Developing a system dynamic model for product life cycle management of generic pharmaceutical products: Its relation with open innovation

Mousavi, Atefeh; Mohammadzadeh, Mehdi; Zare, Hossein Article Developing a system dynamic model for product life cycle management of generic pharmaceutical products: Its relation with open innovation Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Mousavi, Atefeh; Mohammadzadeh, Mehdi; Zare, Hossein (2022) : Developing a system dynamic model for product life cycle management of generic pharmaceutical products: Its relation with open innovation, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 8, Iss. 1, pp. 1-19, https://doi.org/10.3390/joitmc8010014 This Version is available at: https://hdl.handle.net/10419/274319 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/   Citation: Mousavi, A.; Mohammadzadeh, M.; Zare, H. Developing a System Dynamic Model for Product Life Cycle Management of Generic Pharmaceutical Products: Its Relation with Open Innovation. J. Open Innov. Technol. Mark. Complex. 2022,8, 14. https://doi.org/10.3390/ joitmc8010014 Received: 12 November 2021 Accepted: 4 January 2022 Published: 6 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Open Innovation: Technology, Market, and Complexity Article Developing a System Dynamic Model for Product Life Cycle Management of Generic Pharmaceutical Products: Its Relation with Open Innovation Atefeh Mousavi 1, Mehdi Mohammadzadeh 1and Hossein Zare 2,3,* 1Department of Pharmacoeconomics and Pharma Management, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran 19968-35113, Iran; [email protected] (A.M.); [email protected] (M.M.) 2Johns Hopkins Center for Health Disparities and Solutions, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA 3 Global Health Services and Administration, The School of Business, University of Maryland Global Campus, Adelphi, MD 20774, USA *Correspondence: hzar[email protected] Abstract: The purpose of this study is to identify elements that influence the sale of generic pharmaceutical products during their life cycle in order to achieve more comprehensive planning and to prevent a decline stage of the product life cycle (PLC). We used a system dynamic model to identify the behaviors of demand, supply, and competition as three major subsystems of PLC in generic pharmaceutical products. We first investigated the PLC patterns of 527 medicines to identify their “reference mode”, determined the causal loop of the pharmaceuticals phase of PLC based on both an in-depth literature review and experts’ opinions, and finally simulated a quantitative dynamic model based on real-world data between 2012 and 2019 from Iran. Based on the results, “total demand and accurate forecasting”, “marketing efforts”, and “R and D activities of a firm” are the most critical factors involved in the formation of a generic drug PLC. An increase of 20–50% of manufacturers’ marketing and R and D activities can raise sales by more than 50% in the decline stage of the PLC. The product life cycle can give generic manufacturers more insights into the processes leading to declining sales of their products. PLC may help to prevent a product from entering the decline stage even if the total demand for a generic drug is dropping in the market. Keywords: system dynamic; pharmaceutical industry; product life cycle; open innovation dynamics 1. Introduction Product life cycle management (PLM) is a strategic process to manage a company’s products effectively, from production to exit from a market [ 1 ]. PLM improves the processes of a company’s product development and provides an ability to use product-related information to make better business decisions [ 2 ]. In recent years, the healthcare sector has sought helpful protocols and instruments for better decision-making to improve quality of care and to reduce expenditures and insufficient use of resources. By using the PLM concept in the medical sector, we can create synergy between industrial products and patients [ 3 ]. However the nature of the healthcare system—with many actors (new patterns of diseases, patients with different attitudes and expectations, and too many providers [ 4 ]), rapid changes in the market, technology, rules and regulations, and new competitors’ products—make PLM applications very dynamic. As a result, implementing the PLM strategy needs a stronger evaluation system for new opportunities such as the development of markets, regulations, and technology [ 5 ]. The use of product life cycle management in the pharmaceutical industry has also become an undeniable necessity. Evidence has shown that companies with a comprehensive strategy for PLM J. Open Innov. Technol. Mark. Complex. 