introduction to integrated business planning

Copyright 2011, All Rights Reserved

Integrated Business Planning

An Introduction to Newtonian Economics

By Robert C. Whitehair, PhD


Introduction

Do you understand the full impact your management decisions have on your organization? In general,

this can be quite difficult to determine! For example, if your area of responsibility is inventory

management, can you say for certain what will happen if you change any given management policy? If

you change safety stock levels, do you know what will happen to sales? To manufacturing? To

purchasing? If you take steps to minimize *YOUR* costs, do you know what will happen to overall

costs?

In general, understanding the interacting effects of decisions in an organization is an extremely difficult

task. It is possible, perhaps even likely, that reducing certain inventory costs will increase the cost of

manufacturing or purchasing. Is the net effect positive or negative? Alternatively, decisions regarding

inventory policy could reduce sales or significantly alter customer satisfaction levels. How do you

determine the best method for aligning purchasing, manufacturing, inventory and sales decisions? How

do you align your decisions with overall corporate strategy?

Many $Billions have been spent by organizations attempting to make better management decisions.

Despite these staggering investments, companies – well-run companies! – go bankrupt. In less extreme

situations, business strategies that initially appear promising, too frequently result in unmitigated

disaster. On a daily basis, countless decisions result in unintended consequences that have negative

implications for your organization as a whole. This has almost certainly happened to you! If you are a

senior manager or executive, this has probably happened numerous times!

Why?

Why is it that we can build, launch, and operate spacecraft with such precision that we can land them on

a tiny asteroid millions of miles away but we cannot reliably manage a mortgage portfolio?

Why is business decision making more akin to alchemy or mysticism than to science?

One problem is that the most basic element of conventional economic analysis is wrong.

You probably didn’t want to hear that.

On the other hand, you might have already known or suspected that this was the case. A surprising, and

growing, number of people have come to this realization.

It is critical that you understand this point so it will be repeated: The most basic element of

conventional economic analysis is wrong.

Today, conventional economic science is based on the concept of the marginal analysis, or the

marginal value, of decisions or actions 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, Washington 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 and it is the basis for the definition of

Contribution Margin. It is used in virtually every economics text book and it is the basis for decision

management tools.

Unfortunately, as stated above, this definition is problematic for use in supporting decision making.

Since all decisions are about the future, it should not be used for any task, activity, or automated

solution related to any kind of “planning.” There are two primary reasons:

- The definition of Marginal Value/ Contribution Margin does not take into account the causal

drivers of economic activity and consequence.

- The definition of Marginal Value does not take into account interacting effects.

This paper introduces a new approach to economic analysis called Integrated Business Planning™ (IBP).

IBP is an empirical science that resolves the 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. Because of the analytical precision it enables, and its

correlated structure, IBP is also referred to as Newtonian Economics™.

IBP corrects conventional economic analysis and 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. Opportunity Value can be thought of as the causal driver of economic activity and

consequence.

The Three Laws of IBP build on the concept of Opportunity Value to establish a formal basis for

economic analysis of integrated systems and a principled design, engineering, and management

framework for building and operating a new generation of high-value solutions. In effect, these laws

define a formal approach for analyzing all the interacting effects relevant to any and every economic

decision.

Conclusions reached with IBP analysis are often 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.

The basis of IBP is the concept of Opportunity Value. The next section provides an introductory example

and comparison with Marginal Value/ Contribution Margin.


IBP Basics: Opportunity Value vs Marginal Value/ Contribution Margin

Consider this question: If Product A has a Marginal Value/ Contribution Margin 35% higher than Product

B, which one should you make?

The answer might seem obvious. Conventional economic analysis would say to make the product with

the higher Marginal Value/ Contribution Margin. But the reality is that very few organizations know

how to answer this question correctly! IBP is the only analysis technique capable of answering these

types of questions correctly!

The figure below shows a fictitious Candy Company that will be used in a simple example demonstrating

IBP analysis techniques. For the purposes of this demonstration, some tedious details must be

established. It is important to pay attention to these details and to understand what is about to happen.

In the context of our very simple Candy Company, we are going to ask an important question. It’s a

question that is asked countless times every day, “What should we do?”, or, “What is the best

decision?”

