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Pilot projects to test innovations in the manufacturing process are very costly—and risky. Using @RISK to simulate changes in process design allows us to answer some difficult questions without actually running trials of the process.
Ignacio Quijas
Technology Manager, Met-Mex Peñoles
precious metals refiner
@RISK and DMAIC

@RISK is useful at each stage of the DMAIC process to account for variation and hone in on problem areas in existing products.

Define. Define your process improvement goals, incorporating customer demand and business strategy. Value-stream mapping, cost estimation, and identification of CTQs (Critical-To-Qualities) are all areas where @RISK can help narrow the focus and set goals. Sensitivity analysis in @RISK zooms in on CTQs that affect your bottom-line profitability.

Measure. Measure current performance levels and their variations. Distribution fitting and over 50 probability distributions make defining performance variation accurate. Statistics from @RISK simulations can provide data for comparison against requirements in the Analyze phase.

Analyze. Analyze to verify relationship and cause of defects, and attempt to ensure that all factors have been considered. Through @RISK simulation, you can be sure all input factors have been considered and all outcomes presented. You can pinpoint the causes of variability and risk with sensitivity and scenario analysis, and analyze tolerance. Use @RISK’s Six Sigma statistics functions to calculate capability metrics which identify gaps between measurements and requirements. Here we see how often products or processes fail and get a sense of reliability.

Improve. Improve or optimize the process based upon the analysis using techniques like Design of Experiments. Design of Experiments includes the design of all information-gathering exercises where variation is present, whether under the full control of the experimenter or not. Using @RISK simulation, you can test different alternative designs and process changes. @RISK is also used for reliability analysis and – using RISKOptimizer - resource optimization at this stage.

Control. Control to ensure that any variances are corrected before they result in defects. In the Control stage, you can set up pilot runs to establish process capability, transition to production and thereafter continuously measure the process and institute control mechanisms. @RISK automatically calculates process capability and validates models to make sure that quality standards and customer demands are met.

 

 

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