The Data Heads

Last week I had a few minutes to sit down with Chris Bowman, one of our senior implementation specialists, to talk about a project that he had just completed for a foods company in upstate New York. I was really impressed with the story he told me.

If you’ve ever wondered what you can do to help your staff remember when it is time to collect data, you’ll want to keep reading.

This question about making sure that people collect data seems to be a hot topic. And it makes sense because there are two opposing forces at work. First you need data from your staff. You can’t make decisions based on data if your people don’t collect the data. But working against you (and them) is the fact that everyone is already busy. Even well-intentioned people will often only oil the squeaky wheel.

(Of course if you need data, and everyone is too busy, the best solution might be to automate the whole process. But for a variety of reasons automation may not be possible or desirable. We’re talking here about those situations.)

Chris’s solution makes sure that your data wheel squeaks. His solution is very slick and may set the new standard for how customers ensure data are collected in a timely manner.

A little background…

This customer has several checks they want to take periodically, many at different intervals. They want make that as easy as possible.  As Chris put it, “The customer is trying to make it easy to enter multiple sets of data  without having to exit this planned session and launch that planned session, or exit this planned session and launch that planned session.”

And it gets even more interesting. “They’re going to enter the same data over and over again,” Chris said. “But periodically they need to change traceability values, change products and what not, I don’t want to ask them every single time.

At the core of the solution is what Chris calls the “Control Center.” Here is a picture of it:

So what is going on here?

The most important thing is in the left hand window, the one with the green and red blocks. A red block means that a check is overdue. This window is automatically refreshed every few minutes. A green block means that check isn’t due yet. In this example, the Weight, Process and Line checks are all overdue.

Once one of the checks goes red, all the user has to do is click on the Next Action button (in the upper right corner) and select that check from a list. The Control Center launches the check, passing along all the traceability information along.

Of course one of the Next Actions the user can select is to change traceability, or to select a new product.

This customer also wanted to display live control charts for the two most important checks at the same time. They could display any GainSeeker chart or dashboard they wanted, and of course they could display any number of charts on the screen too.

What do you think? Would your organization benefit from the Control Center idea? Would you like more technical information? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

One thing I like about blogging is that you can call something by its real name. Last month my business partner and our Alpha Geek, Byron Shetler, attended the OPC Foundation’s Interoperability Conference 2010. When we refer to it in our press release, that’s what we call it.

But in this blog I can call it what it really was: a Geek Fest.

What is a Geek Fest, you ask? Maybe a picture is worth a thousand words:

If you look carefully, you’ll see Byron all the way down on the right side of the table. He is the guy leaning forward in a white polo shirt.

Click on the picture to open it in another window. Then click on it again to blow it up to full size.

What I love about this picture is the story that the tangle of network cables and power strips and half-empty coffee cups tells. Two tables. Maybe 45 people. Ear buds jammed in ears cranking out tunes. (Bonus point if you can name who that is. Double bonus if you can guess the tune.) Hands on foreheads in deep thought. All crammed in a conference room with enough computing power to launch a rocket.

And just in case you were wondering – it is all guys.

So why did we go to this particular Geek Fest, and what did we accomplish?

OPC is an open connectivity standard that defines how shop floor equipment and manufacturing software communicate with each other. Think of how printer drivers make it possible for all the software on your computer to communicate with your printer. OPC does the same thing for shop floor controls and equipment.

We developed the OPC interface to GainSeeker Suite a few years ago, and wrote up a nice case study about how the GainSeeker OPC interface was so easy to deploy and saved a customer a lot of money.

At the time, we tested the interface with two of the most important vendors of OPC drivers (Kepware and Matrikon). We used them because they provide stand-alone demo systems. We needed the demo system because we can’t implement a full-fledged factory information system. After all, we don’t have a factory. And as our customers have implemented the solution we’ve verified that it works with GE Fanuc, Rockwell Automation, and several others.