2022,8, 14. https://doi.org/10.3390/joitmc8010014 https://www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2022,8, 14 2 of 19 have achieved much success in the financial and non-financial fields such as promoting patient adherence, increasing revenue, improving clinical benefits, and increasing the growth phase of the product life cycle (PLC) [ 6 ]. Drug development has usually focused mainly on the management of clinical trials results. Meanwhile, the industrial sector needs more comprehensive approaches such as PLM to introduce new products and to remove defective products from markets that could reduce operating costs and accelerate the development process of products [7]. Effective PLM strategies can also help open innovation in companies. Besides developing new products and technology, open innovation in a firm can be achieved through innovation in other prosses such as business models, marketing activities, or manufacturing [ 8 , 9 ]. Product life cycle management encompasses all aspects of innovation management during the life cycle stages through integrating information and processes [ 10 , 11 ], which were demonstrated in our SD model. Besides managing innovation related to developing new products in the early stages of the lifecycle [ 12 ], PLM can coordinate product information through all lifecycle stages. Also, PLM supervises the company’s resources and shifts them from parts that may be wasted to somewhere that could be spent on innovation processes [13]. Identifying the main elements affecting the PLC over time has been considered the first step toward efficient product life cycle management [ 14 ]. Due to the lack of sufficient studies for product life cycle management in the generic pharmaceutical companies, in this study, we determined the elements to explain the PLC behavior of pharmaceutical products as a complex system of health care in Iran. Generic medicines are essential in offering the same therapeutic effect as brand medicines with more affordability and accessibility [ 15 – 17 ]. As a generic pharmaceutical market, the Iranian pharmaceutical market has grown rapidly in recent decades [ 18 ]. For example, the capacity of the Iran’s domestic pharmaceutical industry increased from 30% in 1979 to 95% in 2016 [ 19 ]. Despite the acceptable progress, they are facing some domestic and international challenges to sale their products. For example, due to the concentration on price-setting by the government (Ministry of Health and Medical Science; [MOHME]) they have little chance to use price competition. As a result, they have tried to raise their market share by increasing their marketing efforts or maximizing profits by decreasing costs [ 20 ]. This unusual competitive market increases the importance of PLM in generic pharmaceutical companies. Our main objective is to evaluate this system’s behaviors over time to identify the main factors influencing the pharmaceutical PLC and creating the decline phase. Recognizing the elements which lead to the decline stage of PLC in a competitive market of the generic pharmaceutical industry, a company will be able to avoid this phase through proper strategies and maintain their market share and performance. Related literature and study background. Little is known about the life cycle of pharmaceutical products. For the first time, in 1967, Cox studied the PLC of 754 pharmaceuticals and determined different types of behavior in the PLC of medicines [ 21 ]. Other studies have tried to classify pharmaceutical sales patterns in different groups of medicines [ 21 , 22 ], and some of them have investigated the factors affecting PLC. For example, in 1991, one study by Jernigan and Smith discussed that the bell-shaped pattern is the most common form of PLC in medicines [ 23 ] or Henry Grabowski (1990) found that increasing the price of drugs leads to increase competition and shorter PLC of medicines [ 24 ]. In 1994, Bergstrom and Hoog found that switching between prescription medicines to over-the-counter (OTC) ones can influence the PLC pattern and increase sales volume [ 25 ]. Bauer and Fischer (2000) have studied the differences between new and old drugs in the cardiovascular group. They found that early entrants gain maximum sales faster than new entrants [ 26 ]. Fischer et al. (2010) reported that the quality and entry order of pharmaceutical products can affect the maximum sales and the time to reach the maximum in PLC curves [ 27 ]. Some studies have shown the positive effect of advertisements to physicians, pharmacists, and the patients on the sales of prescription and non-prescription drugs [ 28 – 30 ], and discussed that R and D and quality could affect sales in the pharmaceutical industry [ 31 ]. PLC studies in other J. Open Innov. Technol. Mark. Complex. 