Case Study: Performance Excellence Improves Manufacturing Productivity

Opportunity: A $12MM mid-west manufacturer of sport medicines, supports, guards and other athletic appliances utilized a machine-based operation to produce small, rosin-filled bags. Powdered rosin was augered from storage tanks and fed into a wheel which rotated to open slots into which the rosin was dropped. As the wheel turned further, this measured quantity of rosin was dropped into a hand-held cloth bag, which was then placed onto a conveyor belt which carried the bag through a trimmer and stitcher. The closed rosin bag was then removed manually and collected in a paper sack, before being placed in a box for shipping. As in many machine-based operations, the output capability of the production machinery was assumed to be the primary determinant of productivity. However, since many peripheral human performances were also involved, the possibility of enhancing those performances to improve overall productivity warranted investigation.

Performance Excellence Audit: Interviews with employees and managers, and observations of the production process identified several performances which influenced operational effectiveness, and indicated a situation where less than optimal performance was accepted:

Operating and Management Process Improvements: Prep time was emphasized as an essential element of the production process, and guidelines were established. The importance of the operators' roles in promoting productivity was emphasized, and a method of measuring and graphing fill and prep cycle-times was developed to empower operators to monitor their own performances. Performance graphs were posted daily to encourage recognition from others for performance improvements. Process assurance was improved by training the supervisor to give positive recognition and to visit more frequently, especially to reinforce faster prep cycles.

Results: Clearer prep-time guidelines and improved process assurance resulted in a significant improvement in a performance which was presumed to be nearly optimal. Fill times, which were essentially controlled by the speed of the machine, did not vary more than a minute, but prep times decreased 77 percent, from an average of 13 minutes to only 3 minutes. Total cycle times were reduced nearly 24%, from 34 minutes to 26 minutes, which increased the number of cycles run per day from 13 to 17.

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Revised: April 13, 2008.
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