newtonian economics ix - part i for distribution

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NEWTONIAN ECONOMICS

An Introduction to the Science of Integrated Business Planning

By Robert C. Whitehair, PhD


Part I – Formal Concepts and Such


Table of Contents Introduction .................................................................................................................................................. 4

The Need for Integrated Business Planning .................................................................................................. 8

Evaluating the Explanatory and Predictive Power of Economic Science .................................................. 9

The Need for an Integrated Perspective for Decision Analysis ............................................................... 14

Integrated Business Planning Basic Definitions .......................................................................................... 20

Enterprise ................................................................................................................................................ 20

Enterprise Diagrams ................................................................................................................................ 20

Basic Elements of Enterprise Diagrams .................................................................................................. 22

Purchase .............................................................................................................................................. 23

Inventory ............................................................................................................................................. 23

Conversion .......................................................................................................................................... 23

Sales .................................................................................................................................................... 23

Financial Report .................................................................................................................................. 23

The Law of Universal Marginal Economic Analysis – The Basis for the Science of Decision Making ......... 25

Opportunity Value versus Marginal Value Implications ......................................................................... 31

The Three Laws of Integrated Business Planning ....................................................................................... 33

First Law of IBP ........................................................................................................................................ 33

Second Law of IBP ................................................................................................................................... 36

Driver-Based Analysis .......................................................................................................................... 37

Infinite Degree of Freedom ................................................................................................................. 41

Third Law of IBP ...................................................................................................................................... 42

Summary of Part I ....................................................................................................................................... 49


Introduction

“Do you know who Isaac Newton was?”

I asked my young daughter that question because her third grade class was being introduced to physics.

The first page of her homework packet had a silhouetted image of a man sitting under an apple tree so,

before she could respond, I said, “He was the guy who invented the apple.”

My daughter tilted her head to one side, smiled knowingly and said, “Daddy! Don’t be silly! Isaac

Newton didn’t invent the apple. He invented the apple pie!”

Regardless of the disputed nature of Sir Isaac Newton’s relationship with apples, there is certainty with

respect to Newton’s cultural significance – he is without question one of the most influential people in

human history. While Newton is well-known for many contributions, his biggest impact has not been

with respect to any single branch of science or mathematics but to mankind’s general view of reality. By

establishing an understanding of the causal mechanics of the universe, Newton completely transformed

how we think and ushered in the Age of Enlightenment. Today, almost 300 years after his death,

Newton is still relevant and his approach to understanding the physical sciences has a similar potential

for transforming how we think about another branch of science – economics.

Prior to Newton, humanity’s view was that the machinations of the universe were controlled by an

unknowable power, by an omnipotent being or beings. Observable phenomena such as floods,

avalanches, and falling apples were caused by the whims of supernatural entities. Science and theology

were one and the same.

Newton revolutionized how we see the world and human existence. Instead of unknown, mysterious

powers dictating our daily lives and determining our fate, Newton gave us an understanding of natural

forces and a framework for understanding their interactions such that, today, we can explain all

observable phenomena in causal terms as well as manipulate nature and bend it to our will.

The causal reasoning Newton pioneered has imbued us with such a deep reliance on rational analysis

that we no longer look to the supernatural as a means of explaining or predicting phenomena. Our

reliance on rational, causal explanations has become so deeply ingrained that when we do encounter

the mysterious, we dismiss it because we know that inevitably our understanding of the causal forces

involved will soon reduce it to the mundane.

Basically, Newton gave us the ability to understand and explain why things happen in causal terms and

to predict what will happen in terms of the same causal relationships.


More specifically, Isaac Newton articulated principles that we now refer to as the Law of Universal

Gravitation and the Three Laws of Motion. These laws are the foundation of Newtonian Physics.

Newtonian Physics, which is commonly referred to as Classical Mechanics, is still widely used on a daily

basis. In 1969, humans walked on the moon. Newtonian Physics was used by the engineers, scientists

and technicians who made it possible. Newtonian Physics is used to design airplanes. When they don’t

fly, Newtonian Physics determines why. Without Newtonian Physics, we would not have automobiles,

pitching machines, skyscrapers, satellite television or the Golden Gate Bridge.

But this is old news. You probably studied about Isaac Newton at some point and you probably had the

exact same silhouetted image of a man sitting under an apple tree on your homework package. You

probably even know some good jokes about apples.

And you probably do not care.

More than likely, unless you work for NASA or design airplanes, you do not see any connection between

Isaac Newton and the pressing issues that dominate your daily activity. More than likely, you are far

more interested in recent financial crises, the plummeting value of your home, whether or not you will

have a job in six months, and a wide variety of other anxieties.

Isaac Newton may seem an interesting fellow, but you may see little economic value for you, personally,

in further study of his life or work.

However, before moving on to other, non-Newtonian topics, you might want to consider a few

observations:

- No one really seems to understand why the recent financial crisis happened or how to fix it.

- It is very unusual for a “modern” economy to go more than 10 years without a debilitating

financial crisis.

- Financial systems are, seemingly, wildly out of balance but the dire predictions for what should

happen because of these imbalances are not being realized. Conversely, events that are initially

perceived to be inconsequential are proving to have profound global significance.

- Economists are incapable of both explaining what has happened and accurately predicting what

is going to happen. Economists still cannot agree on an explanation for the cause of the Great

Depression of the 1930’s let alone the more recent “Great Recession” of 2008.

- Large companies have invested $billions on tools for planning and financial analysis yet still

cannot determine how decisions will affect financial performance.

- Extraordinarily large, profitable companies go bankrupt. How is this possible? Especially

considering how much they have invested in planning and analysis software?

- For virtually any and every economic decision of consequence, there is no clear, well-defined

course of action that every stake-holder agrees upon. How is it possible that for some, a specific

decision is the only sure course for avoiding disaster while for others, that same decision will be

the cause of unfathomable calamity?


These observations are interesting because of the extent to which they illustrate the sharp contrast

between Newtonian Physics and modern economic science. Similar to the state of natural science in

Isaac Newton’s time, today’s economic science bears more resemblance to a theological system of belief

than to a principled system of known cause and effect capable of explaining observed phenomena and

predicting future consequences. Economists can be overly sensitive and will take umbrage at this

comparison, but we only need to consider the ongoing debate regarding fiscal versus monetary stimulus

to see elements of truth in it.

But this book is not about alienating economists. It is about empowering you. Consider the following:

- What if you could accurately forecast the financial and operational consequences of key

business decisions faced by your organization?

- What if you could consistently make these calculations with a precision comparable to the

analyses generated by aircraft engineers?

- What if you could accurately anticipate, and prepare for, a business competitor’s response?

- What if you could increase your organization’s profits by 3% - 8% of sales? Effectively doubling,

tripling, or quadrupling profits?

- What if you could demonstrate a plan for balancing the US Federal budget deficit without

raising taxes?

- What if you could help members of your local community understand the implications of

allocating funding to schools?

- What if you could show how to reduce unemployment? Or provide affordable universal

healthcare? Or establish a workable social safety net? Or prove to your children that there is,

indeed, such a thing as, “too many stuffed animals!”

By this point, it may not surprise you to learn that analogies to Newton’s Law of Universal Gravitation

and his Three Laws of Motion can be used as the foundation for a new form of economic analysis

capable of addressing these issues and many more. This new form of analysis is called Integrated

Business Planning™ and will be referred to as “IBP.” IBP makes it possible to understand, explain and

predict the behavior of economic systems in causal terms and to perform financial analyses with

unprecedented accuracy and precision. In effect, IBP represents a new economic science. The

fundamentals of IBP are defined in this book and are presented along with example analyses and

practical applications.

Before diving into specifics, some clarifications are in order.

First, it is important to note that in the context of IBP, the word “planning” should be interpreted in its

broadest possible sense to mean “any kind of decision making.” IBP should not be thought of as a

narrow approach to any specific analysis such as “budgeting” or “performance management.” Instead,

it should be thought of as a foundation for every form of economic analysis including:


- Planning

o S&OP

o Operational Planning

o CPM

o Strategic/ CapX Planning

o Treasury/ Cash Flow Planning

- Budgeting

- Cost Analysis

o Standard Costing

o Activity Based Cost Analysis

o Detailed Unit Cost Analysis

- Scheduling and Sequencing

- Price Optimization

- Competitive Analysis

- Root Cause, Differential, Sensitivity and Risk Analyses

- Predictive Analytics

- Managerial Accounting

- Deming Analysis

Second, the concepts presented here represent an enormous amount of work involving a wide range of

contributors. They are not the casual musings of a small group of individuals. The original academic

research was supported by multiple government agencies as well as several large, international

corporations and well-respected academic institutions. Successful commercial applications of IBP have

spanned over 100 different industries and thousands of analyses and have conclusively demonstrated

the potential value and decisive competitive advantage IBP can deliver.

Finally, the principles of IBP and example analyses will be demonstrated using a commercially available

software application called Enterprise Optimizer® (EO) from River Logic, Inc. Spreadsheet

representations will also be used to illustrate IBP concepts but the mathematical analyses required in

IBP are beyond the capability of a spreadsheet tool. For the purposes of the analyses presented here,

an educational version of EO is sufficient and can be found on the River Logic web site,

www.riverlogic.com.


The Need for Integrated Business Planning

This book is about a new approach to economic science called IBP. Given that it will require time and

effort to understand IBP, the natural question to ask is, “What is wrong with the current economic

science?”

In general, economic science, or simply “economics,” is intended to analyze decision making. Its

purpose is to help us make good decisions. Ideally, for a given situation, economics would enable us to

make the best possible decision. Economics is especially relevant to questions involving production and

distribution of goods and services.