2022,8, 14 3 of 19 products have highlighted the importance of price and competition on the PLC curves. For example, Bass (1969) suggested that people buy a product under the influence of factory advertisements or other buyers’ suggestions. [ 32 ]. Bass’s model explains the diffusion theory, which shows how an innovation spreads through users’ perceptions and interactions [ 33 ]. System dynamic model. Many studies used the system dynamic modeling to show the different relationships between variables. It has been used to model the different relationships and feedback between the variables in a specific system over time [ 34 ] and to analyze the system behavior such as a company, guiding policymakers to manage companies better [ 35 ]. The SD approach has been widely used in recent years to solve problems in different healthcare fields, such as supply chain management, healthcare policy, technology and information, aging, and population [ 36 ]. For example, Kazemi et al. (2011) proposed a system dynamic (SD) model for PLC and suggested several practical factors in the formation of PLC such as quality, price, product attractiveness, and consumer satisfaction [ 32 ]. Using a system dynamic approach, Safri et al. (2012) showed a causal loop for PLC of short-life products and described the factors involved in this cycle. They suggested three new, effective items on PLC such as uncertainty of demand, product innovation, and research and development of the manufacturer [ 37 ]. More recently, Carlos (2020) suggested a system dynamic model to evaluate the supply chain in the pharmaceutical industry. In his study, government, biological and economic environments, pharmacies, hospitals, and patients were the main subsystems that established the system behaviors [ 4 ]. Moosivand et al. (2019) developed a system dynamic model for the generic pharmaceutical supply chain. They suggested that collaborative relationships with suppliers, new technologies investment, and the establishment of information technology can optimize the supply chain and raise its resiliency [ 38 ]. Wu and Mao (2017), Yaghoubi and Hayati (2018), and Akhlaghinia et al. (2018) have tried to show the drug supply chain actors through a system dynamic model and elevate the system performance by showing different scenarios outcome [ 39 – 41 ]. Meanwhile, Abdollahiasl (2013) developed a comprehensive system dynamic model that qualitatively captures all national drug policy elements such as availability, quality, and affordability. Each of these subsystems encompasses their related variables, which are in a relationship with each other [ 34 ]. As reported by literature, since the PLC concept is formed with various factors over time, by using the SD method, we can show the different relationships involved in this set and the effects of each one on the sales of pharmaceutical products. Using this approach, we can find the problem’s sources and also show the possible outcome of specific strategies to establish sustainability in PLC of generic medicines. Additionally, in this study, we show how effective PLM planning could influence open innovation in companies through our SD model. 2. Materials and Methods 2.1. System Dynamic (SD) Modeling Steps The SD approach demonstrates the dynamic relationships to understand their possible consequences, and simulates dynamic relationships to discover the effects of different amounts of intervention, scheduling, delay, and feedback [ 42 ]. We explain the SD in the four following steps. 2.1.1. Step 1: Problem Definition and Reference Mode One of the essential steps in dynamic system modeling is the problem definition. In this step, the causes of existing problems in a complex system and its current situation will be investigated to identify the key factors’ behaviors known as the “reference mode”. For defining the problems in a system, the system dynamic method focuses on endogenous factors in a complex system and tries to decrease unimportant exogenous variables [ 32 ]; an exogenous variable is a variable that affects the system but is not affected by it. Therefore, removal of the variable is unobstructed since there is no actual closed system that can consider all of the variables associated with this system. It is preferable to simplify the J. Open Innov. Technol. Mark. Complex. 2022,8, 14 4 of 19 system as much as possible, to include the main system-related variables, and to remove external variables that are not affected by the system [43]. 2.1.2. Step 2: Developing a Causal Loop Diagram In system dynamic modeling, a causal loop diagram is used to establish the relationship between different variables in the PLC system. It is very applicable for explaining the system behavior and for identifying the model boundaries [44]. 