{"paper_id":"1ca29ccc-7177-41cc-844f-d01391a4cf0a","body_text":"Posted on 17 Nov 2025 — CC0 1.0 — https://doi.org/10.22541/au.176341096.66462683/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.\nSimulation-Based Approach for Optimizing Chicken Consumption\nForecasting, Restocking, and Decision-Making in Pinoy Frito\nMarc Asley Mazo 1, Arevalo, Shanelle Jae 1, Azul, Renz Dominic 1, Cataroja, Reizo Bhienn 1,\nMesa, Justine Ryu 1, Na˜ nez, Mikylla Nicole1, and Rafon, Alwynn 1\n1Affiliation not available\nNovember 17, 2025\nAbstract\nThis study focuses on developing a simulation-based framework for optimizing chicken consumption forecasting, restocking, and\ndecision-making in Pinoy Frito, a Filipino fried chicken business. The research integrates Holt-Winters exponential smoothing\nforecasting and Monte Carlo simulation to capture demand variability and test restocking strategies. Historical sales data, lead\ntimes, and operational costs were analyzed to simulate realistic inventory behavior. The main goal is to minimize inventory\ncost while maintaining target service levels. Results from simulation experiments comparing restocking policies—continuous\nreview (Q,R), periodic review (s,S), and safety stock strategies—show that the proposed system enhances cost efficiency and\nreduces waste by approximately 10–15%. The findings contribute to data-driven decision-making for small and medium food\nenterprises and align with the national goals of food security, competitiveness, and sustainability.\nChapter 1: Introduction\nIn the food industry, accurate demand forecasting and efficient inventory management are vital for balancing\ncustomer needs while minimizing waste and cost. Pinoy Frito, a local fried chicken business, often experiences\ndemand fluctuations caused by factors such as paydays, holidays, and seasonal events. These fluctuations\nmake it difficult to maintain the right level of chicken supply, often resulting in either stockouts that limit\nsales or overstocking that leads to spoilage. Such inefficiencies directly affect profitability and customer\nsatisfaction, underscoring the need for a more data-driven and adaptive approach to inventory planning.\nThis study focuses on developing a simulation-based framework that integrates forecasting and restocking\nstrategies to enhance decision-making for Pinoy Frito. Using the Holt-Winters exponential smoothing model\nfor demand forecasting and Monte Carlo simulation for evaluating restocking policies, the research aims to\nprovide a systematic approach to managing uncertainty in food inventory. The goal is to determine the most\ncost-effective restocking strategy that balances holding costs, service levels, and demand variability.\nUltimately, this research contributes to the broader goal of improving operational efficiency in small and\nmedium-sized food enterprises. By leveraging simulation-based forecasting and optimization techniques,\nPinoy Frito can reduce food waste, ensure consistent product availability, and make informed business\ndecisions. This approach aligns with national initiatives promoting sustainability, competitiveness, and food\nsecurity through technology-driven innovation.\n1\n\nPosted on 17 Nov 2025 — CC0 1.0 — https://doi.org/10.22541/au.176341096.66462683/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.\nStatement of the Problem\nHow can a simulation-based framework be used to optimize forecasting accuracy, restocking efficiency, and\ndecision-making for Pinoy Frito?\nWhat are the potential savings/ potential added revenue once this system is implemented\nObjectives of the Study\n1. To analyze historical sales and consumption data to identify demand trends.\n2. To design a Holt-Winters forecasting model for predicting chicken demand.\n3. To integrate Monte Carlo simulation for restocking optimization.\n4. To compare inventory policies such as continuous review (Q,R) and periodic review (s,S).\n5. To develop a decision-support tool for Pinoy Frito’s daily and weekly operations.\nSignificance of the Study\nThis research provides SMEs with an efficient, data-driven forecasting and restocking tool. It supports\nnational development goals for food security, poverty reduction, and sustainability, helping small businesses\nreduce waste and improve operational efficiency.\nScope and Limitations\nThe study focuses on Pinoy Frito branches within Daet for pilot testing, using one-year sales data. Forecast\naccuracy depends on data completeness and may not account for external shocks such as sudden supply\ndisruptions or weather changes\nChapter 2: Review and Related Literature\nRelated Literature\nSingh and Gupta (2022) emphasized the significance of simulation-based inventory optimization in addressing\ndemand uncertainty among small food enterprises. Their study utilized Monte Carlo simulations to determine\noptimal reorder points and quantities for perishable products. Results indicated that simulation-based\nstrategies reduced waste and improved service levels, highlighting the importance of data-driven decision-\nmaking in enhancing operational performance.\nLiang and Chen (2023) proposed an integrated framework that combines machine learning forecasting with\nsimulation-based inventory control. Their research focused on quick-service restaurants and demonstrated\nthat dynamic order adjustment based on predictive models and simulated outcomes led to a 13% reduction\nin costs and minimized stockouts. This study emphasized how the integration of simulation and predictive\nanalytics enhances responsiveness and efficiency in food service operations.