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MULTI-PRODUCT, MULTI-STAGE PRODUCTION PLANNING MODEL AND DECISION SUPPORT SYSTEM SUGGESTION FOR F&B INDUSTRY

BROWSE_DETAIL_CREATION_DATE: 02-09-2016

BROWSE_DETAIL_IDENTIFIER_SECTION

BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: EngD

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: Production management. Operations management,

BROWSE_DETAIL_CREATORS: Tirkeş, Güzin (Author),

BROWSE_DETAIL_CONTRIBUTERS: Çelebi, Neşe (Advisor),

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Demand estimation in F&B industry, Time series, Trend analysis,Decomposition, Fuzzy, Holt-Winters, Optimization, Production planning, Mixedinteger linear programming, Decision support system 


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A great deal of research has been undertaken in recent times on facility capacityexpansion and production planning problems under deterministic and stochasticdemand in the literature. However, only a small portion of this work directlyaddresses the issues faced by the food and beverage (F&B) industry especially insmall and medium size enterprises.In F&B industry; decision to increase the production capacity depends on a reliabledemand forecasting. In food production, forecasting the timing of demands is crucialin planning production scheduling to satisfy customer needs on time. Severalstatistical models have been used in demand forecasting in F&B industry. Most ofthe studies applied linear models such as autoregressive moving average (ARMA),autoregressive integrated moving average (ARIMA) for linear cases; non-linearARMA, Holt-Winters (HW) exponential smoothing, various kinds of data miningtechniques utilizing ensemble learning, artificial neural networks (ANN), geneticalgorithms with radial basis function and fuzzy logic systems used with non-lineardata, to predict future sales. Naturally, the model to be used for prediction is stronglyivrelated to the characteristics of the data such as the ‘trend’ or the ‘seasonality’observed. This study presents the results by examining the effectiveness of demandforecasting using time series analysis and possibilistic-probabilistic approaches inproduction scheduling problem of a real world multi-stage and multi-line sherbet andjam production company producing multiple products for both retail and wholesalesharing a limited capacity when demands are uncertain. A time series model for longterm forecasting is developed by obtaining monthly sales data from company fromJanuary 2013 to December 2014. Triple exponential smoothing method of HWmultiplicative with seasonality, trend analysis and seasonal decomposition methodsare compared and the best fit model is adapted in order to predict 2015 demand. Asperformance measures, the mean absolute percentage error (MAPE) ratio is used asevaluation metric. Following this approach possibilistic and probabilistic modelswere applied to predict the demand data for situations possessing uncertainties. Afterthe forecasting model, a mixed integer programming model is utilized for productionplanning and scheduling, containing a module caring for inventory planning. Theadaptability of the model to the cases dealing with ‘uncertainty’, keeps the model asan efficient base for the future studies with the given case. As the final stage, adecision support system was designed to help the users to deal with cases withvarying demand structures touching to extremities.


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