2.1.3. Step 3: Developing a Stock and Flow Diagram The casual loop model is then used to develop stock and flow diagrams based on the nature of variables. The stock variable is any accumulation of resources such as people, material, money, etc., and the flow variable is the rate stock variables converted to each other [45]. 2.1.4. Step 4: Testing the Model Confidence tests for the SD models include structure and behavior tests. The structural tests compare the structure of the SD model with the real system structure, so the mathematical equations are compared with the relationship between the elements in the real system. Also, behavior tests determine whether the model behavior corresponds to the actual system behavior [ 45 ]. In Figure A1 we have summarized these steps (please see Appendix Afor more details). 2.2. Data Collection and Analysis Problem definition: This study identifies the factors involved in the formation of PLC behaviors of generic pharmaceutical products by developing a comprehensive system dynamic model. To identify the “reference mode” and problem definition, we obtained the sales data of 527 generic medicines chosen randomly from official drug statistics [ 46 ] between 2002 and 2019. Using Origin Pro 2018 software [ 47 ], we plotted the PLC graphs and the fitted regression line with the R-square more than 0.8 [ 27 ]. Then we used the three following steps to run a system dynamic model: • First, we conceptualized the dynamic hypotheses based on an in-depth literature review and experts’ opinions. • Second, we determined the causal loop of the pharmaceuticals PLC and confirmed their related relationships through a questionnaire to identify the causes of reference mode formation. • Third, we ran a quantitative dynamic modeling based on real-world data and experts’ opinions regarding the Iranian pharmaceutical industry. Data. We used data from 2012 to 2019 for the Valsartan 80 mg (drug A, generic form of Diovan ® ), which belongs to the Osvah Pharmaceutical Co. in Iran (Company A). We chose to use Valsartan 80 mg since the Valsartan 80 mg sales pattern has the greatest R-square (0.8) based on our reference mode (one-peak sales pattern). Also, we had access to Valsartan 80 data between this time, but we did not have access to other products for the study time periods. This is public available data that have been published by the Codal website annually [48]. Variables and analysis: We followed three steps here; first, we identified the most common variables that have been reported by peer-reviewed literature, then and as a second step, we asked eight experts from the Iranian pharmaceutical industry to confirm the variables (See Appendix A, Table A1 for the PLC variables). At the final step we asked six academic experts to confirm the relationships between the variables through a designed system dynamic questionnaire. The final confirmed relationships between the PLC elements were entered into Vensim 6.4 DDS software [ 49 ] as the casual loops diagrams, and the qualitative system dynamic model were developed. We used a casual loop model to develop stock and flow diagrams based on the nature of variables. Using the formulated J. Open Innov. Technol. Mark. Complex. 2022,8, 14 5 of 19 equations based on the developed stock and flow diagrams in Stella 8.0 [ 50 ], we have performed the SD analysis in a eight-year period from 2012 to 2019 with one year intervals. We also tested the model by the following methods: boundaries adequacy, sensitivity analysis, and comparison of the model behaviors with the actual system. 3. Results 3.1. PLC Subsystems of Generic Pharmaceutical Products Our findings showed that the PLC has three endogenous subsystems that establish the PLC behaviors: supplier’s subsystem (API supplier, producer, and distributors), demand subsystem (disease, patients, physician, and pharmacies), and competition subsystem. Environmental factors were considered as exogenous elements, which were omitted from the quantitate simulation (See Figure 1.). Each of the three subsystems included some related factors that have been presented in Appendix A, Table A1. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 5 of 20 academic experts to confirm the relationships between the variables through a designed system dynamic questionnaire. The final confirmed relationships between the PLC elements were entered into Vensim 6.4 DDS software [49] as the casual loops diagrams, and the qualitative system dynamic model were developed. We used a casual loop model to develop stock and flow diagrams based on the nature of variables. Using the formulated equations based on the developed stock and flow diagrams in Stella 8.0 [50], we have performed the SD analysis in a eight-year period from 2012 to 2019 with one year intervals. We also tested the model by the following methods: boundaries adequacy, sensitivity analysis, and comparison of the model behaviors with the actual system. 