Title: Demand forecasting for inventory control and production planning
Authors : Hosang, Jürg
Heitz, Christoph
Mönkeberg, Sigrid
Proceedings: From research to applications : proceedings
Conference details: IPLnet 2002 Workshop: From research to applications, National Network of Competence of the Swiss Universities of Applied sciences on "Integrated Production and Logistics", Saas Fee, 10–11 September 2002
Publisher / Ed. Institution : IPLnet
Publisher / Ed. Institution: Yverdon-les-Baines
Issue Date: 2002
License (according to publishing contract) : Licence according to publishing contract
Type of review: Not specified
Language : English
Subjects : Statistical analysis; Customer behaviour; Stochastic modelling
Subject (DDC) : 658.5: Production management
Abstract: A simple stochastic model is proposed to forecast the demand for injection molding products. The model is based on individual orders, as characterized by their arrival time and the amount ordered. Both properties of the orders are described as random variables, the order arrivals by their rate and the quantities ordered by their mean and spread. Demand data for 383 products were analyzed, i.e. their model parameters estimated from the data. Based on visual inspection and quantitative analyses, it was concluded that 334 products could be described very well with the proposed model. The demand for some products is dominated by a few customers who regularly order large quantities. It was suggested to exclude these orders from the analysis and to treat them separately, e.g., by settling contracts with the relevant customers. For 36 products there were not enough data to estimate the model parameters and/or decide on the validity of the model. For another 13 products, there were systematic deviations of the data from the model. Many of the latter cases could be attributed to products which are demanded by only very few customers. For conclusion, the vast majority of the products could be described successfully with the simple approach proposed which is also suited for implementation in an operational forecasting system. Equations were developed which allow the demand to be predicted along with its uncertainty for forecasting horizons of arbitrary length. Today, smoothing techniques, based on time-aggregated demand data are widely used for predictions. One main advantage of our model over such approaches is that the problem is avoided of choosing an adequate period over which demand is to be aggregated. As a consequence, high-demand and low-demand products can be modeled equally well. Moreover, our model is not limited to one-period forecasts, i.e., one-aggregation-period-ahead predictions and its parameters are theoretically well defined and easy to estimate.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Conference Paper
URI: https://digitalcollection.zhaw.ch/handle/11475/4382
Appears in Collections:Publikationen School of Engineering

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