21st Century Economics

The intense competitive business environment is often the motivation for a careful and extensive cost-cutting effort within a business and it is also often part of the motivation for consideration of any mergers or joint ventures with other utility organizations. Since labor costs are often seen as one of the most expensive items on the budget, it is natural to look to cutting the size of the payroll as one of the first areas to review to lower overall expenses. This is a very common practice, especially in a merger situation of two organizations that each have a full operational and support staffs, since it is often not necessary to retain everyone to meet the needs of the new, combined organization.

There is, however, an additional important consideration. Many business researchers now view the labor force of an organization as a critical, perhaps, the most critical strategic resource that can affect competitive advantage. When viewed as a critical strategic resource that should be evaluated on the same scale and level as other strategic elements, labor takes on an entirely different perspective. This can be even more important in a merger situation, which is, by definition, a critical strategic decision.

This is called the “resource-based view” and is the object of a serious field of theory, research and practice called Strategic Human Resource Management (SHRM). More and more companies are looking beyond the results of managerial efforts to determine the knowledge, skills, abilities, even traits and motivations critical to achieving strategic objectives.

Corporations have long sought to identify their “core competencies” - those things that the organizations do that contribute to their sustained competitive advantage. Human resources have always been included in competitive analyses, but focus until recently was more on the results of people’s efforts, not the behaviors contributing to them. Now that has changed.

Rather than take the view that the deregulated environment or the new merged organization can best be addressed by offering the lowest electric rates by cutting costs and that cutting costs equates to downsizing the staff, SHRM dictates that the prudent objective is to achieve sustained competitive advantage through the strategic use of all available resources, including human resources. This approach may result in downsizing the staff, if it is done to achieve sustained competitive advantage, however, an organization would be ill advised to downsize potentially valuable staff just to cut costs or to increase product sales.

If you are a consultant or a programmer, becoming an advocate for improved organizational communications can be a very lucrative career move right now as there is an increasing demand for people that can analyze, redesign and implement improvements in organizational communications. This report will describe some of these methods.

Many industry analysts feel that the entire information revolution and all the buzz-words and technology can be reduced to simply “corporate communications” - the movement of information from one place to another. I have found that there are, in fact, four central elements to corporate communications that are progressively created or developed by the support mechanisms that the IT department in the organization provides. These four elements are:

Data: This is not yet usable information. It is numbers or letters collected and saved in various forms - usually DBMS files. A number like “6″ has no meaning other than quantity relative to another number like “5″.

Information: Data that is given “context” is information. The number “6″ has much more meaning and usefulness if we know it is in the column of numbers labeled “Weight in Pounds”. Now “6″ is information. But 6 Pounds of what? This information may not have meaning until it is applied to some environment or situation.

Knowledge: If we now add that the “6 Pounds” is the shipping weight of a product being ordered, we have knowledge. This is data, in context and applied to some environment or situation.

Wisdom: There is a forth level of intelligence for this number. Wisdom usually comes from exposure to more than one number and over more than one occasion. For instance, if I have ordered this same product many times before, I have a history of experience with its weight. If all other times in the past, the weight was “5 pounds”, then I can speculate that there is an error or a problem with the “6 pound” number for this order. I would then be able to act on that knowledge, therefore applying my wisdom.

Now let’s see how this applies to “corporate communications” and your utility’s Strategic Planning. The IS department is involved in the collection of a lot of “data”. The providers of this data help you create information with these numbers by giving them context. The numbers may represent various power management numbers or financial figures.

The IS Department then turns this information into knowledge about the environment using software like Maximo and Lawson. This gives the numbers application.

Up to this point, the IS Department has moved data and applied it in a defined environment of financial or operational context. What is most often done now is to give the results back to the “customer” - finance, operations, etc.. They then provide the experience to create or apply wisdom to this generated knowledge. The IS Department could assist in this final stage.

