The Data Heads

Last week a colleague sent me a link to a new white paper that you should take a look at. It is published by SAP and titled “Reaping the benefits of next generation dashboards.” You can download your own copy from The Dashboard Spy.

The white paper describes the problems it sees with current dashboard and business intelligence solutions (they are inflexible and too cumbersome to use). And it offers a punch list of features for what it describes as the “Next Generation Dashboard”. Here is the list:

Next Generation Dashboards must:

• Be easy to build and customize
• Provide a consolidated view from any data source
• Leverage visualization to make information easy to consume
• Offer engaging interactivity for further analysis
• Provide the information in a personalized and easy to understand format
• Allow developers to extend new features or integrate to new technology

The white paper concludes with a list of the benefits users can expect to see from these next generation dashboards.

As I read the report I wondered how this vision of the Next Generation Dashboard matched our vision of the data driven organization as defined by the Data Cost / Value Matrix. (If you haven’t already taken the Free On-line Gap Analysis you might want to do that before you read more. It only takes a few minutes.)

The Data Cost / Value Matrix  identifies four aspects of Data Cost and four aspects of Data Value. You can read more about this at the background page.

Let’s take the four aspects of Data Costs and see how the white paper approaches them:

Complete: We collect all the data we need, and no more than is necessary.

The white paper seems to begin with the assumption that we have all the data that we need, and that all data are good, reliable, and necessary.

My experience is that most organizations are smothered in data. Typically it is the wrong data. All too often organizations focus their attention on the data they CAN get, and do not spend enough energy on the data they SHOULD get.

When we make the wrong data more actionable we have gained nothing.

I think the Six Sigma Master Black Belt described in this case study from a financial services firm was right on track when she engaged in manual data collection first because she “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.”

This white paper overlooks this issue.

Automated: We write down very little data. In fact, we type very little data into computer systems. We use bar codes, RFID or other identification technologies. We capture data from digital equipment whenever possible. Wherever possible we have eliminated human interaction with data collection, and we are confident through data driven statistically valid measurement system analysis that the data are reliable.

The white paper is very strong on the first part of this because of its emphasis on the integration and interconnectivity of data systems. At the same time, it seems unaware of Measurement Systems Analysis and the contribution it should make to this process. It may be the MSA is too technical and therefore beyond the scope of this kind of white paper. However, the world envisioned by the white paper - where everything is fully automated - overlooks the premise that we need to be thoughtful about our data.

One of my favorite business quotes is by Peter Drucker: “Nothing is worse than making more efficient what should not be done at all.”

Integrated: We have specialized data systems to run various aspects of our business, but we don’t have silos of data that are used for only one purpose when the data can be useful to other applications. Put another way, data is never entered more than one time anywhere in our business.

This is one area where the vision of the next generation dashboard is in close alignment with the Cost / Value Matrix.

Accessible: Anyone can get to the data they need at any time. We don’t have to rely on specialists to write special queries or export data. We’ve learned that our people do not need to be programmers to make good use of data.

Clearly the white paper is aligned with this aspect of reducing the cost of data. This comes up several times in the article, as in this quote: “the next generation of dashboards empower non-IT professionals to design and connect business data to a dashboard interface.”

So the white paper endorses integration, accessibility, and automation. It seems to fall short on the issues of completeness and data reliability.

Lets turn out attention to the four aspects of Data Value.

Product Release & Control: We use data to validate that our products are acceptable for shipment. This data is primarily accept/reject type data, and may be based on either measurements or some other kind of pass/fail criteria. The pass/fail criteria is based on the voice of the customer.

The white paper jumps on this with both feet. Under the heading “Leverage visualization to make information easy to consume” it suggests a product release and control strategy as one of the fundamental ways users should consume information: “In addition to robust data visualization, next generation dashboards provide methods to visually alert a user when performance indicators are out of tolerance, then enable the exploration of details with point and click simplicity.”

This is a great example of a Product Release & Control approach to the world: Test a result against the tolerance ( tolerance = specifications = Voice of Customer) and alert the user when something fails. Clearly this is a huge advantage to companies to get this kind of information - especially if it is provided in real time so that prompt corrective action can be implemented.

Process Control: We apply statistical process control tests to key products and processes. These activities use the Voice of the Process to determine the stability of our process. We react immediately to instability and unexpected variation.

On the issue of Process Control, the white paper falls completely silent. There is no indication that the authors understand this critical point of delivering value with data.

It may be that this is an oversight. More likely it is a point of value that is not appreciated by the authors. Making this a point of value assumes that the user understands the difference between Voice of Process and Voice of Customer. In my experience this distinction is not commonly understood. Even people who have been through Six Sigma training or who are certified quality engineers sometimes confuse the issue. We readily understand “outside the specs”. We’re far less likely to embrace or distinguish the more abstract “out of control”.

This is an important short-coming in this vision of the Next Generation Dashboard.

Continuous Process Improvement: We use data to close the loop on our processes and drive continuous improvement. All of our people are trained to use this data to look for hidden sources of variation and correlation between key input and key output variables.

According to the white paper, the fundamental benefit of implementing Next Generation Dashboards is to improve processes:

Notably, C-level executives use business intelligence to improve processes, ensure compliance, optimize marketing efforts, and increase sales. And department managers can use the information to improve their operations and monitor the performance of their groups.

In another section of the white paper, the authors note that Next Generation Dashboards should “enable the exploration of details with point and click simplicity.”

