Jun 032013
 

Numbers. They have such a comforting certainty to them, don’t they?

Words can be interpreted. But, numbers, they have that beautiful mathematical ring of truth. I was thinking about this the other day, when I got a number from a friend. I was helping him review a model he had made, and I asked him what the median result was from the model. He told me 16.42%. I ask him, “Do you believe it’s 16.42%?” He responded, “Yes, 16.42%.” This was a very smart guy, with multiple advanced degrees in engineering from a great school. However, the data set from which he was calculating this percentage came from a group of people who are giving him estimates of the money they had spent on certain activities, as well as data from an accounting system. And yet, he was quite positive that the result was 16.42%.  I.e., he thought that the result he calculated from the inputs had enough precision to generate FOUR significant figures.

Now, I’m sure that he would have realized, if he had sat down and thought about it for a second, that expecting this kind of precision when the inputs had virtually no precision of all, at least not the precision of four significant figures, was ludicrous. However, that’s the great thing about computers, especially when using spreadsheets like Microsoft Excel.  They will give you as much precision as you want. In fact, to what does Excel default… two significant digits behind the decimal point.

What I find really funny about this is that most engineers have learned the hard way, over time, that there is this thing called “tolerance stack-up.” In other words, no matter what you specify on a CAD model or drawing, a machine only has so much physical capability to hold that dimension. Therefore, engineers become very proficient at specifying tolerances. In recent years, they have even become much better at understanding the stack up of these tolerances on the final dimensions of a part. In fact, there are very sophisticated software packages dedicated to helping engineers do this.

In more general usage, Monte Carlo modeling became all the rage 10 to 20 years ago. Monte Carlo was an attempt to recognize the inherent noise in numbers that we measure, and how that uncertainty affects the models that we make, especially financial models.  However, the funny thing is that when it comes to calculating product costs, people ignore the precision question, and just assume they have the precision they wish they had.

Take a look at the figure below . Let’s go through a simple product costing in concept. For the part we are looking at, we first need to know the physical quantities that are used in making it. For example, we need to know the mass of the part, but that’s a tricky thing, because we have scrap and varying amounts of mass could be used up in certain processes. So, we might be +/-1-3% in our estimate of how much was used. Similarly, we need to know how much time is actually spent on each machine. However, this varies batch to batch, and measurements aren’t always so accurate. There may be many processes that make up the part, including extra inspections and re-work. Let’s say our measurement of the time it takes has a range of 5 to 15%.

Product Cost Management Ignorance is Bliss

Until this point in the analysis, at least we’ve been dealing with physical quantities, not financial quantities. But, if we move to financial qualities, the problem gets much worse. Even material rates are not such a certain thing. They move around over time with various surcharges for this and that from the different material providers. And, the number depends on what material is sent t0 what lines, etc. Labor rates and overhead rates are far more black magic. Accountants with green eye shades spend endless hours calculating these rates from monstrous ERP systems, using Byzantine Activity Based Costing allocation schemes. We hope that the allocated rates are accurate to the real truth on the floor, but I don’t think we can really expect them to be more than +/- 10-20% from what’s really going on.

Never fear though! At the end of the calculation, we have calculated that this particular part cost is $93.45. Why $93.45? Well, that’s what our spreadsheet model or our product cost management software told us. And, of course, a cost NEEDS to be within 10% of what we think the real cost is.

If the product cost management user actually calculated the tolerance stack-up of the uncertainties of the inputs that went into that cost, they would probably find that the costs are more than +/-10% from the true cost. If they seriously considered the possible precision, would they say the part cost $93.40-93.50? I doubt it. Would they say it costs $92.00-93.00? Nope . They probably would say that the part could cost between $88-$97. But, a range like that is not very comforting . It’s much more fun to hit that little “$” format button on Excel or cut & paste the number from the product cost management software .

It’s $93.45. That’s what it is. Because ignorance is truly bliss in the world of Product Cost Management.

 

 

Mar 182013
 

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:

CUSTOMER: So, how accurate is your software?
PCM TOOL COMPANY: What do you mean by “accurate?”
CUSTOMER: Uhh, um, well, ya know. How ‘good’ is the number from your software?
PCM TOOL COMPANY: Do you mean do we have miscalculations?
CUSTOMER: No, no, I mean how accurate is your software to the ‘real’ cost?
PCM TOOL COMPANY: What do you consider the real cost?
CUSTOMER: Uhh, um, well, I guess the quotes that I get from my purchasing department from our suppliers.
PCM TOOL COMPANY: Oh, I don’t know, because it depends on how close the quote is to the true cost of manufacturing the part plus reasonable margin.  Are you confident your quotes are correct proxies for the true cost of manufacturing.
CUSTOMER: Hhmmmmmmm… yeah, I think think so
PCM TOOL COMPANY: OK, how close do you expect the costs from our PCM software to be to your quote [or internal factory cost or whatever source the customer believes is truth]?
CUSTOMER: Oh, you know, I think as long as you are +/-10% of the quote, that would be alright.

 

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. 

Three shall be the number of the quoting and the number of the quoting shall be three. Four that shalt not quote; neither shalt they quote two, excepting thou proceedest to three.

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.
Variance in the Cost Forecast from Quotes Hiller Associates

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% .

