نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری رشته مدیریت صنعتی، گروه مدیریت، دانشکده مدیریت و اقتصاد، دانشگاه گیلان، رشت، ایران
2 دانشیار گروه مدیریت، دانشکده مدیریت و اقتصاد، دانشگاه گیلان، رشت، ایران
3 استادیار گروه مدیریت، دانشکده مدیریت و اقتصاد، دانشگاه گیلان، رشت، ایران
چکیده
این پژوهش به بررسی تأثیر استراتژیهای بهبود بر شاخصهای کلیدی عملکرد در صنعت آب و فاضلاب با بهرهگیری از رویکرد پویایی سیستم پرداخت. هدف اصلی مطالعه، توسعه مدلهایی جامع برای شبیهسازی روابط پیچیده و تعاملات میان استراتژیها و متغیرهای عملکردی با هدف ارتقای پایداری و مدیریت عملکرد بود. شاخصهای عملکرد به دو دسته ورودی (شامل دوره وصول مطالبات، سرانه بدهی آحاد مشترکین، آب به حساب نیامده و سهم هزینه نیروی کار به فروش) و خروجی (شامل نسبت گردش داراییها، پوشش سرانه مشترکین، رشد بازار آب و فاضلاب و درجه حرفهای کارکنان) تقسیم شدند. براساس ادبیات پیشین، اسناد بالادستی، گزارشهای شرکتهای آب و فاضلاب و نظرات خبرگان، ۱۴ استراتژی بهبود برای ارتقای عملکرد شناسایی و تبیین گردید. سپس دو مدل پویایی سیستم برای تحلیل این شاخصها طراحی و توابع ریاضی مرتبط با هر متغیر تعریف شد. شبیهسازی سناریوهای تدوینی نشان داد که اعمال استراتژیها، به کاهش دوره وصول مطالبات و سهم هزینه نیروی کار به فروش، و همچنین بهبود نسبت گردش داراییها، پوشش سرانه مشترکین، رشد بازار و ارتقای درجه حرفهای کارکنان منجر شده است. این پژوهش ابزاری کارآمد برای تحلیل سیستماتیک عملکرد و تصمیمگیری استراتژیک در صنعت آب و فاضلاب ارائه نمود.
کلیدواژهها
موضوعات
عنوان مقاله [English]
Improvement Strategies in the Water and Wastewater Industry: Dynamic Modeling and Performance Management
نویسندگان [English]
- Nahid Foroughi 1
- Mahmoud Moradi 2
- Keikhosro Yakideh 3
- MohammadRahim Ramazanian 2
1 Ph.D. Candidate in Industrial Management, Management Department, Faculty of Management and Economics, University of Guilan, Rasht, Iran
2 Associate Professor, Management Department, Faculty of Management and Economics, University of Guilan, Rasht, Iran
3 Assistant Prof., Management Department, Faculty of Management and Economics, University of Guilan, Rasht, Iran
چکیده [English]
Abstract
This study investigated the impact of improvement strategies on key performance indicators in the water and wastewater sector using a system dynamics approach. The main objective was to develop models for simulating the complex interactions between strategies and performance variables to enhance sustainability and performance management. Performance indicators were categorized as input indicators (including accounts receivable period, per-capita subscriber debt, non-revenue water, and labor cost share of sales) and output indicators (such as asset turnover ratio, per-capita subscriber coverage, market growth, and employee professionalism). Based on the literature, policy documents, company reports, and expert opinions, 14 improvement strategies were identified and incorporated into two system dynamics models. Results indicated that implementing these strategies reduced accounts receivable periods and labor costs while improving asset turnover, service coverage, market growth, and employee skills. These findings demonstrate that system dynamics modeling is an effective tool for strategic decision-making and performance improvement in the water and wastewater sector.
Introduction
ith the increasing complexity and rapid changes in competitive environments, organizations require innovative approaches to enhance performance and achieve sustainable competitive advantage. Performance management is a fundamental approach that, in addition to evaluation, encompasses continuous feedback, goal setting, training, and incentive systems (Aguinis & Pierce, 2008). Prior research has emphasized that analyzing causal relationships and dynamic interactions within organizations, particularly under multi-factor conditions, can enhance decision-making (Tseng & Levy, 2019). In this context, systems thinking and simulation models have gained importance as tools for predicting policy impacts and designing improvement strategies (Shafiee et al., 2021; Eidin et al., 2024). Nevertheless, many studies have primarily focused on ranking and benchmarking performance rather than addressing operational interventions (Kameli et al., 2023). In the Iranian water and wastewater sector, limited resources and deteriorating infrastructure further highlight the necessity of adopting advanced analytical approaches (Hejazi et al., 2024). Accordingly, this study applies system dynamics modeling to examine how improvement strategies influence key input and output indicators in the water and wastewater sector, while providing a framework for enhancing sustainability and supporting strategic decision-making.
