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.

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.

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?

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?

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.

As I talk to people in many different businesses, I’m often amazed at how much time they spend extracting, massaging, and scrubbing data for analysis and reporting. I call this “The Data Shuffle“. A friend of mine at Minitab calls it “The stuff we do that we call our job.”

My informal surveys of Six Sigma Black Belts and Green Belts tell me that there is often a 20:1 or 25:1 ratio of massaging and scrubbing data to analyzing that data. And Six Sigma people aren’t the only ones doing the data shuffle. I’ve seen it in all kinds of environments and for all kinds of reasons.

Any way you look at it, The Data Shuffle is not value added activity. It is a business cost that enables better communications with customers and better business decisions.

So why might you not want to show this post to your boss?

Because if you’re doing the data shuffle this post points out that the data shuffle can be eliminated through automation, and that part of your job can be eliminated.

And in this economy I’m guessing you don’t need any more reasons to have your boss eliminate your job.

So what can you do instead?

OK – here is the gutsy move:

Start by studying this matrix:

(You can read more about the Cost Value Matrix.) Chances are good that if you’re doing the Data Shuffle, you’re down in quadrant D where the cost of data is High and value of the data is Low.

There is also a good chance that right now your company is very interested in reducing costs. If we automate the Data Shuffle we can shift towards Quadrant C.

So take this matrix to your boss and say something like:

“Look – I know the company is interested in saving money. The economy is tightening up all around us and we need to lean out operations so we’re more competitive… blah blah blah.”

Then say: “I know how to cut some significant Non Value Added costs out of our system. I can help you do that, but if I’m successful, it will eliminate part of my job. Instead of working myself out of a job I’d like to invest this freed time in helping the business make better use of that data. By making better use of the data we can reduce scrap and material costs, improve customer satisfaction… blah blah blah. In other words, I want to help us move up to Quadrant A.”

This gives your boss three options: business as usual, or eliminate the data shuffle and your job, or eliminate the data shuffle and free up time to do more important and value added work. If he chooses business as usual you might want to keep your resume up to date because of the way the economy is going. If he chooses to eliminate the data shuffle and your job, you will at least have learned how to eliminate the data shuffle and you may be able to take that skill into a new business that will appreciate the value of moving to Quadrant A. And if he chooses to reinvest in you and in process improvement, everybody wins.

So give me some feedback: How much time do you spend on the Data Shuffle today? How would this strategy work in your organization?

A couple weeks ago I published an audio interview with Jay Bronec about his ‘Ah-Ha’ Moment when he realized that he was spending valuable company time doing non-value-added work. In that interview he described how he is automating his company’s (QualiFine) KPIs by integrating our CRM and Web data using GainSeeker.

Today I followed up with him to see how that project was coming. He took me on a webex tour of his project and I was impressed. He is using GainSeeker Suite to mine data and analyze his target market. Then he ports that over to Minitab for some advanced regression analysis that predicts class size based on how many people have registered for the class and how many days are left to sign people up. It is very cool.

But after we talked for a while, I turned on the tape recorder and asked him a question:

“Jay, whats the value you’re offering your customers? Why does it matter to people if they implement GainSeeker and get training in an economy like we have today? Why would anybody want to spend money on that today when things are crashing around us?”

For a little over seven minutes we discuss the Data Cost / Data Value Matrix and how it applies to saving money in an uncertain economy. We touch on dashboards, CMM (Coordination Measuring Machine) data collection, and how real-time data can catch problems before you waste a day’s production.

Follow this link to hear some great insights into the value of real-time automated data.

Recently Larry Coburn, the Sr. VP of Operations at Crown Audio, gave a presentation at the Aberdeen Manufacturing Excellence Summit. We recorded his talk, and you can watch it on-line.

I really enjoyed Larry’s talk because he doesn’t pull any punches. He understands how manufacturing drives the economy, and he has a powerful antidote to all the economic gloom and doom that we’re hearing:

I don’t like being a victim. When I hear manufacturing guys talk about being victims, I say, “It’s your choice. You can be a victim if you want, but I don’t recommend it.” I think you should always work to not be a victim. Do what you got to do and be a leader.

Woven in the presentation is one insight that you don’t want to miss. Larry tells the story of touring a competitor’s site (a laugh out loud experience – this guy has nerve) and learning that they’ve invested $8Million in a new factory that produced a solid product in a fraction of the time it took them to make their product. He brought that news back to his staff and challenged them to implement lean and quality improvements without an $8Million investment.

We just can’t give up. And how we gonna do it without eight million dollars? Because if we take away the demand (for product) that eight million is a boat anchor they got to pay, not us.

His team’s efforts resulted in a 90% improvement in Fabrication Lead Time, a 90% improvement in Circuit Board Assembly Lead Time, a 85% improvement in Efficiency, and a 92% reduction in absenteeism. All without an $8 Million capital expenditure.

During the presentation you’ll hear Larry talk about the importance of having the Right Data, in the Right Format, at the Right Time.

Nothing’s worse than working on two‑week-old data trying to make some good decision’s on it. It just drives you crazy. You can’t get anywhere.