Physical Noise  Commercial Noise
Comes from selection of different machines, routings. Comes from market conditions, emotions, and transient conditions
Quantifiable in general by understanding the selections. Very difficult quantifiable
+/-10% of the “factory average” +/-20%+ on top of Physical Noise

 

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:

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

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

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:

Source of the Cost Forecast Common Variance Inherent in the Forecast
Range among 3 quotes +/-15%
95% confident interval (engineering confidence in quotes) +/- (15%+15%)
Physical noise within one single supplier +/-10%
Physical noise plus Commercial noise within one single supplier +/- (10%+20%)
Internal range among cost experts +/-30%
Best Case PCM Tool used by experts +/-30%
Non-cost expert using PCM tool +/- (30%+ 5%?)
Common [Universal] expectation of PCM Tool Cost Forecast +/-10%

 

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.

 

Jan 292013
 

Hiller Associates recently was the keynote speaker at aPriori’s first customer conference.  It was a great opportunity to both teach and learn from experts that came from a wide range of industries and geographies.

Hiller Associates’ President, Eric Hiller, discussed several topics, of which we’ll mention two here.  The entire presentation can downloaded for FREE.  Just click on the slide below and get the presentation:   Best_Practices_for_Starting Your Procuct Cost Management Journey or Improvement.

Variance in Cost Numbers

One of the main themes discussed was the possibility of getting an “accurate” cost, meaning how possible is it to get a cost that is within a certain percentage of a fixed point of reference, such as a supplier quote.  There are several ways to look at this problem that we may discuss in subsequent weeks on this blog.

Eric Hiller at aPriori STARS 1 2012 product cost Hiller Associates

Eric Hiller presenting the keynote speech at the aPriori Customer Conference

However, in summary, the presentation asked the question:  what cost variance is inherent in your system already?   For example, if your 3 quotes from supplier have a range of 30% from highest to lowest, then is it realistic to expect the cost that you calculate in a product cost management software to be closer than 30% away from a random quote?

It was refreshing to see how open the audience was to these concepts.   The reactions to the variance concept went from wide-eyed amazement from people who were new to the cost management field, to thoughtful reflection from the veterans.  In fact, the veterans reacted like men who had been reminded of a truth that they knew all along.  Often, such common truths are forgotten due to immersion in the day-to-day challenges of keeping a company profitable.  We call this concept having a “blinding flash of the obvious” – a BFO.  Everyone in the room had that BFO, and no one wanted to argue about it.  Instead, there were many comments throughout the conference that further explored this concept.

Culture is the biggest loser

Another theme of the presentation was driven by the latest research in Product Cost Management done by Hiller Associates.  Those who follow us regularly know that we segment problems in our consulting work into four root causes:  Culture, Process, Roles/People, and Tools.

Our latest research shows that cultural problems are the clear bottleneck in most firms’ Product Cost Management journeys.  The respondents overwhelmingly agreed.  When Eric ask the attendees which area was their firm’s biggest PCM bottleneck, the conference participants voted as follows, based on a rough estimate of hands in the air:

Best Practices for Product Cost Management Hiller Associates

CLICK TO GET FULL PRESENTATION

  • Culture 60-70%
  • Process 20-30%
  • People/Roles 0-5%
  • Tools     5-10%

That’s fairly shocking at a conference whose organizers are a Product Cost Management TOOL vendor.  [Next time HA will have to set our honorarium higher for taking the pressure off of any problems with the vendor’s product!]  Joking aside, culture is obviously the  biggest problem and it is not an easy thing to change.  In fact, companies often buy a PCM software tool hoping that it will somehow magically fix their bigger cultural problems.

It reminds one of obesity problems.  Many companies have a culture of binging on product cost during design.  In purchasing & manufacturing they continue with cost obesity denial — not know what the cost calorie count is until the parts arrive at the door with an invoice.  However, instead of changing their cost eating and exercising habits, they look for a magical cure in the form of a software tool.  Let’s call this “the shake-weight approach” to product cost management.

We’re not disparaging the shake-weight, or any other home exercise equipment.  Certainly, all home exercise equipment can help you lose weight, just as we are sure that all of the PCM Tools can help one reduce cost.  But, you have to use these tools regularly and properly.  PCM software tools are too much like home exercise equipment.  People buy them thinking that the tool will magically solve cost obesity.   They use the tool twice and then it sits in the corner unloved, unused, and unmaintained… and, yet, people wonder why they are still product cost obese!  It’s not the tool that the problem, it’s your culture.  Much like changing your eating lifestyle, changing the PCM culture is really hard and tricky to do.  That’s why cultural issues are often at the forefront of most of the engagements that we do with clients at Hiller Associates.

However, it was refreshing to see that the attendees at aPriori’s conference did seem to understand this problem, or at least were very open to the idea.   So, maybe we are making progress on this point.  Or, maybe  HA needs a TV show “The Biggest Cost Loser” in which Hiller Associates works with companies to increase product profit with weekly product cost “weigh-ins.”  What TV viewer wouldn’t watch that kind of riveting drama…

Now, get out there and do some product cost push-ups!

If you would like to see the entire presentation from the conference, just click on the slide image above and get the presentation:  “Best Practices for Starting Your Product Cost Management Journey or Improvement.” 

 

Jan 292013
 

 

If you would like to download the presentation (Best Practices for Starting Your Product Cost Management Journey or Improvement)  for FREE, please fill out the form below and click the button.

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