The Data Heads

Back in August I gave a sneak preview of a new case study that I was working on. Yesterday I finally completed it and published it on our web site. You can read the entire study and download a copy to share with colleagues.

My favorite quote?

“We can’t credit GainSeeker with all of these benefits. We still had to do the work. But we would never have been able to capture the changes we needed to make if we didn’t have GainSeeker. We’d never have been able to do any of this if we didn’t have the system. So truly it deserves the credit. GainSeeker is the tool that enabled our people to make the changes.”

Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 800-958-2709.

I’m working on a case study with one of my customers that I think you’ll be interested in. I’m just beginning to put it together now, but I thought you’d appreciate a sneak preview. I’ll let you know when the final article is ready.

Last fall this customer came to us with a sizeable integration and customization project. It came at a time when the financial and manufacturing world seemed to be falling down around us. I was, frankly, surprised that they wanted to spend that kind of money at the same time that banks and investment firms were collapsing, the stock market was imploding, and businesses were shedding employees like autumn leaves.

But we worked with him through our standard process of defining the project and formalizing a Statement of Work. We launched the project right around the new year. During that process, my customer agreed to meet with me in six months to do a post-mortem on the project. He said he’d be willing to open his books so we could evaluate - objectively - whether the project was paying for itself.

We finally got together last month - seven months after we finished our deployment. True to his word, he did open his books to me and demonstrated - with CFO-approved numbers - that he had paid for the initial investment in less than three months.

Many organizations look for a two-year payback. He had achieved his in an eighth of that time.

Now, seven months into the project, he had documented an ROI of 171%.

That got my attention.

We started by reviewing the work we had done with his team.  This was a truly collaborative effort. His engineers had done an exceptionally fine job of building the foundation for the project, and then worked with my staff to implement the solution. Together they did a fantastic job of automating and integrating a variety of work flows and data systems. The result was a streamlined process for tracking repair and rework processes across multiple departments.

It was the classic tactic of “reduce the cost of data”. I knew that going into the debriefing meeting. And I expected that the ROI would be based on the efficiencies gained by eliminating islands of data, removing duplicate data entry, and integrating disparate data systems.  I expected that he paid for the project by eliminating staff (I knew the company was going through a downsizing concurrent with our project) through automation.  Clearly we were helping this customer move laterally on the Data Cost / Value Matrix from expensive data to low cost data.

As we dove into the data, I found a number of surprises.

First, he didn’t eliminate any jobs because of this project. As he reduced rework he reassigned the rework staff to more productive activities. They shifted from non-value-added status (overhead) to value-added production staff.

Second, reducing the cost of the data contributed only about 2% to the ROI. It was such a puny number. I had expected reducing the cost of the data would account for maybe 50% or 60% of the cost savings.

The lion’s share of the ROI came from improved throughput. Cheaper, more reliable, and more accessible data enabled his staff to drive defects out of the process. Reducing defects increased first pass yield. This resulted in lower WIP (work in process), faster product delivery cycle times, and improved order to cash cycle times.

How are you looking at ROI? Do you ever understate (as I was tempted to do) the benefit you get from the value of the data? Use the ShareThis button below to mark this page, leave a comment, tweet me, schedule a conversation, or call 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 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.

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.