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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References
Bryson L (2005) Brewers, Do You Know Where Your Kegs Are? The New Brewer. Sept/Oct, http://www.beertown.org
Clark D, Simmons K (2005) A dynamic 2D laser mark, Industrial Laser Solutions, Aug, pp 19–21
Gonzalez RC, and Wintz P (1987) Digital Image Processing, 2nd edn. Addison-Wesley, Reading, Massachusetts
Hagan MT, Demuth HB, and Beale MH (1996), Neural Network Design, PWS Publishing, Boston, MA
Klette R, Zamperoni P (1996) Handbook of Image Processing Operators, John Wiley, Chichester, England
Pankoke I, Heyer N, Stobbe N, Scharlach A, and Fontaine J (2005) Using RFID technology to optimize traceability, Proceedings of the European Brewing Convention, Prague, pp 1–7
Perryman M (1996) Practical use of RF tags for tracking beer containers. Brewer’s Guardian, Nov, pp 29, 33
Russ JC (1995) The Image Processing Handbook, 2nd edn. CRC Press, Boca Raton, Florida
Schneider M (2003) Radio Frequency Identification (RFID) Technology and its Application in the Commercial Construction Industry. M.Sc. Thesis, University of Kentucky
Till V (1996) Keg Tracking – a Method to Control the Keg Fleet. Proceedings of the Twenty Fourth Convention of the Institute of Brewing Asia Pacific Section, pp 170–173
Till V (1997) Keg tracking – a method to control a keg fleet; experiences and advantages. Proceedings of the European Brewing Convention, pp 737–746
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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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