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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.17 No.4 pp.662-668

Developing a System Dynamic Model for Pharmacy Industry

Nazli Akhlaghinia, Ali Rajabzadeh Ghatari, Abbas Moghbel, Ali Yazdian*
Department of Industrial Management, Tarbiat Modares, Tehran, Iran
Department of Electrical Engineering, Tarbiat Modares, Tehran, Iran
* Corresponding Author, E-mail:
May 23, 2018 July 25, 2018 September 17, 2018


The pharma industry is beginning to realize the benefits of the internet of thing (IOT), especially through modularization, which is already in the detailed implementation and usage stage. pharmatical manufactures that implement IOT technologies can more easily meet requirements for serialization and have the opportunity to leverage intelligent data that are already required in the pharmaceutical manufacturing environment. Using system dynamics model, this study evaluate alternative IOT strategies through investment lens, to provide managers guidance for IOT decisions. In this paper we propose a model of system dynamics for IOT in pharma industry. This study evaluates alternative IOT investment strategies to provide managers guidance for efficient decisions. This paper investigates the typical production logistic execution processes and adopts system dynamics to design cost-effective IoT solutions. The internal and external production logistic processes are first investigated separately. Using sensitivity analysis, the optimal IoT solutions are evaluated and analyzed to provide guidance on IoT implementation. Internal and external production logistic processes are then combined into an integrated structure to offer a generic system dynamics approach.



    The industrial internet of thing, similarly, is a network of equipment with sensors that collect data in real time and communicate them to other machines or people using the cloud or internal company systems. Manufacturing equipment may already be connected and controlled by supervisory control and data acquisition (SCADA) systems that feed data into manufacturing execution systems (MES), which collect and manage manufacturing data, and distributed control system, which control equipment.

    The IIOT is an additional system which has characterized by “big data” that has the potential to be harnessed to improve manufacturing efficiency. Manufactures are using big data and advanced analytics to scrutinize the massive amounts of data being collected by their smart machines. These evaluations are yielding valuable insights into plant workflow, including predictive maintenance needs before equipment breaks and tracking parts as they move through supply chain. Pharmaceutical manufactures that implement IOT technologies can more easily meet requirements for serialization and have the opportunity to leverage intelligent data that are already required in the pharmaceutical manufacturing environment.

    Digitization of processes and data across the value chain along with the emergence of IOT has transformed the pharma industry. Although IOT is still in its nascent stages of adaption in the life science industry, the use of smart devices and machine – to-machine (M2M) communication leveraging SMAC technology comes at a time when the industry is grappling with patent cliffs and declining R&D productivity. Drug compliance and adverse drug reactions (ADR) are two of the most important issues regarding patient safety throughout the worldwide healthcare sector.

    ADR prevalence is 6.7% throughout hospitals worldwide, with an international death rate of 0.32% of the total of the patients. This rate is even higher in Ambient Assisted living environments, where 15% of the patients suffer clinically significant interactions due to patient’s non-compliance to drug dosage and schedule of intake in addition to suffering from polypharmacy (Linjakumpu et al., 2002).

    Therefore, polypharmacy is frequent in hospitals where patients receive treatment for multiple illnesses and/or drugs for prevention. Most of these injuries are caused by ADR, which prolong hospital stay, increase costs, and nearly double a patient’s risk of death. polypharmacy and as a consequence ADR together with drug non-compliance are also common in Ambient Assisted Living (ALL) environment. Old people often have difficulty remembering the drug dosage and times of drug intake that cause dangerous effect on their health (Linjakumpu et al., 2002).

    In this paper we propose a system dynamic models for IOT in pharmacy sector. IOT is applied to examine drugs in order to fulfill treatment, to detect harmful side effects of pharmaceutical excipients, allergies, liver/renal contradictions, and harmful side effects during pregnancy. The IOT design acknowledges that aforementioned problems are worldwide so the solution supports several IOT identification technologies: barcode, radio Frequency identification, Near Field communication, and a new solution developed for low income countries based on IrDA in collaboration with the World Health Organization in this model affiliate to work of Antonio J. Jara - Antonio F. Skarmeta Miguel A. Zamora, we design a drugs checker based on the Internet of Things to monitor treatment for drug compliance and to detect ADR (Atzori et al., 2010).