3. Results 3.1. PLC Subsystems of Generic Pharmaceutical Products Our findings showed that the PLC has three endogenous subsystems that establish the PLC behaviors: supplier’s subsystem (API supplier, producer, and distributors), demand subsystem (disease, patients, physician, and pharmacies), and competition subsystem. Environmental factors were considered as exogenous elements, which were omitted from the quantitate simulation (See Figure 1.). Each of the three subsystems included some related factors that have been presented in Appendix A, Table A1. Figure 1. Conceptual model of the product life cycle in pharmaceutical products. 3.2. Determination of Reference Mode Table 1 presents the results of the historical sales data of 527 drugs between 2002 and 2019 and the fitted regression line with R-square greater than 0.8. Sales trend of more than 20% pharmaceutical products of domestic manufacturers was observed in the overshoot and collapse pattern (Table 1). We were able to explain the interactions which caused the formation of our ‘reference mode” overshoot and collapse behavior, which included an upward and downward trend of this behavior that may cause the oscillating behavior seen in 50% of the drugs in this study. Figure 1. Conceptual model of the product life cycle in pharmaceutical products. 3.2. Determination of Reference Mode Table 1presents the results of the historical sales data of 527 drugs between 2002 and 2019 and the fitted regression line with R-square greater than 0.8. Sales trend of more than 20% pharmaceutical products of domestic manufacturers was observed in the overshoot and collapse pattern (Table 1). We were able to explain the interactions which caused the formation of our ‘reference mode” overshoot and collapse behavior, which included an upward and downward trend of this behavior that may cause the oscillating behavior seen in 50% of the drugs in this study. J. Open Innov. Technol. Mark. Complex. 2022,8, 14 6 of 19 Table 1. Different pattern of product life cycle in the generic pharmaceutical products. PLC Type Linear (Upward and Downward Trends) Binominal (Upward and Downward Trends) Overshoot and Collapse Oscillating No Line Fitted Number 60 54 110 267 36 Percent 11.38 10.25 20.87 50.66 6.8 3.3. System Dynamic Casual Loops for PLC According to the conceptual model (Section 4.1), dynamic hypotheses were designed in three parts: suppliers’ subsystem, demand subsystem, and competition subsystem. 3.3.1. Subsystem of the Supply-Side Figure 2presents the qualitative system dynamic model. As presented, the manufacturer has received active ingredients from the primary manufacturer to produce the final product. The availability of raw material and a capacity budget were crucial to maintaining the production capacity of a company. More sales to distributors can lead to more income (and profit), and the company can raise its capacity (Positive feedback loop R1). However, there are two balancing loops (negative feedback loops B1 and B2) that adjusted the loop R1: the effect of sale forecasting and supply raw materials (Loop B1) and the costs (Loop B2) that control the production and sales to distributors. Moreover, the other primary variables related to the manufacturer are the marketing (Feedback loop R3) and R and D activities (Feedback loop R2), which affect the sales to distributors and the drug quality, respectively. J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 6 of 20 Table 1. Different pattern of product life cycle in the generic pharmaceutical products. PLC Type Linear (Upward and Downward Trends) Binominal (Upward and Downward Trends) Overshoot and Collapse Oscillating No Line Fitted Number 60 54 110 267 36 Percent 11.38 10.25 20.87 50.66 6.8 3.3. System Dynamic Casual Loops for PLC According to the conceptual model (Section 4.1), dynamic hypotheses were designed in three parts: suppliers’ subsystem, demand subsystem, and competition subsystem. 3.3.1. Subsystem of the Supply-Side Figure 2 presents the qualitative system dynamic model. As presented, the manufacturer has received active ingredients from the primary manufacturer to produce the final product. The availability of raw material and a capacity budget were crucial to maintaining the production capacity of a company. More sales to distributors can lead to more income (and profit), and the company can raise its capacity (Positive feedback loop R1). However, there are two balancing loops (negative feedback loops B1 and B2) that adjusted the loop R1: the effect of sale forecasting and supply raw materials (Loop B1) and the costs (Loop B2) that control the production and sales to distributors. Moreover, the other primary variables related to the manufacturer are the marketing (Feedback loop R3) and R and D activities (Feedback loop R2), which affect the sales to distributors and the drug quality, respectively. Figure 2. Qualitative system dynamic model of the pharmaceutical product life cycle. 