In another conversation he told me:

We’ve always had tons of data, but not enough knowledge. GainSeeker gives us the knowledge to eliminate the finger pointing and focus on solving the problems. Plus it gives us the data in real-time, so now we walk into a production meeting with an up-to-the-minute yield report, with a prioritized list of what we need to work on.

If you’d like more information about Crown’s journey, you can also check out this feature story in Industry Week Magazine.

In my last post I described an interesting conversation with a customer about his company’s pilot deployment of GainSeeker Suite. You may recall that because of staff turnover, this plant was collecting data but not doing anything with it. The company was feeling pressure from a significant increase in raw material costs, and because nobody in the business knew how to use GainSeeker (because of the staff turnover) GainSeeker was not helping them reduce costs.

I had sat down with the corporate staff guy and the plant quality manager. We had started to review some of the data she had been collecting and used GainSeeker’s Analysis Wizard to drill down on the data and found that Shift A had the highest variance among three shifts, and the six or eight operators on that shift had very different results. (Click on the chart to expand to full size.)

Once we had this chart displayed on the screen, I right-clicked on the chart and then selected the ‘Control Limit Legend’ option. That displayed a list of the 8 different operators, along with the mean and range (with related control limits) of the data for each operator.

Here is how we interpreted this table, along with the chart:

It is clear that one of these operators (710) has a very different process. When you look at the control chart for this operator it is much more stable than the other operators, and when you look at the average for each of the operators, Operator 710 is running at about 173.4g compared to as high as 174.9 for some of the others. (See the yellow highlighted cells in the table). That’s a shift of about 1.5g.

Now here is something you need to know: the critical dimension is weight. Weight is critical because a minimum weight has to be met, but anything heavier than the minimum is given away – the company doesn’t get paid for it. So getting as close as possible to the minimum will reduce material costs – substantially.

How much?

We went out to the internet and found a site with typical raw material prices for this commodity. At the volume they were running, the difference between Operator 710 and Operator 214 came out to $457 per day. This is a 24/7 operation, so the annual cost savings between the two adds up to $166,861. And this only one line. This plant ran nine lines. So across the plant the potential savings of over $1.5 Million.

Who was the comedian who said “A million here. A million there. Pretty soon we’re talking real money?”

The other thing that will be obvious to you if you click on the chart is how much more stable the process is in Operator 710’s hands. Operator 214 would be foolish to try to adjust his average down because with the variation he is running, he’d be below specification too often.

Operator 710, on the other hand, could shift his process closer to the lower specification without jeopardizing quality.

So actually the impact could be even greater because the lower specification is 167.6g. If the process is tightly controlled with minimum variation, you can shift it towards the lower spec, reduce material consumption by as much as half a million a year!

Here is another way to visualize what they’re trying to do:

So is there money to be made here?

Looks like a safe bet to me.

Not long ago I had an interesting conversation with a customer, a corporate quality systems guy from a multi-plant corporation.

We had installed GainSeeker as a pilot project at one of his plants, and he and I met to plan deployment to another division. Unfortunately a couple weeks after we launched the pilot, corporate had completely upset the management apple cart at the plant and brought in (among others) a new quality manager. We met the QM and I’m sorry to say she had that deer-in-the-headlights look:bright and capable but completely overwhelmed.

In short, GainSeeker was installed but not being used except to collect a bunch of data. Not to put too fine a point on it, the QM’s training reminded me of the Dilbert cartoon where Dilbert says to the new hire: “This is your mouse. Move it around if you see anyone coming. And remember if you ask any questions you’re bothering me.”

My corporate contact bemoaned the lack of evidence to take back to his boss, which led us back into a conversation that we had touched on many times before: “Why are you deploying GainSeeker in the first place? Where is the money?”

As we talked it became clear that material costs were going through the roof, and the company needed to find ways to control and reduce those costs.

Eventually we went back to the QM’s office and her computer where we spent about 10 minutes digging into the data that they were alreading collecting.

What I found surprised all three of us. Here is a control chart of the data, our starting point. (Click on the chart to see it full-sized in a new window.)

A quick glance at this chart shows some fundamental instabilities. (And I wouldn’t recommend putting specs on the chart, but that is another topic.) But looked at this way, the chart doesn’t help us get to the underlying issue – reducing material cost.

I wanted to see if GainSeeker’s Analysis Wizard could help us see inside the data to identify any underlying causes.

In a few seconds I had learned that Shift A had the highest variance among three shifts. Not only that, but the wizard automatically drilled into that shift and pinpointed clock number (operator) as the number one driver of variation on that shift. You can see that in the following chart.

If you click on this chart it will open in a new tab or window, and you’ll see that the data for Operator 710 looks quite different from the others.

So how does this translate to the bottom line? I’m out of time for now, but stay tuned: that will be the subject of my next post.

Speaking of staying tuned…

Are you new to staying tuned to blogs? The best way to stay tuned is to set up an RSS Reader. There are lots of good sources on the web that explain how to do that. Here is one, and here are several more on video.