can be summed up as follows:

• The number of loans in IF-I and IF-II has dramatically decreased, and as a result, the interest paid to out-of-chain counterparties will be much lower than the interest paid to our counterparties.
• Whenever an entity relies on external financing sources, such as banks or credit institutions, these external sources may reject the entity’s request for a loan for various reasons. However, such risks of not being able to fund a business are approximately equal to zero when internal financing for the company is established.
• As the financial risk decreases, the level of service will inevitably enhance. In this way, various challenges such as lost sales or back ordered demands, dissatisfied customers, or, as in the case of the investigated case study, humanitarian problems are ultimately going to be significantly minimized.
• When a supply chain’s financial flow is strengthened, the network will likely have the opportunity to benefit from two aspects. Firstly, it is projected that higher sales will be achieved due to overcoming the previously discussed challenges. Secondly, it can be argued that higher in-chain transactions would result in the paid interests retained within the chain. Whenever there is an increase in the number of transactions and credits, this interest amount will be increased correspondingly to become a significant value.
• According to the results of the analysis, it can be seen that the amount of capital sedimentation has decreased. This, in turn, will impact all of the flows within a chain (financial, physical, informational, etc.), resulting in a much faster and more dynamic movement.
• By lending petty cash from one member of the supply chain to another, an entity can gain both direct and indirect benefits (such as receiving daily returns on petty cash and lowering overall interest payments in the holding’s supply chain) by improving the earnings of the holding’s supply chain and reducing overall interest payments.
• Several benefits could be derived by the top management of the holding in several aspects. As a result, several possibilities can be realized, such as merging or dissolving companies, better control of financial flows, more accurate and inclusive strategic planning, improved network design, etc.

From the above outcomes, several managerial insights are summarized in Figure 16:

By using these management suggestions, it is possible to improve the financing chain in the field of health and improve the performance of companies and organizations in this field.

6. Conclusions

This paper focuses on a three-level supply chain within the pharmaceutical industry, using internal resource-valuation tools and developing a new financing structure to study an integrated supply chain. The proposed structure of the chain involves building a local treasury to accumulate excess liquidity from some members of the chain to use that liquidity to fund the activities of those members who face budget deficits. This study intends to build three simulation models based on a real-life case study dealing with the drug supply chain related to the Shafa Darou Investment Company in Iran to analyze the advantages and disadvantages of implementing this idea. The study results indicate an improvement in the total working capital of the supply chain at large. In EF, which is relatively common in the industry, each supply chain member obtains a loan from a bank or credit institution to finance their activities. According to IF-I, the supply chain members cooperate to establish a primary treasury in which the checks of the members of the supply chain are exchanged. Whenever a liquidity deficit and a company cannot bridge it by selling checks, it becomes necessary for the company to obtain a bank loan to make up for the deficit. It is in IF-I and IF-II that a secondary treasury is established together with the primary treasury, through which some members can share excess liquidity and earn a profit. Thus, to cut its liquidity deficit, each company will try to do it by first selling checks, and if that is not enough, then by applying for loans from the local treasury if necessary. It will be essential for the company to secure a bank loan if both methods do not work. As a result of the study, it has been shown that IF-II not only levels the liquidity of the members of the supply chain as a whole but also reduces the amount of interest paid to the banks and the number of loans obtained. Establishing both a primary and secondary treasury makes good use of the excess liquidity of the better-positioned chain members, maintains the financial independence of companies from foreign institutions such as banks, and is associated with reduced risk and more excellent stabilization. Lastly, relying on the secondary treasury to deposit the excess liquidity of the members instead of making deposits in the bank causes the overall profit of the supply chain to circulate only among its members, results in better internal financial flow, and creates more added value. In future studies, it is possible to evaluate the supply chain (identify strong and weak members) by assessing each member. Also, the optimization of each member can be considered instead of the main company. In addition, the length of the debt settlement period could be varied based on various strategies. Non-Iranian banking and financial systems (different interest rate assumptions) can be another interesting subject for future investigations. The production or service delivery speed in the entire supply chain can be assumed to be unfixed, and other fluctuations can be considered in the future. Reverse factoring and dynamic discounting are some of the SCF mechanisms that can be considered for extending this research.