Last Time Buy systematically mines full lifecycle demand from parts that have been through their entire lifecycle to identify clusters. The software then identifies cluster(s) with profiles that match the last time buy part in demand through end of production and applies those matching profiles to generate a forecast for the last time buy part through end of service.
Leverages demand of parts that have already gone through end of life
Identifies common characteristics among those parts and groups them through clustering techniques
Utilizes regression to identify best pattern from these common patterns
Creates more accurate forecasts based on sophisticated analysis of real-life trends — instead of generic, simple equations
Reduces obsolescence costs by avoiding excess situations at EOS