The Data Heads

Over at the LinkedIn Continuous Improvement, Six Sigma & Lean Group, I posted a discussion question that generated some great responses. Here was my original question:

Lean Six Sigma practitioner’s ad hoc data survey
Recently I was talking with a black belt who told me “I know exactly what you mean by the data shuffle. Three hours of my 13 hour day yesterday were spent doing the shuffle.”

The data shuffle is all the compiling, massaging, and manipulating data that you do to get something useful so that you can make meaningful business decisions.

My experience is that all that busy work undermines the cultural transformation that is needed in Lean Six Sigma deployments. In other words, if data were easier to get to, the corporate culture would be more apt to make data driven decisions.

I have a couple of blog posts on the topic.

For an example of the data shuffle, see http://www.hertzler.com/blog/dataheads/index.php/2008/12/doing-the-data-shuffle/

For more background on the relationship between data and cultural transformation, see http://www.hertzler.com/blog/dataheads/index.php/2009/03/hitting-my-forehead-with-the-palm-of-my-hand/

Is the data shuffle alive and well at your organization?
How much time do you spend doing the data shuffle for your projects?
Does the data shuffle undermine cultural transformation?

Here are excerpts from some of the responses:

From Shaun Wurzner:

Regrettably, [Six Sigma] arrived at a point where managers and business leaders had to demonstrate Six Sigma improvements and for fear of at best, never being promoted or at worst, being let go…. Incorporating the fear of employment with continuous improvement was in my opinion, a disastrous train wreck. As a result, managers (and I AM GENERALIZING) felt compelled to generate data to show improvement. Regrettably, the more paperwork and reports, the better chance of being viewed as a valuable results oriented contributor…

To tie back into Evan’s original question, another fundamental requirement of Six Sigma was the ability to access data to characterize process, identify sources of variation, and drive process improvement through various means… The ability to capture timely data continues to be one of the fundamental issues that challenges companies and practitioners alike…. I can tell you that so many companies drive their business without fundamental data collection and analyze tools. As an example, how many times have you seen someone apply an algorithm to a data set without checking it for normality and these are statisticians or quality “experts”. Not to fault them, but everyone is so preoccupied with the end results that they by-pass some of the fundamentals.

This is a powerful indictment: “Managers feel compelled to generate data to show improvement.” Wow.

But why should we be surprised? It is entirely consistent with Deming’s 14 Points, especially #8 (Drive out Fear.), #10 (Eliminate slogans, exhortations, and targets for the work force.), and #11 (Eliminate numerical quotas for the work force and numerical goals for management.)

Maybe it is time to dust off your 1982 edition of Deming’s “Out of the Crisis”. We did not learn those lessons well.

And from Terri Jostes:

The 7 types of waste are now known as the “7 +1″ or 8 types of waste with the addition of “Creativity” or “Human Potential” as the 8th type of waste. All the wasted effort of overprocessing, rework and motion (in the form of moving data from one database to another) associated with the “data shuffle” causes our intelligent, highly trained, well-paid green belts and black belts to spend countless hours in unproductive, frustrating activity.

Why do companies allow this to happen? In some companies, it is seen as a rite of passage for a belt or a necessary, if unsavory, part of the job. Some belts even like doing this type of work – they’re good at it! Of course they are…they spend a lot of time doing it!

Evan’s right…It’s time to be strategic about our data gathering, compilation and analysis. In order to create a “data driven” culture, clean, reliable data has to be readily available to all levels of the organization. Letting the “data shuffle” continue in our organizations guarantees lost opportunity in our improvement activities and operational performance.

I love that idea: “Rite of passage.” Did you have to go through this rite? What is your story?

And from Forrest Breyfogle:

Evan, you indicated that the data shuffle is all the compiling, massaging, and manipulating data that you do to get something useful so that you can make “meaningful business decisions.” If the result was to make meaning business decisions that would be one thing and would be waste in that the analysis was not performed efficiently. However, it appears to me that the problem is worse in that often there is playing games with the numbers to make a situation appear better than it is… We need to blend analytics usage within an overall business governance system.

Excellent point: This isn’t just an efficiency issue; we need better governance. Can better data help that?

