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January 2018 | ISE Magazine 39 troducing stage, an expert, often a Six Sigma black belt, provides the basic Six Sigma training to an improvement team. Implementation progresses. The teams are enthusiastic, changes are imple- mented and system performance shows an upward trend. With the improvement project completed, the improvement expert moves to another. Generally, in the struggling stage, a senior member of a team steps in to provide explanation or additional training. The team, however, strug- gles with process mapping and Six Sigma analysis, and implementation progresses haphazardly. Amid considerable confusion, many team members re- gress to the old ways of working. Dissatisfied teams introduce additional changes slowly, and system performance plateaus. In the dying phase, no team member steps up. Minus process mapping and analysis, team mem- bers focus on their daily routines and old, com- fortable ways of working. System performance re- gresses to the pre-implementation level and shows a downward trend. Why they hold on too tightly Like the firefighters, the improvement team mem- bers held on to Six Sigma tools because tools were costly, too many simultaneous activities were tak- ing place and the members failed to realize the power of small changes. The team believed the Six Sigma tools were costly for three reasons. First there was the hype. Meetings touted the methodology's benefits, while numerous presenta- tions and emails emphasized that Six Sigma relies on objective data, uses robust statistical analysis and builds models for smart decision-making. Second, improvement teams received a lot of training and know that Six Sigma certification could cost $5,000 or more per person. This doesn't include statistical software. And while team mem- bers had high school diplomas or college degrees, they had little or no statistical background and were suitably impressed with exotic terms like "process (versus functional) view," DMAIC (define, measure, analyze, improve and control) methodology, histograms, Pareto charts, sam- pling, statistical inferences, regression analysis, analysis of vari- ance (ANOVA), design of experiments (DOE) and statistical process control (SPC). Third, there was intense data collection from different ERP (enterprise resource planning) databases, data scrubbing and data validating activities, followed by statistical model build- ing efforts. Obviously, with that much data and analysis, team members were bound to find things that needed remedial ac- tions or interventions. Over many Six Sigma projects, these efforts generated 100 to 300 pages of tables and graphs, many of which were proudly displayed in hallways, conference rooms or offices. But after the Six Sigma project was done, the new way of working implemented and the improvement expert moved on, carrying on the improvements fell on the shoulders of the im- provement teams. They already were burdened with daily job requirements and had difficulty maneuvering through ERP databases to clean and validate data. FIGURE 1 Six Sigma failures A system's performance for Six Sigma projects often goes through three different stages: Introducing, struggling and dying. FIGURE 2 Production not operating After a period of improvement, setup times, machine outages and lead-times all started trending down.