As with every science, economics has many branches. The two most prominent branches are

macroeconomics and microeconomics. Macroeconomics addresses issues of broad scope, especially

those relevant to entire economies such as inflation and unemployment. Microeconomics addresses

issues pertaining to individual agents in an economy such as corporations.

IBP encompasses both micro- and macroeconomic science.

This chapter is not intended to serve as a replacement for a textbook nor is the purpose to critically

evaluate every aspect of economic thought. Instead, the evaluation sought here is to determine

whether or not modern economics provides analytical capabilities comparable to Newtonian Physics.

The evaluation will be made in terms of two narrowly defined questions:

- Does modern economic science have explanatory and predictive power comparable to

Newtonian Physics?

- Does it provide an adequate context for integrated, systemic analysis?

Ultimately, the relevance of any science is based on its ability to solve problems we want solved. The

importance of Newtonian Physics is rooted in the vast extent of the solutions it has fostered. Modern life

would not be possible without Newtonian Physics.

Thus, determining the extent to which economic science is comparable to Newtonian Physics will enable us

to evaluate its suitability for use as the basis for business management solutions. If IBP does, in fact, have the

potential to transform economic science in a fashion comparable to the manner in which Newtonian Physics

transformed 17th century natural science, then we can assume IBP has the potential to similarly foster a new

set of powerful solutions that will enable a new era of economic prosperity.


Evaluating the Explanatory and Predictive Power of Economic Science

Below is a picture of the Rialto Bridge in Venice, Italy. It is made out of stone and was completed in

1591. The Rialto Bridge is an indispensable part of the Venetian logistical system. Prior to the current

stone bridge, there was a wooden bridge. In 1444, the wooden bridge collapsed under the weight of a

large crowd. It was rebuilt, but, in 1524 it collapsed again.

The 1444 collapse was directly related to a lack of understanding of Newtonian Physics. The bridge

engineers of the time did not understand the forces acting on the bridge or their magnitude. As a result,

they were unable to predict that the bridge would collapse if it were to be occupied by too many people.

In fact, their lack of understanding was such that they did not address the problems correctly when they

rebuilt the bridge. Consequently, it collapsed again in 1524. They finally solved the collapsing bridge

problem with the stone structure that still stands today.

The Golden Gate Bridge is shown below. It was completed in 1937. It is far more ambitious than the

Rialto Bridge in terms of structural tolerance for forces due to traffic flow, weight, current, wind, etc.

The rebuilt Rialto Bridge functioned for approximately 80 years before it again collapsed. The Golden

Gate Bridge has functioned exceedingly well for approximately 80 years and it is still satisfying all its

design requirements including withstanding earthquakes. It is useful to compare the performance of

the two bridges; Without Newtonian Physics, the modest Rialto Bridge failed. With Newtonian Physics,

the awe-inspiring Golden Gate Bridge succeeded.


Using these bridges as metaphors can help us evaluate the status of modern economic science. While

we can clearly see that physical science has advanced since the 16th century, what can we say about

economic science? Has the economic science of today progressed to the same extent as physical

science?

An emphatic, “Yes!” is an understandable initial response. From a comparative perspective, it might

seem clear that today’s economy is based on a science better represented by the science of the Golden

Gate Bridge than the primitive science of the Rialto Bridge. Surely today’s economic science represents

an advance over 16th century economic science comparable to the advance in physical science

represented by Newtonian Physics over 16th century physics. From the perspective of the 16th century,

our modern economy is truly a marvel. Per capita production, income, and consumption have increased

many fold. While problems undeniably exist, the world has never experienced such wealth, luxury and

general bounty.

But is this initial response correct? Has economic science really advanced to the same extent as physical

science? Even if we stipulate that today’s economy is more advanced than the economy of the 16th


century, we need to understand the extent to which our economic advances are the result of advances

in economic science. We need to ask the question, “Does our ‘advanced’ economy necessarily imply

that our economic science has advanced as well?”

It is helpful to note that there were actually two Rialto Bridges completed in 16th century Venice, neither

of which was engineered using Newtonian Physics. The first, a wooden structure, collapsed. The

second, a stone structure, is still in use today. Although the stone bridge was not designed with the

benefit of Newtonian Physics, 16th century engineers overcame design problems by significantly

overbuilding it to meet tolerances far beyond anything it might ever be expected to encounter.

Although this approach does not represent the best use of resources, it does satisfy the requirements

for producing a bridge that does not collapse and it is a useful heuristic design technique that has been

employed countless times.

So, upon further reflection, perhaps it is the stone bridge that is the best metaphor for today’s

economy? Perhaps the advances represented in today’s economy are not the result of advances in

economic science but are the result of heuristic management techniques that are applied in such a way

that we have merely “overbuilt” our economic infrastructure such that we now have a “stone economy”

that is more robust than the “wooden economy” that preceded it but that is not as advanced as a

“Golden Gate economy.”

Given the ambiguous result of the metaphorical analysis, let us look at the question more formally.

From a formal perspective, scientific theory has several well-defined characteristics that we can use to

address the metaphorical question. Specifically, a scientific theory must explain observed phenomena

and predict consequences. For now, let us formalize the question we seek to answer as:

Does modern economic science have explanatory and predictive powers comparable to Newtonian

Physics?

In the late 1920’s and through the 1930’s, the world experienced a period commonly referred to as the

Great Depression. Can our economic science explain what caused the Great Depression?

If you go to Amazon.com and search for books on the Great Depression, you will get at least 2,000 hits.

If you scroll through just a few of the titles, you will find that many of these books explicitly address the

cause of the Great Depression. Unfortunately, you will find a great deal of inconsistency and

contradiction in the explanations. For example, there are some who define the cause in terms of

misguided steps to return the world to a monetary gold standard. There are others who lay the blame

on decisions by nations to move off the gold standard. The economic scientists in these opposing camps

routinely use impolite language to refer to those of differing opinions so it is unlikely that the alternative

perspectives can be reconciled into a consistent view.

A cursory review of the titles found on Amazon quickly identifies at least 10 sharply contrasting views on

the cause of the Great Depression. Those 10 different explanations are from just the first two pages of

hits and include “sunspots.”


Although economic science can, at some level, explain the cause of the Great Depression, the results of

this test are discouraging in the sense that the various explanations should at least be consistent if not

identical. But this approach to evaluating economic science’s explanatory power might be

unreasonable. The macroeconomic view taken might be too broad in scope. Instead, let us focus on a

more practical, microeconomic application - explaining why large corporations fail.

Every year in the United States, hundreds of thousands of companies fail and declare bankruptcy.

Focusing on a few of the largest corporate failures in US history, we have:

- Lehman Brothers, bankruptcy filed on 9/15/2008, assets: $691B

- Washington Mutual, bankruptcy filed on 9/26/2008, assets: $327.9B

- Worldcom, bankruptcy filed on 7/21/2002, assets: 103.9B

- General Motors, bankruptcy filed on 6/1/2009, assets: $91B

- CIT, bankruptcy filed on 11/1/2009, assets: $71B

Now let us ask the question, “Can economic science explain why any of these corporations failed?”

Starting with the top of the list, using the Amazon approach, you will find the failure of Lehman Brothers

has multiple explanations including:

- Colossal failure of common sense

- Betrayal

- Financial market manipulation

- Lack of government oversight

- Excessive government oversight

Again it is clear that there is no consistency in the explanations. In fact, several are quite obviously

contradictory. Certainly, there is no consistency in explaining the cause in terms of economic science.

The same is true for all the other bankruptcy examples.

In contrast, if you go to www.wikipedia.org and search for the article, “List of bridge failures,” you will

find a table listing bridge failures. For each incident in the list, there is a field specifying the cause of the

failure. For example, the Green Island Bridge in Troy, NY collapsed on March 15, 1977. The cause is

specified as, “Flooding undermined the lift span pier resulting in the western lift tower and roadbed

span of the bridge collapsing into the Hudson River.” For every instance of bridge failure, there is a

clear, unambiguous causal explanation.

http://www.wikipedia.org/


It would seem that modern economic science has failed the “explanatory power” test. We will forgo

declaring a definitive conclusion for the moment and instead take up consideration of “predictive

power.”

What is the predictive power of modern economic science?

Ronald Reagan was the President of the United States from 1981 to 1989. Under his administration, the

United States budget deficit ballooned to historical highs. According to modern economic science, large

budget deficits inextricably result in an increase in price inflation.

Unless, of course, they do not. Economic science also predicts that inflation is a function of monetary

policy – how much money is pumped into, or removed from, the economy by the central bank.

So, in summary, economic science predicts that the result of President Reagan’s large budget deficits

should be an increase in the rate of price inflation unless the budget deficit is offset by a tightening of

monetary policy.

Price inflation actually decreased during President Reagan’s administration. Therefore, economic

science would predict that monetary policy was tightened.

It was not.

Ronald Reagan’s administration ran huge budget deficits while at the same time the Federal Reserve

implemented a relaxed monetary policy. Economic science predicts that this situation should lead to

increased price inflation. Again, price inflation went down during the Reagan administration, from over

11% in 1981 to under 2% in 1987.

More recently, late in the first decade of the 21st century, the US government increased the annual

budget deficit to over $1,000,000,000,000. (In case you lost track of all the zeros, that number is one

trillion dollars.) Simultaneously, the Federal Reserve dropped interest rates to historical lows.

And that is what caused the rampant price inflation of 2010.

Only, there was no rampant price inflation in 2010. The biggest concern in 2010 was price DEFLATION!

(Quick! Go check what your house is worth!)

It would thus appear that modern economic science’s predictive power is on an equal footing with its

explanatory power.