    The capabilities and opportunities of IOT have already been presented for the healthcare sector. The necessary requirements to manage drug compliance and ADR are implemented via IOT, which allows access to drug information and uses service by the internet for checking ADR, drug under packaging, drug under coding, damage report, recovered patient monitoring.

    The main technologies utilized in IOT for drug identification range from existing technologies such as barcodes to new technologies such as Radio Frequency Identification (RFID) together with its version for smart phones Near Field Communications (NFC).

    The life science industry had been more reactive than proactive in technology adoption, primarily because of tight regulations and domain complexities. But over the past few years, falling R&D productivity, increasing cost, compliance none – adherence, large number of patents expiring and increasing stakeholder expectations with respect to drug efficacy put tremendous pressure on pharma companies.

    Some early adopters have already started exploring IOT to enable end-to-end digital integration across the value chain. IOT-based smart device such as “organ in chip,” which allows organization to run real-life diagnostics scenarios, are already gaining traction. Clubbing the output from these device with big data analytics and cognitive systems has the potential to provide unprecedented opportunity, thereby drastically improving R&D productivity.

    Another example of growing influence of smart devices is “chip in a pill”-a special ingestible pill-that on consumption captures health status, including drug effects on key organs, and sends to a wearable device. This data is then sent as a report over cloud to HCP for diagnosis.

    Use of smart devices in clinical development, supply chain and patient engagements can not only help reduce time to market for drugs but also the real time data feeds can be ploughed back to proactively detect errors across the value chain and, thus improve regulatory compliance. Data from wearable devices can be used by HCPs to prescribe personalized medicines (PM) that will improve drug efficacy manifold and will reduce treatment period.


    Wu et al. (2006), used system dynamics approach to construct a performance measurement model for pharmacy supply chain management (Wu et al., 2016).

    Costa, (2013), in deploying lean in healthcare: evaluating in US, hospital pharmacies, 2014 evaluating the use of digital technology in us hospital pharmacies. They analyses two prescribing technologies, namely no carbon required (NCR) and digital scanning technologies to quantify the advantages of the medication ordering, transcribing, and dispensing process in a multi hospital health system with comparison between these two technologies.

    Azad et al. (2017), in the drug identification and interaction checker based on IOT to minimize adverse drug reaction and improve drug compliance computer design) of CINCAP to develop and test in silico capsule and tablet formulations. They propose an innovative system based on IOT for the drug identification and the monitoring of medication. We simulating this model in this article with system dynamics. Azad et al. (2017), big data analytics: understanding its capabilities and potential benefits for healthcare organization, they recommend five strategies for health care organizations that are considering to adapt big data analytics technologies. Their finding will help organizations understand the big data analytics capabilities and potential benefits about data-driven analytics strategies.

    Wang et al., (2016), in data mining application in health care, they discuss data mining and its application within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management and the detection of fraud and abuse. This article highlight the limitation of data mining and discuss some future directions (Tan, 2006).

    In 2016, in the paper of Retail medicine in an Era of IOT and medical errors in the age of Ubiquitous connectivity, discuss about IOT platform for preventive care scenarios in health care (Datta, 2016).

    Huang and Cheng, (2014), in paper of RFID Technology Combined with IoT Application in Medical Nursing System, design a medical nursing system which is based on RFID, WSN, and NFC technology.


    3.1. Pharmaceutical Intelligent Information System

    The Pharmaceutical Intelligent information system (PIIS) is composed by a knowledge-Based System which contains a rule-engine system to detect the possible interactions between prescribed drug patient, and an ontology where is described the drugs concepts and patients information. Drug verification can be carried out BY a PIIS. Furthermore, the information from a patient’s health profile can be either from the Electronic Health Record (HER) located at Hospital Information System (HIS), a personal Health Record (PHR) defined in the internal memory of the personal device, or from a personal health card based on RFID.

