Amanat Ur Rahman^1,, Marzia Tuz Zahura¹

With the progress of technology and globalization, competition has risen and so has the need to optimize the Supply Chain. More enterprises are now focusing on supply chain efficiency in order to increase its profit margin and customer satisfaction. A part of the solution for increasing supply chain efficiency lies in the ability to make accurate forecast of demands, since, it interacts with multiple component of the supply chain network. Use of statistical, heuristics and machine learning algorithms is very common for future time series prediction, however, the accuracy of prediction by these models are significantly affected by the uncertainty, imprecision, and the size of source dataset. Grey theory has shown its effectiveness for its quick, brief and accurate prediction for vague, incomplete and imprecise data sets. In this paper, the grey one order one variable model GM (1,1) is applied for demand prediction in a case where the source data is brief and highly uncertain. The effectiveness of GM (1,1) is tested against one of the most commonly used and established forecasting method, exponential smoothing technique, for vague, imprecise and incomplete dataset. Based on the simulated results, the prediction accuracy of the grey prediction model has been observed to be a better fit than that of exponential smoothing technique. The average relative error (ARE) from the grey prediction model satisfies the level 2 of the accuracy scale and also achieving a mean relative simulation accuracy of 95.5%. Hence, based to the observed results, it can be established the GM (1,1) can be effectively used for any future time series prediction in such cases.

grey theory, demand forecasting, GM (1 1), exponential smoothing, supply chain