Monday, April 16, 2012

Quality Happiness = Breaking Management Barriers

This month, let’s talk about happiness and job satisfaction in the quality field, worldwide. Are you happy on the job? If yes, why? If no—what would help you raise the voice of quality with a passion?”
Paul Borawski concluded his latest blog by posing the above question.  I interpreted Paul’s description of Raising the Voice of Quality as the ability to be Engaged, Enabled, and Energized in my job.  Some of my colleagues have eloquently described their happiness in their blog posts.  I want to concentrate on the latter question, what would help me raise the voice of quality with a passion, or saying it another way, what are the barriers to my ability to raise the voice of quality and what can be done?
First barrier:  Management insistence on selling marginally bad product (or service) as good. I lived this first hand when I was a quality manager in the paperboard packaging industry.  The plants had quality inspectors and I use the term euphemistically as it really meant quality sorters.  The paperboard packaging manufacturing process has inherent failures in the process and there have been technological advances to improve these failures but invariably there will always be times where people would have to inspect thousands of cartons to determine good or bad based on tolerance limits that are stated in words and not visually.  To solve this one, there needs to be management appreciation of preventing problems in the first place and ensuring tolerance limits are understood by everyone.
Second barrier:  Management thinking that training solves all problems and an annual training program is better than no training at all. Guy Wallace will probably tweak me a bit but I strongly believe management has no comprehension of the purpose of training other than a short-term imparting of skills. Many believe that if we have annual training on key topics that will be enough to prevent problems from occurring in the first place.  What needs to be done is a willingness to take time to analyze a problem deeply and not just throw money at it in order for the problem to be trained out of people.
Third barrier: Management over-emphasis on the value of money and under-emphasis on the value of time.  Again, going back to my packaging days, there were times where the customer has this hot job that has to be done now.  We break the schedule, rush it through the process, send it to the customer, and the customer rejected the product because it did not meet quality standards.  By the way, run it again because we still need the product. Current accounting systems do not capture the waste generated from running it again nor the required sorting just to make enough “good” product to make the shipment.  Breaking this barrier requires culture change, intestinal fortitude, and communication with the customer.
So how can ASQ help me increase my passionate quality voice?  Talk about doing things right the first time (we already do this, keep it up, and spread it to more channels).  Talk more about preventing problems and how that ties to preventing costs as a comparison to throwing money at short-term fixes. Finally, we need to change management’s paradigm.  Stop listening to Wall Street, start listening to Main Street.  It all comes down to people.  Providing the best customer experience one PERSON at a time.  Sure sounds like Deming to me!
Until next time.

