Düzgit, Z.Kuzuoğlu, A.B.Kalelioğlu, H.Kolay, H.Güler, M.B.Akyol, S.Toy, A.Ö.2024-07-182024-07-182020978303031342597898115094902195-4356https://doi.org/10.1007/978-3-030-31343-2_15https://hdl.handle.net/11411/618619th International Symposium for Production Research, ISPR 2019 -- 28 August 2019 through 30 August 2019 -- -- 233539There are some factors to be considered while developing production planning policy of chocolate and chocolate-based products. One of these factors is that chocolate is a perishable food, therefore it has limited shelf life. Another factor is that; its demand is not always easy to forecast. Holidays, mothers’ day, teachers’ day, valentines’ day and new year are examples of the peak periods. For these special days, demand generally follows a seasonal demand pattern where seasonality may also contain trend for some specific product types. Moreover, there are two religious holidays (Ramadan Feast and Feast of Sacrifice) in Turkey whose dates shift each year. This phenomenon makes forecasts challenging. Underestimating demand causes loss of customer goodwill, lost customers and market share whereas overestimating demand causes excess inventory to keep in stock and risk of fat blooming. Accurate forecasting is critical since it provides a fundamental input for the production plan. The study is conducted in a chocolate company in Turkey. The company does not implement a systematic planning method for chocolate production, instead the planning is based on past experiences with respect to experts’ opinions. The objective of this study is to determine the optimal production and ending inventory levels for the period of 2018 so as to minimize total production and inventory holding cost subject to production, inventory and capacity related restrictions. A two-phase optimization method is adopted as a solution method. Firstly, a mathematical model is developed for the monthly aggregate production planning on product group basis. In order to solve the problem, monthly demand for product groups are forecasted based on the sales data of the previous two years. Secondly, a mathematical model for weekly disaggregate production planning is developed for each end item using the outputs of the aggregate planning as input. The disaggregate production plan gives the weekly planned production and inventory levels for end items for year 2018 with minimum deviation from the aggregate plan. Although the proposed solution model is implemented for year 2018, it can be used for the coming years by updating some parameters. © 2020, Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessAggregate Production PlanningDemand ForecastingDisaggregate Production PlanningProduction PlanningSeasonalityShelf LifeAggregatesCompetitionForecastingPlanningProduction ControlAggregate Production PlanningDemand ForecastingDisaggregate Production PlanningInventory LevelsProduction And İnventoryProduction PlanningProduction PlansSeasonalityShelf LifeSalesConference Object2-s2.0-8507621313710.1007/978-3-030-31343-2_15181Q4169