Simpler is better: Metrics not working? Check your complexity.

Are your metrics working against one another?

In the former Soviet Union (USSR), an professional weight-lifter was promised a bonus every time he broke a world record. So, being a shrewd “capitalist” (I guess), he decided to break world records one at a time—one or two grams at a time. Naturally, he got many, many bonuses, but it isn’t exactly what his handlers had in mind.

Some organizations pay rewards to their marketing department based on a “new customer” metric—the number of new customers garnered over a specific period of time. Of course, the idea is to build the customer base.

Meanwhile, many of these same businesses reward their sales department to meet or beat quarterly sales goals. So, in far too many cases, their salespeople are out burning through customers—alienating them through high-pressure sales techniques—in order to make their quarterly bonuses.

They may also reward their inventory managers to keep their inventory lean. So, while the salespeople are out making customers angry while getting their end-of-quarter orders (to get their sales bonuses), the warehouses are leaning out their inventory to meet end-of-quarter inventory numbers in line for their own bonuses. So, when many of those orders need to be delivered, they will be late—making the an already alienated customer all the more angry with the treatment endured.

I could go on, but I won’t. I’m sure you get the idea and you’ve suffered (or at least heard of) some similar well-intentioned reward systems go awry.

Some have suggested that these are “reward-motivated abuses,” seeking to blame the employees for doing exactly what management has told them to do, and for which management has agreed to reward them. How, then, can these be “abuses”?

Too much complexity

The problem here is not that the intended goals are not worthy: of course companies want more customers, more sales and lower inventories. The problem is the inherent conflicts evoked by the presence of too many “levers” being offered without linkages.

What’s lacking is a unified goal upon which the whole “system”—the whole organization—can be measured and each participant in the process of reaching that goal might be appropriately rewarded.

Simpler really is better.

“Simple” levers and the “simple” linkages

The linkages are simple, but in order to prevent your organization—or any part of your organization—from sacrificing tomorrow’s profits for today’s bonuses, your (for-profit) organization’s singular goal should be simple as well. Eliyahu Goldratt set it forth so clearly years ago: The goal is to make more money tomorrow than you are making today.

This simple goal is very easy to understand and very measurable—and it will help prevent improper actions like the following (an many, many more):

• Burning through customers to make short-term sales goals—because of the reduced long-term Throughput and the added operating expenses required to capture new customers
  
• Slashing inventory to reach inventory goals at the risk of alienating customers—because it drives Throughput down and operating expenses up
  
• Using company politics to cover or support inefficient or ineffective work efforts or policies—because it drives operating expenses higher (among other things)

Think about it. I think you’ll really like “simplicity” compared to “complexity” once you understand how it can drive your whole “system”—your whole organization—toward improvement (instead of piecemeal).

What do you think?

[Cross-posted at Kinaxis Supply Chain Expert Community]

Decision-making about ROI and your technology spending

Dan Gilmore wrote in “The ‘Probability’ of Supply Chain ROI” propounds properly and rationally the fact that any “forecast,” including forecasts of ROI (return on investment) should not be a single number. Rather, as anyone properly trained in statistical methods will tell you, it should be a range of numbers. The range of numbers would generally be calculated based on a single calculated value plus and minus values that represent the confidence intervals or, simply put, how likely the statistician believes his estimates the calculates will approximate reality. A larger range indicates lower levels of confidence and a smaller range higher confidence levels.

Now, while Gilmore is mathematically correct, the fact remains that most small-to-mid-sized businesses (SMBs) simply do not have anyone trained in statistics on their payroll and they are not likely to go out and hire a statistician to produce ROI forecasts for their IT projects – since this would, by definition, automatically reduce the ROI of the enterprise as a whole in the short term.

Back on a growth trajectory

Gilmore makes another comment in his article with which I wholeheartedly agree: “[T]here is some evidence that companies are in fact looking at investments that can help them to get back on a growth trajectory (read: increasing Throughput) without having to add much in the way of head count (read: Operating Expenses) by achieving productivity gains.” Given the world-wide economic malaise that is showing some signs of lessening (for the moment, at least), Gilmore’s description probably suits the vast majority of SMBs across the U.S. and beyond.

