The Data Heads

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?

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?

Yesterday the ASQ (American Society for Quality) issued their Quarterly Quality Report for December 2008. If you haven’t read it already, I encourage you to take a look. Based on a survey of only 47 persons, you have to wonder about how far you can take the conclusions, but I found the findings helpful.

Over 75 percent of the respondents were seeing specific responses to the economic downturn. These include reduction in force, reduction in training efforts, a reduction in budget for quality activities, and backing away from quality initiatives. This was in line with what I expected.

I was surprised, however, to see that when presented with a list of negatives, 20% of these respondents wrote in more positive responses. These include:

a noticeably increased emphasis on quality, especially in the area of preventive action; an increase in continuous improvement activities designed to provide competitive advantage; concerted efforts not to cut back, but rather improve and add programs and take a sharper focus on quality now; and also an increased desire to use quality improvement activities to reduce expenses.

This seems to be what lead the authors to conclude:

Quality practitioners say that over the past year they were more likely to have more opportunity rather than less opportunity to become involved in business development activities (such as new product development, establishing business strategy, meeting with customers, and working with sales and marketing). Perhaps this means there is some hollowing out among the quality troops - fewer people and less budget - but not necessarily a shrinking in the size or ambitiousness of their quality programs in a strategic sense. They still have big ambitions, but they’re forced to do more with less.

The survey asked how the downturn is impacting what people are paying attention to. Not surprisingly, ‘Cost Cutting’ ranked at the top and ‘Growth Through Acquisition’ ranked at the bottom. The question for me is how will cost cutting be achieved? If through downsizing, then quality staff should be concerned; if through Becoming More Efficient or Waste Reduction, then there may be hope.

The proof may be in one of the statistics presented near the end of the report. I was disappointed to see once again the gulf between the quality professionals and their leaders. Nearly 80% of the quality pros believe their profession confers a competitive advantage, while only 34% of top management agree.

With that kind of gap it’s going to be hard to connect quality efforts to top management’s interests.

The report concludes that organizations are reacting in fundamentally different ways to the economic challenges:

On the one hand are those going into crisis mode, cutting back and de-emphasizing quality initiatives. On the other hand are those that continue to invest in quality and innovation as a competitive advantage even in the face of economic uncertainty… Organizations that refuse to panic, that move ahead judiciously with new initiatives, and that don’t cut too deeply will be better positioned to excel when the economy rebounds.

So far our experience is that Hertzler Systems is connecting with this latter group. Our customers continue to see opportunities to invest in real-time data to help reduce costs, improve efficiencies, reduce waste, and incrementally improve existing processes and products. We’re especially finding that asset utilization and Overall Equipment Effectiveness (OEE) are valuable. We’ve been able to support our customer’s intent to pay more attention to these issues.

But what is your experience? How well are you aligned with your leadership? Or if you’re in leadership, how well are staff lined up with you?