The Data Heads

Yesterday my colleague Chris called me. “I just had a really interesting call from Bob,” he said. (Bob isn’t his real name. “Bob” is a customer who doesn’t want me to divulge his or his company’s name.) Bob’s company makes  packages filled with something that you can find in most grocery stores. They make a lot of these packages.

In one of my last conversations with Bob he told me “I’m almost embarrassed to tell you how much money GainSeeker is saving us by helping us reduce over pack (give away). The ROI on our deployment is shocking.”

We love it when we hear these stories – even if Bob won’t let me do a full-blown write up with all the gory details.

But Bob’s call to Chris raised an interesting question. Chris told me that Bob said he now believes they’ve been under-reporting the payback on his GainSeeker deployment. After six months or eight months of reducing over pack, Bob is discovering that his plant has made significant increases in yield.

He had been counting the savings realized by not giving away the product, but he wasn’t thinking about where the product he had been giving away was going.

It seems to me that the answer to this question hinges on whether the major constraint to his system is plant capacity, or demand for the product. If demand for the product is unlimited (we could sell every package we make) then any reduction in over pack goes into new packages and brand new sales. That is like picking up free money off the floor.

On the other hand, if the constraint on the system is capacity for production, then any reduction in over pack reduces the total cost of filling the package. If you have an order for 1000 packages and you can produce them in 7 hours instead of 8 hours, then you save one hour’s cost (energy, labor, etc.) and use less materials.

This reminds me of a chapter out of Goldratt’s The Goal. Do you remember when Jonah walks through the plant with the hero Alex and his team and discusses bottlenecks? They get to the heat treat area and the consensus among the team is that the parts waiting in the queue (WIP) are worth only a few thousand dollars. Jonah helps them realize that in reality those parts represent over a million dollars of revenue stuck in WIP. (p155-156)

I think Bob’s controller will have to weigh in on the final answer. What is clear to me is that Bob was delighted with GainSeeker Suite’s power to collect and analyze data so he could reduce material  costs and increase yields.

After I wrote most of this I had a chance to talk directly with Bob, and I learned a little more. He produces to order, not to stock, so improving yield does not lead directly to new sales. Improving yield means that he can produce the same output at lower costs. If the cost of over pack already includes all his overhead costs (energy, labor, materials) then I think it would be double-dipping to count increased sales. However, at some point it does seem that increased capacity would result in increased sales. He told me that last year he had the place open half the Saturdays of the year to meet demand. Now they’ve eliminated overtime and increased volume. Can he get his accounting people to recognize this? We’ll see.

What do you think? How are you tracking the ROI on your data efforts? How might GainSeeker help you? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I mentioned in yesterday’s post announcing the release of GainSeeker Suite Version 8 that I have been working on a case study about the new report writer.

You can read the case study here, and download a PDF version to share with others.

A little bit of the back story:

This project came about during a training class we held for Valeo (the subject of the case study) early in the new year. During the class, Stacey (who is quoted extensively in the case study) made the comment “You’ll never find a kid who wants to grow up to be a data jockey.”  What a great comment.

Mel (the guy from our staff doing the training) was intrigued and scratched away at it. What did he mean by data jockey? Why had he become a data jockey? Who cared about the results of his data jockey work? What difference would it make if we could eliminate the data jockey work? What would it take to eliminate the work?

At the time, Version 8 hadn’t been released, and Mel had had only minimal exposure to the power of the new Dynamic Reports module. He came back and started asking his colleagues “So this is what they really want. Could Dynamic Reports handle it?” Dale, one of our senior developers whom I sometimes call Obi-Wan Kenobi, put together a prototype and we were off to the races.

In manufacturing circles it not at all uncommon to talk about “The hidden factory.” The hidden factory is rework. Another case study about our customer Titleist includes this analogy from the rubber and plastics industry:

Another benefit of reduced scrap is that equipment is freed to do productive work. A shop with a 5 percent scrap rate and 20 molding machines has one machine dedicated to making scrap. Using real-time production data to eliminate scrap is the equivalent of buying a new machine.

Working as a Data Jockey is a hidden factory in our offices. Like a machine producing scrap, it is not value added. Eliminating the data shuffle – freeing the data jockey – pays huge benefits to your organization. The case study outlines some of them.

What about you? What is your experience as a Data Jockey? What have you done to eliminate this hidden factory in your office? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Back in August I gave a sneak preview of a new case study that I was working on. Yesterday I finally completed it and published it on our web site. You can read the entire study and download a copy to share with colleagues.

My favorite quote?