And from Terry Burton:

Today’s turbulent economy requires much more targeted Lean and Six Sigma efforts that produce an accelerated level of tangible results. That means a rapid and perfected “value-added” execution. We need to stop the data shuffle, and the training of the masses and put the money on the table – Or it’s all just another bandwagon! Too many organizations are stuck on this”mad belt” and “data shuffle” mode with their Lean and Six Sigma deployments.

One of the largest inefficiences we observe that compounds the data shuffle is that people are using multiple versions of the truth (facts). This is prevalent in organizations where the formal enterprise system has broken down, or where people struggle with their own personalized kluge spreadsheets to get (data shuffle) the facts. Some people tend to grab and shuffle the data that is available, rather than think through the specific data elements needed to solve the problem.

Think about the obvious. Not only are these folks inefficient due to data shuffling, but they all have different versions of the facts when they’re done! Now this leads to the wrong actions and firefighting, and the data shuffle becomes a viscious cycle. Their organizations would be better off if they made their “data shufflers” sit there and do nothing! Unfortunately, these organizations are not improving at the rate of the economy so despite all the belts, they are falling behind.

The largest challenge with Lean and Six Sigma is quickly thinking through and acquiring the right data to make the right decisions and get the right results. Some of our clients have reached this “utopia:” Real-time, visual event-driven metrics, a single version of the facts, decide-act-measure in real time. Based on true potential, we see that well over 80% of Lean and Six Sigma deployments are failing. People are hanging up the window dressing and getting their belts, there’s a lot of data shuffling and charts, but cultural transformation is not happening.

This economic meltdown is a great opportunity to rethink the “what’s” and “how’s” of your Lean and Six Sigma deployments and shift into a higher gear of new results.

Important concept: “A single version of the truth.” This reminds me of a conversation I had with a test engineer at an audio amplifier company who told me “In the past, if I wanted that data, I had to go mine it myself… People would be reasonably questioning my political motives for saying ‘We had 94% first pass yield last week. Now we have this standard called Hertzler, and you can go get the same data I just got…”

And from David Back:

I can only echo the points made and pass on some of my experience with management teams. Management by facts is a major change for many leaders and it is far more than setting up data cemeteries and then having specialist analysts or Belts providing insight in parallel to routine business processes. Managers need to buy in to the strategic nature of the change and be equipped with the clear concepts to act in this way. The size of this step is generally underestimated, old habits die hard ! It falls to all of us that understand this issue to promote the change at every possible opportunity.

“Data cemeteries…” What a great term.

And from Ken Place:

Although I agree that “the data shuffle” is a problem, I see more instances where data is just not available, what is available cannot be used, and new or additional data collection activity is required before an effective Lean or Six Sigma effort can/should even begin. As a Master Black Belt, many of my Black Belt students come to the first week of class ready to hit the ground running with a project. When we discuss the details of “Good” data they realize that they must return to square one and generate some history of the current state before progressing. Rather than massage the data to make it useful I find they must disregard current data and re-collect. Clearly non-value added but seldom fixed at the root of the problem, The Business Management System (BMS). In my opinion the BMS, formerly known as the QMS, should be designed with the correct, robust metrics in place for each process. Those metrics should reflect the health of each process as it contributes to the system, so that a company is able to asses where their next opportunity for continuous improvement exists. Without ongoing correct measures of at least efficiency and effectiveness, projects are not properly prioritized and work cannot begin right away. Yes metrics should be strategically established and available at any time for accurate monitoring, measuring and continuous improvement efforts.

At the risk of over-simplyfying, build the measurement system and the the rest will be easier.

What do you think? Is the data shuffle alive and well at your organization? How much time do you spend doing the data shuffle for your projects? Does the data shuffle undermine cultural transformation? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

A recent article on SearchDataManagement.com about a Gartner Group conference on Business Intelligence (BI) discusses the fact that business intelligence is probably the number one priority for CIOs, but most companies have not translated that prioritization into high value.

That conclusion doesn’t surprise me. I’ve been arguing for sometime that most businesses under-utilize their data assets. Here are a couple of blog posts, looking at topics like BI as an oxymoron, technology and culture, and the key drivers of Best-in-Class manufacturing.