Furthermore, many others besides me have written that a firm stand on return on investment will be the hallmark of technology spending in the 2010 and beyond. So, I can hardly fault Gilmore for suggesting that SMB executives and managers need to become increasingly sensitive to and realistic about ROI for every kind of investment in their firms’ futures.

Too much complexity already

Despite my agreement with Gilmore on theoretical grounds regarding forecasts – including ROI forecasts; and despite my agreement with him regarding the goal of companies to get back on a growth trajectory through wise investment of capital resources, I must disagree with him on the matter of adding useless complexity to the return on investment forecasting process.

Allow me to explain why I use the harsh term “useless” to describe such an effort in the development of a ROI forecast for an IT project.

First  of all, let me say that statistical methods ought to be applied where they make sense. Statisticians generally agree that a valid statistical sample must contain at least 30 members. This works great where you have 30 dogs, 30 cows, 30 houses, 30 automobile, 30 miles of roadway, and so forth for comparison. Then, of course, you need to factor for environmental differences. Thirty or more cows all in the same pasture, eating the same foods, and enjoying the same climate would make a pretty good statistical sample for some studies of cows. On the other hand, three Holstein cows in northern Minnesota, two long-horns in west Texas, 15 black whiteface cows in eastern South Dakota, and ten mixed-breed cows in central Florida are not likely to constitute a good “sample” for cow studies.

Why?

Simply because there are too many environmental dissimilarities surrounding the cattle. By the time these factors were accounted for, (generally speaking) any results would have such a large confidence interval as to make any prediction almost meaningless.

When considered as a whole, a typical SMB has tens of thousand of variable at work within the enterprise. Any number of those variables are likely to dramatically separate it any “sister” enterprises in a sample group used to forecast ROI outcomes.

Of course, the fact that traditional ERP – Everything Replacement Projects – are going to affect the whole enterprise is a big part of the problem of predicting ROI outcomes. With tens of thousands of variables at play, picking the winning number is far more challenging than winning the lottery.

Reducing the scope reduces the complexity

First of all, a good many SMBs today have a “pretty good” ERP system in place – regardless of its brand. Unless there is some pressing reason to undertake a traditional ERP – Everything Replacement Project, it is probably a far better idea to consider a New ERP – Extended Readiness for Profit project instead.

Narrowing the scope of the project reduces the complexity. And, reducing the complexity increases the likelihood that your ROI forecast will be more on-target. Allow me to give you a couple of examples:

• Warehouse automation – Suppose your Current Reality Tree (CRT) shows you that, in order to increase Throughput, you need to enable your warehouse to be able process 80 to 100 shipments per hour, rather than the present 30 to 40 per hour. Furthermore, you need to do this while holding the line on Operating Expenses.
  
• Business intelligence – After reviewing their Current Reality Tree and laying out a Transition Tree (TrT), your marketing and sales team has assured you that, if they are provided tools with which to better understand customers and markets, they will be able to segment the market and create UROs (un-refusable offers) and increase Throughput by 130% in two years. No additional staff or other increases in Operating Expenses would be required.

If your executive management team were to elect to pursue either of these projects – or both – the goals are specific and measurable – as would be the expected outcomes. ROI calculations become simple:

Where T = Throughput (Revenues less Truly Variable Costs), OE = Operating Expenses, and I = Investment.

Simple. Elegant. And ROI calculations are far more likely to be right than any calculation around traditional ERP – Everything Replacement Projects.

©2010 Richard D. Cushing

The New ERP - Part 4

What executives and managers in most organizations lack is a sound "theory" about how their own organization works and responds to its environment as a "system." They know how each department works -- more or less -- but they have never really stopped to think how the "system" works as a whole.

Asked directly, most executives and managers could not tell you -- with specifics -- why three of the initiatives that they have undertaken in the last two years seem to have delivered some improvement (but not all that they expected). Nor could they describe for you precisely why another five of the initiatives they labored over delivered no measurable results -- assuming that they actually did no damage to the organization. (Of course, this whole conversation assumes that you can actually get such executives or managers to admit that things they tried produced no results, in fact. Generally, they have willingly pushed out of their mind those matters over which they have expended precious time and energy to no effect -- only to give up in disgust. Then they tried the next management fad in its place.)