“We can’t credit GainSeeker with all of these benefits. We still had to do the work. But we would never have been able to capture the changes we needed to make if we didn’t have GainSeeker. We’d never have been able to do any of this if we didn’t have the system. So truly it deserves the credit. GainSeeker is the tool that enabled our people to make the changes.”

Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

At today’s web seminar How Best-in-Class Food Processing Companies Drive Profits, Increase Efficiency and Reduce Risk, my colleague Tom Albrecht offered a number of free resources for individuals who would like more information. (If you missed the live presentation, you can still view the recorded version.) We decided to put links to all of these resources on one page so that you can use this as a starting point.

Here are the resources:

• The Aberdeen Group’s Event Driven Manufacturing Intelligence Benchmark Report.
• A supporting white paper that examines the role of real-time data in improving profitability and customer satisfaction.
• The Aberdeen Group’s Cost of Quality Benchmark Report.
• Free gap analysis software that helps you rate your ‘Gap to Entitlement‘ to a data driven organization
• Case Studies and video presentations from McCormick Flavor Division, Hormel Foods, Consolidated Biscuit, and Del Monte Foods. Additional case studies and white papers are available from the Portfolio pages on our website.

Of course, if you’d like to link to this, share it with a friend or make a comments, please do so. Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

This week at the Quality Expo I ran into an old friend who described a business transaction that left me shaking my head.

He told me about a company that went to their customer and negotiated a 10% price increase for the product they supplied. The customer agreed to it because the supplier was using the increase to fund new high-speed vision inspection equipment. The new equipment would enable the supplier to 100% inspect the product they’re supplying and guarantee that the customer would receive only good parts.

On the surface it’s hard to argue against that. Evidently the customer had been very frustrated with this supplier because they had had to put up with a lot of defects. They seemed eager to shift the effort to inspect and sort good from bad to the supplier, and were even willing to share in the cost. They must have felt 10% was a pretty good deal. (Given all the estimates out there that total cost of poor quality is 30 – 40% of sales, I can see how they could reach that conclusion.)

So why am I shaking my head?

If your definition of good quality is “no bad parts” then this is a perfect solution.

But “no bad parts” is only one definition of quality. And it is the wrong one.

Don’t get hung up on the “parts” language. At the risk of over-simplifying, it doesn’t matter if you’re talking about the diameter of a metal part, the time it takes to close a call in a support center; the amount of yellow ink printed on a magazine cover, the weight of peanut butter in a jar, or the sales of a particular product by a sales rep. All of these are processes, and all have targets and acceptable limits (specifications).

Regardless of the product or the service, “no bad parts” is a poor definition.

Maybe a picture would help:

What the graphic shows is that the green dot is our target and when our output is on target, it is the best that it can be. It also says that the further you get from target, the worse the quality.

“No Bad Parts” says that “Best” and “Fair” are the same. They’re not.

Think about this: A product that is at the yellow dot is closer to the red dot than the green one. It is closer to Unacceptable than it is to Target. When you’re out there in the boondocks of your specifications, you’re a long ways from target, and it doesn’t take much to push you over the edge.

A far better definition of good quality is “on target with no variation.”

Twenty-seven years ago W. Edwards Deming published his 14 Points to guide businesses “Out of the Crisis”. Point 3 of Deming’s 14 points was “Cease dependence on inspection to achieve quality.” My friend’s story tells me that we’re still depending on inspection to achieve quality. Is there any doubt that we’re still in crisis?

How about you? What examples do you have of inspecting quality into a product? How is it working for you? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I’m working on a case study with one of my customers that I think you’ll be interested in. I’m just beginning to put it together now, but I thought you’d appreciate a sneak preview. I’ll let you know when the final article is ready.

Last fall this customer came to us with a sizeable integration and customization project. It came at a time when the financial and manufacturing world seemed to be falling down around us. I was, frankly, surprised that they wanted to spend that kind of money at the same time that banks and investment firms were collapsing, the stock market was imploding, and businesses were shedding employees like autumn leaves.

But we worked with him through our standard process of defining the project and formalizing a Statement of Work. We launched the project right around the new year. During that process, my customer agreed to meet with me in six months to do a post-mortem on the project. He said he’d be willing to open his books so we could evaluate – objectively – whether the project was paying for itself.

We finally got together last month – seven months after we finished our deployment. True to his word, he did open his books to me and demonstrated – with CFO-approved numbers – that he had paid for the initial investment in less than three months.

Many organizations look for a two-year payback. He had achieved his in an eighth of that time.

Now, seven months into the project, he had documented an ROI of 171%.

That got my attention.

We started by reviewing the work we had done with his team.  This was a truly collaborative effort. His engineers had done an exceptionally fine job of building the foundation for the project, and then worked with my staff to implement the solution. Together they did a fantastic job of automating and integrating a variety of work flows and data systems. The result was a streamlined process for tracking repair and rework processes across multiple departments.

