The Data Heads

I’m tickled that The Elkhart Truth picked up our story about our tree planting project today. With Arbor Day coming up this Friday they devoted part of a page to local efforts to support trees, and our story was the lead.

When I announced the tree project in this space, I mentioned that I would come back later and share another baby step we’re taking to reduce our carbon footprint. Compared to all those trees, this one seemed small – almost trivial. But then I ran the numbers and it turns out that this one small step cut our electrical consumption by 16%. All of a sudden it didn’t seem so trivial after all.

Back in early February I started writing down the reading on our electrical meter every morning when I got to work. I subtracted the prior day’s reading to get the amount (in Kilowatt Hours) that we used the prior day.  If I missed a day (or the weekend), I just took the difference and divided it by the number of days to get the average rate for the period.

After about a month I pasted the data into GainSeeker Suite SPC Software and came up with this chart:

I also calculated the average daily cost and sent out this memo to my staff:

Here is the challenge: Lets pay attention to your own personal habits and see what we can do to chip away at our electrical consumption. Here are a couple of things you can do:

• Turn off monitors and desktop pcs on evenings and weekends.
• Minimize use of space heaters.
• Unplug any chargers or DC devices that aren’t actually charging anything. (An AC/DC charger consumes power if it is plugged in and not charging anything.)

You may have other ideas. Please try them out.

There are certain computers (like our servers) that need to be up all the time. But lets see what we can do if we try these minimal steps.

I’ll take the difference between now and sometime towards the end of April and buy lunch with the money we don’t give to the utility company. If it is a dollar a day it will be pizza. If it is more, well it will be nicer.

Then we continued monitoring the meter every day. Here are the before and after results on one chart. The Anchor Point (the vertical red line in the center) marks the day the email went out, and the shift in our process.

According to GainSeeker stats, the average daily consumption dropped from 1.9KWH to 1.6KWH. This is a 16% reduction – achieved by something as simple as turning off equipment that isn’t being used!

We had a nice party!

Why is this important? Here are some statistics about my home state, Indiana, of which I am less than proud:

• Indiana produces about 95% of its electricity in coal-burning generating plants.
• Indiana ranks 5th in the nation as the largest producer of carbon dioxide air emissions from electric power plants in the United States (122,094,588 metric tons).
• Indiana ranks 3rd in the nation in terms of the number of metric tons of sulfur dioxide air emissions (responsible for fine particle pollution and acid rain).
• Indiana ranks 4th in terms of the number of metric tons of toxic nitrogen oxides emitted into the atmosphere (responsible for acid rain and smog).
• Indiana ranks 1st in the nation for the amount of carbon dioxide emissions per person from all Indiana energy sectors.

All of this reminds me of the quote that was attributed to Pogo, the famous possum: “We have met the enemy, and he is us.”

So what are you waiting on? Turn off or pull the plug on stuff you’re not using. Its a great place to start.

And while I’m thinking of it, maybe Electrical Consumption needs to be on our list of KPIs (Key Performance Indicators).

What are you doing to reduce your carbon footprint? You can 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.

Several years ago I started practicing what I preach, at least when it comes to making better use of data in my business. What did I do that was so radical? I started using control charts to track my key business metrics. Imagine that!

We set up a simple data entry process that my controller uses. It takes him a few seconds once a month to key in a few numbers at month end, and again every other week after cutting payroll and payables. And there is another set of numbers that we automatically extract from our call center system.

My key metrics are around revenue from a couple of sources, expenses in a couple of key, controllable categories, cash, profitability, and the number of open support calls. It isn’t perfect, but it gives me a view into the business that I would not want to live without.

Sample KPI Desktop from GainSeeker Suite

I really like the control chart format. It is such a knowledge-rich way to look at data. I know there are people who claim they can look a column of numbers and understand them. When I do that, my brain goes numb. But I find the graphic representation of data on a run chart very easy to follow. In a glance I can see the history and any statistically significant shifts in the process. I can also group data by time period so it is easy to compare quarter to quarter, or year to year.

I implemented this long before we introduced a dashboard module for the GainSeeker Suite.  I’ll get in trouble for admitting that in spite of all those cool dashboards, I still prefer the control charts.

How do you look at KPIs? What are the KPIs that matter in your job and your business? Comment, tweet me, schedule a conversation, or call us at 800-958-2709.

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.

If you’re just tuning in, this post is part of a series aimed to summarize Overall Equipment Effectiveness (OEE) in clear, unambiguous language. This series started a few days ago with comments about a feature story in Industry Week magazine about OEE.