Recently, the OPC Foundation has been pushing hard for vendors to test their systems at an interoperability workshop. The workshop runs for a full week. It isn’t a trivial commitment.

(As a side note, I have to mention that before the Foundation started pushing interoperability testing, several of our direct competitors were members of the OPC Foundation. Last time I checked, they had all dropped off the list. Now if you search for “SPC” on the Foundation website, we’re the only fully-featured SPC system vendor listed. Everyone else has an all-encompassing factory management system that happens to include a control chart somewhere in their product.)

These workshops bring vendors together from all around the world so that you can really test the interoperability of your system in all kinds of conditions. At the workshop, Byron was able to test the interface with software from these vendors:

• Yokogawa Electric Corp.
• Takebishi Corp.
• Software Toolbox
• Siemens AG
• OSISoft, Inc.
• MSIndustrie Software GmbH
• MatrikonOPC
• Kepware Technologies
• InduSoft LLC
• GE Intelligent Platforms
• Emerson Process Management

We weren’t surprised that the testing went smoothly. But it was great to conduct so many tests at one time. Byron says he built a database of OPC servers and tags, and then wrote a template in GainSeeker Suite that let him select a server and put it through its paces. Once he had the infrastructure set up, he could conduct a new test in a matter of minutes.

And as Byron put it: “Our testing addressed all of the functionality that we need to support our customers. It was cool to get all these companies in one room, bang on each other’s software, and prove that everything works as advertised.”

Did you get that? “It was cool to bang on each other’s software.”

That’s what I call a Geek Fest!

If you’re not looking for a Geek Fest, but you are looking for an easy way to get to process data, OPC may be the way to go. 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.

One of the really important aspects of the Data Cost / Value Matrix is how reducing the cost of data can help you get more value from data.

This came up again the other day as we were debriefing following a recent deployment for a new customer. This customer is about to undergo a major cultural transformation because we helped them automate real-time data collection and analysis. In the process, they will shift from a Product Control to a Process Control way of looking at the world. Ultimately this will reduce costs, increase customer satisfaction, and (we hope) drive new business.

This customer makes electronic devices. Their products are complex pieces of equipment, and at a couple places on the production line, the various component parts and the final assembly pass through an automated test stand. This test stand runs the unit though dozens – even hundreds of electrical tests. A unit has to pass all of those tests to be released to their customer.

Sounds like a winner, right? One hundred percent inspection. Nothing bad ever escapes to the end user. And we have the data to prove it. Boy, do we have data. Entire databases of rows and columns of numbers. Every one of them in spec.

Except it doesn’t work that way. Some units pass all the tests with flying colors – and then fail out of the box or early in the life of the product. Here is what the data for one test parameter looks like plotted on a histogram (this isn’t actually the customer’s data – it’s just representative):

In this example, everything is well within spec – it should all be good.

When you look at this data in time series on a control chart it looks very different:

Viewed this way, this data tells a completely different story. If you look closely you can see a couple of signals that the process is unstable, even though everything meets spec. In a complex environment with dozens of critical – and interconnected – variables, these unstable values often directly correlate to the early life and out of the box failures.

Learning to rely on the control chart is the shift from Product Control to Process Control. It is often a tough shift to make – especially when everything is in spec.

What makes it even harder, however, is the sheer volume of data they need to sift through to find those signals. Hundreds of test parameters, hundreds of units tested every day. Automation is the only way you can keep up with the data. By automating the data collection and analysis process you can empower people to make that cultural change.

What about you? How can reducing data costs make it easier to get more value from your data? Use the ShareThis button below to mark this page, or leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Forrest Breyfogle posted a great question over at Linked-In:

Does our corporate leadership’s relationship-building strengths make it a challenge for them to truly understand and resolve the fundamental system improvement and re-engineering needs of the financial crisis?