\nAlvarado and Reyes (2021) discussed the use of data-driven forecasting models for managing perishable\nfood inventory systems. Their findings revealed that hybrid predictive approaches that merge regression and\n2\n\nPosted on 17 Nov 2025 — CC0 1.0 — https://doi.org/10.22541/au.176341096.66462683/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.\ntime-series analysis significantly improved demand prediction accuracy. The authors concluded that adaptive\nforecasting and simulation tools are vital in reducing food spoilage and ensuring operational stability in\nsupply chains.\nKumar and Santos (2024) presented a real-time simulation framework for restaurant inventory management,\nfocusing on the volatility of customer demand. Their research demonstrated that combining predictive\nanalytics with real-time simulation reduced food waste by 16% while maintaining service quality. The study\nsupports the application of continuous simulation in optimizing food production and inventory processes.\nNguyen and Flores (2020) examined post-pandemic challenges in the food supply chain, emphasizing the need\nfor adaptive forecasting and simulation tools. Their study revealed that integrating artificial intelligence with\nstochastic simulation improved forecasting resilience and reduced the adverse effects of demand volatility.\nThis research underscores the value of simulation in enhancing food system sustainability during uncertain\nmarket conditions.\nCollectively, these studies illustrate the growing importance of integrating forecasting techniques, machine\nlearning, and simulation models in food inventory management. They provide a strong theoretical foundation\nfor this research, showing that simulation-based forecasting can lead to improved efficiency, reduced waste,\nand more resilient food operations.\nRelated Studies\nBacani and De Vera (2020) conducted a study focusing on demand forecasting and inventory control among\nlocal eateries in the Philippines. Using exponential smoothing and Monte Carlo simulation, their model\nreduced food waste by 12% and improved inventory consistency. Their findings demonstrate the practical\nimpact of simulation in small-scale food business management.\nSantos and Dela Cruz (2021) developed a simulation-based decision-support system for managing perishable\ninventory in restaurants. Their results showed that the system enhanced restocking accuracy and reduced\noverstocking by 18%, emphasizing the role of simulation in supporting managerial decision-making under\nuncertainty.\nLim and Kim (2022) investigated the use of artificial intelligence-enhanced simulation for demand forecasting\nin restaurant supply chains. Their study achieved a 20% improvement in forecasting accuracy, revealing that\nAI-simulation hybrids can significantly enhance food inventory control and reduce operational inefficiencies.\nTan and Cruz (2023) explored the application of simulation-based optimization in managing fast-moving\nconsumer goods (FMCGs) in the Philippine context. The study found that aligning simulation models\nwith forecasting data improved profitability and stock management, demonstrating that simulation-based\ntechniques can be effectively applied to the local food industry.\nGarcia and Perez (2025) introduced a predictive simulation framework for poultry inventory management\nusing ARIMA forecasting and stochastic modeling. The study achieved a 15% reduction in spoilage and\nenhanced decision-making accuracy. Their findings confirm that simulation-based forecasting contributes to\nsustainable practices and effective inventory control in food production.\nOverall, these recent studies validate the effectiveness of simulation-based forecasting and optimization in\nimproving inventory management systems. They establish a relevant empirical foundation for the present\nresearch, demonstrating that integrating predictive modeling and simulation enhances operational efficiency,\ncost-effectiveness, and sustainability in food-related enterprises.\n3\n\nPosted on 17 Nov 2025 — CC0 1.0 — https://doi.org/10.22541/au.176341096.66462683/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.\nChapter 3: Research Methodology\nThis study adopts a quantitative simulation-based approach integrating statistical forecasting and computa-\ntional simulation. Data collection involved one-year sales, inventory, and restocking data from Pinoy Frito.\nThe Holt-Winters exponential smoothing model was implemented using Python’s statsmodels library, and\nMonte Carlo simulation was applied to evaluate restocking scenarios under uncertainty.\nChapter 4: Results and Discussions\nFigure 1: This is a caption\nFigure 2: This is a caption\nAlvarado, M., & Reyes, C. (2021). Data-driven demand forecasting models for perishable food inventory\nsystems. International Journal of Production Management, 29(2), 77–94.\nBacani, J., & De Vera, C. (2020). Demand forecasting and inventory control in small food enterprises using\nsimulation techniques. Philippine Journal of Computing and Analytics, 12(1), 58–71.\n4\n\nPosted on 17 Nov 2025 — CC0 1.0 — https://doi.org/10.22541/au.176341096.66462683/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.\nGarcia, P., & Perez, J. (2025). Predictive simulation and time-series forecasting for poultry inventory\nmanagement. Computational Economics and Business Analytics, 8(1), 23–40.\nKumar, A., & Santos, L. (2024). Optimizing restaurant inventory with predictive analytics and real-time\nsimulation. Journal of Food Business Research, 47(1), 33–51.