    Verifying a drug before consumption is especially important for polypharmacy cases and among elderly patients for detecting ADR. The drug verification is done by means of the smart – phone or smart-watches or wearable technology. The patients is alerted when a drug consumption is safe. PIIS is composed of a database with drug information. It is knowledge-based system which contain engine system to detect the possible interactions between prescribed drugs character and EHR system.

    3.2. Patients Profile

    The profile is accessible through the PIIS database, or personal health RFID CARD, which contains

    • 1. The Anatomical, Therapeutic, chemical drug classification

    • 2. Dose, Dose Number of drug

    • 3. Drug name

    • 4. information about possible individual side effects of drug

    • 5. information about intervention of use of multi drug

    • 6. An ingredient that cause an allergic

    The challenge for the future of manufacturing is highly flexible with adaptable automated production. Today, there are still a lot of manual processes in drug substance and drug product preparation. Managing the data and being able to make automatic adjustment or decision based on the data implies advances in the management of the data and the willingness to trust in the systems that are collecting data.

    Medical information technology and healthcare service are closely related to the national welfare and the peoples live hood cloud computing and internet of integration in the application of modern medicine would be a great breakthrough. Because in large scale cloud computing has its advantage such as high reliability, virtualization, high promote resource sharing, cost saving, build medical monitoring and management system with high efficiency. Internet as an important support to realize the safe, efficient and high quality of the medical monitoring and management system with high efficiency.

    IoT also bring great convenience to hospital, especially in the patient monitoring and tracking management. With the rapid development of internet, cloud computing and internet integration of medical monitoring and management platform is to provide new opportunities for the hospital, even in social fields. On the one hand, the internet of things can benefit from cloud almost unlimited capacity and resources to make up for technical constraints. Specifically, cloud computing can provide an effective solution to realize management of internet services and composition and use of things or data applications.

    Essentially, the Cloud acts as intermediate layer between the things and the application (Liu et al., 2015).

    3.3. Description of IOT Model

    In this model we have the variable such as: hardware infrastructure, software infrastructure, labor, this variable impact on production rate. every drug package has drug ID and the total operation in drug factory such as production rate, the state of drug under coding, coding rate, drug under packaging, packaging state, shipment rate to pharmacy, pharmacy inventory, are monitoring with IOT infrastructure.

    In each package of drug we have chip that controlling communicating data with IOT systems. With this chip if we have damage in transforming drug from factory to pharmacy the damage report sending with sums to suppliers.

    With wearable tech and smartphone and NFC tag every drug surveyed with EHR DRUG history & pharmatical intelligent system. Each difference between PIS and EHR systems identified for detection allergies and adverse drug reaction that impact on drug dose that each patients should consumed.

    With this infrastructure we have improved quality and gaining competitive advantage (Figure 1).


    Simulation of the model was conducted using vensim PLE, a fully functional system dynamics software package from ventana systems, inc. (Harvard, MA, USA). The unit time frame selected was a month, and the model was run over a period of 30 months, representing a medium- term security planning horizon. Attempting to simulate for longer periods would entail greater uncertainty and less meaningful results because longer term predictions are difficult to accurately make in health care. The model was run under variety of conditions to understand the impact of different IOT investment policies in health care industry, damages, and patients recovered, ADR detection. This was done to help managers make effective decisions concerning IOT platforms.

    4.1. Base Scenario

    Base scenario was calibrated for small organization, using median values for dimensionless constructs and a set of plausible values for other constructs. This involved an assist base of 90000$ investment in IOT business model and patient population of 15. in addition the hardware IOT infrastructure tool investment was set at 100000$ at the start of first year, with software IOT investment expenses of 750000$ every six months. After running the model, the numbers of attacks, total damages, and overall patients recovered were tracked. These results appear in Figure 2.

    4.2. Alternative IOT Investment Scenarios

    After establishing that the model was structurally sound and that its behavior was consistent with expectations, the model was used to investigate the impact of different pharma IOT business model investment decisions. The IOT hardware technology investment was varied from 100000$ to 800000$, cumulative damages, and infrastructure cost were compiled for these scenarios and are presented in Table 1. all of these scenarios based on experts comments.