Although this example might seem overly simplistic, you should pay attention because this is an

interesting question. It is interesting because the question is almost *NEVER* answered correctly!

Based on an informal survey, fewer than 10% of respondents answer correctly. For the record, this is a

straightforward problem. There are no tricks or gimmicks. All costs are variable so the correct answer

does not depend on some arbitrary allocation of fixed overhead costs. So read carefully and see if you

can beat the odds and answer correctly!

The Candy Company makes two products, Nutcrunch and Megacrunch, using the five process steps as

shown. Nutcrunch has three ingredients, chocolate, sugar, and nuts. Megacrunch has only two

ingredients, chocolate and sugar. For the purposes of this example, assume the cost of ingredients is as

follows:

- Chocolate = $2/ unit

- Sugar = $1/ unit

- Nuts = $5/ unit


The process of making Nutcrunch produces 80 units of Nutcrunch per hour. Each unit of Nutcrunch

consumes 4 units of Chocolate, 2 units of Sugar, and 1 unit of Nuts. Nutcrunch has a cost for packaging

of $0.10/ unit and a $5/ hour processing cost.

The process of making Megacrunch produces 120 units of Megacrunch per hour. Each unit of

Megacrunch consumes 3 units of Chocolate, 3 units of Sugar and 0 units of Nuts. Megacrunch has a cost

for packaging of $0.05/ unit and a $5/ hour processing cost.

Both Nutcrunch and Megacrunch are processed on a machine called a Mixer. The Mixer has a labor cost

of $240/ hour and can be operated up to 40 hours/ week.

The price of Nutcrunch is $40 and the price of Megacrunch is $30.

With this information, we can calculate Marginal Values/ Contribution Margins for Nutcrunch and

Megacrunch, as shown in the figure below.

Based on the analysis shown, Nutcrunch has a Marginal Value/ Contribution Margin of $21.84/ unit. In

other words, the incremental value of one more unit of Nutcrunch is $40 and the incremental cost is

$18.16. By the same reasoning, Megacrunch has a Marginal Value/ Contribution Margin of $16.91.

So here is the key question: What should the Candy Company produce to maximize profits?

Based on conventional economic theory, the answer seems clear – since Nutcrunch has by far the higher

Marginal Value, the Candy Company should make only Nutcrunch. The consequences of this decision

are shown in the Net Income statement below.


The decision to make only Nutcrunch seems obvious. Nutcrunch’s margin is almost $5 more than

Megacrunch. To understand the difference between the two choices, the Net Income statement can be

recalculated assuming the Candy Company only makes Megacrunch. This is shown below. In this figure,

the column labeled “Solution” is the result of making only Megacrunch. The column labeled “Budget” is

the result of making only Nutcrunch and is the same result as above. Finally, the column labeled,

“Solution – Budget” is the difference.

In summary, the Candy Company’s Net Income is $11,280 higher when it makes only Megacrunch – the

product with the lower Marginal Value/ Contribution Margin. This probably seems counter-intuitive.

How can making only the product with the *LOWER* Marginal Value/ Contribution Margin generate a

*HIGHER* Net Income?

First, let us examine the question of how to maximize profits using Opportunity Value analysis. In the

book, Newtonian Economics Part I, the Law of Universal Marginal Economic Analysis defines the


Opportunity Value of an action, A, in terms of 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 or decision 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.

As an example, consider a scenario where only Megacrunch is made by the Candy Company. In this

context, the Opportunity Value for Nutcrunch is -$3.52. In other words, if the Candy Company makes

one more unit of Nutcrunch – the product with the HIGHER Marginal Value – Net Income will be

reduced by $3.52.

The figure below shows the financial consequences of the Candy Company making one more unit of

Nutcrunch. The previous result is shown in the column labeled “Budget” and the difference from the

current solution is shown in the “Solution-Budget” column. As shown, Net Income goes down by exactly

the amount of Nutcrunch’s Opportunity Value.

Although this appears to demonstrate the accuracy and relevance of Opportunity Values, it still does not

explain why making the product with the higher Marginal Value – as defined by conventional economic

science – causes Net Income to be reduced.