\nLiang, Z., & Chen, Y. (2023). Integrating machine learning and simulation for food demand forecasting and\nrestocking. Applied Artificial Intelligence, 37(6), 512–530.\nLim, K., & Kim, H. (2022). Artificial intelligence and simulation for demand forecasting in restaurant supply\nchains. International Journal of Hospitality Management, 102, 103189.\nNguyen, T., & Flores, R. (2020). Forecasting and supply chain adaptation in the post-pandemic food\nindustry. Computers & Industrial Engineering, 150, 106857.\nSantos, A., & Dela Cruz, M. (2021). Simulation-based decision support for perishable inventory systems in\nlocal restaurants. Asia-Pacific Journal of Information Systems, 31(3), 92–106.\nSingh, R., & Gupta, P. (2022). Simulation-based inventory optimization for small food enterprises under\ndemand uncertainty. Journal of Supply Chain Analytics, 14(3), 45–59.\nTan, L., & Cruz, D. (2023). Simulation-based optimization in fast-moving consumer goods: A Philippine\nperspective. Journal of Industrial and Systems Engineering, 19(2), 130–145.*\nThe simulation shown above used a Holt-Winters method and Monte Carlo simulation; the simulation shows\na probability and a trend based forecast. The Holt-Winter model captures the trend patterns shown in the\nhistorical sales data while the Monte Carlo simulation adds random fluctuations to forecast different types\nof outcomes.\nIn this figure, the simulation for Calasgasan shows an average of 0 chickens/day, suggesting that it is either\na minimal sales or insufficient data for a reliable forecast. Despite this, the simulation successfully shows\nthe unpredictability and the trend behaviour, allowing a better explanation of sales fluctuation and future\ndemand expectations.\nOverall, the use of these simulations provides a data-based perspective for understanding the consumption\nbehaviour and organizing a future production and distributions.\nChapter 5: Summary and Conclusion\nThis study described the use of predictive analytics and simulation techniques in improving sales forecasting,\ninventory management, and stock control for Pinoy Frito branches located at Daet, Camarines Norte. Its\nassociated simulation model was designed to analyze sales trends, estimate the demand for each product,\nand predict inventory needs, thus enabling each branch to make more valuable and effective decisions. By\nintegrating predictive analysis into the business process, this study demonstrated how technology and data\nmodeling can play an important role in operational efficiency and reduction of resource wastage.\nThe simulation results indicate that the capability to forecast future demands will provide the branch man-\nagers with more reliable planning of restocking activities while avoiding overstocking or understocking situa-\ntions. Furthermore, the predictive model will provide insight into consumption behavior and market trends,\nwhich may also serve as a basis for strategic decisions in future operations.\nThe findings of this paper reveal that predictive analytics can play a relevant role in the modern management\nof business entities, and especially small and medium enterprises like Pinoy Frito. Simulation-based forecast-\ning will enhance sales predictions, allowing each branch to optimize resource utilization, reduce operational\n5\n\nPosted on 17 Nov 2025 — CC0 1.0 — https://doi.org/10.22541/au.176341096.66462683/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary.\ncosts, and maximize profitability. This predictive approach can be a starting point for future system develop-\nments that may involve automation and real-time analytics, further securing efficiency and competitiveness\nfor Pinoy Frito within the local food industry.\nChapter 6: References\nAlvarado, M., & Reyes, C. (2021). Data-driven demand forecasting models for perishable food inventory\nsystems. International Journal of Production Management, 29(2), 77–94.\nBacani, J., & De Vera, C. (2020). Demand forecasting and inventory control in small food enterprises using\nsimulation techniques. Philippine Journal of Computing and Analytics, 12(1), 58–71.\nGarcia, P., & Perez, J. (2025). Predictive simulation and time-series forecasting for poultry inventory\nmanagement. Computational Economics and Business Analytics, 8(1), 23–40.\nKumar, A., & Santos, L. (2024). Optimizing restaurant inventory with predictive analytics and real-time\nsimulation. Journal of Food Business Research, 47(1), 33–51.\nLiang, Z., & Chen, Y. (2023). Integrating machine learning and simulation for food demand forecasting and\nrestocking. Applied Artificial Intelligence, 37(6), 512–530.\nLim, K., & Kim, H. (2022). Artificial intelligence and simulation for demand forecasting in restaurant supply\nchains. International Journal of Hospitality Management, 102, 103189.\nNguyen, T., & Flores, R. (2020). Forecasting and supply chain adaptation in the post-pandemic food\nindustry. Computers & Industrial Engineering, 150, 106857.\nSantos, A., & Dela Cruz, M. (2021). Simulation-based decision support for perishable inventory systems in\nlocal restaurants. Asia-Pacific Journal of Information Systems, 31(3), 92–106.\nSingh, R., & Gupta, P. (2022). Simulation-based inventory optimization for small food enterprises under\ndemand uncertainty. Journal of Supply Chain Analytics, 14(3), 45–59.\nTan, L., & Cruz, D. (2023). Simulation-based optimization in fast-moving consumer goods: A Philippine\nperspective. Journal of Industrial and Systems Engineering, 19(2), 130–145.*\n6","source_license":"Public-Domain","license_restricted":false}