Expert systems in management science

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Expert Systems in Management Science

Philip COOPER Palladian Software Inc., Four Cambridge Center, Cambridge. Massachusetts 02142, USA

Decision support software as we know it has failed in its goal of bringing managers and the management sciences closer together. Conventional software requires computer literacy and detailed technical modeling skills. Top managers, on the other hand, are prized for an entirely different set of skills: experienced judgment, innovative thinking and the ability to see the "'big picture". Even if they could build complicated mathematical models, work closely with computers and had timely access to relevant details, most managers would not have the time.

This paper contends that the gap between managerial and analytic/computer competence can be bridged with expert systems meeting the following criteria: 1. Require no user training 2. Interact and explain themselves 3. Learn from and adapt to users 4. Incorporate state-of-the-art analytic techniques

Palladian Software, Inc., of Cambridge Mas- sachusetts is using artificial intelligence technology to build such expert systems for managers. An example of one application is provided.

I. Introduction

Over the past fifty years, the management of corporations has taken on more of the complexion of a science than an art. Of course, the human element is still paramount; no one would claim that creativity, initiative, ambition, and people skills lend themselves to quantification and numerical analysis. Or that we could dispense with people and manage by machine. However, these talents are no longer enough, by themselves, to manage the affairs of a large business.

North-Holland Future Generations Computer Systems 2 (1986) 217-223

Business is becoming ever more complex and that complexity rests on a foundation of numbers. Dispersion of ownership, the informational demands of more sophisticated capital, detailed governmental reporting and accounting require- ments, complex bank and financial market ~ rela- tionships, taxes, and global competition coupled with near limitless opportunities and alternatives for new business, products, markets have created tremendous demands for planning, analysis and reporting. Stakes are high and getting higher. Margins for error have just about disappeared. There has been an ever increasing demand for sophisticated analysis to lessen the chances of decisions being made on improper oi impotent information.

To attempt to deal with some of this complexity, corporations have turned to formal planning and analysis. Whether strategic or of a day by day tactical nature, these techniques and methodologies are mathematical. For example, the accounting discipline has become more and more powerful and complicated. There is cash accounting, accrual accounting, value added accounting, and many others. "Financial engineering" has yielded techniques such as discounted cash flow analysis, IRR, Payback, measures of profitability, ratio analysis and scores of other tests and measures. Statistics has evolved techniques such as Monte Carlo simulation, regression analysis, correlation techniques and many others for business analysis. The relatively new science of Operations Manage= ment has brought linear programming, mixed integer programming and other optimization techniques into the fold.

More recently, the advocates of "formal strategic planning" have introduced matrix oriented ranking paradigms, and a variety of financial-mea- sure based evaluative techniques such as sustaina- ble growth, market to book, P /E and other techniques to assess the value and potential of business. On the frontier we see such exotica as contingent claims analysis, options pricing theory and so on have been added to the armory.

The pace of modern business results in great pressure on the decision making process. Time has become one of the most critical factors of any

0376-5075/86/$3.50 ¢) 1986, Elsevier Science Publishers B.V. (North-Holland)

218 P. Cooper / Expert Systems in Management Science

decision. Herein lies one of the dilemmas of modern management science. Decisions are needed in very little time, but time is needed to do thorough analysis. The management science techniques mentioned are fundamentally mathematical techniques. They are computationally difficult and time consuming. The availability of high speed computers might lead one to conclude that the mathematical complexity of analytic techniques need not provide a barrier to their [effective] use. This, as we shall see, is a dangerous fallacy.

2. Managers of Mathematicians?

In response to the increased demand for the use of quantitative management science, computer "decision support" software was developed to make their use more convenient. It is an interesting philosophical speculation whether the coming of the computer age was the cause of much of this complexity or was an attempt to respond to it. Personally, I subscribe to the former view but the ends are the same. Traditional decision support software was developed with a primary view towards making the manipulation of mathematics easier than by working with pencil, paper, tables and mechanical devices such as slide rules.

For our purposes, we define such traditional software products to be computer programs which are designed to assist managers in analyzing, planning, simulating, modeling, and hypothecating business situations. These software products in- clude modeling and simulation languages of all types from large, non-procedural, mainframe based products to smaller matrix-oriented spreadsheets. All the normal arithmetic, algebraic and matrix algebraic (rows and columns) operations, as well as elements of database, statistics, specialized financially oriented mathematics (such as NPV analysis), query capability, "what-if analysis" and the like are usually included to one degree or another. More recently, a strong presentation graphics capability is being incorporated in these products.

Unfortunately, while these tools have made it more convenient to write computer models (for those inclined to do so) they have fallen short of their goal of bringing management science and management together. This is because management requires experience, judgement and perspec-

tive. Mathematics is, in these regards, sterile. It is clear that what computers have not been able to do is to bring human experience and judgement to their quantitative legerdemain. We are drowning in data, but thirsty for information. We are equipped with powerful tools, but often without an understanding how to apply them. It has developed that while a modeling language or spreadsheet is easier to work with than, say, For- tran; it is not really of any more use to the operating manager because of inherent assumptions made about the user which we will examine in more detail shortly.

