Operation Improvement Inc.

Follow us on “X” (Twitter)

A helpful post contrasting the definitions of Lean versus Six Sigma made me think about the skeptical reaction many have when the latest improvement buzz phrase or acronym appears in media. There are always successful and credible case studies, but many are left thinking that surely a key ingredient has been left out of the tasty recipe.

The “Silver Bullet”

For years, we attributed this to the wish for a “silver bullet”, a quick solution to that performance, time, cost or risk problem. Perhaps the way solutions are presented (Six Sigma versus lean versus Kanban versus KPIs versus dashboards, team building, etc.) contributes to the misunderstanding and misuse.

Maybe it is the way that older strategies are renamed, rebranded, and upgraded with larger and larger data requirements. If SPC didn’t work, then maybe DOE, regression (sorry: “Predictive Model Building”), multiple regression, data lakes, simulations, linear algebra and AI are the answer.

Certainly, greater computational power, technology improvements and more data is a positive; but these various methods and tools should not be treated as “siloed” solutions. There is often a missing ingredient in many approaches, and that is integration of these tools with each other and with a conceptual view of the processes one wishes to improve.

Quality, Performance & Value

Many struggled with TQM (“Total Quality Management”) because of the tendency to conflate “Quality” with “Performance”. To clarify this I would ask teams, “What would Total COST Management represent? What is the value of an absolutely perfect wedding cake one day late?” When they came to see quality as value to the customer, TQM began to be integrated with the famous conceptual formula from Project Management: “Value=FunctionOf(Performance, Time, Cost, Risk)” (Not every chicken dinner needs to be a $1000/plate celebrity chef masterpiece to have value.)

When high scoring “Quality as Value” KPIs do not tell us that customers were disappointed – we must add the knowledge that metrics and measures are not the same; that actionable descriptive statistics will rely on homogeneous groups and that outliers and trends can hide in a statistical analysis that ignores time sequence and variability.

Variability & Outliers

When descriptive statistics is integrated with probability functions and Statistical Process Control (SPC), we began to get a near real-time picture of variability, quick recognition of outliers and objective evidence of homogeneous groups for acceptance testing – We then need to integrate this with an action plan for outliers. We need tools to connect causes (“Things and how they behave”) with effects (changes in variability and outliers.)

Visualizing Process Dependencies

When Cause/Effect or “dependency” diagrams give us visual relationships between “What we do” and “What we get”, we become able to integrate this with our metrics, measures, process and product targets and data collection strategies. With this additional tool, we can integrate team building, leadership exercises, sustainment training and “learn, share, teach, improve” training with experiment days.

The “Base Camp”

When we finally have what we call, a “base camp” from which further improvement can occur; then we are ready to try and test advanced optimization techniques, large data set tools, technology upgrades and more.

Whether our improvement initiatives began with process inputs, customer deliverables, a bottleneck process or in quality, lab and measurement; we continue to integrate – to connect what we know and have learned about one area to others and we may use one of the various “value chain” techniques.

Continuous Improvement

We match inputs of one internal process to the outputs of another to optimize and align. As variability is reduced, buffer stocks can be gradually reduced and Just-In-Time techniques can be incorporated.

There are other tools in the process improvement toolbox, some of which are optimized for manufacturing and some for the service industry. The principle is the same. Regardless of which techniques come first and where in the process we begin, there is need for integration into an expanding process knowledge database structured to support -human- intelligence and conceptual understanding of work and deliverables.

Actionable Metrics

An analysis, dashboard, diagram or computer output that has not been integrated into the sum of what we know is -not- actionable. If you have seen daily, weekly, and monthly status report on paper or screen that do not drive action, you know exactly what I mean. It’s the Monday morning “Read and File” process, and that may be why these approaches sometimes fail.