By adding various communications enhancements to the movement of this data and information around within your utility, the IS department could significantly enhance the creation of wisdom from this process - in essence to capture a part of the total intelligence of the organization and automate it. How? By Corporate Communications in a variety of forms. Here’s how:

1. If more people can see and participate in the communication process, there are more exposed to the information, more experience to draw from and hence a larger pool of corporate wisdom. This can be achieved through E-mail, Intranets, on-line computer-based training (CBT) and groupware. This supports cross-training, improved understandings of why and how a process work and speeds the process along while keeping it accurate.

2. Capturing the processes, to the extent possible, so that some of the wisdom is transferred back into the automated system. For instance, document management systems and groupware software can be programed to automatically route documents to the correct next person in the approval or review chain without manual intervention. This is a captured process that is no based on experience with the current procedures.

When expanded into the realm of expert systems, it is possible to capture complex decisions based on the accumulated experience of many different people over a long period of time. For instance, the power management software might be programmed to automatically adjust distribution switches based on detected loads. This is an automated decision based on experience of operators that has been captured in the power management software. Expert systems can be very powerful in their application to the electric utility environment since decision trees can be fairly well defined.

3. An extension of the expert system concept is to capture the decision rules rather than the decisions themselves. For instance, rather than say that a certain action occurs with particular reading reaches a set threshold, the software might examine the rate of increase and warn the operator that the set threshold could be reached within the next few minutes or hours. This is a form of wisdom that computers are very well suited for - tracking and interpolating numbers to support the prediction of potential future events. Another example might be that at the current spending rate, the department will use up all of its maintenance contract funds three months before the end of the contract - therefore, you might make a decision to reduce the number of calls for support to a minimum and only for very serious problems.

There are some very sophisticated aspects of this kind of automated wisdom. People that are working on the latest state-of-the-art in this area call their software “Database Event Alerters”. These can fall into one of four types of notification: synchronous, asynchronous, interrupt and time-based. There is an equal number of “trigger” and “event registration” types with a variety of application responses. Some of the biggest names in software are now creating database event alerter software add-ons to their DBMS packages, including CA, Borland, IBM, Informix and Microsoft.

4. Finally there is the automated creation of wisdom that may not have previously existed in the corporate experience of the staff. This is done by allowing the software to use special and very sophisticated algorithms to look for “patterns” in the data and information. This most often is done with massive volumes of data contained in “data warehouses”. The process is called “data mining” and it refers to being able to find and identify valuable gems of intelligence among the massive volumes of data that might not be otherwise recognizable to the human operators.

For example, if the software was programmed to look for time-based events, it might find that whenever there is a large decrease in evening load from a rural manufacturing plant there is a large increase in the account of the local county government. IT would further identify that this occurs in the Fall through early Winter. If this were provided to an operator by dates and times, he might find that they correspond to major sporting events and the manufacturing plant lets employees attend the county-sponsored stadium for nighttime football games.

Principle of Regression to the Mean: A notion worked out by Sir Francis Galton (1822-1911), an English gentleman-scientist that, in any series of random events clustered around an average or a mean, an extraordinary event was most likely to be followed, just by luck of the draw, by a rather more ordinary event. One application is contrarian investing which works simple because regression predicts that the worst stocks get better.

Representativeness is a mental problem-solving method that is a sort of short-cut the mind takes in dealing with real-world problems that are so complicated they would choke a computer. The mind handles these complex problems by assessing the evidence intuitively and compares it to some mental model. If the two match, then the mind concludes that the event is more likely. For instance, to decide if a particular football team will win a game, the mind compares the team to its internal model of what an ideal team is like. If the two models match, then the mind concludes that the team will win. This works well for most of the time but does poorly when the derived conclusion runs counter to the laws of chance and probability.

Availability is a mental short-cut that occurs when people judge the likelihood of something happening by how easily they can call other examples of the same thing to mind. Availability, too, appears to be a wonderful way to tackle complex problems because, in general, commoner events are more easily remembered. However, it does not always work for less well known subjects. For instance - does the letter K appear more often as the first letter in a work or the third letter in a word? Most people judge that K is commoner at the beginning of words because its easy to recall words that begin with K. Actually K appears about twice as often as the third letter in words. People overestimate the probability of large vividly imaginable causes of death and underestimate the likelihood of more common but less dramatic causes of death simple because vivid accidents are easier to picture in the mind.