According to IBP analysis, the issue is the interaction between Nutcrunch and Megacrunch. If we

examine the problem definition, we see that there is only one point of interaction – the Mixer.

Manufacturing either product consumes some of the limited hours (40) on the mixer. Therefore,

because the Mixer is a bottleneck, increasing production of either product will require reducing

production of the other product. In order to calculate the net economic impact – the Opportunity Value

– of increasing production of one product, we must “net out” the lost margin associated with reducing

production of the other product.

In the scenario above, increasing production of Nutcrunch requires reducing production of Megacrunch.

Nutcrunch has a process rate of 80 units/ hour, so producing 1 unit requires 0.0125 hours on the Mixer.


In contrast, Megacrunch has a production rate of 120 units/ hour. In 0.0125 hours, 1.5 units of

Megacrunch can be made. Therefore, in order to make 1 more unit of Nutcrunch, the Candy Company

has to make 1.5 fewer units of Megacrunch.

So, although Net Income will be increased by the Marginal Value of Nutcrunch, it will be decreased by

1.5 multiplied by the Marginal Value of Megacrunch since we are forced to make 1.5 fewer units of

Megacrunch.

Marginal Value of Nutcrunch = $21.84.

Marginal Value of Megacrunch = $16.91.

Therefore, OppValNutcrunch = $21.84 – (1.5 * $16.91) = -3.52

In other words, because of the constraint on available Mixer hours, selling one more unit of Nutcrunch

requires selling 1.5 fewer units of Megacrunch. Therefore, the net economic impact of making and

selling 1 additional unit of Nutcrunch is the Marginal Value of Nutcrunch less 1.5 times the Marginal

Value of Megacrunch, or -$3.52.

Alternatively, we can think of the decision regarding what products to produce in terms of the Mixer.

Given that we have 40 hours of available Mixer time, we can make 3,200 units of Nutcrunch or we can

make 4,800 units of Megacrunch. The implications for Net Income are:

Net Income from making Nutcrunch = 3,200 units * $21.84/ unit = $69,880.

Net Income from making Megacrunch = 4,800 units * $16.91 = $81,160.

In summary, even in very simple situations, the conventional definition of Marginal Value/ Contribution

Margin should not be used as a basis for making decisions intended to optimize financial performance.

As might be expected, the severity of the problem increases with the complexity of an enterprise’s

interactions. For the Candy Company, enterprise interactions are easily understood in terms of different

production rates on a shared resource. In real-world enterprises, interactions are far more complex and


much more difficult to understand. The next section provides a glimpse into the significance of IBP

analysis in real-world enterprise where the interactions are far more complex.

Integrated Business Planning

Very few people answer the question about the Candy Company correctly. In more complex enterprise

systems, answering such questions is virtually impossible. Consider the diagram below that represents a

large chemical manufacturer that will be referred to as “Chem.” Insights garnered from IBP analysis of

this enterprise are representative of what can be expected from any and every IBP analysis.

What might those insights be? What can you expect to learn from IBP analysis of a real-world

enterprise?

Quite simply, you should expect to be surprised. You should expect that almost everything you think

you know about your organization is … well… wrong. To some extent, this won’t surprise you. Your

general intuitions are probably correct but your existing enterprise analytics are almost certainly

incorrect. Ironically, you might find this a great relief or even a vindication of your intuitions! For many,

IBP analysis is far more closely aligned with their intuitions and serves as a welcome validation that

refutes what they have long felt to be misleading analysis based on cost accounting, “BI,” or various

forms of conventional economic analysis.

Although IBP analysis is relevant to every decision an enterprise faces, in order to better understand the

potential for IBP, it can be helpful to focus on one set of decisions – sales. For example, consider the


diagram below with four tables comparing Opportunity Values with Marginal Values/ Contribution

Margins associated with sales of products made by Chem. Actual product codes have been obscured at

Chem’s request.

The four quadrants represent distinct categories of opportunity related to sales activities:

- Reduce Activity – The upper left quadrant shows activities for which both the Opportunity

Values and the Marginal Values/ Contribution Margins are negative. These are activities that

Chem should consider reducing or eliminating.

