Document Type

Conference Proceeding

Conference Track

Sales Promotion/ Retailing

Publication Date

2013

Abstract

The simple moving average forecasting technique (SMAFT) uses a naïve arithmetic measurement for smoothing time-series data for various situations purposes, such as sales prediction. This paper attempts to rectify the contextual procedure of SMAFT by transforming the method into a judgmental bootstrapping approach, combining the statistical techniques of the X - chart (x-bar) and the Hurwicz's Criterion. The proposed modeling approach generates a dual forecasting value, presented by the grand mean, x , of the x-bar chart and the expected weighted payoff of the Hurwicz's Criterion, which is used to improve the accuracy of the final forecast. This model will serve the need for a cost effective technique to address routine forecasting, especially for companies with large numbers of items.

About the Authors

Usama Saleh received his MBA degree from Lancaster University, UK, in 1992. He is the solo entrepreneur of the www.handymarketing.net for marketing training and consultancy. Meanwhile, he is a free lance marketing instructor at a few academic institutions in Egypt. Previously he held the post of the marketing lecturer at CBA, Jeddah, KSA, (2003-2008). He had also been affiliated with a few international companies in areas of production, operation management, and marketing research. This paper is his 9th contribution to AMTP in row.

Gamal Haikal is Assistant professor of Economics working for the Arab Academy for Science Technology and Maritime Transport (AASTM), one of the organizations of Arab League. Currently, he is acting as assistant dean for international affairs - College of International Trade and Logistics – AASTM. Dr. Haikal received his doctoral degree from Ain Shams University on July, 2008. He is currently a member of the Economics and Political Sciences Institute, Institute of Professional Managers-South Africa.

Copyright Statement / License for Reuse

Digital Commons@Georgia Southern License

Included in

Marketing Commons

Share

COinS