How do people formulate strategy? They first decide what their opponents are likely to do. Then they decide how they will respond. Then they decide how their opponents will react, and so on. The theory of Representativeness dictates that the more detailed these future scenarios become, the more likely they will seem - since detail makes an account more strongly resemble the real world. But imagine a scenario involving just seven such assumptions, each of which has a 90% chance of being right. Its overall odds would actually be somewhat less than 50-50 (.9x.9x.9x.9x.9x.9x.9=47.8%). Actions that acknowledge a high degree of uncertainty are often very different than actions that don’t.

“It’s frightening to think that you might not know something, but more frightening to think that, by and large, the world is run by people that have faith that they know exactly what’s gong on!”

Ignoring the Base Rate or background data against which the probability of an event is judged is a common error. People will the odds are in their favor - “it won’t happen to me”. Aids, cancer from smoking, losses in the stock market, criminal activity are all examples of this. This leads to a strong overconfidence effect. This is a classic example of how the human mind suppresses uncertainty. We’re not only convinced that we know more than we do but that we what we don’t know must be unimportant.

The notion that people are “risk averse” as decision theorists put it, has endured since the 17th century and has become a part of many economic models. People tend to avoid risks when seeking gains but choose risks to avoid losses. People need a strong inducement to gamble but they will expose themselves to tremendous risks in order to avoid a loss. The effect is particularly pronounced in jobs and careers, second only to life and death situations. People avoid risks when seeking to save lives, but choose risks when seeking to avoid deaths.

Prospect Theory says that there is something about the human mind that so abhors a loss that giving up some quantity of money, commodity or privilege is never fully offset by an equivalent gain. “Losses loom larger than gains”. People avoid fair bets not because they are “risk averse” but because they are “loss averse” - the prospect of the gain isn’t worth the pain of the loss. People find it easier to give up a discount (forgo a gain) than pay a cost (suffer a loss). A loss seems less painful when it is an increment to a larger loss than when it is considered alone.

Framing is the principle that if a problem is framed (presented in a different manner) then the response will be different, even if the problem has not changed. In general, the frame that takes the broader view of a situation is more easily defended.

If you are a consultant or a programmer, becoming a BPR facilitator can be a very lucrative career move right now as there is an increasing demand for people that can support the analysis and process improvement of businesses - collectively known as being a “change agent” for “organizational change management”.

Today’s global economy, enhanced and hastened by rapidly changing technologies of all types, is putting pressure on companies to increase the efficiencies of all their business processes. Likewise, budgetary constraints are putting similar pressures on government administrative processes. Many organizations around the world have turned to business process reengineering (BPR) as a methodology to achieve these efficiencies. A majority of BPR projects really do not do any “engineering” at all. That is, many BPR practitioners are not using proven quantitative analytical techniques to analyze and design business processes. This defeats the entire purpose of BPR and results in few BPR projects that actually implement new process designs based on a consideration of reliable performance metrics or reliable expected differences between competing alternatives. Instead, all too frequently, BPR projects tend to have decisions based on subjective notions of “what should work well.” This is wrong and often does not work. When it doesn’t work, BPR is blamed for the failure. In fact BPR dictates the “proper” method.

Stumbling Between Vision and Implementation

Intuitive notions about what would work well for a business process can provide excellent frameworks for creating the “vision” of the new process. Every BPR project should have a clear vision (see Barrett, “Information Systems Management”, Spring 1994). However, the vision has to be implemented successfully to realize the benefits. This is where many BPR projects stumble badly - not because implementation is so difficult, but because careful analysis of exactly what to implement has not been done. There are an infinite number of ways to “achieve the vision.” The problem is in selecting the “right design for the new process that, once implemented, can be expected to achieve the desired results with a high degree of confidence.