- Unrecognized Opportunity – The upper right quadrant shows activities where the Opportunity

Values are negative but the Marginal Values/ Contribution Margins are positive. These are

“Unrecognized Liabilities” – activities that Chem believes to be profitable but that actual

decrease Net Income.

- Unrecognized Opportunities – The lower left quadrant shows “Unrecognized Opportunities” –

activities that Chem believes to be unprofitable but that actual increase Net Income, in some

cases, quite substantially!

- Expand Activity – The lower right quadrant shows activities that should be considered for

expansion where both the Opportunity Values and the Marginal Values/ Contribution Margins

are positive.


Examine the tables defining the four quadrants and you will notice that even in situations where the

Opportunity Values and Marginal Values/ Contribution Margins have the same signs, the values differ, in

some cases considerably.

All decisions in an enterprise can be viewed with a similar representation. In other words, all decisions

related to manufacturing processes can be viewed in terms of these quadrants. There will be some

activities that should be considered for reduction, some that are Unrecognized Liabilities, some that are

Unrecognized Opportunities, and some that should be considered for expansion. Purchasing, logistics/

distribution, inventory management – every kind of decision an enterprise makes can be viewed

similarly.

In general, there are two kinds of value associated with this analysis, explicit and implicit. With respect

to the explicit value, this information can be used directly in a variety of decision support solutions. For

example, it can be used to set pricing on a periodic basis or to define sales commissions to encourage

activities that will take advantage of Unrecognized Opportunities. This information can also be used to

support sales and negotiation processes. What prices should be used in a new customer contract?

What volume can be promised for delivery?

Based on actual results from IBP analyses, an enterprise should expect the explicit value from IBP to be

an increase in Net Income equal to 3% to 8% of SALES. This typically represents a doubling or tripling of

profit.

The implicit value from IBP analysis is reported to be even greater. The implicit value is derived from a

significantly improved understanding of the enterprise’s key performance drivers. At least to some

degree, what you currently think are the key drivers of your business may or may not be the actual key

drivers. Minimally, the magnitude of their influence on financial consequences is almost certainly

misunderstood.

For example, Chem thought a key driver of their business was minimizing inventory cost. Prior to IBP

analysis, they attempted to set inventory levels throughout their supply chain at “safety stock” levels

that minimized overall inventory levels. They quickly discovered that this strategy had huge negative

opportunity values. Through IBP analysis, they came to understand the following:

- Holding greater quantities of inventory allowed for longer production runs that dramatically

reduced manufacturing costs.

- Holding increased quantities of “high-quality” product and implementing substitution/

downgrading policies allowed them far greater flexibility in terms meeting customer demands.

This increased customer satisfaction and actually reduced costs across the supply chain. This

strategy is represented in the Chem model by the icon labeled, “Approvals” at the bottom/

middle of the diagram.

- Outsourcing was a huge opportunity for enhanced profitability. In situations where Chem’s

production capacity was insufficient to meet the demands of a new customer contract, they

found that they could realize extremely high Opportunity Values by submitting contract

proposals with high prices and then, when they won the contracts, outsourcing production to


competitors at prices that guaranteed them high Net Income. This strategy is represented by

the icon labeled, “Purchase Product” at the top/ middle of the diagram.

In summary, every kind of decision an enterprise makes can be analyzed with IBP in terms of the

quadrants used in the example above. This analysis will provide the enterprise with two kinds of value –

explicit improvements in operational decision making and implicit improvements in terms of

understanding the actual key drivers of success for the enterprise.

Conclusion

The basic element of conventional economic analysis, Marginal Value/ Contribution Margin, should not

be used for any form of analysis related to “planning.” Please 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.”

Integrated Business Planning, IBP, resolves the 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 defines Opportunity

Value as the optimal, net economic impact of an action or decision. Opportunity Value can be thought

of as the causal driver of economic activity and consequence. The Three Laws of IBP define a formal

approach for analyzing all the interacting effects relevant to any and every economic decision and

establish a formal basis for economic analysis of integrated systems and a principled design,

engineering, and management framework for building and operating a new generation of high-value

solutions.