It was the classic tactic of “reduce the cost of data”. I knew that going into the debriefing meeting. And I expected that the ROI would be based on the efficiencies gained by eliminating islands of data, removing duplicate data entry, and integrating disparate data systems.  I expected that he paid for the project by eliminating staff (I knew the company was going through a downsizing concurrent with our project) through automation.  Clearly we were helping this customer move laterally on the Data Cost / Value Matrix from expensive data to low cost data.

As we dove into the data, I found a number of surprises.

First, he didn’t eliminate any jobs because of this project. As he reduced rework he reassigned the rework staff to more productive activities. They shifted from non-value-added status (overhead) to value-added production staff.

Second, reducing the cost of the data contributed only about 2% to the ROI. It was such a puny number. I had expected reducing the cost of the data would account for maybe 50% or 60% of the cost savings.

The lion’s share of the ROI came from improved throughput. Cheaper, more reliable, and more accessible data enabled his staff to drive defects out of the process. Reducing defects increased first pass yield. This resulted in lower WIP (work in process), faster product delivery cycle times, and improved order to cash cycle times.

How are you looking at ROI? Do you ever understate (as I was tempted to do) the benefit you get from the value of the data? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

In my last post I shared the Data Cost / Value Matrix and described companies that I’ve known that live in each of the four quadrants. While most people aspire to Quadrant A (Low Cost and High Value data), most don’t live there in reality. Many actually live in Quadrant D (High Cost and Low Value).

While these anecdotal descriptions of the four quadrants are useful, they don’t offer much guidance on what to do about your current reality.

For that reason, I developed a quick and dirty Gap Analysis that helps you quantify where you are today. Used properly, the Gap Analysis can point you to some actions that can help you become more data driven.

(I use the term Gap Analysis because it helps you evaluate the Gap in your current performance and your performance potential. Sometimes I like to say that your performance potential is how things would work if God ran the process.)

Anyhow – here is the Gap Analysis Tool. Just answer the four questions below, then click Continue to answer four more questions. Then Click Results to see where you score.

Once you have your score, use the Back button to review your answers to each question and plot your strategy for improving your business.

Here are some questions that might help you develop a new strategy:

• Is your score better on the horizontal axis (Data Cost) or on the vertical axis (Data Value)? If you’re firmly in Quadrant C (Low Cost and Low Value) it is obvious that you need to work on increasing the value of your data. If both scores are low, look for the low hanging fruit. Often this is found in data completeness and automation. Automation will free up time from the data shuffle so that you can work at making better use of the data that you’re collecting.
• Where are your lowest scores? Often bringing one score up from Never to Seldom or Seldom to Sometimes will do a lot to improve your performance.
• Are your scores balanced across all eight categories, or are some significantly better than others? As a rule, I’d encourage you to seek a balance across all aspects, rather than strive for excellence in one aspect at the expense of the others.

These are just some of the ways you can use this data to become more data driven. Here is some information about a more complete Gap Analysis that we can help you with too.

In the meantime, does your score on this Gap Analysis reflect the reality of your business? Tell me what you think. Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

If you haven’t been following it, the discussion about the data shuffle has been continuing over at LInkedIn.

Laura Wright posted a comment yesterday:

Is there SOME value to the ‘data shuffle’? E.g., deep knowledge that can help the green or black belt discern nuances to their process analysis that otherwise wouldn’t be had…and so a better solution comes to light? Don’t get me wrong – I do believe data shuffle is wasteful…but some fruit can be gleaned from the exercise.

I think this is a great question, and I agree with her comment. But I also wanted to push the topic out a little further.

I was still trying to formulate a response when Terri Jostes weighed back in with a comment that said what I wanted to say far better than I could have:

I agree with Laura in that there is no substitute for an intimate knowledge of your data. Understanding where it came from, what it means and the process used to acquire the data is absolutely critical. But after that’s been figured out, a mechanism for streaming process data to managers and process improvement experts has to be put in place to free your belts from the ongoing task of “cleaning up” the data or linking files from multiple databases so it can be used.

In the interest of full disclosure I need to point out that Terri is a former user of the GainSeeker Suite. She comes to this after having lived with the data shuffle and found a different way of life. Actually some years ago I wrote up a case study about the experience of an unnamed Master Black Belt (who I just ‘outed’) at a financial services firm. Here is a link to read the case study, Building a Six Sigma Measurement System in Financial Services. At the end of the case study is a section on Lessons Learned, and the first lesson addressed this very point. Here is an excerpt:

Upstream manual data collection – According to the MBB who led the cycle-time-reduction initiative, the initial effort of capturing data manually first paid huge dividends as the deployment progressed. By engaging in manual data collection, the MBB was able to gain valuable insight into the nuances of the various operational definitions used by the process owners, and in the way the information system supported or did not support those definitions.