If you have any remaining doubts, consider the following. During “earnings season,” anxious business

analysts join conference calls to hear publicly traded companies announce their latest results. A huge

percentage of the announcements are “surprises.” In the third quarter of 2010, over 88% of the

announcements constituted “surprises.” If the predictive power of economic science was on a par with

Newtonian Physics, an “earnings surprise” would be a far less common event.


By now, the point should be clear – modern economic science has limited power as a tool for

explanation and prediction. It does not really matter how this compares with Newtonian Physics, the

point is that economic science can, and should, be advanced and improved.

Although it is reasonable to conclude economic science has not advanced in a manner comparable to

Newtonian Physics, it may not yet seem relevant to you. The discussion has focused on abstract issues

like money supply and inflation and, if you still have a job and your house is still worth more than your

mortgage, these issues may not seem relevant to the economic issues you face daily.

As mentioned at the beginning of this chapter, the relevance of any science is based on its ability to

produce solutions. Clearly we have seen that the explanatory and predictive power of modern

economic science is not comparable to Newtonian Physics. The obvious implication is that modern

economic science is inadequate for solving problems that are relevant to you. These problems that

modern economic science fails to solve include;

- Successfully managing the financial performance of companies

- Designing effective supply chains

- Efficiently managing a department of a company

- Correctly pricing a product

- Anticipating market demand and competitive response

- Predictive modeling for budgeting

- Managing the national economy to minimize unemployment while maintaining negligible price

inflation

- Many, many more…

In contrast, IBP addresses all these issues by establishing a scientific basis for principled approaches to

designing, engineering and managing solutions to economic problems. The next section discusses a key

element of the principled approach, the need for an integrated perspective for decision analysis.

The Need for an Integrated Perspective for Decision Analysis

Engineering is the process of using scientific knowledge to design, build and manage solutions.

Conventionally, we think of engineering in terms such as “mechanical engineering,” “electrical

engineering,” or “aeronautical engineering.”


As with all other sciences, economic science is broadly used as the basis for a wide range of solutions.

However, we usually do not think in terms of “economic engineering.” The reason for this is that the

word “engineering” suggests a level of precision and robustness that does not exist in solutions based

on current economic science. As a consequence of the failure of its explanatory and predictive power,

solutions based on economic science are universally heuristic in nature. This issue is addressed formally

in subsequent chapters. For now, you can think of the word, “heuristic” as implying, “unreliable and

inaccurate” and it is important to understand that, because of this heuristic nature, it is not possible to

accurately predict the performance of any solution based on current economic science. In some

instances, solutions might have positive benefits. Under other circumstances, those exact same

solutions might have negative consequences.

Again, specific details will be addressed formally in subsequent chapters. For now we will focus on one

specific, conceptual issue – the need for an integrated perspective for decision analysis.

As a basis for discussion, consider a small company, the Candy Company, that makes candy. The Candy

Company has a department, “Procurement,” that buys ingredients; a department, “Production,” that

produces finished candy products; a department, “Sales,” that sells candy; and a “Finance” department

that manages the company’s money.

The Procurement managers have been told to minimize the cost of the ingredients. The Production

managers have been told to maximize capacity utilization. The Sales managers have been told to

maximize unit sales. The Finance managers have been told to minimize cost and maximize the return on

capital. Each of these managers uses one or more solutions to support their decision analysis processes.

These processes are managed independently of one another.

Before further consideration of the Candy Company, let us revisit the bridge metaphor. Imagine we had

three groups of engineers building a bridge. The first is responsible for aesthetics and is told to make

the bridge visually appealing. The second is responsible for maximizing the traffic flow over the bridge.

The third has been told to minimize the cost of materials. The result of their efforts is shown in the

figure below.


For maximum impact, it is hoped that you will agree the resulting bridge is a mess. If you do not, please

note that due to inadequate attention to basic design principles, the bridge collapsed due to the weight

of a flock of pigeons roosting on the left-most tower.

The point is simple and obvious. It is not possible to design a bridge by decomposing the process into

independent activities that have no interaction with one another. No competent engineer would ever

engage in a process where the decision analysis associated with a project was managed in such a way.

Instead, design, engineering and management processes must be coordinated based on a unifying

objective they are all attempting to achieve.

How can the person designing the trusses determine how to distribute the compression forces from the

superstructure if the person designing the superstructure is doing so independently? And what about

the forces from the roadbed? How much traffic is the bridge intended to support? What is the average

traffic per day? Per hour? What is the expected maximum weight it is expected to support?

If you want a bridge to function successfully, the people designing, building, and operating it must do so

in an integrated, coordinated way. It would be foolish for engineers to try to make design decisions

about a bridge in isolation.


Yet, that is the definition of modern economic analysis.

Today, decision support solutions invariably take a silo approach where activities within a silo are

evaluated independently of activities in other silos. Returning to the Candy Company example, the

Production managers have been told to maximize capacity utilization. There is no reason to believe that

the decisions that result in maximizing capacity utilization will be consistent with the objectives of the

Procurement managers who have been told to minimize the cost of the ingredients. For example, if a

Production manager succeeds and increases capacity utilization, it is likely that this will result in an

increase in procurement costs. Such a scenario is common in industries where inputs have upward

sloping cost elasticity curves, or “cost response functions.” In other words, if the cost of an input

increases with the quantity consumed, increasing capacity utilization can increase procurement costs.

An example of such a cost response function is shown in the figure below. The price of the input

material is shown on the Y axis and the quantity purchased is shown on the X axis. In most process

manufacturing industries, the general shape of the cost function shown in the diagram is considered the

standard shape of all cost response functions. One of the reasons for this can be easily understood in

terms of transportation cost. With each incremental unit of input, the transportation cost per ton of

input increases due to the fact that all the locally available input material has been consumed and it is

necessary to bring in the additional material from sources that are further away.


Similarly, the objective of the Sales managers, to maximize unit sales, is likely to conflict with the

objective of the Finance managers who have been instructed to maximize profit. In general, selling

more units requires dropping price. As we have already seen, increasing production is likely to increase

cost. Therefore, the obvious strategy of a Sales manager – to increase unit sales by dropping price – will

have the unintended consequence of increasing cost and, almost certainly, the ultimate impact will be

felt in terms of reduced profits.

These problems exist even within a department or division. Consider the following figures that

represent process steps in a supply chain. Each of the steps constitutes a silo in which decisions are

made independently of decisions in all other silos. Conventionally, each manager is given a mandate to

minimize the costs within their silo.

The typical result is shown in the figure below. Each silo manager responds by pushing cost out of their

silo. Though not always the case, it often happens that the net effect is to increase overall cost.


The “integrated” aspect of IBP is formalized in subsequent chapters but its importance is such that it is

presented here at a conceptual level. This is a theme that will be stressed throughout this book. The

goal of IBP is to integrate all decision making in a single, unified perspective. Unfortunately, in most

cases, this is not a practical goal. Because of this, “integrated” decision analysis should be understood to

mean analysis that spans multiple decision silos.

Ultimately, the critical consideration is that, in order for economic science to achieve the predictive and

explanatory power of Newtonian Physics, the analysis must take a more integrated perspective than

current economic science supports.


Integrated Business Planning Basic Definitions

The purpose of IBP is to establish an economic science with explanatory and predictive powers

comparable to Newtonian Physics. In so doing, IBP will establish a principled design, engineering, and

management framework for a new generation of high-value solutions.

This chapter defines key elements that will be used to formally define the science of IBP and the

engineering methods it enables.

Enterprise

Newton defined the Law of Universal Gravitation and the Three Laws of Motion in the context of the

motion of objects in the physical universe. Their purpose was to establish a foundation for explaining

observations and predicting consequences of actions. Newton expressed observations and predicted

consequences in terms of force, mass and velocity.

IBP is defined in the context of the enterprise and is intended to be used to explain observations of

enterprise behavior and to predict the consequences of enterprise activities. Enterprise activities will

frequently be referred to as “decisions” or “actions.” These three terms will be used interchangeably

throughout this book. In this context, an enterprise is an organization of process activities that can be

mapped to measurable operational and financial consequences. In IBP, observations and predicted

consequences will be expressed in terms of process activity, material/ energy balances, and financial

results.

Examples of an enterprise include companies organized for commercial purposes, a department or

function within such a company, a government or government agency, a service organization such as

the Red Cross or a hospital system or a school, a regional or global economy, a supply chain, or a

competitive market.

Enterprise Diagrams

Anyone who has studied physics is familiar with “free-body diagrams” such as the one shown in the

figure below. Free-body diagrams show the relative magnitude and direction of all the physical forces


acting upon an object in a given context. The arrows in a free-body diagram reflect the magnitude and

direction of the forces acting on the object. Each force arrow in the diagram is labeled to indicate the

exact type of force.

The example diagram depicts four forces acting upon an object. Any number of forces can be

represented by a free-body diagram. The only rule for drawing free-body diagrams is to include all the

forces that are acting on an object in the given context.

IBP uses an analysis approach analogous to free-body diagrams called enterprise diagrams. An example

of an enterprise diagram for a steel mill is shown below. Enterprise diagrams show the flow of all the

process activities associated with an enterprise in a given context. In effect, an enterprise diagram

shows the financial and operational forces acting on an enterprise in a given context. Enterprise

diagrams will be used to explain IBP concepts and to perform IBP analyses. The direction of the links in

an enterprise diagram represents the flow of process activity. There can be any number of objects and

links in an enterprise diagram.

Interpreting enterprise diagrams is intuitive. In the steel mill example, “Hot Strip” is being purchased,

stored in inventory, and processed in a “Pickling Line.” “Pickled Strips” can then flow in one of four

directions; to a “Cold Reverse Mill,” to “Galvanization,” to a “Tempering Mill,” or to a “Finishing Line.”

