The Data Heads

Over the years I’ve had many conversations with people who own or run plastics molding companies. Persistently I’ve heard them say:  “We don’t worry too much about scrap because we can always regrind it. It’s not waste.”

I’ve always found myself stuttering in response to that.

On the one hand, it seems obvious to me that any time you have to do something more than once, your costs have to go up. On the other hand, these are smart people and they know their business. I make software, not plastic.

Recently I got into one of these conversations with the owners and the CFO at a molding company. They had told me they have a 10% - 12% scrap rate, but “We don’t worry too much about scrap because we can always regrind it. It’s not waste.”

At one point the CFO grabbed a pocket calculator and started punching numbers. In about 30 seconds he announced: “I come up with __X__ dollars.” (I can’t tell you what the number was, but I will tell you that it made all of us sit up in our chairs.)

Here is how he came up with his numbers. (This example is for a fifty million dollar company with a scrap rate of 11 % and a COGS (Material Cost of Goods Sold) of 52%).

Use this information to calculate the Scrap Material Costs, and then use that to calculate the number of Regrind Pounds produced each year.

With this estimate of Regrind Pounds produced each year, we need to calculate the actual cost of regrind:

Now that we know the number regrind pounds produced each year and the actual cost of regrind per pound we can calculate the annual cost of regrind:

This process made a lot of sense to all of us, and put the true cost of regrind at this company in a different light. In today’s economy, can anybody afford to squander over six percent of their revenue on a non-value-added activity like regrinding scrap?

I thought you might be interested in running these numbers for your own business, so I put together a little spreadsheet that you can download and plug in your own figures and come up with your own cost of regrind value. Are there any other costs of regrind that we should have included in our model?

The spreadsheet includes a space to show the impact of stepwise reductions in regrind. Obviously you can’t eliminate regrind because of job change overs, planned or unplanned down time, and so forth. But what is the value of reducing scrap by 1%” or 2% or 5%?

Does this model apply to your business? Please comment, schedule a conversation, or call us at 800-958-2709.

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.

Since starting this blog, I’ve been tapping into a variety of lean / quality / continuous improvement blogs. It’s one way to connect with other people around the world, and keep from isolating myself.

I set up Google Reader so I can check all of the blogs from one site, and it takes only a few minutes each morning to scan them and catch up with what others are seeing and saying. I find this especially useful given all the dark economic news we get from main stream media.

Here is my list of the best of the blogosphere…

Evolving Excellence is hosted by SuperFactory. He has a great post today comparing Intel and AMD’s response to the recession.

Curious Cat is worth reading for the title alone. I like this video he posted about Honda’s philosophy.

Elegant Solutions looks for simplicity beyond complexity. His recent post about Campbell’s Soup highlights the importance of focus.

Gemba Panta RIE has some great content. I appreciate his recent post about rethinking the USPS because it (the post, not USPS) is really outside the box.

Going Beyond Lean Six Sigma has some really important ideas about business governance and data systems, and the interaction between Lean, Six Sigma, Theory of Constraints and other improvement systems.

Got Boondoggle? has some really good posts about improving work through Lean, Six Sigma and the elimination of waste. This post about moving towards simplicy connects work life to personal habits.

Lean Blog is a collection of thoughts and experiences about Lean. Many of his posts are related to lean implementations in the hospital setting, but others, such as this one about Deming and incentives, are more general interest.

Lean Six Sigma Academy posts thought-provoking content about the lean and Six Sigma. For example, this post connects his experience as a customer at the Pottery Barn with his knowledge of Lean.

Learning about Lean has the tag of “A journey to respect people and eliminate waste.”  I like that. I also like his simple example that finally explains Kanban in a way that even I can understand it.

My Business Musings is a collaborative exchange of ideas and perspectives about business. I’m a guest blogger on the site (If I do say so myself, I’m especially pleased with my last post there “In Gut We Trust“). But I like the other author’s too, including this post about 4 mind-sets and 7 stages of business life.

Shmula has some great posts about business and technology. This post about how Jeff Bezos, CEO at Amazon.com led an impromptu 5 Why problem solving effort is a great example of how leaders should lead.

ISixSigma blogs publishes content from several authors. One of these is Gary Cox, with his cartoon strip about Six Sigma practices. This strip is typical - if it wasn’t true, it wouldn’t be funny.

The Sales Performance Improvement Forum looks at applying continuous improvement to the sales process. I’ve known Mike Webb for some years, and I appreciate his perspective. Here is a good example of his content on selling in technical environments.

That’s my current reading list. Do you have a blog I should add?

Oh - and send me an email (ejmiller at hertzler dot com) if you like an xml file that you can import into your reader with all these blog feeds.

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.