While an automated system has proved invaluable for collecting and analyzing massive amounts of transactional data, it is essential to develop an intimate, hands-on relationship with data in order to understand the system that produced it. This principle applies to any initiative or project that is focused on deriving long-term, leveraged benefit from an automated measurement system.

This lesson was reinforced later when the MBB implemented a similar measurement system in another part of the business. In this second application, she believed she knew enough about the system to go straight to automated data collection, but she discovered that there was no shortcut to forming a thorough understanding of the data by collecting it manually first. The second application took far longer to deploy, with many more false starts before realizing success.

Does this sound familiar to you? Use the ShareThis button below to mark this page, or leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I love this post from Thomas Wailgum at CIO.COM. His title says it all: “Wake Up People! Forget Twitter and iPhone Apps, and Focus on SAP and ERP Apps.”

I’ve been on Twitter now for several months and I confess that I find it addicting. I still can’t decide if it is irrelevant or useful.

Wailgum argues that:

They are nothing more than a costly distraction, stealing your attention from the massive problems that you, your company and the business world now face: We’re in a deep recession (perhaps a depression), and your company’s core IT systems are going to be called on to do more and more (with less and less).

If you’re a Twitter fan, you’re likely to bristle at this argument.

I’ve certainly had fun following people on Twitter, and I’ve even made some connections and learned some things that I wouldn’t otherwise know. But it doesn’t drive my business. In fact, I think Wailgum hit the nail on the head: Business enterprise software will drive value in your business, not Twitter or Facebook.

Recently we sponsored a research study by the Aberdeen Group.  The report looks at specific practices and technologies that manufacturers have in place, and look at the productivity and profitability of those businesses. Here is a key finding from this research that supports Wailgum’s argument:

This is the first benchmark produced by the manufacturing practice (Aberdeen Group Research analysts) showing a direct correlation between Best-in-Class operational performance across On Time Delivery, OEE and Yield metrics that enables significantly higher profitability. In fact, the Best-in-Class enjoy over 33% higher operating margins than both Industry Average and Laggards.

So what is it that Best-in-Class performers do that generates a 33% higher margin than Average or Laggards? What behaviors drive this kind of performance benefit?

A glance at the top four or five high impact differentiators between Average and Best-in-Class performers reveal a common theme: real-time data. Here are the top differentiators of Best-in-Class performance in manufacturing companies:

• Continuous Improvement Teams leverage analytics and real-time visibility into operations
• Production release and control leverages real-time data
• Production optimization uses real-time data from production processes and responds to process deviations
• Plant floor exceptions are monitored in real-time

Twitter isn’t on the list. Neither is Facebook.

Nothing on this list is very sexy or even new. Nothing is based on derivatives. It is all the fundamental block and tackle stuff that actually makes a difference to the bottom line. A 33% difference in the bottom line.

If you want to read more, please download the Event Driven Manufacturing Intelligence Report and my companion white paper, “The Role of Real-Time Data in Improving Profitability and Customer Satisfaction.”

Then tell me whether you think Twitter and Facebook are irrelevant. You can leave a comment, tweet me (ironic isn’t it?), schedule a conversation, or call 800-958-2709.

Now I need to tweet about this new post!

Several years ago I started practicing what I preach, at least when it comes to making better use of data in my business. What did I do that was so radical? I started using control charts to track my key business metrics. Imagine that!

We set up a simple data entry process that my controller uses. It takes him a few seconds once a month to key in a few numbers at month end, and again every other week after cutting payroll and payables. And there is another set of numbers that we automatically extract from our call center system.

My key metrics are around revenue from a couple of sources, expenses in a couple of key, controllable categories, cash, profitability, and the number of open support calls. It isn’t perfect, but it gives me a view into the business that I would not want to live without.

Sample KPI Desktop from GainSeeker Suite

I really like the control chart format. It is such a knowledge-rich way to look at data. I know there are people who claim they can look a column of numbers and understand them. When I do that, my brain goes numb. But I find the graphic representation of data on a run chart very easy to follow. In a glance I can see the history and any statistically significant shifts in the process. I can also group data by time period so it is easy to compare quarter to quarter, or year to year.

I implemented this long before we introduced a dashboard module for the GainSeeker Suite.  I’ll get in trouble for admitting that in spite of all those cool dashboards, I still prefer the control charts.

How do you look at KPIs? What are the KPIs that matter in your job and your business? Comment, tweet me, schedule a conversation, or call us at 800-958-2709.