Why did TQM Fail?

-	Employees take their cues from mgmt. more
-	Executive & middle mgmt. oversights more
-	Didn't integrate quality into organizational structure more

Why do major change initiatives fail?

-	Part 1: The eight major reasons for failure.
-	Part 2: Consequences of these errors?

Confessions of a shot messenger

• What is Data "Sanity"?
• How can data be a source of waste?
• What are the basics of collecting data?
• How does 'statistical thinking' differ from the 'Statistics from Hell 101' I was taught in the past?
• Why is plotting the dots so powerful?
• What do you mean, 'Whether or not you understand statistics, you are already using statistics'? [5 scenarios]
• Seminars, Keynotes, Conference Breakout Sessions

• Resources

"Data Inventory" Considerations What was the original objective of these data? Is there an unambiguous operational definition to obtain a consistent numerical value for the process being measured? Is it appropriate for the current objective? How are these data accumulated/collected? Is the collection appropriate for the current objective? How are the data currently being analyzed/displayed? Is the analysis/display appropriate, given the way the data were collected? What action, if any, is currently being taken with these data? Given the objective and action, is anything "wrong" with the current number?

Practical Accumulated Records Compilation "The more you know what is wrong with your data... the more useful it becomes." --Prof. John Tukey

Passive Analysis (through) Regressions Correlations

Profound Analysis Relying (on) Computers

Planning After Research Completed

Spell 'PARC' backwards... and that's what you've got!

• Statistics: The art and science of collecting and analyzing data-asking the right questions.
• Simple, efficient data collection

Coming up with a good solid set of metrics and actually using it to manage will save thousands of hours of time wasted reviewing charts and graphs in meetings and reading reports on statistics that do not really matter... Armies of employees do nothing but collect, summarize, and report data. Armies of managers and technical professionals spend time reviewing these data and attempting to pull out something meaningful from the mass of charts they receive each week…

Some research and experience of Marc Graham Brown has shown seven potential immediate benefits from taking an organizational commitment to "data sanity" seriously:

• An 80 percent reduction in the volume of reports generated on a monthly basis by a corporate finance function,
• A more than 50 percent reduction in the amount of time spent in monthly senior management meetings,
• A 60 percent reduction in the pounds of reports that were printed each day, reporting performance data,
• An increased ability to focus on both the long- and short-term success of the organization,
• A better balance between meeting the needs of customers, shareholders, and employees,
• The elimination of up to an hour each day spent by managers reviewing and attempting to interpret unimportant performance data,
• A way to make the vision and values real to employees and to track progress toward achieving the vision and living the values.

Designing your own new and improved measurement system may not be as much work as you think; it will save you much time later. In fact, organizations claim to be able to save at least a couple of hours per week that used to be wasted reviewing and trying to make sense out of meaningless data. If you figure two hours per week per technical or managerial employee times 48 weeks times the labor rate per hour, you're talking a lot of money. Of course, like most projections of this nature, it will be difficult to find these dollars on the balance sheet. A side benefit of reengineering your measurement system is that you will be making better business decisions.

• The word "data" is plural
• Data are important
• Collect data on:
  • Things you care about
  • Things your customers care about
  • Upstream indicators related to things you and your customers care about
• If data exist, question them
• Look for opportunities to verify opinions with data
  ("In God we trust.. all others bring data!" & The plural of "anecdote" is NOT DATA!)
• Clarify operational definitions and measurement procedures before collecting data
• Plan carefully for data collection; consider:
  • How you will present the data once you have collected them
  • What they might tell you
  • How you will want to stratify or localize
• Make data collection as easy as possible
• Collect data over time
• Present data in a picture

1. Collect meaningful data
2. Collect data over time
3. Use data to identify root causes of problems

1. Why collect the data?
2. What methods will be used for the analysis?
3. What data will be collected?
4. How will the data be measured?
5. When will the data be collected?
6. Where will the data be collected?
7. Who will collect the data?
8. What training is needed for the data collectors?

Descriptive statistics: "What can I say about this individual widget / patient / student?"

Enumerative statistics: "What can I say about this specific group of widgets / patients / students?"

Analytic statistics: "What can I say about the process that produced this group of widgets / patients / students?"

In everyday work, we are concerned with "analytic" statistics because we manage processes, and our goal is prediction.

Most courses teach statistics from an enumerative perspective and deal with estimation. A huge assumption being made is that the potential elements being sampled have come from a stable process or the assumed "infinite population."

In the real world, a stable process is the exception and not the rule! Hence, the analytic framework is needed, a vital component of which is assessing the stability of the process that produced one's data.

So, statistical thinking is looking at data from an analytic perspective, and it has three assumptions:

• All work is a process that is potentially measurable,
• All processes exhibit variation, which impairs their predictability,
• There is benefit in understanding the variation that keeps processes from being predictable 100% of the time and reducing the inappropriate and unintended variation.

People generally do not perceive that they need statistics - the need is to solve their problems.

Current Realities of the Quality Improvement World

Given the current rapid pace of change in the economic environment along with the "benchmarking," "re-engineering," and "total customer satisfaction" crazes, there seems to be a new tendency for performance goals to be imposed from external sources, making improvement efforts flounder when

• Results are presented in aggregated row and column formats complete with variances and rankings,
• Perceived trends are acted upon to reward and punish,
• Labels such as "above average" and "below average" get attached to individuals/institutions,
• People are "outraged" by certain results and impose even "tougher" standards.

Realities:

• Taking action to improve a situation is tantamount to using statistics,
• "Traditional" statistics have severely limited value in real world settings,
• Understanding of variation is more important than using techniques,
• Statistical thinking gives a knowledge base from which to ask the right questions,
• Unforeseen problems are caused by the exclusive use of arbitrary numerical goals, "stretch" goals, and "tougher" standards for driving improvement,
• Using heavily aggregated tables of numbers, variances from budgets, or bar graph formats as vehicles for taking meaningful management action are many times futile and inappropriate,
• There is poor awareness of the true meaning of "trends," "above average," and "below average."

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