Tuesday, April 10, 2012

The Coming of “Big Data” and what it means for Quality

I have been reading, with my personal skepticism filter on, about the latest and greatest on “Big Data.”  Big Data is defined as the ability to collect a voracious amount of data and using the data to make decisions.  A new type of position, data engineer, has been created to oversee this significant increase in volume of data, the physical maintenance of the hardware, and the creation of relevant datasets.
Our ability to collect data has increased logarithmically. Cost of storage has dropped dramatically in the past 10 years.  We have more access to varying types of data than ever before.  Yet, with all this data, has our decision making ability increased at the same proportion as our ability to store it?  I would contend absolutely not.
To start answering the question I would first request you read Caribou Honig’s guest blog post on Forbes.  The post’s contention, and rightly so, is that Big Data should not be the focus.  We need to focus on “Better Data.” As quality professionals we have espoused this concept for years. If we don’t analyze the data germane to the issue at hand we will not create the appropriate decision or solve the problem that is pertinent to the current reality state.
So how does one create Better Data?  The days of dumping the entire database into a spreadsheet to create that magical pivot table are over.  Here is my strategy to deal with huge databases.
·         Understand the data elements that are in the database
·         Determine the data elements that you need to evaluate from the problem statement or business objective
·         Create a list of expectations as to what you need from data.
·         Validate the data elements to the process from which the data is coming from
·         Communicate the expectations to the data miner, database guru
·         Test drive the dataset using the expectations list
You will notice that this type of exercise requires some different skills and data that are often overlooked.  First, you have to know what your process produces and how the data represents the process. I have seen way too many processes being managed by data that has no connection to process inputs or outputs. Next, you have to have to be somewhat skilled in segmentation.  Segmentation is not just splitting the data; there has to be logic to the segments. The ideal collaboration for data segmenting is the process subject matter expert, the database guru, and the analyst.  Data segmentation is crucial in that it can provide atypical looks that may provide different perspectives on problems or decisions.
Another type of knowledge is statistical intent.  The typical statistical knowledge of a data analyst is usually in the realm of that first college level stats class. Data analysts forget the power of visualization and do not understand that data dumps are often static in nature which means that unless you understand the type of data, although you have a lot of it, you may only end up with a single data point.  Meaning, you are making assertions from only one perspective.
I do see one positive application of Big Data: greater ease of statistical modeling.  To me, this is the one area not readily utilized by the Quality profession. Having the data gives us greater ease in modeling process behavior, if we only use it.  Statistical modeling is very powerful as a way to pilot changes and predict future performance.
The Big Data drive opens up some huge opportunities for the Quality professional.  It is a road with potholes.  But if we trust our Body of Knowledge, use the tools appropriately, we can increase the quality of business decision making. Just make sure you do your due diligence.
Until next time!

Thursday, April 5, 2012

In celebration of baseball’s Opening Day

A lot of you who know me know that I am a diehard Philadelphia Phillies fan (check my twitter profile), watching my first game as a 6th grader on the safety patrol in old Connie Mack stadium. So, it was quite interesting to read in April’s Quality Progress, an article (“Fair or Foul”) from two ASQ members who provide statistical consulting services to the Los Angeles Dodgers on their take of the Oakland A’s application of econometrics into Major League Baseball, recorded in the book and movie, Moneyball. 
Let’s get the bad out of the way first. I was disappointed with the online version of the graphs. Graphs were often visually busy and axes did not clearly delineate differences when describing Team ERA. What I was also disappointed was the lack of application of the author’s findings to the game of baseball nor an understanding of the historical era in which the decisions Billy Beane had to make to at least put a presentable product in a market where revenue generation, the prime source of baseball salaries, was more difficult than any other market, save Tampa Bay or Miami.  In short, I found that the article was written like a typical statistical journal article; it does well to present the facts but leaves little for historical context.  
What I did like about the article is the fact that they proved you cannot rely on a single statistic to base decisions.  Of course, the original model from which Moneyball is based ALSO doesn’t rely on a single statistic, like is mentioned in the article, but the article does prove out a successful formula for competition.  Good hitting will win games but good pitching prevents runs from scoring.   What I did want to read about is taking the author’s assertions, how do the latest playoff teams rate?  For example, the St. Louis Cardinals where the hottest team in baseball at the end of last year, ending the year as baseball’s top hitting team.  They also came back to become the best pitching team when Chris Carpenter came back from injury to lead an above average pitching staff to World Series victory.
Again, historical context is missing.  I have to give Billy Beane credit for understanding the economic landscape of baseball; balancing out whether to trade for financial gain (trading Tim Hudson and Mark Mulder) or holding them until the end of the season to at least keep fans in the seats (Barry Zito). How do injuries impact the statistics? Are there teams who exhibit the success factors the authors mentioned (I know the Phillies did last year)?
So what does all this mean to us quality geeks?  All that time crunching the numbers is useful only if you are going to use them in context. To crunch numbers they have to support a story.  When I was teaching Black Belts, my favorite line when asked how to do a project presentation was to tell me a story. Make the DMAIC deliverables tell a story about your progress and support the decisions your team makes.
Until next time, enjoy Opening Day, and may your favorite be successful, except when playing mine!