Literature Review
The system dynamics approach, introduced by Forrester (1961), is a framework grounded in systems science and computer-based simulation that enables the analysis of complex system behavior and the prediction of long-term policy effects. Features such as feedback loops, stocks and flows, nonlinear relationships, and mutual interactions make it an effective tool for modeling organizational and infrastructural systems (Mustafee et al., 2010; Mielczarek, 2016). Numerous studies have demonstrated that system dynamics is an effective method for performance analysis and strategic decision-making, including in wastewater network asset management, automotive supply chains, urban and water resource management, and complex projects (Mohammadifardi et al., 2019; Norouzian-Maleki et al., 2022; Calderon-Tellez et al., 2024). However, most studies have focused only on partial analyses of performance indicators, and integrated evaluations of multiple strategies and both input and output indicators remain scarce. In the water and wastewater sector, the development of separate models for resource-related input indicators and performance-related output indicators, along with the simulation of their interactions, remains limited. The present study addresses this gap by introducing two distinct models and analyzing the combined effects of improvement strategies, thereby providing an innovative and context-specific framework for comprehensive performance assessment in this sector.
Methodology
This descriptive-analytical study employed a system dynamics approach to examine the long-term effects of 14 improvement strategies on key performance indicators in water and wastewater companies. Eight input indicators (accounts receivable period, per-capita subscriber debt, non-revenue water, and labor cost share) and output indicators (asset turnover ratio, per-capita subscriber coverage, market growth, and employee professionalism) were identified based on the literature and expert opinions. Two system dynamics models were developed, encompassing financial, operational, and human resource subsystems. Reinforcing loops represented the positive effects of network expansion, reduction of non-revenue water, and employee motivation, while balancing loops captured the moderating effects of accounts receivable management and constraints associated with sales growth. These models enabled the simulation of improvement scenarios and the identification of key leverage points affecting system performance.
Results
Using Sterman’s five-step modeling process (2000), input and output system dynamics models were simulated for the selected indicators. Variable relationships were established based on financial data, industry standards, water and wastewater regulations, and the opinions of ten experts to evaluate the long-term effects of improvement strategies on key company performance metrics.
Structural and behavioral tests confirmed the validity of the models, and sensitivity analysis showed that the provincial pricing coefficient had the greatest impact on net sales, with a ±20% change resulting in approximately a 20% change in sales. In contrast, other key parameters, such as the conversion rate of unauthorized connections and government funding allocation, had minimal effects (less than 0.1%) on the growth rate of service units. These results indicate that the models are stable with respect to input variations, with the primary sensitivity associated with pricing policies.
Eight scenarios were developed: four aimed at improving operational efficiency and reducing financial constraints, and four targeting productivity enhancement and market growth. Simulation of the input model showed that the simultaneous implementation of incentive/punitive strategies, private sector involvement, and network expansion reduced accounts receivable issues, controlled resource losses, and optimized labor costs. The output model demonstrated that a combination of developmental actions, tariff adjustments, and human resource empowerment improved asset productivity and service coverage, stabilized market growth, and enhanced employee skills and expertise. Overall, the results indicate that the coordinated and targeted application of strategies creates an optimal balance between short-term efficiency, financial sustainability, and long-term development. The developed models provide water and wastewater managers with a systematic tool to support strategic decision-making and evaluate long-term impacts.
Discussion
This study demonstrated that, through system dynamics modeling, the simultaneous interaction of managerial, financial, and infrastructure strategies significantly affects the efficiency and sustainability of water and wastewater companies. Separating input and output indicators and designing distinct models enabled the analysis of the combined effects of 14 improvement strategies, showing that enhancements in accounts receivable management, reduction of resource losses, and optimization of labor costs were accompanied by increased asset productivity, network expansion, and improved employee skills. The results confirmed that a combination of incentive-based strategies, financing of smart technologies, and infrastructure development can effectively balance short-term objectives with long-term goals. Financial resource constraints and external factors, such as inflation and demand fluctuations, emphasize the importance of active management and complementary policies. Overall, the findings indicate that successful management in these companies requires an integrated and synergistic approach across human capital, financial structure, and infrastructure.