We’ve been working our way across the formula for OEE:

OEE = Availability * Performance * Quality

In this post we’re finally ready to define Quality.

Quality

As with Availability and Performance, Quality is a ratio, expressed as a percentage.

Quality = Number of Good Pieces / Total Pieces Produced

We know the total number of pieces produced from the examples in the prior posts. We just need to count up the number of defective pieces and calculate the number of good pieces.

Quality = Good Pieces / Total Pieces Produced

Quality = 1109 / 1187

Quality = .9342 (Multiply by 100 to express in percentage = 93.4%

The number of good pieces should include only those pieces that went through the process without rework or adjusting. In other words, Quality should be a measure of First Pass Yield.

How do you calculate Quality at your facility?

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

In my last post I began digging into the details of calculating OEE (Overall Equipment Effectiveness). The formula for calculating OEE is straightforward:

OEE = Availability * Performance * Quality

But each of these key components relies on some standard operating definitions. In the last post we looked behind the scenes at Availability. This post dives into the definition of Performance.

Performance

As with Availability, Performance is a ratio, expressed as a percentage. Unlike Availability, Performance can be calculated based on either the Ideal Cycle Time or the Ideal Run Rate.

Here are the components of Performance:

Performance = Ideal Cycle Time / (Operating Time / Total Units Produced)

Ideal Cycle Time is the optimal or expected time that you can expect to achieve with your process. Some people call this the Theoretical Cycle Time.

You can also flip this calculation upside down if you know the Ideal Run Rate instead of the Cycle Time. (Run Rate is the reciprocal or inverse of Cycle Time.)

Performance = (Total Pieces/ Operating Time) / Ideal Run Rate

Since the Ideal Run Rate and Ideal Cycle Time are theoretical values, and they may be inaccurate, Performance is always capped at 100%. That way if you underestimate the ideal, it will have a limited impact on OEE.

In the example in the last post we calculated Operating Time as 343 minutes. This was based on the following data:

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

Actual Operating Time = 338 Minutes (Total time in shift [480 Minutes] – scheduled breaks [30 Minutes] – machine setup [95 minutes] – Other down time [17 minutes]).

With just two more pieces of information, we can calculate Performance:

Performance = (Total Pieces/ Operating Time) / Ideal Run Rate

Performance = (1187 / 338) / 4

Performance = .8787 (Multiply by 100 to express in percent = 87.9%)

In some situations it may be more convenient to calculate the Performance based on Ideal Cycle Time. For example, we could use this same example and express the ideal cycle time as one unit produced every 15 seconds.

Performance = Ideal Cycle Time / (Operating Time / Total Units Produced)

ideal cycle time = 15 seconds = 0.25 minutes (we need to convert to the same unit of time measurement)

Performance = 0.25 / (338 / 1187 )

Performance = .8779 (Multiply by 100 to express in percent  = 87.8%)

How do you calculate Performance at your facility?

In the next post we’ll look at what goes into calculating Quality. In the meantime comment, schedule a conversation, or call us at 800-958-2709.

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.

We’ve been getting some good press recently for some work we’ve been doing with a well know foods company. This project implemented a real-time Overall Equipment Effectiveness (OEE) dashboard so they could collect and report on Key Process Indicators (KPIs).

As the project manager states, “The Hertzler System’s color-coded and real-time display of production line performance has given our operators a heightened sense of ownership over the plant’s performance. Rather than analyze performance reports the next day, employees can act on real-time data from the performance boards, leading to faster issue resolution and better overall performance. Furthermore, this system will allow us to maintain a historical record of our performance, which will guide our long-term improvement efforts.”

This project actually began several years ago when we began collecting package weight data from check weighers on the production line. The company used this data to help reduce overpack and save money.

With that success under our belts, they asked us to help them collect downtime and other data associated with OEE, and to display these Key Process Indicators (KPIs) automatically on flat panel displays on the factory floor.

Corporate had mandated that they get this information out to the workforce, and they were manually updating white boards with markers at the end of each shift. It was a time consuming, error-ridden process.

Here is a picture we took from inside the plant showing the shop floor data collection station, with the large flat panel display suspended from the ceiling.

And here is a screen capture of the OEE Dashboard display. This was custom-developed for this customer and combines data from a variety of sources. The column labeled Downtime Reason scrolls to show all of the reasons for downtime during a particular hour.

Research shows that real-time data is one of the key strategies that differentiates Best-in-Class performers. You can read more about that research in this Aberdeen Report on Event Driven Manufacturing Intelligence and in our accompanying white paper on the The Role of Real-Time Data in Improving Profitability and Customer Satisfaction.

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.