Pickled Strips that are galvanized are subsequently processed in the Finishing Line. Pickled Strips that

are processed in the Cold Reverse Mill can then flow to Galvanization, to “Batch Annealing,” or to the

Finishing Line. Eventually, “Finished Inventory” can flow to one of three markets, “North,” “South” and

“West.”

The steel mill model contains a freestanding object called, “Financial Report.” In this enterprise

diagram, the financial consequences of the process activities are mapped to the Financial Report object.

Enterprise diagrams are used throughout this book to demonstrate concepts, to capture knowledge and

to perform analyses. In general, enterprise diagrams are intuitive to build and interpret.


Basic Elements of Enterprise Diagrams

Conceptually, enterprise diagrams are based on network flow models that are widely used in

mathematics and engineering. Network representations are commonly used in mathematical analysis of

business processes in the fields of Operations Research and Management Science. They are popular

because they are easy to learn and intuitive to understand and because it is commonly accepted that

any business process can be represented with a network flow model. Network representations consist

of two simple elements, nodes and links. Some nodes are customized to represent the beginning of a

flow through a network and are referred to as source nodes. Other nodes are customized to represent

the termination of a flow and are called sink nodes.


Nodes are commonly referred to as “objects” and that convention will be used throughout this book.

Enterprise diagrams are a specialization of network representations. Enterprise diagrams consist of four

types of process activity objects; purchase, inventory, conversion and sales. In addition, a fifth type of

object called a financial report is used to represent financial consequences. A very important aspect of

enterprise diagrams is that the objects and links have semantic meaning relevant to enterprises.

The objects used to define enterprise diagrams, as well as their semantic meanings, are defined in the

following subsections. Using these simple objects and links representing flow between them, it is

possible to construct an enterprise diagram of any organization.

Purchase

Purchase objects are a variant of source nodes in network models and are used to represent the process

of acquiring something from outside the enterprise. Purchase objects are so named to clearly

differentiate the intuitive semantics they imply from generic sources nodes.

Inventory

Inventory objects are intermediate nodes used to define materials and physical storage. Inventory

objects can be used as source and/ or sink nodes in enterprise diagrams to indicate process flows that

begin with existing inventory or that terminate with ending inventory.

Conversion

Conversion objects are intermediate nodes used to represent the transformation of a flow of material as

well as the delivery of services and the use of resources in the transformation process. The semantics of

conversion processes are such that it is necessary to differentiate these intermediate nodes from

Inventory objects. Conversion objects can also be used as source or sink nodes.

Sales

Sales objects are a variant of sink nodes and are used to represent output sold and delivered to

customers. Sales objects are so named to clearly differentiate the intuitive semantics they imply from

generic sink nodes.

Financial Report

Financial Report objects can be used as source, intermediate and sink nodes for financial flows. In

enterprise diagrams, process activity is mapped to Financial Report objects. It is very important to note


that Financial Report objects are not merely used to report consequences. They are integrated in the

flow of process activity and financial consequences and can be used to constrain both.


The Law of Universal Marginal Economic Analysis – The Basis for the

Science of Decision Making

Hank Morgan was the superintendent of a manufacturing enterprise. He understood the principles of

Newtonian Physics and Classical Mechanics quite well. In Mark Twain’s novel, “A Connecticut Yankee in

King Arthur’s Court,” Hank is magically transported back in time to the 6th century. In the story, Hank

Morgan leverages his superior knowledge to become “Boss,” take over medieval England, and transform

it to his liking. Because of his superior knowledge, he is able to defeat all his enemies, achieve great

renown, and be held in high esteem.

It‘s fun to imagine being in a similar situation – one in which you have knowledge that gives you a

decisive competitive advantage over everyone else. It goes without saying that you would use such

power only for good!

If you understand the concepts presented in this chapter, you will have such knowledge. The decisive

competitive advantage you will gain will be dramatically improved decision making. This chapter will

help you understand why so many enterprise planning decisions turn out to be mistakes, how to avoid

making similar mistakes, and how to correctly evaluate alternative courses of action in order to make

the correct decisions.

In general, the purpose of economic analysis is to serve as an aid in decision making by evaluating the

potential merit of each alternative course of action. Today, all economic science is based on the concept

of the marginal analysis, or the marginal value, of decisions where, “Marginal analysis, quite simply,

balances the additional benefits from an action against the additional cost.” (From microeconomic

webnotes by Dr. Robby Rosenman, School of Economic Sciences, Washingtion State University.)

Mathematically, the marginal value of an action A, “MVA” is defined as:

MVA = (incremental benefit of A) – (incremental cost of A)

This is the universally accepted definition of marginal value. It is used in virtually every economics text

book and it is the basis for all existing analytical tools and is especially relevant to the concept of

Contribution Margin.

It is, unfortunately, problematic. The nature of the problem corresponds closely to issues Isaac Newton

faced.


Newton’s Law of Universal Gravitation is the centerpiece of Newtonian Physics and it directly addresses

problems in the widely accepted theories of his predecessors and contemporaries. It is helpful to review

how Newton resolved those issues because his approach is directly relevant to IBP.

Have you ever heard of Johannes Kepler? Prior to Newton, Kepler defined Laws of Planetary Motion.

He was a remarkable individual of immense intellect. You could easily make an argument that he was

one of the greatest scientists and mathematicians of all time. But there aren’t any amusing anecdotes

involving fruit that anyone remembers about Kepler. His silhouette doesn’t appear on the cover of any

homework packets. Very few people remember ever hearing his name. The reason no one remembers

Kepler is because his Laws of Planetary Motion were flawed in ways analogous to the flaws in the

prevailing definition of marginal value.

Kepler’s Laws of Planetary Motion are impressive in that they come very close to predicting the

movement of planets. One of the issues is that their utility for explaining the root cause of planetary

motion, and, more importantly, the motion of any large object, is negligible. The other issue is that

although they are pretty good, they are not accurate enough to be used for predictive modeling. Their

predictions for planetary motion are incorrect in ways that cannot be adjusted in some standard

manner.

The figure below is a graphical representation of Kepler’s laws. Like Newton, Kepler defined three laws,

but his laws only define the relationship between each planet and the Sun. Kepler’s laws do not define

the relationships between each planet and all the other planets. This is an important point. But even

more importantly, Kepler’s laws only explain how the planets move, they do not explain why. His laws

cannot be used as a foundation for a system of causal analysis. In other words, Kepler’s laws have no

predictive or explanatory power.


Newton’s Law of Universal Gravitation corrected flaws in Kepler’s approach and established a basis for

causal analysis that is still used today. Newton’s approach differed from Kepler’s in that it first defined

the causal mechanism – gravity – that explained observed phenomena and then predicted expected

behavior as a causal effect of gravity. A critical aspect of the Law of Universal Gravitation is that it

defines the interactive force between every physical body and every other physical body.

Thus, there are two critical flaws in Kepler’s system – it does not provide for systemic considerations and

it makes no provision for causal explanation. The problems with the definition of marginal value are

identical to those of Kepler’s system.

More specifically, the definition of marginal value does not take into account the relevant context of the

system in which benefits and costs are being determined.

Consider the following situation in the context of the Steel Mill depicted in the enterprise diagram

shown in the previous figure. Assume sales are being made of a range of products including Pickled

Strips and Cold Reverse Strips, both of which have been tempered in the Tempering Mill. In addition,

assume the Tempering Mill is at maximum capacity. What is the marginal value of selling one more ton

of Pickled Strips that have been tempered?

Based on the prevailing definition of marginal value, this would be:

marginal value = revenue(1 ton of tempered Pickled Strips) – cost(1 ton of tempered Pickled Strips)

This may seem reasonable but consider the shortcomings of Kepler’s approach. He did not take into

consideration the effect planets have on one another. His system defines planetary motion solely in

terms of the Sun and each individual planet. Because of this, predictions of planetary motion based on

Kepler’s laws are incorrect. Predictions of financial consequences made using marginal analysis (or

contribution margin) are incorrect for the same reason!

Furthermore, marginal value calculations are incorrect in ways that cannot be corrected using some

standard method. They are not incorrect in a directional manner such that you could say, “marginal

value calculations are always a bit too high” or “marginal value calculations are always a bit too low” or

“marginal value calculations are always within 5% of actual.” Instead, all we can say is that marginal

value calculations are incorrect. They might be too high, they might be too low, they might be negative

when we think they are positive, and they might be incorrect by a significant amount or only by a small

percentage.

With respect to the Steel Mill, in order to determine the marginal value of selling one more ton of

tempered Pickled Strips, we need to understand the implications not only in terms of the cost and

benefits of that specific action, but also in terms of the impact it will have on other activity. Since the

Tempering Mill is at maximum capacity, if we sell one more unit of tempered Pickled Strips, we will not

have enough capacity to process all the other activity that was occurring in the Tempering Mill. For the

purpose of this example, let us assume this means we have to reduce the amount of Cold Reverse Strips


we temper by one ton. This means we will need to reduce the sales of tempered Cold Reverse Strips by

one ton and forfeit the marginal value of that action.

Now we can recalculate the marginal value of selling one more ton of tempered Pickled Strips by

adjusting for the loss of the marginal value of one ton of tempered Cold Reverse Strips:

marginal value = revenue(1 ton of tempered Pickled Strips) – cost(1 tone of tempered Pickled Strips)

- marginal value(1 ton of tempered Cold Reverse Strips)

The potential magnitude of this problem can be understood if we instantiate our example with some

actual numbers. For the sake of argument, assume the revenue from selling one more ton of tempered

Pickled Strips is $300 and the cost of selling one more ton is $200. Given this, selling an additional ton of

tempered Pickled Strips has a marginal value of $100 and might appear to be a fortuitous event. But

what if the marginal value of selling a ton of tempered Cold Reverse Strips is $50? Then the net value of

the fortuitous event drops by 50%. i.e., we have to subtract the marginal value of Cold Reverse Strips,

$50, from the marginal value of tempered Pickled Strips, $100, leaving a net of $50.