In this latest story, I especially like this prescription for addressing the problem:

IT workers must reconsider how they deliver information to end users. Traditionally, on one end of the spectrum, users either access information through static reports or through ad hoc queries, Schlegel said. Instead, IT should focus on developing interactive reports to meet both demands.

On the other end of the spectrum, more sophisticated users often create their own spreadmarts, which by definition fall outside the view of IT, to make up for the limitations of ad hoc queries. IT departments should develop data discovery environments that empower users to do the analysis they need, but which also let them connect that analysis back to the organization.

That sounds to me like a dashboard that users can drill into to get to the underlying data. It sounds to me like an analysis wizard that guides users to the underlying sources of variation in a process.

IT people do sometimes lose sight of their real goal. As one conference attendee, Chad Erman, head of BI for Southwestern Energy put it, “We noticed a lot of people think in terms of reports, instead of BI or key metrics. What I constantly had to remind them … is: What is the question you’re trying to answer? Then work to achieve that goal.”

Getting business leaders the right tools can go a long way to enabling that shift.

What about you? Are you getting high value from BI? If not, what are your road blocks? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

One of the discussion forums I try to monitor is Elsmar Cove. Sometimes it goes pretty geeky (I’m sorry, its just the word that comes to mind.) But sometimes it has some great questions. Here is one from that ‘Shesha’ posted today.

Hi,

Just wanted to know, what are the different methods you all had used OR are using to improve the Quality Culture in a organisation , mean to say to change the mindset of the people in a organisation towards implementation of process and Quality related stuff.

Thanks & Rgds, Shesha

I had to post the following response:

My experience is that when you want to change culture you have to provide the tools to make it so. Everyone talks about getting commitment from the top, and of course all that is true. But if that commitment is shown by table thumping and speeches and bands and banners it is a bunch of hogwash. (Pun intended – in reference to an earlier comment about how committed chickens and pigs are to preparing the farmer’s breakfast.)

If you want a quality culture that is data driven (which is what many people mean when they say they want a quality culture) the most important tool is the right data, in the right form, and right now.

Actually, this isn’t just my experience. The Aberdeen Group has published a couple research reports that put some dollars behind this. Best-in-class performers pay attention to building real-time data systems. Failing to do so undermines culture. And culture eats strategy for lunch.

You can download this and some supporting white papers from my website.

Four reports are especially on topic:

Aberdeen Event Driven Manufacturing Intelligence Report

The Role of Real-Time Data in Improving Profitability and Customer Satisfaction

Aberdeen Lean Six Sigma Benchmark Report

Leveraging Technology to Transform Culture

One of my staff members calls this “Evan’s Soapbox” and it is true.

CIO Magazine recently published a great blog post called “To Hell with Business Intelligence: 40 Percent of Execs Trust Gut“.

Based on separate research published by Accenture and Aberdeen, the post says that nearly half (40%) of major corporate decisions are based on ‘gut’ and not on data.

The number one reason? Sixty-one percent of the ‘gut deciders’ do so because good data was just not available.

Recently I visited one of these businesses. Ok, maybe they weren’t part of one of these studies, but they are a ‘gut decider.’ They’d love to do something different, but they can’t get to the data.

This company has an archaic, manual data management system. Every day they write down stacks of data on pieces of paper. They file (and forget) these in a filing cabinet. Some of it makes it into a homegrown (MS Access) data system.

Normal – busy – people can’t access this data.

But that’s OK, because nobody trusts that data that are there.  Even with Checkers checking the Checkers checking the people recording the data, nobody believes the data.  These people have no choice but to make almost all of their decisions based on gut.

In this situation, the obvious question is “So how’s that working for you?”

It was obvious to all of us that it isn’t working at all. How can I say that? Here are a couple of reasons:

• Customer complaints torpedo new business opportunities.
• High scrap rates siphon down profitability.
• Product returns clutter a warehouse, some of it retained as “inventory” for years.
• The list goes on…

Deciding from the gut is expensive.

I’ve found that when real-time, actionable data are readily available, people use it to make decisions – good decisions.

