Begin with some reading. Paul Barringer developed the Weibull Process method and his website www.Barringer1.com has some great articles on the application of this method.
Get a data set of daily production data over a 12 month period.
Input the daily production data to a Weibull data analysis package. You can choose fromIsograph’s Availability WorkbenchTM (AWB) Process module, Wes Fulton’s WinSmith, or Reliasoft Weibull++ . The Isograph AWBTM is an integrated package which has modules including AvSim for System Availability, RCMCost for optimising maintenance decisions, Lifecycle Costing simulation and Process Reliability. Whereas the other packages are stand alone and require information to be transferred. The Process Reliability module has been set up so that the graphs are preconfigured and the production beta, reliability of the process, and lost production can all easily be seen.
Determine the Production Beta, the Reliability of the Process, establish the name plate and determine the production losses due to production variation or special cause losses.
Focus improvement efforts on “where the losses are greatest” – process variation or special cause.
Analyse the data and choose an improvement method. Use Root Cause Analysis (RCA) to address special causes (www.apollorootcause.com), use Reliability Centered Maintenance (RCM) to improve maintenance plans, use System Analysis to address plant configuration, logistics and equipment design. (www.armsreliability.com)
Model improvements in a Reliability Block Diagram such as the AvSim Module of AWBTM (or alternate such Raptor, Maros, etc).
Use the System Availability model to generate predicted production data.
Use Cost Benefit analysis to show the reduction in production losses (gains) versus cost.
Share your business case with your production manager and celebrate!
Run production data each quarter and monitor losses and their nature.