Even worse, what if tempered Cold Reverse Strips have a higher marginal value? What if their marginal

value is $300/ ton? In this case, the marginal value of selling an additional ton of tempered Pickled

Strips is not $100 – it’s $100 - $300 = -$200, or negative two hundred dollars!

This example was intentionally simplified. In real enterprises, production rates can vary dramatically

and this can exacerbate the problem of determining marginal values. For example, what if the

production rate for tempering Cold Reverse Strips was much faster than that for tempering Pickled

Strips? What if the rates differed by a factor of three? In this case, in order to make an additional ton of

Pickled Strips, the Steel Mill has to make THREE fewer tons of Cold Reverse Strips. If we assume the

marginal value of the Cold Reverse Strips is only $50, we again end up with a negative marginal value:

$100 – (3 * $50) = -$50.

By expanding the definition of marginal value to include consideration of the systemic impact of an

action, one of the problems with the prevailing definition of marginal value is corrected and we gain an

integrated perspective. This is analogous to one of the issues Newton addressed in Kepler’s system

when he defined gravity as a force that exists between all bodies, not just between the Sun and an

individual planet.

It is extremely important to note that, in general, building a suitable mathematical representation of

interacting systemic effects is an extremely difficult task. For the vast majority of IBP analyses, it will not

be necessary to build the mathematical representations manually nor will it be necessary to have a

detailed understanding of the specific mathematical techniques and manipulations used. The important

points to know and understand are the key concepts. For analysis, software built specifically for IBP


analysis will be used and it will not be necessary to understand the mathematical details any more than

it is necessary to understand how electrical circuits work on silicon chips in order to use a pocket

calculator to calculate mortgage payments.

The second issue that needs to be addressed is causality.

Kepler only mapped out how planets move. He never addressed why they move in a manner that could

be used for general purpose analyses of physical systems. Kepler’s system is incapable of explaining why

things happen in a causal way or accurately predicting consequences. The same is true for the definition

of marginal value.

In terms of our Steel Mill example, we need to understand the causal factors underlying the

determination of marginal value. More specifically, when we force one more ton of tempered Pickled

Strips to be sold, what caused us to make and sell one less ton of tempered Cold Reverse Strips?

Perhaps we could have expanded capacity in the Tempering Mill by adding an overtime shift? Or maybe

we could have increased the process rate in the Tempering Mill? Perhaps we could have purchased a

ton of tempered Cold Reverse Strips from a competitor and substituted it for our own production?

In IBP, we address causality through the mathematical optimization of an objective function. In the

case of the Steel Mill, the decision to make and sell one less ton of tempered Cold Reverse Strips was

caused by the optimization of the objective function, “maximize profit.” In other words, the decision to

make and sell one less ton of tempered Cold Reverse Strips was the decision that maximized the Steel

Mill’s profit. If we had instead added an overtime shift or pursued any other option, it would have

resulted in a smaller profit. In general, any objective function can be used provided the terms in the

objective function can all be measured in the context of the enterprise. For example, any of the

following can be used as objective functions:

- Optimize (or maximize) profits

- Optimize (or maximize) cash flow

- Maximize unit sales

- Optimize profits subject to minimum sales growth of 2%

- Maximize unit sales subject to minimum cash flow requirements

We can now define the Law of Universal Marginal Economic Analysis in terms of the Opportunity

Value™ of an action, A, as well as all activities B and C where A’s impact on B results in a decrease in

benefit and where A’s impact on C results in an increase in benefit.

For action A, the Opportunity Value, “OppValA”, is calculated as:

OppValA = OPTobj ((marginal benefit of A) – (marginal cost of A)


– (Sum (OppValB for all actions B displaced by action A)

+ Sum (OppValC for all actions C enabled by action A))

Where OPTobj is a mathematical optimization function subject to the objective function “obj”.

In other words, the Opportunity Value of activity A is the optimal, net economic impact of A, taking into

consideration all the systemic implications A has on every other possible activity in the enterprise.

The above definition is mathematically precise. If it seems unintelligible, the textual definition is

sufficient for all further purposes. For the remainder of this book, we will make little explicit use of this

formal definition for one very simple reason – it is heinously difficult to compute manually. Instead, the

analyses presented here will be generated by River Logic’s Enterprise Optimizer software application.

For most practical purposes, it will be necessary for IBP analysis to be performed on software specifically

built for that purpose. It is not technically feasible to use applications such as spreadsheets, simulation

tools, OLAP tools, or statistical analysis packages to perform IBP analyses. To understand why this is,

please note that the definition is self-referential; the Opportunity Value of A is defined in terms of the

Opportunity Value of B, and the Opportunity Value of B is defined in terms of the Opportunity Value of

A. In order to solve problems of this type, it is necessary to solve a system of simultaneous equations

(or “system of equations”). While it is possible to manually build and solve systems of equations using

tools such as spreadsheets, it is only practical to do so for small problems. IBP problems can easily

require millions of equations with millions of variables in each equation. Such problems are well beyond

the scope of conventional analysis tools.

The “matrix method” is a commonly used approach for solving systems of equations that is well-suited

for IBP analyses. Because of this, IBP practitioners often use the phrase, “the matrix” to describe the

mathematical representation of an IBP problem.

For clarity, the phrase Marginal Economic Analysis will be used to differentiate the analyses performed

in IBP from the conventional definition of marginal analysis. Similarly, Opportunity Value will be used to

differentiate the result of IBP analysis from conventional marginal values and contribution margins.

In summary, all modern economic analysis and all decision science is based on a definition of marginal

value that is flawed in the sense that it does not take into consideration systemic implications nor does

it support causal explanations. As such, it cannot be used to explain enterprise behavior or predict

future consequences. The Law of Universal Marginal Economic Analysis addresses these flaws and

defines Opportunity Value in such a way that IBP analysis can provide causal explanations of enterprise

behavior and accurately predict the consequences of a decision.


Opportunity Value versus Marginal Value Implications

Over 40 text books were reviewed during the preparation of this book. Every one of them used a

definition of marginal value that is virtually identical to the one used here. In addition, every one of

these text books defined contribution margin in terms of marginal value.

If you are a business manager, executive or analyst, you are familiar with contribution margins and

marginal value and you have probably calculated or used them in a business process. If you read and

understood the passages above, you might be thinking some or all of the following;

- “I am not sure I understand – is this really suggesting that the computation of marginal value is

incorrect?”

- “This cannot mean what I think it means – this is suggesting that the very basis for all economic

analysis is incorrect. How can that be possible?”

- “I have been using contribution margin to make decisions for many years, does this mean I have

been making decisions based on incorrect information?”

In a sense, the answer to all these questions is, “yes.” However, we need to keep in mind that we are

dealing with a definition. Definitions should not be thought of as “correct” or “incorrect.” Instead, they

should be evaluated in terms of whether or not they are useful. Therefore, the correct question to ask is

this, “When is the definition of marginal value or contribution margin useful?”

Think of this question in the context of Newtonian Physics. You could argue that Newton, like Kepler,

was wrong. Albert Einstein, among others, demonstrated that Newtonian Physics is “wrong” in the

sense that it is not the best available model for explaining and predicting the behavior of the physical

universe. Even Newton realized the representation of the universe he defined was merely a model of

reality and not reality itself and he consequently spent many years adjusting and improving his theories

on the structure of the natural world.

So the question is not whether or not Newtonian Physics is “correct.” The question is, “When is

Newtonian Physics useful?”

With respect to marginal value and contribution margin, they do have uses. These uses will be discussed

in subsequent chapters and include validating enterprise diagrams, making long-term pricing decisions,

and reporting financial results for past activities.

This leads us to the statement of the first of several General Principles that will be presented.

General Principle I – All decisions are about the future.


Given this general principle, it is clear that any form of analysis used to support decision making must be

based on some form of accurate, predictive modeling. This is a key point and its importance cannot be

overstated – marginal value and contribution margin should not be used as the basis for any form of

decision support related to planning as defined here. In contrast, IBP has very powerful predictive

modeling capabilities and is well-suited for use as the basis for an extremely broad range of

management solutions.


The Three Laws of Integrated Business Planning

A long-standing goal of economic research is the development of empirical methods comparable to

Newtonian Physics. To date, progress has been made but it has been limited and it has failed in terms of

adequately providing a foundation for building solutions.

The Law of Universal Marginal Economic analysis addresses a critical shortcoming by defining a basis for

causal explanation and predictive analysis. In Newtonian Physics, gravitation can be thought of as the

prime mover that explains why there are forces and how to determine their magnitude. Opportunity

Value plays this role in IBP by explaining why there is enterprise activity in terms of economic forces

defined by an objective function.

In this chapter, the Three Laws of Integrated Business Planning are presented. Each is directly analogous

to the corresponding Newtonian law of motion. These laws complement the Law of Universal Marginal

Economic Analysis and establish a basis for designing, engineering, and managing solutions. In

Newtonian Physics, the Three Laws of Motion define the analytical framework that is Classical

Mechanics. The Three Laws of IBP establish a corresponding framework for economic analysis of the

operational and financial forces acting on an enterprise at a micro- or macroeconomic level.

First Law of IBP

Newton’s First Law states that every physical body remains in a state of rest or uniform motion unless

acted on by an external force. The purpose of this law is to establish a frame of reference for

subsequent analysis. So, for example, a frame of reference might be, “my daughter is holding my

favorite coffee mug,” and the analysis might be, “what will happen if she drops it?”

The IBP analog is,

First Law of IBP: enterprise process and financial activity will remain constant unless acted upon by an

external process or financial action.

