The Data Heads

There’s an old adage that you need to know the rules before you break the rules. That applies to everything from playing jazz to running a business.

I got to thinking about that idea after reading Industry Week’s cover story on OEE (Overall Equipment Effectiveness). The IW story discusses the benefits of OEE, and even gives a definition of OEE:

OEE tells users the percentage of time that equipment, when running or required for production, is producing good-quality products at an acceptable rate. It is the product of three ratios, or submetrics: machine availability rate, performance or run rate, and the quality rate. It is calculated by multiplying availability rate by production rate by first-pass quality rate.

This is a good textbook definition of OEE, but it leaves some gaps. This post and others in this series will fill some of those gaps. Once we fill in the gaps in the text book definition we’ll be in a better place to consider if we want to break any of the rules.

Mr. Sanders, my 5th grade teacher, was forever having us convert a story problem into an equation.  Here is what that paragraph looks like as an equation:

OEE = Availability * Performance * Quality

Future posts will go into more detail on Performance and Quality. Today let’s look at Availability in more detail.

Availability

The formula for Availability is simple, but it relies on some operational definitions. Here are all the components for calculating Availability:

Availability = Actual Operating Time / Planned Production Time

Planned Production Time is the time any piece of equipment is scheduled for operation.

Actual Operating Time = Planned Production Time - Down Time.

Down Time is a measure of unscheduled production stops.

Down Time includes the time required for machine setup and changeover, or losses due to equipment breakdowns and material shortages.

We do not include planned shutdown time in this calculation. For example, If a machine is scheduled to run from 10am until 5pm on a specific day, then the Planned Production Time is 7 hours, or 420 minutes. (All of these times are usually measured in minutes or hours.) If a machine normally runs while an employee is at lunch, then the employee’s lunch time is included in the Planned Production Time. If the machine is scheduled to be shut down while the employee is taking a break, then the break is not included in Planned Production Time.

Down Time is usually measured in minutes.  Consider establishing a policy for a threshold for what constitutes a trackable event. For example, you may wish to track stoppages of more than 5 minutes so that you don’t bother with every little hiccup in the system.

Availability, like the other components of OEE, is reported as a percentage, so after we calculate the ratio we just multiply it by 100. Here is a typical calculation:

Planned Production Time = 450 minutes (480 time in shift less 30 minutes for lunch break)

Actual Operating Time = 338 minutes (450 minutes - 95 minutes for machine setup and 17 minutes unscheduled down time.

Availability = Actual Operating Time / Planned Production Time

Availability = 338 / 450

Availability = 0.7511 (multiply by 100 to express as a percent = 75.1% )

This is the text book definition of Availability. How do you calculate it at your facility?

In the next post we’ll look at what goes into calculating Performance. In the meantime 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.

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?

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?

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?

A recent study by the Aberdeen Group found that Best-in-Class manufacturers substantially out-perform laggards.

Duh… Of course Best-in-Class have higher yields, throughput, and profits, and are more likely to deliver product on time. Here are the stats:

What we wanted to know is why. Why do Best-in-Class performers enjoy profits that are 25% higher than Laggards? What do they do that is different? How do they get those kinds of significant differences?

The Aberdeen Report, available here, gives a good start to answering these questions.

As a Data Head, I wasn’t entirely satisfied until I had sliced and diced the data myself. Specifically I needed to see the relative importance of the various components of good performance. What I found surprised me and opened new layers of meaning to the Aberdeen Research.

Based on this add-on research, I wrote a companion White Paper that I hope you’ll download and read: “The Role of Real-Time Data in Improving Profits and Customer Satisfaction“.

Take a look at both of these reports. Then share your comments: how do these findings fit with your experience?

Are you on Twitter yet? I signed up a couple weeks ago and I can’t decide: Is this the biggest boondoggle on the face of the planet, or have we reached the promised land?

The more I dive into it, the more overwhelmed I feel and the more opportunities I see. So much to see, so many people to connect to. If you wish, please follow my twitter stream as I continue to explore this new medium.

While you’re at it, please join my new Twitter Group (in TweetWorks). Check out and join BPI-ThoughtLeaders. I hope that this will become an easy way for people who are interested in business process improvement to stay connected.

Let’s see if this stuff is worth our time and effort.

What does is mean to be data driven?

I’m now a guest blogger at MyBusinessMusings.com.

Late last week I wrote my first post about what it means to be data driven. Check it out.