Abstract
A typical large brewery could have a keg fleet size in the order of hundreds of thousands. For some breweries, the asset value of this fleet is second only to the fixed plant. The annual rate of attrition within the fleet can range from 5% to 20%, a sizable figure in dollar terms with a stainless steel keg costing around USD100. There is a business case for a keg asset management system that can help to reduce the annual rate of attrition and supply chain cycle time. Established solutions such as bar codes and RFID tags are costly as they require a modification to every keg. The feasibility of a machine vision tracking system based on the optical character recognition (OCR) of the keg’s existing serial number is explored. With prospective implementation in the keg filling line, a process is proposed which is based on neural network OCR. A recognition rate of 97% was achieved for kegs with non-occluded serial numbers, with realistic scope for further improvement.
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© 2008 Springer-Verlag Berlin Heidelberg
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Lees, M., Campbell, D., Keir, A. (2008). Machine Vision for Beer Keg Asset Management. In: Billingsley, J., Bradbeer, R. (eds) Mechatronics and Machine Vision in Practice. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74027-8_11
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DOI: https://doi.org/10.1007/978-3-540-74027-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74026-1
Online ISBN: 978-3-540-74027-8
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