A game theory approach for drinking water supply chain management considering water pricing and government intervention

Managing the drinking water supply chain and pricing has gained significant importance in contemporary times due to various factors, including population growth, urbanization, climate change, and water scarcity. The scarcity of water as a finite resource and its availability and quality hold paramount importance for sustaining life, facilitating economic development, and promoting environmental well-being. This paper presents a comprehensive study of the drinking water supply chain, comprising a Public Water System (PWS) and a Bottled Water Plant (BWP) with government intervention. The study investigates the role of the government in motivating the BWP to increase its production greenness level while supporting the PWS to enhance the quality of tap water for citizens. It also evaluates the government's efforts to decrease the final price of tap water by providing subsidies for households and managing tap water demand through increasing public awareness. Furthermore, the study employs dynamic analysis to account for changes in population water consumption and precipitation over time, dividing the households' drinking water demand into three phases. Additionally, the study takes into account rainfall conditions, their uncertainty for water supply, and their impact on PWS profit. We also develop mathematical modeling for water pricing and managing demand. The study employs concepts such as peak pricing and volumetric pricing for each stage and alterations in price that are penalty and incentive-based, depending on the amount of consumed water, season, or time of use. The findings of this study provide valuable insights for policymakers and stakeholders to enhance the efficiency, sustainability, and profitability of drinking water supply chains. The paper offers a practical illustration to showcase the effectiveness of mathematical models in a real-world scenario. It concludes by conducting several sensitivity analyses on the primary parameters to derive insightful managerial implications.