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A clustering analysis of forecasting methods in a multi-objective inventory system

International Journal of Production Economics
Publication Date
DOI: 10.1016/0925-5273(92)90121-m


Abstract This study employs a descriptive model to relate key issues in inventory management — cost and demand estimation, lead time and performance measures of inventory systems. The model assumes a single item, uncertain demand, deterministic review order point and discrete lead time. Sixteen forecasting methods were examined and compared on empirical ground with three data sets. Cluster analysis was first used to group forecasting methods along four defined dimensions. The naive forecasting method was found to be distinctly different from the rest. Furthermore, the naive method consistently provided less accurate forecasts across the three data sets used. Cluster analysis was again used to put the other fifteen forecasting methods into groups. Results indicated that under general conditions regarding the demand pattern none of the forecasting methods have demonstrated the expected advantage relative to other methods. Thus more specific knowledge is required to assign a forecasting method to specific data. Such a model was recently studied by Reinstein, Gibson and Zidman under the naive forecasting method which calls for inventory based on the demand from the prior period. The results also have valuable insights for inventory management.

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