Enterprises conducting IBP analysis of decisions will find surprises that can be grouped into four

categories:

- Reduce Activity – Activities where both the Opportunity Values and Marginal Values/

Contribution Margins are negative.

- Unrecognized Liabilities – Activities where the Opportunity Values are negative but the Marginal

Values/ Contribution Margins are positive, implying the enterprise could increase profits by

decreasing these activities.

- Unrecognized Opportunities – Activities where the Opportunity Values are positive but the

Marginal Values/ Contribution Margins are negative, implying the enterprise could increase

profits by increasing these activities.

- Expand Activity – Activities where both the Opportunity Values and the Marginal Values/

Contribution Margins are positive.


IBP analysis provides the enterprise with two forms of value – explicit and implicit. Explicit value is

realized through operational solutions leveraging an understanding of how a particularl decision under

consideration falls into one of the different decision categories.

Explicit value is realized by the enterprise’s improved understanding of its true key performance drivers.

In terms of quantified value, an enterprise should expect IBP analysis to deliver an increase in Net

Income equal to 3% to 8% of sales.

Ultimately, however, the value proposition is much greater. Any organization that fully embraces IBP

will enjoy not only a dramatic improvement in their financial performance, they will also gain a decisive

competitive advantage they can leverage to gain dominance of their industry. At that point, the full

benefits of IBP are only limited by the creativity of the enterprise!


About the Author Dr. Robert C. Whitehair has led pioneering research and development of Integrated Business Planning

since the 1980’s. Dr. Whitehair has founded multiple companies and research labs related to IBP, most

recently River Logic, Inc., where he is the Chief Research Officer and Director.

In addition to IBP, Dr. Whitehair's research involves theoretical and mathematical foundations of

artificial intelligence, knowledge-based programming languages, knowledge-based decision support

systems, simulation and computational mathematics. Dr. Whitehair earned his Ph.D. from the

University of Massachusetts.

Chronology

Founder, Director, Chief Research Officer, River Logic, Inc. (RLI), Beverly, MA. River Logic established as the market and technology leader for Integrated Business Planning and technology infrastructure enabling the Knowledge-based economy.

Director of Research and Development, EBSCO Publishing (EP), Ipswich, MA. Developer of infrastructure for 3rd

Generation Internet Networks, IBP knowledge exchanges, and value-added processing for content products.

President, CEO, COR Decision Support Systems, Inc. (CDSS), Ipswich, MA. Market leader for technology enabling 3rd

Generation IBP Network Solutions and knowledge exchanges.

Division General Manager, EBSCO Information Technologies (EIT), Ipswich, MA. Market leader for solutions providing state-of-the-art, value-added differentiation for content products.

President, CEO, ArtLog, Inc., Beverly, MA. Collaboration with elements of the Russian Academy of Science, developer of over 100 enabling technologies successfully deployed in IBP knowledge exchanges and 3

rd Generation Internet Networks.

President, CEO, COR Technologies, Inc. (CTI), Beverly, MA. Pioneered development of first Integrated Business Planning Solutions.

President, Co-founder, Probots, Inc., Northampton, MA. Joint IBP R&D with E.I.Du Pont De Nemours and Company of Wilmington, DE; General Electric Corp. of Pittsfield, MA; Gold Hill, Inc., of Cambridge, MA; Mathsoft, Inc., of Cambridge, MA; Mapinfo of Troy, NY; Environmental Science Research Institute (ESRI) of Redlands, CA; CLN, Inc., of Littleton, MA; The Paper Corp. of Des Moines, IA; and Lamb-Grays Harbor Co. of Hoquiam, WA.

Vice-President of Research and Development, Probots Developments, Inc., Vancouver, British Columbia. Developer of the Educational Plan, a large-scale, international project to increase cooperation between Russian science institutes and research/education organizations and commercial enterprises in North America.

Corporate Director, Co-founder, Consultant, Top Level, Inc., Amherst, MA. Developer of high-performance parallel software languages, parallel programming tools, and applications requiring high-performance parallel computations.

