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Published bySylvia Fisher Modified over 8 years ago
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DLA's Aviation Supply & Demand Chain Manager DLA – Hamilton Sundstrand LEAN Event SEPRT 12 John Kazlo/Rosemary Suprenant 7 April 2008
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DLA's Aviation Supply & Demand Chain Manager The DLA perspective Although the performance on metrics has not been bad we wanted to LEAN the process out and make it more manageable DLA was tasking our contractor to create a report for us weekly with about 200 columns of data on it We have seen periods when MA on HI FREQ NSNs has fallen off
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DLA's Aviation Supply & Demand Chain Manager March 2008 Meeting Process improvements did not happen exactly as planned Not all AF data was updated HSC was not able to provide reports in advance of meeting DLA did provide improved forecast spreadsheets Team overcame shortfalls and will have a published forecast by end of March Additional lessons learned gained from meeting Data brought in for validation should be on laptop or in easy to find order (e.g. NSN order) Additional processes need to be worked (e.g. how to forecast IPV parts) Most problem parts are not HSC sole source; primary issues are competitive items
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DLA's Aviation Supply & Demand Chain Manager Post-Publication HSC will use published forecast to reduce lead times of high demand/high frequency NSNs Goal is 50% lead time reduction DLA will provide forecast to demand planners USAF will have knowledge of forecast and can advise as changes are known. HSC will use published forecast to reduce lead times of high demand/high frequency NSNs Goal is 50% lead time reduction DLA will provide forecast to demand planners USAF will have knowledge of forecast and can advise as changes are known
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DLA's Aviation Supply & Demand Chain Manager Benefits Expected Reduced Lead Times for select parts on Strategic Sourcing Initiative contract Improved Availability for end user Better communication of changes in expected demands = better support Contractor - end user relationship improvement (with contracting agency participation) Streamlined pricing for future parts using better forecasting methods
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