The Data Heads

With all the economic doom and gloom in the main stream media, let’s take time to celebrate Del Monte Foods’ Third Quarter Earnings report, which boasts a 14% profit increase for the third quarter.

In a Del Monte Foods press release, Richard G. Wolford, Chairman and CEO of Del Monte Foods, said the company’s aggressive focus on cost reduction is a key part of their strategy:

“The work we have done, combined with continued marketing and innovation investment and an ongoing, aggressive focus on cost reduction, position Del Monte to deliver our fiscal 2009 goals and drive shareholder value.”

During the Third Quarter conference call, Wolford provided more details: “We are focused very aggressively as a company on cost reduction programs and that’s key for us and we’ve got a good history of that, and we plan to redouble our efforts there and that’s going to be important for us going forward. Our target is 2% to 3% of costs, and we’d rather see a three handle on it, and so would our operating guys.”

This dovetails nicely with this news story describing how Del Monte’s Milk Bone Division used GainSeeker Suite to drive a 3% improvement in performance across all packing lines.

Congratulations to the entire team at Del Monte.

How does this compare with your 3rd Quarter profitability? Please comment, 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.

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?

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.

For the last couple weeks we’ve been working on an OEE (Overall Equipment Effectiveness) project for a new client. When I sat down over lunch today with our CTO and Alpha Geek, Byron Shetler, he explained that the biggest issue - as usual - is getting the data. “This company doesn’t have an OPC/PLC network in place yet, so everything we did last week was around manual data entry.

“I set up a couple of GainSeeker templates to collect downtime, scrap, and efficiency, and we’re storing that as either variable or defect data every hour on every machine.” Byron pointed out that once this customer puts his OPC factory network in place we’ll automate the data collection piece.

By the end of the day Friday (after less than a week on site) Byron had a couple of dashboards in place. He couldn’t bring back any screen caps of their dashboard, but it sounds a lot like what you’ll see on the Dashboard page on our website.

GainSeeker Enterprise Dashboard - OEE Example

Byron reported that the customer was pretty happy that we got in and out so quickly. But more important, he’s excited about getting an hourly update on OEE, Quality, Downtime and Efficiency, and being able drill into any of those metrics by machine or operator or downtime reason, or scrap reason.

Byron reported: “This customer is pretty sharp, and he was practically rubbing his hands together in anticipation of getting his hands on that data. Being able to see the big picture and then drill into various components to understand the root cause is a huge opportunity for this company.”

In addition to linking to the OPC network, once it is in place, Byron also sees potential to feed data back from GainSeeker to the customer’s AS400 business system. “Right now the customer has a couple people who sit down and type data from paper records for scrap and production rates back into the primary business sytem.