Conclusion
This study confirmed the effectiveness of the system dynamics approach in analyzing and predicting the long-term effects of improvement strategies in water and wastewater companies. The developed models, by simulating the complex interactions among 14 strategies and key performance indicators, enable evidence-based decision-making with a systems perspective. The results showed that a combination of managerial actions, infrastructure development, and human capital empowerment not only enhances operational efficiency but also strengthens financial sustainability and service accessibility. This study recommends that companies focus on integrated strategies and continuous performance monitoring, while future research should consider the impact of external factors such as climate change and emerging technologies in future models. Overall, this study offers a practical and context-specific framework for comprehensive performance improvement in water and wastewater companies.
کلیدواژهها [English]
- System dynamics
- Simulation:
- Performance indicators:
- Improvement strategies:
- Performance management
- ایزدیار، مهدی، طلوعی اشلقی، عباس و سیدحسینی، سیدمحمد. (1400). رویکرد پویایی سیستم برای ارزیابی عملکرد پایداری شیوههای مدیریت زنجیره تأمین لارج در صنعت قطعهسازی خودرو. مدیریت بهرهوری (فراسوی مدیریت)، 15(4)، 159-192. https://www.sid.ir/paper/999208/fa
- آئیننامههای تعرفهای وزارت نیرو. (1400). وزارت نیرو.
- برنامه استراتژیک شرکت آب و فاضلاب گیلان. (1401). شرکت آب و فاضلاب گیلان.
- برنامه استراتژیک شرکتهای آب و فاضلاب آذربایجان غربی و هرمزگان. (1400). شرکتهای آب و فاضلاب آذربایجان غربی و هرمزگان.
- حجازی، ناصر، جهانگیرفرد، مجید و میاندرق، امیر. (1403). ارائه الگوی مدیریت هوشمند مبتنی بر بهبود بخشی به عملکرد رفتاری سازمان در شرکت آب و فاضلاب استان تهران. مجله مدیریت صنعتی، 19(67)، 82-103.
- جهانیان، سعید، امینی، فائزه و شائمی برزکی، علی. (1397). شناسایی سیاستهای بهبود ظرفیت جذب دانش و تأثیر آنها بر عملکرد سازمانی با رویکرد پویاییشناسی سیستم. مدیریت نوآوری، 7(3)، 143-168. https://civilica.com/doc/1895407
- داودآبادی، محمد و قنادی، مجید. (1393). راهنمای جامع مدیریت مالی و اقتصادی صنعت آب و فاضلاب (به همراه تحلیل مؤلفههای مالی و اقتصادی- از تئوری تا عمل). تهران: موسسه فرهنگی هنری پویه مهر اشراق. https://www.gisoom.com/book/11005661
- داودآبادی، محمد و دولتشاهی، هنگامه. (1400). سالنامه آماری شاخصهای مالی صنعت آب و فاضلاب (سال مالی 1398). تهران: پیک نور.
- دفتر حقوقی وزارت نیرو. (۱۳۸۰). مجموعه قوانین، تصویبنامهها و آئیننامههای آب و برق و آب و فاضلاب (جلد 2، چاپ اول). تهران: وزارت نیرو.
- دفتر حقوقی وزارت نیرو. (۱۳۸۷). مجموعه قوانین و مقررات آب و برق و آب و فاضلاب (چاپ اول). تهران: وزارت نیرو.
- سند توسعه منابع انسانی شرکت آب و فاضلاب گیلان. (1401–1405). شرکت آب و فاضلاب گیلان.
- قانون برنامه پنجساله ششم توسعه. (1396–1400). سازمان برنامهوبودجه کشور. https://rc.majlis.ir/fa/law/show/1014547
- گزارش فعالیت هیئتمدیره بودجه شرکت آب و فاضلاب گیلان. (1400). شرکت آب و فاضلاب گیلان.
- گزارش فعالیت هیئتمدیره بودجه شرکت آب و فاضلاب گیلان. (1401). شرکت آب و فاضلاب گیلان.
