The Data Heads

Update July 7, 2010: KPI Dashboard screen cap added below.

The Mug Hug Mug

I’m finally getting around to reporting on the Monday’s joint Minitab / Hertzler User Group meeting. Jay Bronec of QualiFine took my cheesy idea for a name seriously and gave everyone MUG HUG mugs. They’re pretty cool.

We had great presentations from a bunch of people, but the show stealer was Jamie Dobravec’s. Maybe it is because he was talking about something near and dear to my heart – KPI Dashboards. But I don’t think it was just me.

Jamie has been using GainSeeker for years, beginning back in the DOS days. As a manufacturing engineering he became accustomed to relying on GainSeeker for up-to-the minute shop floor quality data.

All these years later he is now Operations Manager and he found himself longing for timely business metrics – things like month-to-date sales performance, gross profit margin, on time deliveries and first pass yield.

His ERP system had all this information, but he couldn’t get to it.

Well, thats not quite true. He could get to it, but it meant manually running nine different queries on his AS400, and then cutting and pasting data into Excel so he could turn it into a graph. The manual system was so time consuming that he just couldn’t do it and do his real job. And like everyone else, he is operating lean – so there is nobody he can throw the work at and say “I need this report every day at 7am.” So in reality, he couldn’t get to it.

He also considered traditional BI and ERP reporting solutions, and found they had lots of zeros in the price tag. Given the cash crunch of the recession, he couldn’t justify the expense.

Enter Jay Bronec of QualiFine.

Jay’s a smart and savvy guy, and he knows about as much as anybody on our technical staff. And Jamie was already using GainSeeker in his business operations, so he didn’t need to spend any capital. He didn’t need any extra software licenses.

Jamie sat down and sketched out what he wanted. He said it wasn’t on a napkin, but as close as you can get without lunch. And Jay went to work.

Now with a couple of clicks, Jamie gets an up-to-the-minute dashboard of his critical business performance metrics (KPIs). He can drill into the raw data and see trend charts or Pareto charts behind the dashboards. Its all there.

Grayhill KPI Dashboard

I wrote up a short account of the case study and published it on our website. Jay has one with more quotes from Jamie, and a little more detail on his web site.

What about you? Would a dashboard of your organization’s Key Performance Indicators help you make better, more timely business decisions? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

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

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

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

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

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

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

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

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

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

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.

Dr. Neil Polhemus, CTO at StatPoint Technologies (and publisher of StatGraphics) contributed a great article in the current issue of Quality Magazine about selecting statistical software for Six Sigma. In it he lists four criteria for selecting the right statistical package:

1. How strong a background in statistics does the typical operator have?
2. What types of data are operators most likely to encounter?
3. If data are mined for information, how easily can multiple approaches with multiple options be tried?
4. How easy is it to create a report or presentation that can be shared with other colleagues?

I’ll answer each of these questions for GainSeeker Suite before the end of this post, but first I want to surface an unspoken assumption in the article and add a couple of criteria that I think Dr. Polhemus missed in his list.

The unspoken assumption is that one statistical package will serve all the needs of a Six Sigma deployment. My experience is that there are at least two broad categories of statistical software, and each has their place in Six Sigma.

Two Categories of Statistical Software

One category of statistical software is Advanced Statistical Analysis tools. Dr. Polhemus’ article outlines criteria for this group. Products in this category include StatGraphics, Minitab, JMP and others. These systems were developed (originally) for statisticians. Often Black Belts (BBs) and Master Black Belts (MBBs) depend on these packages for in-depth work in the Analysis phase. These systems are less useful for selecting projects. For the most part they operate poorly in the Control Phase. Put another way, these tools are of less use to the Champions and Business leaders who charter projects, and also of less use to Green Belts (GBs) and Process Owners who inherit and live with a project when it is completed.

The other category of statistical software is what I call Real-time Enterprise SPC Solutions. It will come as no surprise that GainSeeker Suite falls into this second group. This category comes out of the Real-Time Statistical Process Control (SPC) world. These packages are designed for ongoing data collection and analysis in a continuous improvement (kaizen) environment. These tools are, first and foremost, a tool for process owners and Green Belts. They are also tools for Champions and Sponsors (business leaders) who are chartering projects and driving business performance.

While there is some overlap between the two categories, they are more complimentary than competitive. In fact, they should readily share data. Data should be especially portable from a Real-time Enterprise SPC solution to the Advanced Statistics Solution. That way BBs and MBBs can readily tap into the enterprise data sources to support their efforts.

So here are the additional criteria that you should look for when you’re selecting a Real-time Enterprise SPC System.

Criteria 5: What does it take to get new data into the system?

Advanced Statistical Analysis Packages begin with an assumption that data are in a file, in rows and columns. In this view, data are static: generated once, analyzed in some way and then saved in a folder somewhere. Real-Time Enterprise SPC Packages assume that we are tapping into a live stream of data. Each new data point contributes to our understanding of the process. (Some of the Advanced Stats Packages are beginning to recognize this, but their core competency is in analyzing a static data set.)