Of course these systems require an investment.  Of course people need training to make good use of data. But given the high cost of the gut, the return on these investments is phenomenal.

Having said all this, transforming into a data driven business is hard. As the CIO post states: “Losing that gut-first instinct isn’t going to be easy, and I’m not sold on whether companies can stomach the change required. ”

Lets hope they can. In this tough economy ready access to actionable real-time data may make all the difference in the world.

In the IT world what is classified as Business Intelligence may indeed be an oxymoron. But most of us need data – or more accurately knowledge – to make good decisions.

What do you think: Gut or Data? What is your experience?

The other day I was showing a colleague the Data Cost / Value Matrix and describing the four aspects of data value.

He made a comment that got me thinking. I was explaining the fourth aspect of data value: Data Visibility and Transparency when he said: “For my customers, transparency isn’t the key issue. The biggest issue is that they are drowning in data they can’t use. If you can make the data they already have more usable then you’re providing value.”

My first impulse is that perhaps I need to relabel the graphic so that “Data Visibility & Transparency” reads “Data Visibility and Usability.”

The more I thought about it, however, the more I realized that all four aspects of data value have to do with making data more usable.

Product Release and Control is the minimal, entry level approach to making data more usable. Data for product release and control validates that our products are acceptable for shipment. It may be accept/reject type data, and may be based on either measurements or some other kind of pass/fail criteria, and is based on the voice of the customer. Product release and control is necessary, but not sufficient.

Process Control makes data more usable because we’re relying on statistical theory to help us understand when we should react and when we should leave things alone. Process Control empowers us to react immediately to instability and unexpected variation.

Using data for Continuous Process Improvement makes the data even more usable. When you can close the loop on processes and drive continuous improvement, you get significant returns on your efforts. Closing the loop means being able to find hidden sources of variation and correlation between key input and key output variables.

The last aspect – Data Visibility & Transparency – adds value because when data is readily visible across all levels in an organization, it changes the way people work. One engineer put it to me this way:

The real time part of it has been cool. I have walked into several meetings and said “As of 5 minutes ago, this week our first pass yield is 93%.”

In the past I would walk into a meeting and report on week-old data. So inevitably there would be questions on “Do we have this fixed?” and the answer would be “I think so.”

We are definitely making a lot more decisions based on data rather than gut feel or incomplete data. We feel like we are getting the entire landscape before we go down and make a decision on what we are going to do.

As I talk to people in many different businesses, I’m often amazed at how much time they spend extracting, massaging, and scrubbing data for analysis and reporting. I call this “The Data Shuffle“. A friend of mine at Minitab calls it “The stuff we do that we call our job.”

My informal surveys of Six Sigma Black Belts and Green Belts tell me that there is often a 20:1 or 25:1 ratio of massaging and scrubbing data to analyzing that data. And Six Sigma people aren’t the only ones doing the data shuffle. I’ve seen it in all kinds of environments and for all kinds of reasons.

Any way you look at it, The Data Shuffle is not value added activity. It is a business cost that enables better communications with customers and better business decisions.

So why might you not want to show this post to your boss?

Because if you’re doing the data shuffle this post points out that the data shuffle can be eliminated through automation, and that part of your job can be eliminated.

And in this economy I’m guessing you don’t need any more reasons to have your boss eliminate your job.

So what can you do instead?

OK – here is the gutsy move:

Start by studying this matrix:

(You can read more about the Cost Value Matrix.) Chances are good that if you’re doing the Data Shuffle, you’re down in quadrant D where the cost of data is High and value of the data is Low.

There is also a good chance that right now your company is very interested in reducing costs. If we automate the Data Shuffle we can shift towards Quadrant C.

So take this matrix to your boss and say something like:

“Look – I know the company is interested in saving money. The economy is tightening up all around us and we need to lean out operations so we’re more competitive… blah blah blah.”

Then say: “I know how to cut some significant Non Value Added costs out of our system. I can help you do that, but if I’m successful, it will eliminate part of my job. Instead of working myself out of a job I’d like to invest this freed time in helping the business make better use of that data. By making better use of the data we can reduce scrap and material costs, improve customer satisfaction… blah blah blah. In other words, I want to help us move up to Quadrant A.”