Unfortunately, most BPR analysts select a design based primarily on intuition or “best practices correlation” (e.g., ABC company implemented a similar process and they are a market leader) without thinking through, and certainly without carefully analyzing, the dozens of details and alternatives associated with implementation inside the subject organization. Some would argue that considering a number of alternatives and/or significant details takes too long and costs too much compared to the benefits received. On the contrary, we argue that considering such alternatives and details is not costly and does not take a long time. In addition, the benefits are enormous - especially when compared to the often hidden cost and risk of project failure.

Lack of Analysis Contributes to Lack of Executive Commitment

End-to end business processes (e.g., customer order fulfillment) are comprised of many (maybe dozens) activities and tasks, utilize a myriad of the organization’s scarce and costly resources, are managed (and non-managed) by a host of written (and unwritten) policies and form the basis for most corporate culture existing in the organization.

Aeronautical engineers prototype their airplanes. Why shouldn’t you prototype your business process planes?

When an aeronautical engineer prototypes an airplane, she must use calculus. She can take pictures of other airplanes and make drawings of her new airplane. She might even use algebra for answers to her design questions. Eventually she must prove to herself her design will produce an airplane that will actually fly. Why? Because top management will want to have a high degree of confidence her airplane will fly before committing time and money to go ahead with the project. Any aeronautical engineer will tell you she must use calculus to help her prove her airplane will actually fly. This is because calculus is the only proven tool telling her how all aspects of her airplane design will interact during simulated flying time. She has no choice if she wants detailed answers with a high degree of statistical confidence.

Exactly the same reasoning applies to a business engineer who wants a reliable business process plan. She must use simulation (actually, real-time discrete-event simulation, as we explain later). She can study designs of other business process planes, make static sketches of new ones with drawing or diagraming tools and personally visit similar business process planes of other organizations. She will also use algebra or spreadsheets to try and get answers to her design questions. She too must prove to herself her design will produce a business process plane that will actually fly. Why? Because top management needs to be confident her business process plan will fly before committing the organization’s time and money to build the business process plane and operate it with real customers.

The “U” shaped graph has two peaks at the top of the “U”. The inner one - the one with the high Y or profit values corresponds to businesses that compete by selling an item at good profits. But this end of the curve is also low on the “X” axis values meaning that it has little market share.

This is the “niche” market. To a small but focused market, you can sell a service for a high profit. Let’s look at the drug companies as an example. They make a drug that addresses a disease that only 10,000 people in the whole US have a need for. They ask and get $5.00 per pill. A small market that has a narrow but high demand for a product can command a high price for that product.

The other end of the “U” is also a high profit point but with much improved market share. This is characterized by Japanese autos. The price is not the lowest on the market but they still command a large market share. Why? The answer is that at this end of the graph, businesses “differentiate” themselves in some manner from their competition in order to command a price that is not the lowest. In the case of Japanese cars, the differentiation is “quality”. Buyers will pay more for perceived or real quality because that is important to them. Japanese cars have invested in both the real and perceived sense of quality.

Volvos, on the other hand, differentiate themselves as the “safe” car. Mercedes Benz is the “rich man’s car”. Land Rover is the car of choice for safaris…and so on… All these cars are significantly more expensive than their competition but they still command a large share of the market because they differentiate themselves in the eyes of the buyer.

The bottom of the “U” is characterized by low profits and only a medium market share. A good example of this is McDonald’s hamburgers which are sold at nearly the cost to make them. There is very little profit because they are so cheap. (McDonald’s, as a company, makes their profits off drinks and fries). Their market share is a portion of the fast-food market in that they are appealing to those buyers that want that kind of food, fast and cheap. This means that they are very vulnerable to price fluctuations from their suppliers and from their competition.

Another aspect of the older business model is that any given marketplace can typically support up to three top competitors. Others can and will enter the market but they will play a distant second place to the top three.

Sears-Wards-Pennys…..
Ford-Chevy-Chrysler…..
McDonalds - Burger King - Wendy’s.

If you are a consultant or a programmer, becoming a BPR facilitator can be a very lucrative career move right now as there is an increasing demand for people that can support the analysis and process improvement of businesses - collectively known as being a “change agent” for “organizational change management”.

One of the basic precepts of a properly executed BPR analysis effort is that a graphic representation of the processes, information system or both is created to better visualize the enterprise. This graphic representation, if done properly and with the right software tool, will be an accurate computerized model of the enterprise.