Clearly these are signs of commitment to continuous process improvement. I wonder, however, if the proliferation of dashboard tools will be matched with a similar effort to make sure people use the data in meaningful ways.

My mother spent her career teaching fourth grade. I’m old enough to remember the alarm bells she rang when pocket calculators were first introduced: “But will these kids actually understand the answers they’re coming up with? Or will they just get wrong answers faster and assume that they’re right because a computer spat it out at them?”  The older I get, the more I see what she warned against. This situation is a grown up version of the same problem.

Data Visibility & Transparency: Our data is readily visible at all levels of the organization. Every stakeholder, from process owners to the leadership team, can put their fingers on the performance data that matters to them. Information is summarized in easy-to-understand dashboards that help them separate signal from noise so they don’t react to the wrong things. Furthermore, they can readily get to the underlying data to better understand the drivers of their key metrics.

This is actually a pretty good summary to the Next Generation Dashboard White Paper. Clearly the authors “get” this vision.

In summary, the white paper is in alignment on many aspects of the Data Cost / Value Matrix. There are a few points where it falls short. Most notably if falls short in its vision of the importance (or the real cost) of complete and accurate data, and the value it places on the Voice of Process.

The fundamental assumption of this white paper is that business processes can be characterized by data. If we set aside the (very important) questions about the reliability and repeatability of data for just a minute, the question that comes to the forefront is “what theory shall we apply to the data that characterizes this business process?” Or, in the words of Dr. Deming, “By what method?” shall we reach our goals?

If we fail to attend to the Voice of the Process, our efforts will certainly be suboptimized. GainSeeker Suite and GainSeeker’s Enterprise Dashboard implement all of the requirements outlined by the white paper for the Next Generation Dashboard and they make it very easy to pay attention to the voice of the process.

What do you think? How important is the Voice of the Process in your dashboard? What are you doing today to build dashboards for your business?  Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

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.

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.

Since writing the series of posts about calculating OEE, I’ve been thinking about how you can best use OEE data to drive improvements in throughput and profitability.

Recently I showed this OEE Dashboard to a customer.

He said to me,

“Evan, this is exactly what I’m looking for. Today we’re collecting OEE data on paper and in spreadsheets. It is so cumbersome because we have to compile and massage the data, and by the time we actually see a problem it is too late to do much about it. If we could display this on the plant floor and people could react right away, it would make a huge difference.”

I have no doubt that he is absolutely right: making the data visible in an understandable form will make a huge difference.

Here is the “Yes, And…”

Yes, making the data visible has a positive effect. And with the right tools you can convert that data into knowledge about your organization so your people can focus their efforts on the right things.

Here is a new video I’ve just posted on YouTube that walks you through how you can drill into OEE data for more knowledge.

What do you think? Would being able to visualize and drill into OEE data like this be useful at your plant? How would you use this?

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

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.

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.

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.

This morning I opened my weekly update from LinkedIn and saw the following note: Jay Bronec is working on automating QualiFine’s KPIs by integrating our CRM and Web data using GainSeeker.

I fired off an email and asked Jay to fill me in. He called my a few minutes later from his car. In the middle of the conversation I turned on the tape recorder so you could hear Jay explain how he is automating KPIs using GainSeeker SPC Software.

Jay’s company, QualiFine, is the largest independent Minitab training program in the country. He is also an independent regional representative for our GainSeeker Suite. QualiFine is aligned with Hertzler Systems because Jay wants to help his customers reduce the cost and improve the value of the data they have in their organization.

In this podcast Jay describes his “A-Ha” moment when he realized that he is no different than his customers: his current data collection system is unsustainable and unable to help him make good business decisions.

The other thing that you’ll hear is a connection to Mike Webb’s vision of sales process improvement. Mike has a blog called Six Sigma Selling, and in it he draws the parallels between selling and manufacturing processes. For example, if you provide sales people with better raw materials you’ll have a better close rate. If Jay is successful with his KPIs he’ll learn a lot about his ‘raw materials’.

For the last couple weeks we’ve been working on an OEE (Overall Equipment Effectiveness) project for a new client. When I sat down over lunch today with our CTO and Alpha Geek, Byron Shetler, he explained that the biggest issue - as usual - is getting the data. “This company doesn’t have an OPC/PLC network in place yet, so everything we did last week was around manual data entry.

“I set up a couple of GainSeeker templates to collect downtime, scrap, and efficiency, and we’re storing that as either variable or defect data every hour on every machine.” Byron pointed out that once this customer puts his OPC factory network in place we’ll automate the data collection piece.

By the end of the day Friday (after less than a week on site) Byron had a couple of dashboards in place. He couldn’t bring back any screen caps of their dashboard, but it sounds a lot like what you’ll see on the Dashboard page on our website.

GainSeeker Enterprise Dashboard - OEE Example

Byron reported that the customer was pretty happy that we got in and out so quickly. But more important, he’s excited about getting an hourly update on OEE, Quality, Downtime and Efficiency, and being able drill into any of those metrics by machine or operator or downtime reason, or scrap reason.

Byron reported: “This customer is pretty sharp, and he was practically rubbing his hands together in anticipation of getting his hands on that data. Being able to see the big picture and then drill into various components to understand the root cause is a huge opportunity for this company.”

In addition to linking to the OPC network, once it is in place, Byron also sees potential to feed data back from GainSeeker to the customer’s AS400 business system. “Right now the customer has a couple people who sit down and type data from paper records for scrap and production rates back into the primary business sytem.