From examination of the myers-briggs personality types we note that not everyone thinks the same. One observation is that some people tend to be systems thinkers and others are not. Systems thinkers, according to the Myers-Briggs Type Indicator [MBTI] manual, are those persons who have NT temperament and make up only 15% of the U.S. male population (female is less). My hypothesis is that high level government and business decision makers do not tend to be system thinkers because it takes a lot of relationship building to move upward. If this is true, many of our overall decision makers can have a very difficult time approaching the economic crisis as a system issue that needs process improvement/re-engineering. What are your thoughts?

I think Breyfogle may be on to something. I’ve taken the MBTI, and I’m an NT. I realize that I think differently from a lot of people.

What I’ve learned, however, is that ready access to real-time data helps people who are not systems thinkers see connections they would otherwise miss. That’s why having a good theory of data and making data more visible and accessible is so important. That is how you turn data into knowledge.

The follow-up comments are interesting and reveal a wide range of opinions about the underlying causes (and subsequent improvements needed) of the recession.  My own opinion?

What the MBTI doesn’t address is the question of values raised by some other commentators. Is the term ‘free market’ used in the way classical economists advocated: a market free from monopoly power, business fraud, political insider dealing and special privileges for vested interests? Or is it used in the more modern sense: free for predators to exploit victims without public regulation or economic policemen?

I own a small business and I don’t favor regulation, but I’m outraged at the way the marketplace has become free to enable Ponzi schemes and other scams to proliferate.

As a data guy and a systems thinker I would like to see us make better business decisions based on systems theory and data. But it has to be exercised in a free market that is fair and equitable, and that doesn’t reward theft.

What’s your personality type? Do you agree that it affects the way you view problems?  Which type of ‘Free Market’ do you advocate? You can 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!

Ron Pereira over at LSS Academy wrote a great post about the Japanese phrase Genchi Genbutsu. He quotes Jon Miller, a Lean consultant who is also fluent in Japanese, as translating genchi genbutsu to “actual place, actual thing”.

Pereira’s post caught my attention because he opened with this quote from the father of the Toyoto Production System, Taiichi Ohno: “Data is of course important in manufacturing, but I place the greatest emphasis on facts.”

As a leader in a company that specializes in “Turning Data into Knowledge” I suppose I should feel a little threatened by this statement. On the contrary I think it is spot on.

Real-time visibility to data is a tremendously useful simply because you can’t be everywhere at once. Live OEE Dashboards, real-time control charts, and instantaneous email alarms can let you know when something has changed. And they can tell you if the change is statistically significant so that you’re not reacting in the wrong way to random variation.

But ultimately you have to get up from your desk and go to the work to see for yourself.  All these tools can help you know when and where to go, but they won’t do the work for you.

Pereira’s post includes a great story about what a Toyoto Engineer learned by going to the work.

Where are the weak links in your organization? Do your people know when they have a problem? Do they go to the work when they need to?

Please comment, schedule a conversation, or call us at 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.

Industry Week magazine did a great cover story on OEE (Overall Equipment Effectiveness) in the February Issue.  A couple of key take-aways:

• Adapt OEE to your business situation
• Use the OEE data to drill in and drive improvements
• Don’t optimize OEE at the expense of the business

These ideas point to why GainSeeker Suite is getting so much traction as a tool to deploy OEE.

First, almost every place we’ve deployed OEE has a slightly different definition of the metric. GainSeeker’s flexibility to define and calculate data makes this a piece of cake.

Second, GainSeeker Suite provides great tools for drilling into – slicing and dicing – data.

Third, OEE should be only one of your key business metrics. If you set these up properly (especially in GainSeeker with desktops and dashboards) you can see how OEE is improving and test whether it is actually impacting other critical measures.

I’d like to see more discussion on how automation can help increase the reliability and timeliness of OEE data. What we’ve found as we talk to our customers is that data reliability is a huge issue. Again, GainSeeker Suite can be an important tool for getting better data faster.

Finally, GainSeeker’s Dashboard Module can post OEE data information visually on the floor and greatly increase the visibility of the metric.

What are you doing with OEE?

Comment, schedule a conversation, or call us at 800-958-2709.

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?