A less common step in many BPR activities is the use of simulation to exercise the model into various “what-if” scenarios. I contend that, not only are these essential elements to every BPR activity, but these are THE CRITICAL ELEMENTS to gain the benefits that BPR promises and has the potential to deliver. The combination of modeling and simulation is what allows BPR to totally eliminate the trial-and-error management methods of the past and allows the decision makers to know that the improvement implementation decision will work the first time and will have a predictable benefit. This article will focus on the generic aspects of what modeling and simulation is and how it is applied.

MODELING

Let’s make sure that we understand that there is no single method, technique, or tool that can always produce the best model, despite the claims of the software makers. There is, however, a set of specific criteria that every model must contain for it to be useful to a proper BPR analysis. These criteria can be modeled by more than one physical or logical model but all models should contain the following:

Processes - This is the activity or work that is performed, usually symbolized by a simple box with a process name on it. A test of a process is that it must cause some change or produce some new product.
Information - Sometimes called “data” or “systems”. This is the entity that is moving from one process to the next. It is what is acted upon, changed or created. It can be in the form of paper, mail, network data or electronic images - the media or content is not important. With the exception of manufacturing, information is usually both the input and the output of most processes.

Cost - The essential aspect here is to quantify the value of the process by some significant and useful measure. Costs do not need to be measured in dollars. Costs can be measured in labor hours or in other comparative measures or benchmarks. For instance, a valid measure might be the number of documents processed per person. It is not uncommon to use a separate cost model and technique. One of the most common in BPR is Activity Based Costing (ABC).

Resources - This is what it takes to accomplish the process in the time and cost specified. Resources are generally “expended”, meaning that they are consumed and not reusable. If electricity, gasoline or some other ingredient is needed to perform the process, it is consumed by the activity of the process. Resources also have the quality of being finite and can become a limiting factor in the process.

Time - As with costs, time is an essential element to quantify but it is not limited to measurement by the clock or calendar. A process can be valued as a comparison to past performance or to an industry standard or benchmark.

Regulation - Sometimes called “controls”. Regulation is generally a limitation on the process, cost, time and/or resources. For instance, no matter what is changed, a certain process must be repeated to completion monthly. Regulations are most often imposed from outside the model and the enterprise and cannot be adjusted.

The modeling tool used must provide a means to interrelate these elements in a meaningful manner. For instance, resources may be expended per unit time as in labor hours or for a given number of process outputs as in the case of fuel for deliveries. Likewise, cost must be tied to consumed resources and information flow must be linked to time.

There are a number of modeling tools and techniques currently available. Each one has good and bad features and models the six essential enterprise elements to a greater or lesser degree, however, this writer is not aware of one that models all six elements and their proper interrelationships. To properly model the entire enterprise and all six essential elements, more than one model is used.

One of the most common modeling techniques in BPR applications is called Integrated Definition or IDEF. IDEF models can emulate either the enterprise processes, referred to as IDEF-0 Models, or it can emulate the data or information systems, referred to as IDEF-1X Models. IDEF has been standardized to such a degree that is has its own federal standard (FIPS 182 for IDEF-0 and FIPS 183 for IDEF-1X). Some IDEF tools allow you to create both the process and the data model as a single integrated model. These are among the most powerful tools available.

Despite being a powerful BPR modeling technique, IDEF still does not include cost or time values. One cost model that has shown a good application to BPR analysis is called Activity Based Costing (ABC). The “activity” in ABC models is the same activity or process being modeled in the IDEF-0 model. This allows for easy cross-relationships to be established. Integrated IDEF process and data models with the ABC cost model and the interrelationships are the most ideal models.

Simulation

If the models have been created in a format and method that accurately simulates the six essential elements of the enterprise and models the interrelationships of those elements, then quantitative changes can be made in one or more of the elements in order to simulate changes to the enterprise. For instance, if resources are reduced, what will be the impact on time, output or. Any one or more elements can be changed to see the effect on the other elements. These are relatively simple mathematical relationships that lend themselves to easy representation in spreadsheet form. In fact, most simulations are based on basic spreadsheet formats and can be done using standard spreadsheet programs such as EXCEL.

