There are universalities that seem to cross people and cultures, such as, it’s polite to say “please” and “thank you.” These universalities also occur numerically. For example, designs that follow the Golden Ratiopop up all over the world. Many other aspects of one group versus another may vary, but there are these universal touchstones that pervade the world. The same is true with companies. Granted, one might argue that one company simply is imitating another company and that is why they share a simple practice or the important of a certain number. We believe that this is, indeed, true in most cases. Still, there are a couple of numbers in companies that seem to arise independently in all companies. We are going to talk about some of those universal and independent numbers today with respect to Product Cost Management.

The great universal number in PCM is “10%.” I have met hundreds of companies over the years, both in consulting and when I was the Founder, CEO, and then Chief Product Officer of one of the product cost management software companies. Invariably, a meeting with a company will occur, in which one of the customers will utter the “a” word: accuracy. The dialogue proceeds similar to the following:

Ding! Ding! Ding! – no more calls, we have a winner! The customer has uttered the universal expectation for all costs produced by a product cost management tool, with respect to the “source of true cost”: +/-10%

The universal expectation of customers of Product Cost Management software is that the PCM tool is accurate to within +/-10% of whatever forecast the customer considers the “true cost.”

This expectation is so common that you would think that every customer in the world had gone to the same university and had been taught the same expectation. Of course, that is not the case, but it is a ubiquitous expectation. How did this universal convergence of expectations come to be? We will probably never know; it’s one of the great mysteries of universe, such as, why do drivers is Boston slow down to 4 mph at the lightest sign of snow or rain?

The more important question is: Is the expectation that the cost from a PCM tool should be +/-10% of a quote realistic? To answer this question, we first have to ask:  How truthful is “the truth?” The truth in this case is supposedly the quote from the purchasing department. The reader may already be objecting (or should be), because there is not just one quote, but multiple quotes. How many quotes does a company get? Well, that depends, but we all know how many quotes a typical company gets: THREE!

The universal number of quotes that purchasing gets is 3, and they believe the “true cost” is within +/-10% of whichever of these 3 quotes they select as truth. 

Variance Within Supplier Quotes

Do all the  quotes have the same price for the quoted part or assembly? No, of course not. If they were the same, purchasing would only get one quote. So what is the range among these quotes? That is a fascinating question, one that I am currently investigating. So far, my research indicated that the typical range among a set of three quotes is 20-40%. That seems about right from my personal experience.

But, is the “true” price (cost + reasonable margin) contained within the range of the 3 quotes? Not necessarily. If we assume that quotes are normally distributed (another assumption that I am researching), the range would be much bigger in reality. For example, if we had three quotes evenly distributed with the middle being $100 and these quotes had a 30% range, the high quote would be $115 (+15%) and the low, $85 (-15%). This gives us a standard deviation that, conveniently, is $15. At two standard deviations (~95% confidence or “engineering” confidence), we predict that the “true cost” of the part is between a predicted  high quote of $130 and a low of $70. This is a range of 60% (+/-30%). You can see this on Figure 1.

OK, but what about if we  just source a single supplier. Well, there will be variance in this supplier, as well. This variance breaks down into two types: physical noise and commercial noise.

• Physical noise — the difference in cost that could occur due to physical reasons, such as choosing a different machine (i.e. different overheads) a different routing (sequence of the machines), or even simple human variation from part to part or day to day.
• Commercial noise – differences in pricing driven by the market, emotions, and transient conditions.

Figure 1 – Variance in the Cost Forecast from Quotes (click to enlarge)

Physical noise can easily account for a range of 20% (+/-10%) in the quote that a supplier might provide to an OEM. However, physical noise can be quantified and discovered. A supplier can share what routing or machine they are using. The problem is that Commercial noise is very difficult to quantify. How do you quantify when the supplier believes you hurt him in the last negotiation and now he is going to repay you for it, or that he needs your company as a new strategic customer and will underbid to get initial business? Worse yet, Commercial Noise is often LARGER than Physical Noise in the quote! How big is Commercial noise? That is difficult to say, because we can’t measure it very well, but from our discussions with purchasing groups, at minimum, Commercial Noise adds at least another +/-10% .

Supplier quotes are just one forecast of true cost. There are other forecasts the organization has.

Cost Estimation Experts

What about those people in the organization with the most manufacturing and product cost knowledge? What is the noise in their estimates compared to a source of alleged truth, such as a quote. We are not sure, but we have asked another question about variance to these experts. When asked the question, “How close are you as a cost estimator to the estimates of other cost estimators in your company, people most often reply, “Probably +/-10-20% depending on the complexity of the part cost estimate or assembly.” So, we might say that the cost estimators have at least a 30% range of quotes themselves.

Historical Costs in ERP

What about the historical costs in ERP? How “accurate” are they? There’s actually at least two problems with data in corporate databases. First, sometimes it is just plain wrong from the beginning of its life in the database. However, even if it is correct initially, it gets out of date very quickly. Material cost, labor rates, efficiency, etc. change. Go ask you purchasing buyer how close a re-quote of a current part that has been in the database for three or four years will be to the original quote. To give you an idea of the magnitude of this problem, consider these findings:

• “Experts estimate that anywhere from 10 percent to 30 percent of the data flowing through corporate systems is bad…” 
• “According to [Gartner in ], 25% of critical data within Fortune 1000 companies will continue to be inaccurate through 2007.” 
• “The problem with data is that its quality quickly degenerates over time. Experts say 2% of records in a customer file become obsolete in a month because customers die, divorce, marry and move.”

The Accuracy (i.e. variance to quote) of a Product Cost Management Software

Variance in Different Forecasts for Product Cost (click to enlarge)

So, after all of the discussion of the variance within other cost forecasts, how “accurate” are the forecasts from a product cost management software? Well, if the internal variance among expert cost estimators independently estimating is 30%, the BEST the PCM tool could do would be +/-15%… IF it is controlled by experts. What happens when non-experts use this software? How much does the range increase? Who knows? Obviously, the more automatic and intelligent the PCM Tool, the less the added variance would theoretically be. But, is this added variance +/-5%, +/-10%, +/-20%?  That is hard to say.

The Reality of Accuracy and Variance in Product Cost Forecasts

Regardless of the answer to the above question, the bigger questions are:

1. What is your EXPECTATION of how “accurate” your PCM Tool’s cost forecast is to the quote forecast?
2. Is your expectation reasonable and realistic?

We know the answer to question 1:   Be +/-10% of a my selected quote.

To answer the second question, let’s quickly review what we know:

Hhhmmmmmmm… Houston, I think we have a problem.

It just doesn’t seem that +/-10% is a reasonable expectation.

Bringing Sanity Back to Product Cost Management Expectations

What can you do in your company to help reset these unrealistic expectations? There are three things.

1. First make your colleagues (engineering, purchasing, etc.) aware of the reality of the cost forecasting world. Don’t let them develop uninformed and unrealistic expectations.
2. Don’t focus exclusively on the end cost, but on the physical and immutable concepts that cost is supposed to quantify: mass, time, tooling.
3. Start to quantify the internal variance in your own firm’s cost forecasts. Your firm’s internal cost ranges in quotes, internal estimates, etc. may be lower or higher than the numbers presented here. However, you won’t know until you start to investigate this.

Is this a painful realization?  Perhaps, but you are already living with the situation today.  It is not a new problem in the organization.  If you don’t acknowledge the potential problem, you run the risk of misleading yourself.  If you acknowledge the potential problem, you may be able to solve it, or at least make it better.