"I am not yet convinced regarding the connection between 'knowledge' and 'theory,'" I hear you saying. Then consider this:

How many people had seen apples falling from trees (or witnessed similar events) for how many hundreds or thousands of years before Sir Isaac Newton postulated a "theory" about a force we call gravity? Everyone had experienced gravity and everyone had information about the effects of gravity, but until Newton, no one had any knowledge about gravity.

Once the "theory" was set forth, cause-and-effect experiments could be developed to measure the effects of gravity. Based on the results of these experiments, one could then postulate if-then correlations: if we do X, then Y should be the result.

If management is anything, it is about being able to propose actions with a predictable -- not random -- effect on the "system" to which the action is being applied.

But, what of the second wrong assumption in the chain of reasoning (in the prior post)?

It should be clear now that it is not more information that will help us manage better. Rather, it is a sound theory or logical framework by which to understand how the "system" functions and interacts with its environment. The second wrong assumption is, then, "More information means we can manage better."

The correct approach would be to say: "If we can develop a sound and effective framework or theory by which to interpret the information coming from our organization (our "system"), then we will be able to manage better."

And, since developing a theoretical framework is likely not a function that will be much enhanced by technologies, then the next step is not to rush out to buy new software or hardware. Clearly, the next step should be to find a way to develop such a sound theoretical framework.

[To be continued]

The New ERP - Part 2

So, what's wrong with traditional approaches to ERP? Why do so many ERP implementations lead to disappointing results? Why do so many companies spend so much money on new technologies and then end up reaping so little return on their investment?

Failure No. 1: Not achieving the planned return on investment (ROI)
It remains today a regrettable fact that many small to mid-sized companies considering new technologies have only the vaguest of notions about the ROI that their new investment should deliver. This is not to say that executives and managers haven't thought out ROI, or even that they may not have already "pinned a number" on the ROI that they'd like to see from the expenditure of their time, energy and money.

What they do not know -- far too frequently -- is precisely how the new technology will deliver results. They have not tied the expected results to specific improvements in Throughput, specific reductions in Investment, or specific savings in Operating Expenses. Rather, there appears to be a general consensus among executives and managers -- despite considerable evidence to the contrary -- that investments in information technologies (IT) sort of auto-magically deliver a return on investment (ROI). That, somehow, IT and automation investments bear an inherent capacity to make the company better and more profitable.

Over the more than 25 years that I have been working with IT from both sides of the desk -- as an executive and as a consultant -- there have been fewer than a handful of companies with which I have worked that actually calculated an ROI for their investment in technology. Fewer still had any measurable objectives for specific IT investments beyond some number clearly picked from the air like "increase revenues by 5%" or "cut manufacturing costs by 7%." Almost none of these firms could tie specific technology functional deployments to the expected ROI.

Given these facts, it is no wonder that traditional ERP (Everything Replacement Project) fails to deliver ROI. The executives and managers deploying the new ERP have not based their ROI expectations on much more than "gut feelings" and some vague sense that having more data will make them better managers.

Failure No. 2: "Go-live" delayed inordinately
Substantial delays to "go-live" in Everything Replacement Projects (traditional ERP) are generally attributable to one or more of the following factors:

• Poor decisions related to customizations or modifications -- when they are selected; how the program code is designed, developed and managed; and the methods chosen for testing and deployment
  
  
• Executive management's improper view of the goals and objectives of a valid ERP project -- thus leading to out-of-control scope creep, usually with absolutely no correlation to project ROI
  
  
• The organization being overwhelmed by an Everything Replacement Project -- rather than being focused on leveraging specific technologies for the benefit of the "system" (i.e., the organization) as a whole

[To be continued]

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The danger of "We know!" - Part 2

In Part 1 of this series, I discussed how many executives and managers fail to reap benefits from new methods and ideas -- especially if these new methods and ideas arrive in the form of a "consultant" -- simply because these executives and managers believe that the already know what can be known about their organizations and their industries. This prevents many organizations from growing to their full potential.