- گزارش فعالیت هیئتمدیره صورتهای مالی شرکت آب و فاضلاب گیلان. (1401). شرکت آب و فاضلاب گیلان.
- گزارش فعالیت هیئتمدیره صورت مالی شرکت آب و فاضلاب گیلان. (1402). شرکت آب و فاضلاب گیلان.
- قانون برنامه پنجساله هفتم توسعه. (1403–1407). سازمان برنامهوبودجه کشور. https://rc.majlis.ir/fa/law/show/1809128
- شفیعی، مرتضی، حسین زاده لطفی، فرهاد و صالح، هیلدا. (1399). پیشبینی الگو برای واحدهای تصمیمگیرنده در تحلیل پوششی دادهها. مجله بینالمللی ریاضیات صنعتی، 13(1)، 29-42. https://www.magiran.com/p2192858
- Aguinis, H., & Pierce, C. A. (2008). Enhancing the relevance of organizational behavior by embracing performance management research. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 29(1), 139-145. https://doi.org/10.1002/job.493
- Ayanponle, L. O., Awonuga, K. F., Asuzu, O. F., Daraojimba, R. E., Elufioye, O. A., & Daraojimba, O. D. (2024). A review of innovative HR strategies in enhancing workforce efficiency in the US. International Journal of Science and Research Archive, 11(1), 817–827. https://doi.org/10.30574/ijsra.2024.11.1.0152
- Bai, Y., Langarudi, S. P., & Fernald, A. G. (2021). System dynamics modeling for evaluating regional hydrologic and economic effects of irrigation efficiency policy. Hydrology, 8(2), 61. https://doi.org/10.3390/hydrology8020061
- Bianchi, C. (2012). Enhancing performance management and sustainable organizational growth through system-dynamics modelling. Systemic management for intelligent organizations: Concepts, models-based approaches and applications, 143-161. https://doi.org/10.1007/978-3-642-29244-6_8
- Calderon‐Tellez, J. A., Bell, G., Herrera, M. M., & Sato, C. (2024). Project management and system dynamics modelling: Time to connect with innovation and sustainability. Systems Research and Behavioral Science, 41(1), 3-29. https://doi.org/10.1002/sres.2926
- Carrubbo, L., Cosimato, S., & Gagliardi, A. R. (2025). Towards dynamic decision-making in government as service organization: insights from systems thinking. Transforming Government: People, Process and Policy, 19(1), 108-129. https://doi.org/10.1108/TG-05-2024-0113
- Eidin, E., Bielik, T., Touitou, I., Bowers, J., McIntyre, C., Damelin, D., & Krajcik, J. (2024). Thinking in terms of change over time: opportunities and challenges of using system dynamics models. Journal of Science Education and Technology, 33(1), 1-28. https://doi.org/10.1007/s10956-023-10047-y
- Forrester, J. W. (1961). Industrial Dynamics. Waltham MA, Pegasus Communications.https://books.google.com/books/about/Industrial_Dynamics.html?id=WTXyAAAAMAAJ&utm
- Gilani, H., Shobeiry, S., Biglari Kami, M., & Sahebi, H. (2022). A sustainable redesign model for the water/wastewater supply network: A water–energy nexus approach. Kybernetes. https://doi.org/10.1108/K-04-2021-0320
- Gozali, L., Zagloel, T. Y. M., Simatupang, T. M., Sutopo, W., Gunawan, A., Liang, Y. C., & Suseno, Y. (2024). The important role of system dynamics investigation on business model, industry and performance management. International Journal of Productivity and Performance Management, 73(4), 945-980. https://doi.org/10.1108/IJPPM-07-2021-0399
- Guemouria, A., Chehbouni, A., Belaqziz, S., Epule Epule, T., Ait Brahim, Y., El Khalki, E. M., & Bouchaou, L. (2023). System dynamics approach for water resources management: A case study from the Souss-Massa Basin. Water, 15(8), 1506. https://doi.org/10.3390/w15081506
- Hosseini, A., & Hosseini, S. H. (2022). A System Dynamics Approach to Sustainable Business Model Transformation: A Manufacturing Case. Journal of Systems Thinking in Practice, 1(1), 24-48. https://doi.org/10.22067/JSTINP.2022.76303.1010
- Ibrahim, A. I., & Minja, D. (2024). Reward management practices and performance of governmental agencies in the water sector: A Case of Northern Water Works Development Agency, Kenya. International Academic Journal of Arts and Humanities, 1(4), 407-421. https://iajournals.org/articles/iajah_v1_i4_407_421.pdf