As with Newton’s First Law, the First Law of IBP establishes a framework for analysis by establishing a

basis for validating IBP models and frame of reference for evaluating changes to the enterprise

associated with decisions or actions of some kind.


Consider again the Steel Mill enterprise diagram. How do we know if it is correct? How do we test

whether or not it is an accurate representation of the process activity and financial consequences of the

actual Steel Mill?

The First Law of IBP is stating that we can establish a frame of reference for further analysis by validating

the enterprise diagram against actual data taken from a historical period. In other words, the historical

period can be thought of as a “constant” and we can use the enterprise’s actual process and financial

activity to calibrate the enterprise diagram such that it produces identical results.

Examples of the validation process for an enterprise diagram will be given in subsequent sections. As

will be discussed, the actual validation process is more involved than what is suggested here and will

include sensitivity analyses and other validation methodologies. The key point, however, is that

historical data can be used to establish a frame of reference for IBP analysis.

The First Law of IBP is also stating that, once the frame of reference is established, it can be used to

analyze decisions. Again, we use the words, “decision,” “action” and “activity” interchangeably to mean

effectively the same thing – a change to the enterprise. It is important to stress that the First Law of IBP

does not indicate what kind of subsequent analyses can be performed. In fact, the First Law of IBP does

not in any way limit the kinds of analyses that can be performed.

This is an important aspect of IBP analysis. There is no restriction on the analyses that can be performed

relative to the changes acting on an enterprise. For example, in terms of the Steel Mill enterprise

diagram, potential IBP analyses can include all of the following:

- What is the impact of increasing sales in the North region?

- What would happen to finances if prices drop 10%?

- What would happen to operation process activity if we limit the value of WIP we keep in

inventory?

- What would the impact be if we replaced the current Tempering Mill with new technology?

- What is the best way to allocate $100M of capital?

- How should we respond to a competitor’s price reduction in the North market?

- Any and every combination of the above…

The unrestricted nature of the analyses that can be performed with IBP may be counterintuitive for

users of existing methodologies or tools such as spreadsheets, statistical or simulation packages, or

OLAP tools. Using spreadsheets as an example, in order to analyze a particular decision, it is necessary

to build a specific model. If a different decision is analyzed, it will be necessary to build a completely

new spreadsheet model. In terms of the questions above, a spreadsheet model capable of analyzing the

first question will be inappropriate for analyzing capital expenditures.


In contrast, in IBP, a single enterprise diagram can be used for unlimited types of analyses. Any decision

that is represented in the context of the enterprise diagram can be analyzed. It is not necessary to

change the enterprise diagram to accommodate any particular analysis.

The distinction is that IBP is a science; it is not merely a narrow analytical approach suitable for

addressing a specific issue in a specific manner. Rather, IBP defines the natural structure of the

“enterprise” domain such that any and all relevant forms of analysis can be performed.

In more formal terms, we say that in IBP, variables have “infinite degrees of freedom.” Any variable can

represent either a fixed input or a decision variable to be solved for. More importantly, any

combination of variables can be used to specify the input set and any combination of variables can be

used to define the set of decision variables that are to be solved for. In IBP, there is no restriction

whatsoever on the input variables or the decision variables. Rather, IBP defines HOW to determine a

solution, virtually ANY solution, given a problem defined in terms of an enterprise diagram, an initial

state, and a set of input constraints.

As will be discussed in a subsequent chapter, this is an economic analysis paradigm that provides

analytical power comparable to that of the many engineering disciplines that are rooted in Newtonian

Physics.

It is also important to point out that there are many software applications that use a graphical

representation as a means for specifying mathematical analyses. Enterprise diagrams are quite

different. Enterprise diagrams explicitly represent the structure of an enterprise in terms of the flow of

process and financial activity – effectively all the forces acting on the enterprise. While the enterprise

diagram itself does not represent any particular mathematical analysis, every possible mathematical

representation or analysis of the enterprise can be derived from a validated enterprise diagram.

More formally, we say that, in IBP, the enterprise diagram defines the problem(s) to be solved in terms

of intuitive domain constraints. This terminology will be revisited, expanded and clarified throughout

the remainder of this book. For now, it is important to stress that when reference is made to,

“Constraint Oriented Reasoning,” this is what is meant. In other words, Constraint Oriented Reasoning,

or “COR,” is a process whereby problems are expressed in terms of the natural, intuitive domain

constraints, and problem solving involves a knowledge-based interpretation of the constraint

representation that is used to automatically determine the problem(s) to be solved and to then

automatically build and execute an algorithm to solve the problem(s).


Second Law of IBP

The purpose of IBP is to bring a level of understanding to decision making that enables us to explain

observed phenomena and predict the consequences associated with any particular decision. The First

Law of IBP establishes the necessary frame of reference. The Second Law of IBP establishes the basis for

evaluating a decision by specifying the impact that decision has on the enterprise.

The Second Law of IBP corresponds closely to Newton’s Second Law which defines the impact of a force

applied to a body in a given frame of reference. Newton’s Second Law is simple to state mathematically

as, F=ma, or “force equals mass multiplied by acceleration.”

The IBP analog is defined in terms of the Activity, A, associated with a decision.

Second Law of IBP:

1. The financial consequence, F, of an activity, A, is the product of A, cost, C, and a financial

transaction vector, T, or F=ACT.

2. The Input, I, of an activity, A, is the product of A and a distribution vector, D, or I=AD.

3. The Output, O, of an activity, A, is the product of A and yield vector, Y, or O=AY.

Very often, even the slightest application of mathematical nomenclature makes a simple definition

appear complex. Regardless of how it might appear, the Second Law of IBP is intuitive and easy to

understand.

For example, let’s analyze the decision, “How many apples should we buy?” Assume the cost of each

apple is $.25 and we have decided to buy 10 apples. In this case, the Second Law of IBP tells us the

following with respect to a corresponding “Purchase Apples” process:

1. F = 10* $.25; the cost of the apples is $2.50.

2. I = 10 * 1; 10 apples are input to the apple purchasing process.

3. O = 10 * 1; 10 apples are also output from the apple purchasing process.

Please note that the relationships defined in the Second Law of IBP are with respect to a specific

decision or action. The aggregation of all the individual actions defines the enterprise balance

relationships. Enterprise balance relationships are addressed by the Third Law of IBP.


The financial analysis in this example is simplified for explanatory purposes by ignoring the effects of the

financial transaction vector. For now, a few additional details regarding financial transaction vectors will

be discussed but most details will be addressed through examples in later chapters.

In general, the structure of financial transaction vectors corresponds to double entry accounting. So, for

the apple example, the activity of purchasing 10 apples actually results in a $2.50 debit to a cost

account, Net Purchases, and a $2.50 credit to an asset account, such as Accounts Payable or Cash.

If you are not an accountant, or perhaps even if you are, this might seem overly complicated. The good

news is that for all of the analysis examples in this book, the processing of financial transaction vectors

will be handled automatically. For the sake of clarity, wherever it makes sense to do so, financial

transaction vector details will be simplified and explanations will be given in more intuitive terms.

Now consider an incremental decision, “Should we use 10 apples to make apple cider?” Assume there is

a variable processing cost of $2.00 to make apple cider. Now, with respect to a “Make Apple Cider”

process, we have:

1. F = 1 * $2.00; the cost of making apple cider.

2. I = 10 * 1; 10 units of apples are input to process of making apple cider.

3. O = 1 * 1; 1 unit of apple cider is the output of the process.

In both Newtonian Physics and IBP, it is easy to express the impact of a force or the consequences of an

activity. The difficulty lies in analyzing the impact of a force or an activity in the context of an enterprise.

In the case of our apple related decisions, a full enterprise representation would involve combining the

decision regarding buying apples with the decision regarding making apple cider. While combining the

two examples is a straight forward task, it does involve effort. For example, both decisions have

corresponding input and output variables. Minimally, these must be tagged in some manner to avoid

confusion. Keeping the tags properly sorted and understood is relatively easy with only two decisions

being considered. But real-world enterprises can (and do!) have millions of decisions that need to be

represented. Without modern software tools to automate the generation and processing of these

mathematical constraints, IBP analysis is virtually impossible.

Driver-Based Analysis

A critical aspect of IBP, one that differentiates it from alternative analytical approaches, is that it

explicitly represents the causes of enterprise behavior. More specifically, IBP does not represent cost as

an average value. Instead, IBP represents cost in terms of how the cost is actually incurred. This is

referred to as “driver-based analysis.” Newtonian Physics similarly requires driver-based analysis.


In contrast, prevailing approaches to economic analysis are overwhelmingly based on the use of average

values, especially for costs and prices. The figure below is from a textbook definition of marginal

analysis. Recall from the previous chapter that the conventional definition of the marginal value of

activity A, MVA, is;

MVA = (incremental benefit of A) – (incremental cost of A)

In the diagram above, it appears that the “Marginal Cost” function, labeled “MC,” can be closely

approximated with a straight line. Therefore, current economic science uniformly argues that

definitions for “incremental benefit” and “incremental cost” can be accurately expressed in terms of

average values.

This is a fatal mistake. Even if the prevailing definition of marginal value were suitable for predictive

modeling and causal explanation, which it is not as discussed in the previous chapter, the use of average

values for incremental cost and benefit would render it less than useless.

This observation motivates the statement of the second General Principle.

General Principle II – The only thing we know about average costing (and average revenue) is that it is

incorrect.

To emphasize this principle, consider the Steel Mill example used in the presentation of the Law of

Universal Marginal Value where we added the production of one more ton of tempered Pickled Strips.

There are no circumstances for which it is reasonable to expect the cost (or revenue) of the next ton to

be anywhere close to the average cost or revenue.


