The Data Heads

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.

We’ve been getting some good press recently for some work we’ve been doing with a well know foods company. This project implemented a real-time Overall Equipment Effectiveness (OEE) dashboard so they could collect and report on Key Process Indicators (KPIs).

As the project manager states, “The Hertzler System’s color-coded and real-time display of production line performance has given our operators a heightened sense of ownership over the plant’s performance. Rather than analyze performance reports the next day, employees can act on real-time data from the performance boards, leading to faster issue resolution and better overall performance. Furthermore, this system will allow us to maintain a historical record of our performance, which will guide our long-term improvement efforts.”

This project actually began several years ago when we began collecting package weight data from check weighers on the production line. The company used this data to help reduce overpack and save money.

With that success under our belts, they asked us to help them collect downtime and other data associated with OEE, and to display these Key Process Indicators (KPIs) automatically on flat panel displays on the factory floor.

Corporate had mandated that they get this information out to the workforce, and they were manually updating white boards with markers at the end of each shift. It was a time consuming, error-ridden process.

Here is a picture we took from inside the plant showing the shop floor data collection station, with the large flat panel display suspended from the ceiling.

And here is a screen capture of the OEE Dashboard display. This was custom-developed for this customer and combines data from a variety of sources. The column labeled Downtime Reason scrolls to show all of the reasons for downtime during a particular hour.

Research shows that real-time data is one of the key strategies that differentiates Best-in-Class performers. You can read more about that research in this Aberdeen Report on Event Driven Manufacturing Intelligence and in our accompanying white paper on the The Role of Real-Time Data in Improving Profitability and Customer Satisfaction.

There is an old story about Dr. Deming from back in the mid 1980s when he was working with the big three auto companies.

I don’t know if it is true, but the story goes that someone asked him: “Dr. Deming – fifty years from now, which companies will still be practicing your 14 points?”

He was reported to have growled: “The ones that are left!”

At the time that observation must have sounded somewhat egotistical. Now it seems prescient.

And speaking of prescient, maybe it is time to dust off his book “Out of the Crisis“. When was the last time you reviewed those 14 points?

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?

I’ve been following Forrest Breyfogle for some time. You may know that he came out with a couple of the definitive text books for the Six Sigma DMAIC process several years ago. I have a couple of them on my shelf.

In the last few months, I’ve bumped into Breyfogle at a couple of conferences and he is onto something really important. At the risk of oversimplifying it, he has come to realize that Six Sigma and Lean Six Sigma are, in and of themselves, too narrow in scope. All too often DMAIC projects fix one thing and break something else, and seldom do you find links from individual projects to ultimate business performance.

This is made worse because these process improvement efforts are mostly divorced from the implementation of business dashboards and scorecards. Furthermore, strategic goals are too often created in a vacuum at an executive retreat with little connection to customers and their real needs. (Six Sigma guru’s may be harrumphing in the background, but please, go read his website. There’s a lot of truth in his words.)

Breyfogle proposes an Integrated Enterprise Excellence (IEE) system that helps organizations execute the Three ‘Rs’ of Business: Doing the Right Things, doing them Right, at the Right Time. You can read more about IEE at his website and blog.

You find IT (information technology) and the CIO (Chief Information Officer) at the heart of IEE. Breyfogle “gets” the role of the CIO and IT in continuous improvement. He has a great white paper on this topic, and his latest blog post touches on it.

I haven’t had time to see how far Breyfogle takes his prescription for what IT needs to do to enable business excellence. What I’ve read so far seems very consistent with our customer’s experiences and my vision for how data and IT can – in Breyfogle’s words – “be the catalyst for new improvement initiatives.” It is also consistent with the research we’ve seen out of the Aberdeen Group on the role of real-time data in manufacturing excellence.

Breyfogle thinks he may be on to the “Next Big Thing”, and he may be right. I intend to keep my eye on it.

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.

Andrew Downard had a great post last week on the iSixSigma blog about the maturation of Six Sigma. He argues that Six Sigma has an “Act II” problem. Act II is all about institutionalizing Six Sigma so that it actually delivers on it’s promise. Downard argues that the skill set required for Act II is very different than what is required for Act I.

My language for this is the difference between Explorers and Settlers. In US history the explorers came from outside, mapped the terrain, and then moved on to the next place. Settlers came in and put up a cabin, plowed the earth, and settled in to make a difference. The actors in Act I are Explorers. The Settlers don’t show up until Act II.

But there is another way to look at this. If you do a Google Image Search for Six Sigma DMAIC, and then tally the images by type you get a very interesting perspective on Six Sigma. Of the first 40 images, about one half of them depict DMAIC as a linear process. Here is a typical graphic:

DMAIC as a Linear Process

The other half of the images, however, are cyclical. This one is typical:

DMAIC as a Cyclical Process

I wonder if how we picture DMAIC indicates whether we’re an Explorer or a Settler, or in Downard’s terms, whether we’re in Act I or Act II.

I think it may also lead us to different conclusions about how we view data and data systems. If you’re an Act I Linear Explorer, I’m guessing you’ll see data and data systems as something that you need to conquer. Data is a wild river that holds some great fish that you can eat, but essentially it is blocking you from your next destination.

If you’re an Act II Settler, that same data river is something that can help drive subsequent cycles of the DMAIC process. In Settler terms you can use it to water your crops, drive a new sawmill, and (once you’ve invented them) power electric lights and a toaster.

If you’re an Act I Explorer, tools like GainSeeker Suite won’t make much sense to you. Who wants to build a hydro-electric dam when you’re just trying to get across the river?

But if you’re an Act II Settler, being able to connect to disparate data sources, collect and alarm based on statistical process control rules, and analyze for root cause of process variation becomes essential.