- Ji, X., Zhai, Y., Fu, S., & Lu, C. (2023). Towards the sustainable development of logistics system model: A system dynamics approach. Plos one, 18(1), e0279687. https://doi.org/10.1371/journal.pone.0279687
- Joseph, G., Hoo, Y. R., Wang, Q., Bahuguna, A., & Andres, L. (2024). Funding a Water-Secure Future (No. 41515). The World Bank Group. https://documents1.worldbank.org/curated/en/099050824184024824/pdf/P17294414b3bfa0601be1b181ab4d26aedd.pdf
- Kameli, M., Daneshian, B., & Hosseinzadeh Lotfi, F. (2023). Sequential benchmarking to achieve the closest cross-sectional targets in DEA. International Journal of Applied Operational Research-An Open Access Journal, 11(3), 45-56. https://ijorlu.liau.ac.ir/article-1-642-en.pdf
- Mielczarek, B. (2016). Review of modelling approaches for healthcare simulation. Operations Research and Decisions, 26, 55–72. https://doi.org/10.5277/ord160104
- Mingers, J., & White, L. (2010). A review of the recent contribution of systems thinking to operational research and management science. European journal of operational research, 207(3), 1147-1161. https://doi.org/10.1016/j.ejor.2009.12.019
- Mohammadifardi, H., Knight, M. A., & Unger, A. A. (2019). Sustainability assessment of asset management decisions for wastewater infrastructure systems-Implementation of a system dynamics model. Systems, 7(3), 34. https://doi.org/10.3390/systems7030034
- Moradi Shahdadi, L., Aminnejad, B., Sarvari, H., & Chan, D. W. (2023). Determining the critical risk factors of implementing public–private partnership in water and wastewater infrastructure facilities: Perspectives of private and public partners in Iran. Buildings, 13(11), 2735. https://doi.org/10.3390/buildings13112735
- Mustafee, N., Katsaliaki, K., & Taylor, S. J. (2010). Profiling literature in healthcare simulation. Simulation, 86(8-9), 543-558. https://doi.org/10.1177/0037549709359090
- Norouzian-Maleki, P., Izadbakhsh, H., Saberi, M., Hussain, O., Jahangoshai Rezaee, M., & GhanbarTehrani, N. (2022). An integrated approach to system dynamics and data envelopment analysis for determining efficient policies and forecasting travel demand in an urban transport system. Transportation Letters, 14(2), 157-173. https://doi.org/10.1080/19427867.2020.1839716
- Oladimeji, O., Cross, J., & Keathley-Herring, H. (2021). System dynamics applications in performance measurement research: progress and challenges. Management Decision, 59(6), 1181-1208. https://doi.org/10.1108/MD-11-2019-1596
- Pudjono, A. N. S., Wibisono, D., & Fatima, I. (2025). Advancing local governance: a systematic review of performance management systems. Cogent Business & Management, 12(1), 2442545. https://doi.org/10.1080/23311975.2024.2442545
- Shafiee, M., Hosseinzade Lotfi, F., & Saleh, H. (2021). Benchmark forecasting in data envelopment analysis for decision making units. International Journal of Industrial Mathematics, 13(1), 29-42. https://civilica.com/doc/1886953
- Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. https://industri.fatek.unpatti.ac.id/wp-content/uploads/2019/03/007-Business-Dynamics-Systems-Thinking-and-Modeling-for-a-Complex-World-John-D.-Sterman-Edisi-1-2000.pdf
- Sun, Y., Liu, N., Shang, J., & Zhang, J. (2017). Sustainable utilization of water resources in China: A system dynamics model. Journal of cleaner production, 142, 613-625. https://doi.org/10.1016/j.jclepro.2016.07.110
- Tseng, S. T., & Levy, P. E. (2019). A multilevel leadership process framework of performance management. Human Resource Management Review, 29(4), 100668. https://doi.org/10.1016/j.hrmr.2018.10.001
- Zahedi, R., Yousefi, H., Aslani, A., & Ahmadi, R. (2024). System dynamic model of water, energy and food nexus for policy implementation. Applied Water Science, 14(10), 213. https://doi.org/10.1007/s13201-024-02279-z
 
						
						