W. Edwards Deming put it bluntly: "Information is not knowledge. Knowledge comes from theory."

Unfortunately, what far to many executives and managers have is a lot of information about their businesses and their industries. What they desperately lack is "theory" by which to interpret and understand the information at their disposal.

G. K. Chesterton put it this way in Tremendous Trifles (Beaconsfield, Britain: Darwen Finlayson, 1968): "One of the four or five paradoxes which should be taught to every infant prattling on his mother's knee is the following: that the more a man looks at a thing the less he can see it, and the more a man learns a thing the less he knows it. The Fabian argument of the expert, that the man who is trained should be the man who is trusted would be absolutely unanswerable if it were really true that the man who studied a thing and practised it every day went on seeing more and more of its significance. But he does not. He goes on seeing less and less of its significance."

Think of Sir Isaac Newton and the story of his having begun his development of the theory of gravity because he had seen an apple falling from a tree. Surely there had been tens of thousands of individuals that had witnessed objects falling to the ground under the influence of gravity for several millennia prior to Sir Newton's experience. Yet, no one understood "gravity."

It has only been since Isaac Newton put a "theory" around gravity that men could take what they had experienced with gravity and put it into a framework -- a theoretical context -- that made the experience understandable to them. Furthermore, the framework (the "theory") gave men the opportunity to predict outcomes of certain actions relative to the gravitational affects. This meant that men could plan and execute with some real certainty as to the results they would obtain under "gravity."

Precisely the same is true of business.

Executives and managers have all manner of data in their hands relative to the performance of their enterprises. What they lack is a "theory" by which that data may be abstracted and understood for the purposes of effective management. A framework that will help them bring simplicity out of the complexity before them.

In all too many cases, the missing "data" for beginning the process of ongoing improvement is to be found within the organization at all. The missing component for executives and managers is quite often this simple point: there is a simple method available to help organizational leadership logically analyze what they already know internally.

In the absence of a "tool set" that helps management bring forth "knowledge" from their "information," executives and managers tend to continue "tinkering" with their businesses. They make changes here or there to see if the change helps.

Sometimes such change seems to help, other times the change actually makes things go worse than before. Still other times, the change is made and their is no perceptible affect on the organization at all.

This is no way to run a business -- or any other kind of organization!

Executives and managers are yearning -- sometimes without even recognizing what is lacking -- for a simple, effective tool to help them gain control of their enterprises once again.

[Next time: Gaining Control]

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Getting more of what you want - Part 5

How many things does an executive or a manager need to know to manage an organization -- a "system" -- effectively?

Answer: Exactly 3 things!

Here they are:

1. What needs to change
   
   
2. What the change should look like
   
   
3. How to effect the change in the system
   

This sound easy and hard at the same time, doesn't it?

Well, I am a firm believer in a concept called inherent simplicity, although I cannot take credit for creating the concept. The concept was developed and articulated by Eliyahu Goldratt in his recent book The Choice. The basic thought of inherent simplicity is that underlying all complexity in systems is a concealed simplicity. If that simplicity can be made apparent, then any "problems" within the complex system will require only relatively simple solutions.

Consider a complex manufacturing machine with hundreds of moving and interrelated parts. No one would design such a machine so as to require that one touch every one of the hundreds of parts in order to effect an adjustment in the machine's operations and outcomes. A machine with hundreds -- or even thousands -- of interrelated, interdependent moving parts may often be adjusted to produce different results simply by making changes in a small handful of parts. These simple adjustments are made available because the inter-dependencies between the various moving parts are known and understood -- at least to the persons that designed the machine and wrote the instruction manual.

Similarly, if the entrepreneur can find a tool set that will help him or her decipher, document and understand the inter-dependencies in the organization (i.e., system) as it moves toward enterprise proportions and complexity, then the entrepreneur will also be able to discover the relatively small handful of places he or she needs to "adjust" the "system" in order to produce different results.

Is there such a tool set? Is it readily available? Is it of a nature that the entrepreneur can readily grasp the tools and make use of them effectively?

I firmly believe that there is.

[To be continued...]

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