3.3.2. Subsystem of Demand-Side Advertisement Production amounts Awarness + Total consumption of drug A Price satisfaction Quality satisifaction Product satisfaction + + Number of domestic competitors Loyality + Company income Costs - API availability Population Disease prevalence Portfolio dive rsity Growth rate Quality of drug A + Sales amouts of drug A to pharmacies + Ince ntive by dis tributor + Quality of domestic competitors Price of imported competitors Number of imported competitors Volume of imported competitors Pres cription + ++ Availability + Number of pharmacists + Number of pharmaceis + Stock in pharmacies + + R and D investment + + Production capacity + Sales amounts (to dstributors) + Disease incide nce + Death rate - + Consumtion of imported competitors + + Consumption of domestic competitors + + + + + + Quality of imported competitors + + + + Producer stock + + Number of distributors R1 B2 B3 R3 R2 Sales amounts of drug A to patients + + R5 Distrbutor stock + + B1 Replacement with drug A R4 R6 R7 Consumption of other blood pressure me dications Insurance coverage + API outflow API arrival rate API order rate Order forecast + + + Ordering delay time + - + Supply delay time - Percent of distributor number Total distributors Distribution points + + Number of other BP drugs + Consumption rate of total BP drugs Total consume rs of BP drugs (patients) + + + Price - Periodic sales forecast + + Market share + + + + + + + Figure 2. Qualitative system dynamic model of the pharmaceutical product life cycle. J. Open Innov. Technol. Mark. Complex. 2022,8, 14 7 of 19 3.3.2. Subsystem of Demand-Side The demand subsystem consists of two major elements—“patients and diseases” and “physicians and pharmacies”—and each has its variables and relationships explained below. • Patients and diseases: the number of patients depends on the disease incidence, which is affected by the population. • Physicians and pharmacies: physicians play a role as gatekeepers between patients and pharmacies, and pharmacists as intermediate consumers have an essential role in drug selection. We found that loyalty to a manufacturer was an important factor to increase market share. The manufacturer’s marketing activities can affect loyalty by increasing physicians’ awareness and as a result increase consumption of related products (Feedback loop R4). Two other factors, including “product availability” and “product satisfaction”, along with “advertisement” lead to loyalty and ultimately increase patients’ consumption as a positive feedback loop R5. The intermediate consumers of pharmaceutical products are pharmacists; they usually choose the manufacturer of prescription drugs based on pharmacy inventory (availability), and quality and manufacturer advertisements (Feedback loop R6). Also, the number of pharmacies can enhance availability by increasing the pharmacies’ stock across the country (Positive feedback loop R7). 3.3.3. Competition Subsystem Competition is another crucial component that affects the generic pharmaceutical market. The competition components consist of availability, price, advertisement, and quality of imported and domestic competitors, the volume of imported medicines, and the number of local producers and importers. Additionally, the total number of antihypertensive drugs from other groups affects the sales of drug A. Final causal loops of the pharmaceutical product life cycle. Using the above-mentioned relationships between the subsystems, we developed a qualitative model of the product life cycle by using Vensim v6.4 DDS software. The basis of the system dynamic method is the maximum elimination of external variables of the system that are not affected from within the system [ 43 ]. Therefore, if their effect can be calculated from endogenous variables, these variables can be removed. We excluded the environmental factors as exogenous variables from the quantitative model. We then determined the stock and flow diagram based on the final casual loops of pharmaceutical PLC using Stella software 8.0. We reported our findings in Appendix A, Figure A2. 