Research Scientist, University of Massachusetts, Amherst, MA. Development lab focused on theoretical and mathematical foundations for the development of search-based design theories. Special emphasis on the use of abstractions, approximations and problem reformulations, theoretical search, real-time processing, managing uncertainty, and control of problem solving.

Staff Research Writer, Smithsonian World Television Series/Adrion Malone Production Company, Washington, DC. Script and screenplay development and film production for Smithsonian World.

Consultant, Arthur Andersen & Co., Pittsburgh, PA. Manufacturing consultant specializing in computer integrated manufacturing (CIM).

Research

Integrated Business Planning: A formal extension of contemporary economic theory that provides significant improvement in

the ability to predict and explain the behavior of micro- and macroeconomic enterprises. IBP solutions have been deployed in

over 300 client engagements in many industries and include:


- Harvest Planning: Sugar Cane/ Sugar Beets; Fruit; Grain - Service Industry Capacity Planning: Banking; Insurance; Consulting - Strategic Enterprise Optimization: Various Process Industries - Manufacturing Optimization: Various Discrete and Continuous Process Industries - Trade Promotion/ Market Mix Optimization: CPG; Automotive - Procurement Planning and Optimization: Various Discrete and Continuous Process Industries - Negotiation Management - Integrated Delivery System Planning: Healthcare Industry - Practice Planner: Healthcare Industry - Treatment Plan Analysis and Optimization: Healthcare Industry - Competitive Global Market Analyzer: Grains - Pathfinder Oil&Gas Planner - Pipeline Scheduling and Management - Municipal Water Management - Blending Optimization: Metals; Chemicals; Mining/ Coal; Food Industry - Strategic Pharmaceutical Planning - AFP (Active Financial Planning) and Enterprise Performance Management - Cost-to-Serve Optimization, Negotiation, and Planning - Emission/ Effluent Planning and Analysis (GHG Planning): Various Discrete and Continuous Process Industries - Network Analysis and Planning: Telco - Client Life Cycle Management and Optimization: Consumer Industry - Paper Mill/ Saw Mill Planning and Optimization - Financial Planning and Optimization for the Financial Industry - IT Optimization and Planning - Defense Department related Planning solutions: Quadrennial Review; Force Optimization; Recruiting Planning and Optimization;

Logistical Planning and Optimization - Risk Analysis and Planning (integrate IBP/ Monte Carlo) - Various Discrete and Continuous Process Industries; - Total Energy Management and Optimization

- Product (SKU) Rationalization

PhD Research, ``A framework for the analysis of sophisticated control,'' Department of Computer Science, University of Massachusetts, 1995. PhD research under the direction of Professor Victor R. Lesser that developed theoretical foundations and mathematical formalisms for specifying and analyzing problem domains and sophisticated problem solvers.

Integrated Business Planning: Developed theoretical foundations for describing enterprise management problems and associated design principles for constructing sophisticated problem solving systems.

Modeling of Dynamic Systems and Intelligent Agents, In cooperation with the Russian Academy of Science. Developed mathematical formalisms that support the modeling of dynamic systems of objects and intelligent agents (i.e., agents capable of learning and applying their knowledge in novel situations).

Constraint-Oriented Reasoning, In cooperation with the Russian Academy of Science. Developed high-performance reasoning, analysis and simulation systems based on techniques for both representing domain knowledge as a structured database (or knowledge-base) of constraints and for exploiting the knowledge-base to efficiently define systems of closed-form mathematical expressions that describe relevant properties of objects in a given domain.

Global Integrated Planning SYstem, GIPSY, In cooperation with E.I.Du Pont De Nemours and Company of Wilmington, DE, and General Electric Corp. of Pittsfield, MA. The GIPSY research project investigated techniques for representing and analyzing large-scale industrial processes integrating production processes, financial, and logistical systems.

Temporal Geographic Information System, In cooperation with ESRI (Environmental Science Research Institute), of Redlands, CA, and Mapinfo, of Troy, NY. Investigated issues associated with incorporating a representation of time into geographic information systems.

Parallel Programming Languages, In cooperation with the University of Massachusetts, developed language constructs and implementation techniques for high-performance parallel programming.

introduction to integrated business planning

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