The Data Heads

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.

A couple years ago we sponsored a research report by the Aberdeen Group on best practices in Six Sigma deployments. You can download a copy of the report, and a companion white paper that I wrote for the report called “Leveraging Technology to Transform Culture.” For me the most astonishing thing about that 2006 report was the disconnect between the challenges people say they face in their Six Sigma deployments and their responses to those challenges.

Most of the challenges people face are cultural:

Most people respond to those challenges directly by doing a checklist of initiatives: train employees, introduce change gradually, assign senior management as champions, engage outside consultants, steal talent from the competition, and so forth.

This frontal assault has been going on for years and it isn’t working. That’s the forehead smacking part of this.

Years ago I realized that making data more visible and accessible changed the way people look at themselves, the people around them, and the problems they face. Somehow just making the data visible takes away the personality and political dimensions - the cultural barriers - and helps people focus on solving problems.

Making the data very accessible - visually on the screen in a control chart or a dashboard - and making it available in real-time is a huge benefit. It breaks down all kinds of barriers.

My customer, Royce Binion, then Director of Operations at BAE Systems Controls in Fort Wayne, put it most succinctly when he said to me years ago “Real-time access to accurate, actionable data is the number one tool that has enabled us to move to a data driven culture.” This was way back in 2000 when his plant won the Industry Week’s 10 Best Plants award, and a few years before they would win the Shingo Prize.

This came up again for me this week when I attended a webinar hosted by the Aberdeen Group. They’re doing follow up research to see what has changed in the last 2 1/2 years, and they wanted to share their preliminary findings on best practices in Lean Six Sigma deployments. (If you’d like to participate in the study, you have until April 30. You get a free copy of the report if you do.)

What struck me as I heard this briefing is how little has changed. Cultural challenges are still at the top of the list, followed closely by IT and technology challenges. People still don’t seem to be connecting the dots.

Today I wrote to Cindy Jutras, the author of this research, to get her take on it. She wrote back:

in spite of all the data and IT related challenges from the previous slide (about the challenges people face in Lean Six Sigma deployments), there was not an appropriate response to those challenges. I agree that visibility is key. And our results support that. In general we found those with True Six Sigma have 110% better visibility than Industry Average and 580% better visibility than Laggards. Not surprisingly, they deploy far more IT tools than those not performing as well.

How about you? Are you using real-time access to accurate, actionable data to transform your culture? You can leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

Cool - Quality Magazine saw my post yesterday on Selecting Statistical Software for Six Sigma and reprinted it on their blog. From their home page you can find it by clicking on the Blogs link (left hand column) and then following the link to Quality Remix, or this link will take you directly there.

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?

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

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

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

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

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

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

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

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

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

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

DMAIC as a Linear Process

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

DMAIC as a Cyclical Process

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

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

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

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

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

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’.

Recently a colleague forwarded this email from a friend of his and asked me for my comments:

Please let me know what lean/6sigma book you would recommend. I’ve read a couple over the years but they are a bit dull and won’t fire up our management - is there one that is simple but convincing?

You’d be surprised how hard it is to get their heads out of the sand. Part of the problem is that we are so successful and dominant in our field.

e.g. At a major meeting last week, the exec responsible for customer satisfaction presented data to show that our competitor is the leader in terms of quality and customer satisfaction. He actually said “we don’t want to copy THEM because their margin is lower than ours so their quality is costing them money”. That’s how dumb and simplistic we are.

My response:

The problem you’re facing is way too common, and I don’t think there is any pill you can give your CEO to adjust his attitude. This isn’t what you want to hear, but until your CEO’s hair is on fire, I don’t think there is any book that will make a difference.

If you can find some smoke somewhere above the hairline, and then tie that back to quality and customer satisfaction, you might get somewhere. Sadly there is usually a huge disconnect between the ceo/finance function and quality/performance. Here is an true example:

I’ve been working with a customer (an electronics firm) on a data collection/analysis project. A bright Six Sigma Black Belt proved a direct correlation between the statistically significant variation on a handful of test results and out-of-box failures at the customer’s site. These were units that met specifications on well over a thousand tests, but still failed out of the box. Those OOB failures were threatening the contract: the customer was ready to pull the business.

We put together a proposal using SPC software to capture the signals so engineers would know immediately when they had a unit that was statistically different than the others (even though it still passed all the tests). It was a beautiful solution and everyone was really excited about it.

To sell that proposal to upper management my contact bypassed the (to me) obvious argument that identifing statistically significant variation would isolate defective units, which protects the customer from receiving bad product, thereby saving the contract.

(I can hear all the MBBs in the audience saying I’m not really using statistics right, but they were doing 100% testing here so pardon me.)

Instead my contact framed the proposal around faster product release cycles and the impact that would have on inventory turns. By having real-time data as the units were produced, he argued that they would be able to release production lots from WIP to Inventory 3 or 4 hours earlier. That was an argument that got the finance guy and the CEO excited.

I tell this story because it is an example of how a creative (read politically savvy) middle manager pushed through his agenda for quality process improvement using language that any  CEO understands. No book on SS or TQM or anything else would accomplish the same thing.

So look for smoke above the hairline. Something is keeping your CEO awake at night, and unless it is his girlfriend, you can probably tie it back to quality and customer sat.

Hope that helps, and best wishes.

Regards,

Evan

Even though the CEO and CFO didn’t care, in the first 10 days of the pilot project, GainSeeker real-time SPC software trapped two defects that the test process couldn’t catch. My contact reported: “Three defects in a year is enough to knock us out of ‘Preferred Vendor’ status. Anytime I stop two defects from getting to this customer, I’m a happy man.”