3.4. Model Simulation: PLC System Behaviors 3.4.1. Subsystem of the Supply-Side Figure 3shows the results of model simulation between 2012 and 2019. Based on Figure 3A, flow of raw materials and production rates in the first six years have experienced an upward trend along with the population growth and the increased number of patients, and a decline in 2018 due to reduced demand for drug A and increased again from 2019 onwards due to increasing demand for drug A. Product satisfaction has experienced a general increase due to the company’s growth in R and D activities, which led to an increase in quality between 2012 and 2019. Meanwhile, loyalty declined in 2018 due to a decrease in the manufacturer’s availability and advertising (Figure 3B). These situations, along with the decreased production rate based on incorrect sales estimation in 2019, caused more dropped sales, while total demand has been increasing in the country. J. Open Innov. Technol. Mark. Complex. 2022,8, 14 8 of 19 J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 8 of 20 consumption of Valsartan 80 mg decreased by 32.8% in 2018 compared to 2017 due to the carcinogenic an-nitrous-di-amine reported in the Chinese raw materials of seven domestic companies (the factory A is not one of these seven companies) by the Iranian Food and Drug Administration in July 2018 [51], which caused the recall of this drug and reduced the demand (Figure 3C). After that, in 2019, the total demand grew again by fixing the contamination of raw materials and the domestic factories’ production resumption of this drug. 3.4.3. Competition Subsystem Based on Figure 3D, the number of domestic competitors increased between 2012 and 2019, while the total consumption decreased in 2018 due to the carcinogenic N-Nitroso dimethylamine reported by the Iranian Food and Drug Administration [51]. Concerning the importation of brand medicines, we observed a declining trend in 2013 (Figure 3D). There were two importer companies for Diovan® in the market during the study time (except for 2014, with three importers). After 2015, the importation trend was constant while the domestic production increased mainly during 2012–2019. The general results of the PLC subsystems simulation are as follows: The overall demand trend for Valsartan 80 mg increased during the study period. However, in 2018 we observed a decrease due to contamination of API, recall of drugs, and reduced production of domestic pharmaceutical companies. The volume of imports has been almost constant during this period, which has helped domestic companies dominate the market. During this time, company A has not maintained its market share in the last years of the study period due to unstable management in production, advertising, and R and D. Therefore, this company’s drug has reached the decline stage of the product life cycle. (A) (B) (C) (D) Figure 3. System dynamic simulation result during 2012–2019: ( A , B ) supplier subsystem; ( C ) demand subsystem; (D) competition subsystem. 3.4.2. Subsystem of Demand-Side The results of the SD model simulation in the demand subsystem are presented in Figure 3C. Due to the increase in Iran’s population and the relative stability of the hypertensive disease incidence (15%), the number of users of antihypertensive medicines has been estimated from 10.25 million in 2012 to 10.76 million in 2019 (5% increase). The total consumption of Valsartan 80 mg decreased by 32.8% in 2018 compared to 2017 due to the carcinogenic an-nitrous-di-amine reported in the Chinese raw materials of seven domestic companies (the factory A is not one of these seven companies) by the Iranian Food and Drug Administration in July 2018 [ 51 ], which caused the recall of this drug and reduced the demand (Figure 3C). After that, in 2019, the total demand grew again by fixing the contamination of raw materials and the domestic factories’ production resumption of this drug. 3.4.3. Competition Subsystem Based on Figure 3D, the number of domestic competitors increased between 2012 and 2019, while the total consumption decreased in 2018 due to the carcinogenic N-Nitroso dimethylamine reported by the Iranian Food and Drug Administration [ 51 ]. Concerning the importation of brand medicines, we observed a declining trend in 2013 (Figure 3D). There were two importer companies for Diovan ® in the market during the study time (except for 2014, with three importers). After 2015, the importation trend was constant while the domestic production increased mainly during 2012–2019. The general results of the PLC subsystems simulation are as follows: J. Open Innov. Technol. Mark. Complex. 