Mert PaldrakEfe ErolAtaberk İnanDeniz FıratArtun Erdoğan MiranErcan ÇetinkayaIşılay Nur PolatEfthimia StaiouBurçin KasapPınar AydınErol, EfeFırat, Denizİnan, AtaberkMiran, Artun ErdoğanÇetinkaya, ErcanPaldrak, MertAydın, PınarN.M. Durakbasa , M.G. Gençyılmaz2025-10-0620239789819650583, 9783031991585, 9783031948886, 9789819667314, 9789811937156, 9783030703318, 9789811622779, 9789811969447, 9789819701056, 9789819748051978303124456821954364, 219543562195-435610.1007/978-3-031-24457-5_622-s2.0-85151125533https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151125533&doi=10.1007%2F978-3-031-24457-5_62&partnerID=40&md5=c10a27fd9ef5523ba191e291bfa4ad5fhttps://gcris.yasar.edu.tr/handle/123456789/8574https://doi.org/10.1007/978-3-031-24457-5_62Forecasting customer demand for preliminary products in an accurate way plays a vital role in increasing efficiency of inventory control systems reducing total costs and meeting the requirements of customers on time. Considering this fact the chief objective of the study is to develop a user-friendly decision support system (DSS) to be able to forecast demand for products and minimize the cost of total inventory control costs including ordering and holding costs. Due to the complexity of the problem of this study the project is handled in two parts namely demand forecasting and inventory management. In the demand forecasting part unlike the traditional methods which mostly ignore the statistical behaviour of demand distribution of products we employed Holt-Winters and SARIMA techniques which minimize the error of forecasting by harnessing demand behaviour. In the second part the forecasted demand values are used as inputs for the inventory control system. In this part we developed a Mixed Integer Programming Model (MIP) where the total inventory cost involving ordering and holding costs is to be minimized. To solve the proposed mathematical model IBM CPLEX OPTIMIZER coupled with Branch & Bound Algorithm (B&B) is employed. In addition to this exact solution technique we also used the Benders Decomposition method which is suitable to solve MIP models in a more reasonable computational time with optimality by decomposing the model into master and sub-problem. Besides these two exact-solution techniques to determine the number of products to be ordered from a supplier in a shorter computational time when the problem size is larger a heuristic solution was developed adapted from the Silver Meal algorithm. The results obtained using the aforementioned techniques are compared concerning their solution quality and computational time. © 2023 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessBenders Decomposition, Branch And Bound, Decision Support System, Forecasting, Holt-winters, Inventory Control System, Sarima, Silver Meal Heuristic, Artificial Intelligence, Branch And Bound Method, Control Systems, Decision Support Systems, Integer Programming, Inventory Control, Silver, Benders' Decompositions, Branch And Bounds, Computational Time, Demand Forecasting, Holding Costs, Holt-winters, Inventory-control Systems, Ordering Cost, Sarima, Silver Meal Heuristic, ForecastingArtificial intelligence, Branch and bound method, Control systems, Decision support systems, Integer programming, Inventory control, Silver, Benders' decompositions, Branch and bounds, Computational time, Demand forecasting, Holding costs, Holt-Winters, Inventory-control systems, Ordering cost, SARIMA, Silver meal heuristic, ForecastingSARIMAForecastingBenders DecompositionDecision Support SystemInventory Control SystemBranch and BoundSilver Meal HeuristicHolt-wintersConference Object