This gives your boss three options: business as usual, or eliminate the data shuffle and your job, or eliminate the data shuffle and free up time to do more important and value added work. If he chooses business as usual you might want to keep your resume up to date because of the way the economy is going. If he chooses to eliminate the data shuffle and your job, you will at least have learned how to eliminate the data shuffle and you may be able to take that skill into a new business that will appreciate the value of moving to Quadrant A. And if he chooses to reinvest in you and in process improvement, everybody wins.

So give me some feedback: How much time do you spend on the Data Shuffle today? How would this strategy work in your organization?

A couple weeks ago I published an audio interview with Jay Bronec about his ‘Ah-Ha’ Moment when he realized that he was spending valuable company time doing non-value-added work. In that interview he described how he is automating his company’s (QualiFine) KPIs by integrating our CRM and Web data using GainSeeker.

Today I followed up with him to see how that project was coming. He took me on a webex tour of his project and I was impressed. He is using GainSeeker Suite to mine data and analyze his target market. Then he ports that over to Minitab for some advanced regression analysis that predicts class size based on how many people have registered for the class and how many days are left to sign people up. It is very cool.

But after we talked for a while, I turned on the tape recorder and asked him a question:

“Jay, whats the value you’re offering your customers? Why does it matter to people if they implement GainSeeker and get training in an economy like we have today? Why would anybody want to spend money on that today when things are crashing around us?”

For a little over seven minutes we discuss the Data Cost / Data Value Matrix and how it applies to saving money in an uncertain economy. We touch on dashboards, CMM (Coordination Measuring Machine) data collection, and how real-time data can catch problems before you waste a day’s production.

Follow this link to hear some great insights into the value of real-time automated data.

Recently Larry Coburn, the Sr. VP of Operations at Crown Audio, gave a presentation at the Aberdeen Manufacturing Excellence Summit. We recorded his talk, and you can watch it on-line.

I really enjoyed Larry’s talk because he doesn’t pull any punches. He understands how manufacturing drives the economy, and he has a powerful antidote to all the economic gloom and doom that we’re hearing:

I don’t like being a victim. When I hear manufacturing guys talk about being victims, I say, “It’s your choice. You can be a victim if you want, but I don’t recommend it.” I think you should always work to not be a victim. Do what you got to do and be a leader.

Woven in the presentation is one insight that you don’t want to miss. Larry tells the story of touring a competitor’s site (a laugh out loud experience – this guy has nerve) and learning that they’ve invested $8Million in a new factory that produced a solid product in a fraction of the time it took them to make their product. He brought that news back to his staff and challenged them to implement lean and quality improvements without an $8Million investment.

We just can’t give up. And how we gonna do it without eight million dollars? Because if we take away the demand (for product) that eight million is a boat anchor they got to pay, not us.

His team’s efforts resulted in a 90% improvement in Fabrication Lead Time, a 90% improvement in Circuit Board Assembly Lead Time, a 85% improvement in Efficiency, and a 92% reduction in absenteeism. All without an $8 Million capital expenditure.

During the presentation you’ll hear Larry talk about the importance of having the Right Data, in the Right Format, at the Right Time.

Nothing’s worse than working on two‑week-old data trying to make some good decision’s on it. It just drives you crazy. You can’t get anywhere.

In another conversation he told me:

We’ve always had tons of data, but not enough knowledge. GainSeeker gives us the knowledge to eliminate the finger pointing and focus on solving the problems. Plus it gives us the data in real-time, so now we walk into a production meeting with an up-to-the-minute yield report, with a prioritized list of what we need to work on.

If you’d like more information about Crown’s journey, you can also check out this feature story in Industry Week Magazine.

In my last post I described an interesting conversation with a customer about his company’s pilot deployment of GainSeeker Suite. You may recall that because of staff turnover, this plant was collecting data but not doing anything with it. The company was feeling pressure from a significant increase in raw material costs, and because nobody in the business knew how to use GainSeeker (because of the staff turnover) GainSeeker was not helping them reduce costs.