    The overall trend in these results is moderately predictable. As the level of IOT investment decrease, the number of damages increased and, correspondingly, the damages incurred and overall patients increase as the level of IOT investment increase, the damages, patients all decrease and they do so dramatically. a similar analysis was performed to labor IOT investment rating from 250$ to 370$. The result appear in below.

    4.3. Model Validation

    Validation of system dynamics model is generally performed using two approaches. A structural validation of the model seeks to determine whether it reflects the real world accurately (Forrester, 1980).


    These graphs provide greater insights, though a more telling observation is the relative impact of different forms of IOT investment. Investment in IOT, software and hardware have considerably large impact than detection ADR and help reduce the number of Patients, it improve quality process, gain competitive advantage, reduce damage report, increase production rate. Increase in (hardware and software and labor) investment for IOT infrastructure have considerable impact on production rate and damage report. In all of scenario the investment in IOT business model is consistent. This model indicates that different IOT investment have different implications on damage cost. An examination of the different IOT investment channels reveals that not all investment has the same payoff. Investment in tools for production rate and detecting damage drug package yielded the greatest payoff. In similar vein, a cutback on investment in this area had the most deleterious effect. A variety of tools are available in this category that address specific and overlapping needs. These include SDN network, intelligent sensors, NB-IOT network, zig bee network, LPWAN network, analyctics such as hadoop, artificial intelligence application, and intelligent information systems. This research examined the effect of IOT infrastructure in pharmacy industry and using the system dynamic model to understand the implications of these investment. The incorporates many aspects of an IOT practice including hardware and, improving process, recovery operations, detection and damage reduction. Simulations using the model indicate that investments in IOT tools designed to detecting allergy reaction and damages in production process led to better payoff. The model can be used in various capacities by practitioners and researchers. The model can serve as a decision support tool, recommending the preferred ways in which to expand IOT investments.

    It can also serve as design tool through a systematic explication of the structural relations that link an IOT investments to production rate. In summary, the model provides researchers with rich environment to better understand the implications of IOT decisions under a variety of circumstances: in addition, it assists practitioners in making better decisions concerning IOT tools.



    Proposed model.


    Results of various scenarios.


    The scenario and results


    1. Atzori, L. , Iera, A. , and Morabito, G. (2010), The internet of things: A survey, Computer Networks, 54(15), 2787-2805.
    2. Azad, N. , Ghandvar, P. , and Rahimi, Z. (2017), Online search behaviour of customers in shoe market, Astra Salvensis, Supplement 2, 793.
    3. Costa, F. F. (2013), Big data in biomedicine, Drug Discovery Today, 19(4), 433-440.
    4. Datta, S. P. A. (2016), Retail medicine in an Era of IoT and medical errors in the age of ubiquitous Connectivity, Journal of Innovation Management, 4(3), 3-6.
    5. Forrester, J. W. (1980), Tests for building confidence in system dynamics model, TIMS Studies Manage,Sciences, 14, 209-228.
    6. Huang, C. H. and Cheng, K. W. (2014), RFID Technology combined with IoT application in medical nursing system, Bulletin of Networking, Computing, Systems, and Software, 3(1), 20-24.
    7. Linjakumpu, T. H. , Hartikainen, S. K. , and Klaukka, T. (2002), Use of medications and polypharmacy are increasing among the elderly, Journal of Clinical Epidemiology, 55(8), 809-817.
    8. Liu, Y. , Dong, B. , Guo, B. , Yang, J. , and Peng, W. (2015), Combination of cloud computing and internet of things (IOT) in medical monitoring systems, International Journal of Hybrid Information Technology, 8(12), 367-376.
    9. Tan, H. C. (2006), Data mining applications in healthcare, Journal of Healthcare Information Management,19(2), 64-72.
    10. Wang, Y. , Kung, L. A. , and Byrd, T. A. (2016), Big data analytics: Understanding its capabilities and potentialbenefits for healthcare organizations, Technological Forecasting & Social Change, 126, 3-13.
    11. Wu, J. , Chen, C. C. , and Tsai, R. (2006), Using system dynamics approach to construct a performance measurement model for pharmacy supply chain management,Journal of International Technology and Information Management, 15(1).
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