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How difficult is it to forecast worlds’ most successful products? The case of Apple iPhone

Petropoulos, Fotios and Nikolopoulos, Konstantinos and Litsa, Akrivi and Polychronakis, Yiannis and Assimakopoulos, Vassilios (2012) How difficult is it to forecast worlds’ most successful products? The case of Apple iPhone. In: OR 54:. Operational Research Society, pp. 3-17. ISBN 0903440512

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Abstract

Apple is the largest publicly traded company in the world by market capitalization, as well as the largest technology company in the world by revenue and profit. Accurate forecasting unit sales is of major importance, as even the smallest of the errors will have huge impact in terms of sales management and revenues. The current research investigates extrapolation alternatives regarding iPhone unit sales, probably the most influential product of the corporation. Simple and straightforward time series techniques (naïve, exponential smoothing methods, theta model) are competing judgmental approaches (unaided judgment, interaction groups) performed by three groups of participants in terms of expertise (novices, semi-experts, experts). Moreover, two subgroups (for each level of expertise) were formed, regarding the amount of information provided to participants. The performance of the alternatives was measured in terms of bias and accuracy, using simple error and absolute percentage error. Results indicate that judgmental approaches produced greater forecasts than quantitative methods, while focusing effect is verified to lead to cognitive bias when forecasting future outcomes. As far as performance is concerned, combination of time series extrapolation techniques with judgmental forecasts derived from groups with limited knowledge produces the smallest forecasting errors.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: sales forecasting ; Apple ; iPhone ; statistical forecasting methods ; judgmental forecasting
Subjects:
Departments: Lancaster University Management School > Management Science
ID Code: 60314
Deposited By: ep_importer_pure
Deposited On: 29 Nov 2012 10:03
Refereed?: No
Published?: Published
Last Modified: 05 Dec 2012 16:23
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/60314

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