I had sat down with the corporate staff guy and the plant quality manager. We had started to review some of the data she had been collecting and used GainSeeker’s Analysis Wizard to drill down on the data and found that Shift A had the highest variance among three shifts, and the six or eight operators on that shift had very different results. (Click on the chart to expand to full size.)

Once we had this chart displayed on the screen, I right-clicked on the chart and then selected the ‘Control Limit Legend’ option. That displayed a list of the 8 different operators, along with the mean and range (with related control limits) of the data for each operator.

Here is how we interpreted this table, along with the chart:

It is clear that one of these operators (710) has a very different process. When you look at the control chart for this operator it is much more stable than the other operators, and when you look at the average for each of the operators, Operator 710 is running at about 173.4g compared to as high as 174.9 for some of the others. (See the yellow highlighted cells in the table). That’s a shift of about 1.5g.

Now here is something you need to know: the critical dimension is weight. Weight is critical because a minimum weight has to be met, but anything heavier than the minimum is given away – the company doesn’t get paid for it. So getting as close as possible to the minimum will reduce material costs – substantially.

How much?

We went out to the internet and found a site with typical raw material prices for this commodity. At the volume they were running, the difference between Operator 710 and Operator 214 came out to $457 per day. This is a 24/7 operation, so the annual cost savings between the two adds up to $166,861. And this only one line. This plant ran nine lines. So across the plant the potential savings of over $1.5 Million.

Who was the comedian who said “A million here. A million there. Pretty soon we’re talking real money?”

The other thing that will be obvious to you if you click on the chart is how much more stable the process is in Operator 710’s hands. Operator 214 would be foolish to try to adjust his average down because with the variation he is running, he’d be below specification too often.

Operator 710, on the other hand, could shift his process closer to the lower specification without jeopardizing quality.

So actually the impact could be even greater because the lower specification is 167.6g. If the process is tightly controlled with minimum variation, you can shift it towards the lower spec, reduce material consumption by as much as half a million a year!

Here is another way to visualize what they’re trying to do:

So is there money to be made here?

Looks like a safe bet to me.

Not long ago I had an interesting conversation with a customer, a corporate quality systems guy from a multi-plant corporation.

We had installed GainSeeker as a pilot project at one of his plants, and he and I met to plan deployment to another division. Unfortunately a couple weeks after we launched the pilot, corporate had completely upset the management apple cart at the plant and brought in (among others) a new quality manager. We met the QM and I’m sorry to say she had that deer-in-the-headlights look:bright and capable but completely overwhelmed.

In short, GainSeeker was installed but not being used except to collect a bunch of data. Not to put too fine a point on it, the QM’s training reminded me of the Dilbert cartoon where Dilbert says to the new hire: “This is your mouse. Move it around if you see anyone coming. And remember if you ask any questions you’re bothering me.”

My corporate contact bemoaned the lack of evidence to take back to his boss, which led us back into a conversation that we had touched on many times before: “Why are you deploying GainSeeker in the first place? Where is the money?”

As we talked it became clear that material costs were going through the roof, and the company needed to find ways to control and reduce those costs.

Eventually we went back to the QM’s office and her computer where we spent about 10 minutes digging into the data that they were alreading collecting.

What I found surprised all three of us. Here is a control chart of the data, our starting point. (Click on the chart to see it full-sized in a new window.)

A quick glance at this chart shows some fundamental instabilities. (And I wouldn’t recommend putting specs on the chart, but that is another topic.) But looked at this way, the chart doesn’t help us get to the underlying issue – reducing material cost.

I wanted to see if GainSeeker’s Analysis Wizard could help us see inside the data to identify any underlying causes.

In a few seconds I had learned that Shift A had the highest variance among three shifts. Not only that, but the wizard automatically drilled into that shift and pinpointed clock number (operator) as the number one driver of variation on that shift. You can see that in the following chart.

If you click on this chart it will open in a new tab or window, and you’ll see that the data for Operator 710 looks quite different from the others.

So how does this translate to the bottom line? I’m out of time for now, but stay tuned: that will be the subject of my next post.

Speaking of staying tuned…

Are you new to staying tuned to blogs? The best way to stay tuned is to set up an RSS Reader. There are lots of good sources on the web that explain how to do that. Here is one, and here are several more on video.