- What if the next ton of production required the addition of an additional evening shift? A full

shift of cost would have to be absorbed by a single ton of production!

- What if the next ton required the addition of a weekend shift at overtime rates?

- What if the next ton required the purchase of an entire shipment of coal that then had to be

placed in inventory or discarded?

- What if the next ton required the purchase of additional coal, but there was no coal available in

the local market and it had to be imported at a cost 3x greater than that of the previously used

coal?

- What if the next ton of production increases carbon emissions beyond allowed limits and the

enterprise incurs a $10,000,000 fine?

It is widely known amongst experienced analysts that the use of average values in planning solutions is a

fatal mistake. Real-world cost functions do not look like the contrived graphs from textbooks. Real-

world cost functions are much more likely to look like the figure below, which is based on real-world

data and represents the incremental costs of admitting post-operative patients to an overnight stay in a

hospital.

In contrast to conventional economics, IBP’s driver-based approach means that, in the definition of the

Second Law of IBP, cost, distribution and yield, C, Y, and D, can be functions and not merely fixed,

average values. For example, the figure below shows the cost elasticity function used previously. In this

case, cost, C, is a function of the change in price, P, with respect to the change in quantity, Q.

Mathematically, this is represented as:

C = dP/dQ


Thus, C can be thought of as the Cost Elasticity or the Cost Response Function. In the case where the

activity is a sales event, then C represents a “negative cost” and can be thought of as the “Price

Elasticity” or the “Price Response Function.”

The distribution and yield vectors, D and Y, should be thought of in similar terms.

This is a critical aspect of IBP and it cannot be overemphasized. The driver-based nature of IBP analysis

is a natural extension of the constraint-based manner in which problems are defined. In other words,

using IBP, analysis is performed in terms of problems expressed as enterprise diagrams and constraints

associated with variables such as, “100 tons of coal can be purchased at $2/ ton, an additional 60 tons

can be purchased for $2.25/ ton, an additional 75 tons can be purchased for $2.75/ ton, …” and so on.

The First Law of IBP defines the frame of reference for analysis and the Second Law of IBP defines the

driver-based mechanisms for translating the enterprise diagram and the constraints into corresponding

mathematical analyses. The Third Law of IBP defines how this is extended to integrated analysis.

Before continuing further, it is necessary to clarify certain issues. In particular, in IBP analyses, it will

occasionally be advantageous or even necessary to use average values for variables representing cost ,

revenue, process rate, etc. While this might seem a contradiction to General Principle II, the

contradiction is covered by the third General Principle.

General Principle III – All universally quantified statements are wrong.


For now, General Principle III will be left to stand on its own merits. If these are not readily apparent, do

not be concerned. The will be explicitly addressed in subsequent chapters.

Infinite Degree of Freedom

To conclude this section on the Second Law of IBP, we revisit the issue of “Infinite Degrees of Freedom.”

Consider for a moment Newton’s Second Law which can be expressed mathematically as,

F=ma, or “force equals mass multiplied by acceleration.”

Given this definition, we can perform a variety of analyses. Obviously, if we know m, “mass,” and a,

“acceleration,” we can calculate F. In addition, because of the mathematical transformation rules of

Algebra, we also know that:

m = F/ a, or “mass equals force divided by acceleration,” (Quick Algebra Review: This is derived by

dividing both sides of the equation “F=ma” by “a” and flipping the terms on either side of the equal sign.

The “flipping” is done to conform to commonly used conventions where the variable to be solved for is

on the left of the equal sign. )

We also know that:

a=F/m, or “acceleration equals Force divided by mass.”

This is an example of what is meant by “infinite degrees of freedom.” In other words, a methodology is

said to provide infinite degrees of freedom when it does not in any way constrain the problem to be

solved to a subset of the overall representation. Newtonian Physics provides infinite degrees of

freedom. In contrast, analysis methodologies such as Monte Carlo simulation, which is not an empirical

science like IBP and Newtonian Physics, do not provide infinite degrees of freedom.

Clearly, the Second Law of IBP establishes a similar framework for economic analysis in that it allows us

to define any aspect of an enterprise model as the problem we are trying to solve. More specifically, the

Second Law of IBP is specified in three parts. As with Newton’s Second Law, each of the three parts of

the Second Law of IBP can be mathematically transformed such that any of the variables can be defined

in terms of the other variables.

Furthermore, because the variable A appears in each of the three parts, the three parts are all

interrelated in such a way that Part 1, for example, can be transformed to solve for A and then

substituted into Part 2 to solve for I. This would result in:


I = (F/CT)D. This is derived through multiple algebraic manipulations corresponding to the textual

description above. More specifically, clauses 1 and 2 of the Second Law of IBP state F=ACT and I=AD.

Clause 1 can be rewritten as F/CT=A by dividing both sides of the equation by CT. This result can be

used as a replacement for the term A in the second clause to give I = (F/CT)D.

For those readers who are excited about such things, it should be apparent how IBP establishes a

principled foundation for engineering high-value business management solutions. This is a capability

with the potential to transform how businesses and economies are structured and managed.

For those whose interests are not so inclined, please note that from this point forward, you will never

again need to be able to perform or understand these algebraic manipulations in order to exploit the

power of IBP. All you need to understand is that with IBP, these algebraic transformations are possible

and you can leverage this power for decisive competitive advantage.

Third Law of IBP

Newton’s Third Law is commonly given as, “For every action, there is an equal and opposite reaction.”

The IBP analog is,

Third Law of IBP:

1. For every process activity, the material/ energy and financial flows associated with that process

activity must balance.

2. For every enterprise diagram, the material/ energy and financial flows represented in the

enterprise diagram must balance.

In both Newtonian Physics and IBP, the purpose of the Third Law is to establish a basis for systemic

analysis.

In terms of IBP, the Second Law defines a basis for analyzing all aspects of each element of an enterprise

diagram. The Third Law defines how the balance constraints associated with the Second Law are

extended throughout the enterprise diagram and the integrated system it represents. More specifically,

the inputs, outputs, and financial consequences of any action are defined in terms of the inputs,

outputs, and financial consequences of the interrelated actions and the Law of Universal Marginal Value.

As an example, let’s look at three problems involving the Steel Mill example introduced previously. First,

let’s ask the question, “For a given amount of available Hot Strip, what products should we make and

sell to maximize profits?” For reference, the original enterprise diagram is shown below.


For the initial problem we are trying to solve – determining the output that will optimize profit for a

given amount of input material – we can use the Second Law of IBP and apply it to the process activity

represented by the object labeled, “Purch Hot Strip” to determine O, the output, and F, the financial

consequences associated with that action. We can then use the Third Law of IBP to mathematically

determine the input, I, to the process activity represented by the object labeled, “Hot Strip Inv.” We

know the input, I, for “Hot Strip Inv” because we have determined the output, O, from “Purch Hot Strip”

and because we know, based on the Third Law of IBP, that the output from “Purch Hot Strip” must

balance the input to “Hot Strip Inv.”

Conceptually, the balance constraints related to the Third Law of IBP propagate through the enterprise

model as shown below. The large green arrows show the Input Constraints and the associated Solution.

The smaller yellow arrows show how the mathematical constraints implied by the Third Law of IBP flow

through the enterprise diagram. For each of the yellow arrows, the inputs to the next step(s) in the

process flow are specified by the output of the previous step(s).


Now let’s consider a second problem, “For a fixed sales mix, what input materials should we purchase to

maximize profits?” In this example, as shown in the diagram below, the input constraint is placed on the

three Sales objects and the mathematical constraints implied by the Third Law of IBP flow backwards

through the enterprise diagram to the “Purch Hot Strip” object where the solution is determined.

To be more specific, in this second example, the input for each of the sales objects is defined in terms of

their constrained outputs. This requires a mathematical transformation and substitution based on the

Second Law of IBP that gives:

I = (O/Y)D.

Similarly, the balance requirements defined in the Third Law of IBP define the Output of the object

labeled, “Finishing Lines” in terms of the input to the object labeled, “Finished Inv” and this, in turn,


defines the input to “Finishing Lines.” Thus, for each of the yellow arrows, the output of each step(s) is

defined by the input of the following step(s).

Although they are not trivial, it should be clear that the mathematical transformations required for

these different analyses can be determined from The Second and Third Laws of IBP. It should also be

clear that these are not transformations anyone would want to do manually! Fortunately, as we work

through example exercises, computer software will handle these mundane details for us.

Before continuing to a third example, please note that if for some reason we want to do these analyses

manually, let’s assume for the moment we are using a spreadsheet tool, we will have to completely

rebuild the spreadsheet representation for each variation. This will not only require an extensive

amount of mathematical transformation and substitution, it will also require an understanding of how to


sequence the computations so that required computations are available as needed. These

mathematical manipulations are extremely difficult, even for relatively simple problems. While there

are a variety of complexities to consider, one of the more difficult is the issue of ambiguity.

With this in mind, let’s now consider a third example, “Given a requirement to run our Tempering Mill at

full capacity, what input materials should we purchase and what products should we make and sell to

optimize profits?” The implications of this example are shown in the figure below.

In this example, both the purchase and the sales activities are now solutions. Other balance constraints,

and the associated mathematical transformations, defined by the Third Law of IBP flow through the

enterprise diagram as indicated by the yellow arrows. The Input Constraint is applied to the Conversion

object labeled, “Tempering Mill.”


Above the green Input Constraint arrow is a red circle with two yellow lines, one directed upward and

the other downward. This is an example of an ambiguous balance constraint and a potential constraint

conflict that could result in an infeasible IBP model in which there is no solution that simultaneously

resolves all constraints. The ambiguity is the result of two flows of balance constraints. The first flows

from the Tempering Mill, through the object labeled, “Tempered CRS” to the Finishing Lines, and then

backwards from the Finishing Lines to the objected labeled, “Cold Reversed Strips.” The second flows

from the Tempering Mill, through the object labeled, “Annealed CRS” to Batch Annealing and then to

“Cold Reversed Strips.” (A more thorough review of the preceding examples will reveal similar

ambiguities that we chose to ignore until now.)