2022,8, 14 15 of 19 Appendix A J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 15 of 20 Figure A1. System dynamic modeling steps. Table A1. Product life cycle subsystems and related variables. Variables Unit Supply subsystem Factors related to supply of raw materials 1. Inventory of raw materials Percent 2. Delay in supply of raw materials Number Factors related to manufacturers 3. The amount of advertising activities Percent 4. Production rate Number/year 5. Warehouse stock Number 6. Sales to distributor Number/year 7. Company income Rials 8. Market share Percent 9. Costs Rials 10. Research and development activities Percent 11. Production capacity Number 12. Sales forecast Number 13. Raw material order rate Number 14. Delay in raw material order Number 15. Total number of product portfolio Number 16. Product quality -* 17. Product availability - Factors related to distributors 18. Number of distribution points Number 19. Sales to pharmacies Number/year 20. Discount rate (incentive) Number 22. Percentage of distribution centers Number Figure A1. System dynamic modeling steps. Table A1. Product life cycle subsystems and related variables. Variables Unit Supply subsystem Factors related to supply of raw materials 1. Inventory of raw materials Percent 2. Delay in supply of raw materials Number Factors related to manufacturers 3. The amount of advertising activities Percent 4. Production rate Number/year 5. Warehouse stock Number 6. Sales to distributor Number/year 7. Company income Rials 8. Market share Percent 9. Costs Rials 10. Research and development activities Percent 11. Production capacity Number 12. Sales forecast Number 13. Raw material order rate Number 14. Delay in raw material order Number 15. Total number of product portfolio Number 16. Product quality - 17. Product availability - Factors related to distributors 18. Number of distribution points Number 19. Sales to pharmacies Number/year 20. Discount rate (incentive) Number 21. Percentage of distribution centers Number 22. Warehouse stocks of distributors Percent J. Open Innov. Technol. Mark. Complex. 2022,8, 14 16 of 19 Table A1. Cont. Variables Unit Demand subsystem Factors related to the disease and patients 23. Population Number 24. Mortality rate Number/year 25. Birth rate Number/year 26. Disease prevalence Number 27. Total number of people sought for treatment Number 28. Total consumption of antihypertensive drugs Number/year 29. Number of people treated Number 30. Price satisfaction - 31. Quality satisfaction - 32. Product satisfaction - 33. Existence of insurance coverage 0/1 Factors related to the pharmacies 34. Number of pharmacies Number 35. Warehouse stocks of pharmacies Number 36. Sales to patients Number/year 37. Loyalty to the manufacturer - Factors related to the physicians 38. Product satisfaction - 39. Loyalty to the manufacturer - Competition subsystem Factors related to the competition 40. Number of competitors from other families Number 41. Consumption of competitors from other therapeutic families Number/year 42. Consumption of domestic competitors of drug A Number/year 43. Number of domestic competitors Number 44. Number of foreign competitors Number 45. Price of foreign competitors Rials 46. Volume of competitors imports Number/year 47. Import volume of competitors of the same family A Number J. Open Innov. Technol. Mark. Complex. 2022, 8, x FOR PEER REVIEW 17 of 20 Figure A2. Stock and flow diagrams of generic pharmaceutical PLC in Stella software 8.0. Notes: Some assumption was considered to develop the system dynamic model: (1) The drug is a prescription medicine. (2) Iran’s FDA sets the price of all generic medicines, and the medicine are under the insurance coverage, so all of the producers’ price are the same and price satisfaction was considered 100%). References 1. Stark, J. Product Lifecycle Management Lifecycle Management, 3rd ed.; Springer International Publishing: Cham, Switzerland, 2016; Volume 2. 2. 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Product Lifecycle Management: Driving the Next Generation of Lean Thinking by Michael Grieves; McGraw Hill: New York, NY, USA, 2007; Volume 24. producer stock distributer stock pharmacy stock sales to distributor ~ saletopharmacy API av ailibility patientsale ~ company income Forecast Disease prev alence ~ Number of BP drugs Consumption of total A ~ portfolio div ersity ~ Number of importer of drug A ~ Costs ~ R&D orientation ~ Adv ertisement ~ Distributor Number Quality price satisf action Quality satisf action Product satisf action Loy ality Distribution Points Percent of DN Av ailibility ~ incentiv e ~ death fraction Healthy population bearth rate per y ear supply delay time Consumption of drug A Total consumers of BP drugs market share Death rate ~ Number of domestic competitors of A production rate treatment percent ~ pulation growth Order f orecast ~ Imported medicine price Total consumptions ~ Tot al D Ordering delay time ~ Imported v olume rate Consumption rate of domestic competitors of A ~ Consumption rate of other BP drugs Consumption rate of total BP drugs ~ modif ied index Disease incidenece API order rate API arriv al rate API outf low ~ Index Figure A2. 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