A powerful variation of the cause-and-effect simulation is called “goal-seeking”. This is when you establish the element relationships and then specify a change or goal desired in one element and discover the necessary changes required in the other elements to achieve the desired goal.

There is, in fact, a “trial-and-error” aspect to this modeling and simulation analysis. You change the model and see what happens. You are, however, changing a computer representation of the enterprise, not the real thing. In less than and hour, you can cut the budget, change the staff or alter the processes and analyze the effects with no cost or risk to the organization.

Using your own management decision experience, you can assess the benefits of each change until you decide what should be done. Once you have determined the change required, you can run a series of simulations of intermediate levels of change to establish what might be expected in each phase in the way of cost, schedule and performance improvements.

These become benchmarks to measure progress by during the transition.
If a change is made and the computed improvement is not realized, then you can analyze the change to see if it matched the modeled change or is the model flawed in its representation of the enterprise. One or the other is adjusted and the next phase is implemented. As time and experience continue, the model will become a more accurate representation of the enterprise until it is able to emulate every aspect of operation. If it is maintained, it will continue to be the most perfect form of change test bed for enterprise improvements and analysis.

Summary

Modeling and simulation are not just exercises in computer graphics and math. They represent a method to rebuild and manipulate your enterprise in a form that allows you to determine if your decisions for change will work and to what degree.

Many investors today are interested in the high flying, fast growing and most profitable investments possible. An inviolate law of economics is that high profit comes at high risk. So far, no one has been able to break that law, although many have tried or thought they had. In the long run, it has proved to be true in every case.

Unfortunately, the high risk, usually expressed as a high beta value, can be misleading because many people do not understand that in the area of investments, if you lose 10% and gain 10%, you are NOT back where you started. Let’s take an exaggerated case:

Suppose you invest $1000 in a volatile stock. As it is prone to do, it whips up and down and in the first year after you buy it, it suffers a total of a 25% loss. Being high risk, it also can go up so in the second year it ends with a 25% gain. How much money do you have? $1000? NO! You actually have $937.50. It is down more than 6%! Here’s why.

The first year’s loss of 25% of $1000 is $250 so you end the year down by that amount to $750. Now you start the second year with $750 and go up 25% or $187.50 to $937.50.

You would have to have about 34% gain in the second year to just get back to your original investment.

Now let’s take a much more conservative investment with a low beta. In this case you start with your $1,000 in a blue chip or balanced mutual fund that has a 10 year average return of 10%. At the end of the first year, at that rate, you will have $1100 and at the end of the second year you will have $1,210. That is $272.50 higher than the volatile, high risk, high return stock - or 27% better!

Of course, you invest in the high risk stock because it has more positive net returns that used in this example. More likely is fluctuations of up and down 25% but with more ups than downs but even that can be misleading. Here’s why.

Suppose the stock, from July to January, goes down 25% and then back up 45% by June of the following year. If you were to read their ad or see one of those investment magazines, it would show you a “YTD Rtd” (Year To Date Return) of 45% in the first 6 months of the second year. So you put $1,000 into this stock in July. It goes down 25% by December and you end the year with a balance of $750. But you hang in there and watch it rise from January to June by a whopping 45% - wow! Hmmmmm wait a minute - what have you really got now.

$1,000 down 25% to $750 in 6 months and then up 45% for a second 6 months gain of $337.50 to $1,087.50 by July of the second year. You end the 12 months with $12.50 LESS than the buy that invested in the conservative stock at 10% annual return!

You can change the percentage numbers and shorten or lengthen the periods of time but you get the same result. If you have a volatile stock and it incurs a loss early on in your investment, you have to have a very hard working investment to make up for it later. For instance, if you invest in a stock with an expectation of getting 10% per year but in the first year you take a 10% loss, you now need to have an average annual return of 15.7% for the remaining 4 years to get the original expected 10% average return for the 5 year period. Look at the stock ans see if that is reasonable.