In other words, do the constraints on the object labeled, “Cold Reversed Strips” propagate from the

Tempering Mill through the Finishing Line? Or do they propagate from the Tempering Mill through

Batch Annealing? Or do they flow from the Tempering Mill through Pickled Strips and then through the

Cold Reverse Mill?

The answer is, “Yes.”

Mathematically, all these balance constraint flows must be consistent and any solution must satisfy

*ALL* the implied constraints on inputs, outputs and financial consequences. It is well-known that

these types of ambiguous material balance constraints can be represented with the matrix

representation described earlier. That is not difficult. There are, however, two considerations that are

more problematic. First, it is necessary to formulate the required representation using appropriate

domain semantics. Second, it is necessary to resolve all ambiguity through the use of a mathematical

optimization technique.

For now, it is only necessary to understand these issues at a conceptual level. This book is about the

fundamental principles of IBP, how to apply them to solve real-world problems, and how to leverage this

knowledge to your personal advantage. The specific details associated with these issues are dealt with

more appropriately in technical documents.

With respect to establishing a proper conceptual understanding, the following are the critical points.

You can feel comfortable ignoring technical specifics until you might actually need them, but you do

need to understand the following:

1. IBP is based on well-founded mathematical principles that stand up to close scrutiny in a

manner analogous to Newtonian Physics.

2. IBP analyses require software built specifically for that purpose. They cannot be fully supported

by spreadsheets, simulation tools, statistical analysis tools, OLAP tools, etc.

3. IBP absolutely requires true mathematical optimization techniques.


4. IBP *DOES NOT* require the practitioner to understand anything about science, mathematics or

engineering! All you need to understand is the problem *YOU* are trying to solve!

While some aspects of the above might seem overwhelmingly complicated, you will see in the examples

and case studies that IBP is surprisingly intuitive and easy to use. You will not need to know anything

more about mathematics, economics, physics or apples than you already know. In order for you to use

IBP successfully, the critical issue is that you have a reasonable – not even a thorough! – understanding

of the problem or problems you want to solve. You will soon find that IBP is much more than an analysis

paradigm. Like Newtonian Physics, IBP is an empirical science that provides a model of the real-world

that is sufficiently accurate to enable powerful predictive and explanatory capabilities that serve as a

foundation for engineering principles that can be used to build high-value business management

solutions.


Summary of Part I

In summary, Newtonian Economics is about a new approach to economic analysis called Integrated

Business Planning (IBP). IBP is an empirical science that resolves problems in conventional economic

science to provide analytical power comparable to Newtonian Physics, especially with respect to

predictive modeling and explanatory analytics based on causal analysis. IBP is formally defined in terms

of the Law of Universal Marginal Value and the Three Laws of IBP. The Law of Universal Marginal Value

addresses problems in conventional economic analysis by defining Opportunity Value as the optimal, net

economic impact of an action or decision. The Three Laws of IBP build on the concept of Opportunity

Value to establish a formal basis for economic analysis and a principled design, engineering, and

management framework for building and operating a new generation of high-value solutions.

In Part II – Pragmatic Applications & Decisive Competitive Advantage, practical applications of IBP will

be discussed and case study examples will be reviewed. The practical applications of IBP will be

presented in terms of solutions IBP enables and supports including the following:

- Performance Management and Financial Optimization

- Detailed Unit Cost Analysis

- Data Validation and Correction

- Infeasibility Processing

- Available to Promise/ Available to Deliver

- Available to Pay

- Differential Diagnosis

- Root Cause Analysis

- Sensitivity Analysis

- Risk Analysis and Minimization

- Bracketing the Solution Space


- Monte Carlos Analysis

- Rationalization of Customers, Products and Contracts

- Gap Analysis

- Capital Expenditure ROI Optimization

- Sequencing and Scheduling

- Net Present Value Optimization

- Benchmarking

- Market Mapping

- Budgeting and Active Financial Planning

- Flow Analysis

- Capacity Planning

- Real-time Audit and Assurance

- Price Optimization

- Yield Management

- Inventory Management and Safety Stock Management

- Supply Chain/ Logistical Management and Optimization

- Knowledge Management

- Strategic Enterprise Management, Communication and Goal Alignment

- Balanced Scorecarding

- Six Sigma

In Part III – Through the Looking Glass, IBP is used to analyze a number of economic issues. The issues

addressed include topics such as,

- What caused the Great Depression?

- What caused the financial crisis circa 2008 – 2011?


- Why is unemployment so high in 2011?

- To what extent can the behavior of a competitive market be explained and predicted?

- How should we restructure our financial markets for maximum effectiveness?

- How should governments manage economies?

- Is it even possible to balance a national budget? If so, how? If not, why not?

Portions of Parts II and III will be surprising and, in many cases, controversial. In fact, some conclusions

reached with IBP analysis will initially seem so outlandish that you will dismiss them without proper

consideration. But always keep in mind that IBP is a principled approach that not only allows you to

determine results, but also to verify those results such that you can have confidence in your conclusions

and you can share those conclusions with others. IBP allows others, in turn, to conduct their own

experiments and perform their own analyses to confirm or reject your conclusions. For now, you need

to begin conditioning yourself with appropriate expectations based on a simple, but perhaps disturbing,

set of cascading observations:

- Conventional economic analysis is based on calculations of Marginal Value/ Contribution

Margin.

- The conventional definition of Marginal Value/ Contribution Margin does not accurately

calculate what it purports to calculate. It is therefore reasonable to say that it is wrong.

- Therefore, the conventional definition of Marginal Value/ Contribution Margin is not suitable for

use in analyses supporting decision making processes. This is especially relevant for any

decision process involving some form of planning.

- Therefore, every decision made using conventional economic analysis is highly suspect and very

likely wrong.

- Therefore, every enterprise that has been managed or structured based on conventional

economic analysis has been managed or structured in ways that are highly suspect and very

likely wrong.

- Therefore, in general (though perhaps from a limited perspective), it is reasonable to conclude

that everything you thought you knew about economics is… well, … wrong.

For many, such a concept is preposterous. The suggestion that all economic analysis is, effectively,

“incorrect” or “wrong” is unimaginable. If you are in this group, recall that until comparatively recent


times, the prevailing view was that the Sun orbited the Earth. This view is now considered foolish or,

minimally, uninformed.

But think about this for a moment. How can you tell the difference? Just how foolish is someone that

claims the Sun orbits the Earth? How does one go about deciding the issue one way or the other? In

fact, in either case, the observable phenomena would be EXACTLY THE SAME! Without the aid of

modern technology such as a telescope, there is no way to settle the question one way or the other.

In other words, if for any reason you are feeling intellectually superior because you “know” the Earth

orbits the Sun and not the other way around, you need to understand the following: What you see with

your own eyes and feel with all your senses is exactly the same as what you would see with your own

eyes and feel with all your senses if the opposite was true and it was the Sun that orbited the Earth. For

the vast majority of us, the only reason we “know” the Earth orbits the Sun, and not the other way

around, is because someone told us. Very few of us have even the slightest clue how to prove the

question one way or the other.

But there is a second group. There is another group of people who will react differently. If you are in

this group, the statement that conventional economic analysis is, “wrong” will trigger a very different

response. For you, the reaction will be something like, “HA! I *KNEW* it! I *KNEW* the accountants

were wrong! I just couldn’t PROVE IT!” If you are a member of this group, you have probably known for

a long time that something was not quite right with conventional economic analysis and not being able

to prove it has probably been an enormous source of frustration. It may comfort you to know that you

are not alone. This second group includes notable members such as William Edwards Deming.

So which group are you in? Are you in the group that already knows conventional economic analysis is

wrong and now wants to understand why and what to do about it? Or are you in the group that feels

such a suggestion is preposterous? Or are you somewhere in between?

If you care to do so, it is easy to resolve the issue. It will be easy for you to determine for yourself which

group you are in. To understand how this will be accomplished, consider the following: Ultimately, the

debate over whether the Earth orbits the Sun or the Sun orbits the Earth was settled very simply. It was

settled by a single man, Galileo Galilei, looking through a primitive telescope. If there were no

telescopes, we would still be having the “Geocentric versus Heliocentric” debate. It took the invention

of the telescope to settle matters. Without telescopes, we would not be able to see and understand the

structure of nature – the physical structure of the universe – from a holistic, (more) comprehensive, and

integrated perspective.

Because of advances in computing technology, we now have the equivalent of “economic telescopes”

that will allow you to resolve the question posed above to whatever level of satisfaction you desire.

Such devices are necessary because, without them, conventional economic analysis appears to be

“correct” when in fact it is not. Without “economic telescopes,” it is not possible to understand the

structure of economic systems – enterprises – from a holistic, (more) comprehensive, and integrated


perspective. As a consequence, without “economic telescopes,” our understanding of economics is

wrong. In Part II, you will be introduced to such a device and it will be used in a series of examples.

If you are incredulous regarding IBP and the supposition that conventional economic analysis is wrong,

the intent in not to make you feel or appear foolish. The analogy between physical science and

economic science is very real and relevant. You should not feel foolish or threatened by IBP because the

reality of the situation is that, without advanced computing technologies and mathematical techniques

that have only come into existence recently, conventional economic science appears to be “correct.” At

the very least, until recently, the technology did not exist to prove that conventional economics is

wrong.

So what will it be? Are you ready to look through an “economic telescope?” Are you ready to find out if

everything you thought you knew about economics is wrong?

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