The ability to readily incorporate new data is what makes Real-time Enterprise software so effective in the Control Phase. Users set up automatic data collection once and then monitor the results for exceptions.

Keep in mind too that the system should collect data at all levels of the organization. Good systems will make it easy to collect and manage data from the shop floor to the executive suite. This makes it easy to capture high levels of business metrics which can be used to help prioritize projects.

When selecting a statistical package, be sure to ask:

• Can the system tap into any data source, including front-line process owners, gages, a wide variety of text files and databases, PLCs, PDAs, cell phones, and so forth?
• Can the data entry process be controlled so that only valid data can be entered into the system, in a reliable and repeatable way?
• Can data be collected automatically and without human intervention?
• Is it easy to set up and manage these data collection processes to meet all the various needs across my business?

Criteria 6: Does the system automatically test new data for real-time process shifts?

Real-time doesn’t just refer to the process of connecting to data sources and readily incorporating new data. It also refers to statistically evaluating all new data for expected variation. This is an essential tool for understanding processes. If the system does detect a change or shift, it needs to automatically communicate that to people and systems that can do something about it.

When selecting a statistical package, be sure to ask:

• Does the system automatically detect process changes using appropriate statistical tools?
• Does the system automatically let me know there is a shift through email, pagers, on-screen displays, or other appropriate means?

Criteria 7: Are Data Stored in a Robust Relational Database?

The word “Enterprise” in our category name (Real-time Enterprise SPC Solutions) tells us that we’re not looking for a point solution. There are some fine packages out there that do SPC with Excel spreadsheets. But these programs can create a data management nightmare when you are managing all the data in your business (not to mention the risk of defects being introduced in a spreadsheet environment).

An enterprise system builds a data warehouse in a relational database. This makes it possible to tap into a rich data set for selecting and prioritizing new projects. It also makes it easier to share data (and best practices) across the organization.

When selecting a statistical package, be sure to ask:

• Are data stored in a robust relational database structure with a flexible hierarchy?
• How fast are retrievals on large data sets?
• How easy is it to group or segment data?

Criteria 8: How Easy is it to Slice and Dice the Data?

A good Real-time Enterprise SPC System will collect data at multiple levels of the organization. It shouldn’t be confined to the down and dirty shopfloor data.

By capturing this data – along with information about the data – think of it as demographic information – you can slice and dice the data to find opportunities to improve the system.

At high levels it might mean viewing Overall Equipment Effectiveness (OEE) by Line, and then drilling down into various machines or sliced across all shifts. In a transactional environment it might mean tracking cycle times across all offices, or within offices by customer service rep. Being able to easily slice and dice the data makes it easier to understand the relationship of all the parts.

When selecting a statistical package, be sure to ask:

• How easy is it to drill into various subsets of the data?
• Are automatic analysis wizards available to help prioritize and focus your attention on the critical variables?
• Can you data be rolled up into dashboards and other high level summary views for easy monitoring?
• Can data be easily tagged with demographic information?

These additional four criteria are a good starting point for rounding out your tool box of statistical software for Six Sigma.

Additional information

For more information, check out these white papers and case studies:

• Freeing Six Sigma from the ‘Data Shuffle’
• Data grouping and drill down
• Walking the talk – How Micropump built a comprehensive data system to empower their people with data
• Aberdeen Lean Six Sigma Benchmark Report
• The Role of Real-Time Data in Improving Profitability and Customer Satisfaction
• Building a Six Sigma Measurement System in Financial Services

How GainSeeker performs against Dr. Polhemus’ criteria

I promised at the start of this post that I’d address how GainSeeker Suite performs against Dr. Polhemus’ criteria.

1. How strong a background in statistics does the typical operator have?

GainSeeker Suite serves a wide population of users, and assumes that the typical user has little or no background in statistics. Furthermore the system assumes that the user has many other tasks to perform besides statistical analysis.

2. What types of data are operators most likely to encounter?

GainSeeker Suite is targeted for engineering and manufacturing applications. The product is in use in some purely transactional environments too. The system isn’t particularly robust for managing survey data, but does very well with cycle times and defect/error tracking. The system is not designed for the R&D community.

3. If data are mined for information, how easily can multiple approaches with multiple options be tried?

GainSeeker is an interactive system. Users do not need to write programs that are submitted for execution. Having said that, it is not intended to function as an advanced statistical tool. Instead it readily ports data to other software systems including advanced statistics packages. Of course Gainseeker is an excellent tool for automatically updating databases and analysis.

4. How easy is it to create a report or presentation that can be shared with other colleagues?

GainSeeker pays particular attention to sharing data, analysis and reports with various user communities. Users can access data over the web, including mobile devices, and output can readily be delivered by email. In addition, GainSeeker includes a new Enterprise Dashboard module that provides easy-to-understand role-based summary knowledge.

What do you think? Are there other Critical to Quality Characteristics that we haven’t mentioned?

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.

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.