2.1. "'Operating Engineers"." Middlemen of the Computer Age

Because of the long learning time necessary to gain proficiency in using the tools, only specialists can use current DSS systems. Therefore, specialists trained in quantitative methods and the operation of computers have become the "operating engineers" of management science. Specialists have taken over management science and, as a practical matter, management can only gain access to the power of quantitative methods through the filter of the specialists. In order to have analysis pre- pared and subsequently explained, modified, re- explained and though however many iterations are necessary to deal with the matter at hand, managers must have access to the specialists. However, costs in dollars and time (particularly if the specialists are not located where the management charged with dealing with an issue are e.g.; a division manager examining a new product introduction or investment in cost reducing machin- ery must often call upon "strategic planners" financial analysts from headquarters) are so great that the tools and specialists can only be used for the most important matters.

This is not the way the process should work. To be useful to management, management science must be brought t o the forefront of management and be tools they can use with their own hands. Computer DSS systems which support strategic planning and tactical analysis must adapt themselves to the real world human demands of those managers that they would serve: 1. The "computer literacy", or lack thereof, of the

average manager. 2. The practical and applied experience and

P. Cooper / Expert Systems in Management Science 219

judgement of those managers who ultimately make and implement decisions and who expect the analysis presented to them to take such learning into account. Unfortunately, such is not the case today. Un-

derstanding why goes a long way towards understanding why many management science techniques and financial analytic techniques are either not used, used incorrectly, or viewed with great suspicion by operating managers. It is obvious that the advanced techniques hardly ever get brought to bear despite their great utility for many types of complex decision making for the same reasons.

2.2. Quantities versus Quafities

Why is this the case? There is a curious inverse relationship between the business acumen of the "specialist" and that of a seasoned manager. The greater the proficiency of the specialist is the world of modeling languages, spreadsheets and other mathematically based tools, the less he may understand the vagarities and subtleties of business decision making which are not based on quantifiable things. Or have the practical and applied experience to recognize conditions which might exist in theory, but are unrealistic in the real world. Examples of this abound in most analysis and, we are all familiar with the ritual of a senior manager or management committee beginning the process of reviewing or approving a proposal by an exhaustive examination of the assumptions which went into the analysis and the realism and relevance of values assigned to variables.

Why do the analysts often make the mistake of assuming that that models are accurate enough proxies for the real world that they may be depended upon. We suggest that this can arise by the seductiveness of numbers. Numbers give the appearance of precision by their nature. If the financial convention is two digits after the decimal point, then the world must be resolvable to two decimal points. We are often amused to see the results of calculations presented with four or five decimal places of precision merely because the mathematics employed generated them! The situation is even more disturbing when one contem- plates that the values assigned to variables in the model are often estimates of the future with un- certainty of 25% or more inherent in them. It takes

an experienced eye to spot these things, or antic- ipate them by blending judgement into mathematical models.

The popularity of mainframe modeling systems, coupled with the explosion of inexpensive spreadsheets and other microcomputer software, has led to the wide acceptance of the notion that the world is amenable to two-dimensional mathematical abstractions and, therefore, that computer based decision aiding tools are a necessity to remain competitive. Children are learning to use them at an early age, and familiarity with them is required at all the leading business schools. In- deed, they are even finding their way into the home, though it is not clear they are as useful there as people initially perceived. In all, it is a rare company who does not have some type of DSS system.

3. IJmitations of Conventional Soft,rare

The unhappy result of all this is that conventional decision support software makes five crucial assumptions about the managerial competence of the user:

1. The User Knows What Type Of Problem He Has. This means that the user must know formally what type of problem he is confronted with. It is not enough to say that one's problem is allocating capital among competing opportunities: one must be able to say that the problem is one of priori- tized net present values, or that it is more suitable to a linear programming technique. One must know formally, not just empirically what type of problem is at hand.

2. The User Must Be Able To Build A Model In Equation Form. This tenet first assumes that tenet # 1 is true. Then the user must be able to create a model of equations that properly sets up the appropriate technique(s) and solve them.

3. The User Has Timely Access To Relevant Information. For instance, if NPV is to be used, and it is set up properly, the assumption is that the user knows the correct form of financing, knows the correct discount rate, knows the company's policy on depreciation, etc.

4. The User Knows How To Interpret The Answers. Assuming that 1, 2 and 3 are true, the user must be able to fully interpret the solutions that the model produces. For example, if statistics


are in use and the model produces a mean value and the standard deviation, the user must know what the standard deviation means, and does not mean! If the model produces after tax profit of 5%, the user must know whether that is good or bad compared to company and industry norms.

5. The User ls Computer Literate And Facile With The Program. Obviously, if the user is not familiar with how to use the computer he won' t be able to even call up the program. If the user is not facile with DSS system, he will not be able to build the model! Even if the user has the knowledge and skills to do the above, he still must have the time.

We believe that all these assumptions are almost universally false. Because the five tenets are almost universally false, one can only conclude that the purpose and use of traditional DSS products, assumed by both the vendors and the users, must be to write the equations of the model in an easier fashion than by programming them in For- tran or some other computer language. The knowledge of what to do must rest with the user! Therefore, products intended for specialists are not directly useful to operating managers.

3.1. The Gap Widens

these goals are met, then the specialist will only become more useful as well. Our research shows that, in general, most managers: - Can only describe their business problems em-

pirically. - Do not know, or remember how, to build

mathematical models. - Do not have timely access to appropriate infor-

mation. - Often have difficulty interpreting the results of

such models. - Are not computer familiar or facile with DSS

programs. In any case, most managers do not have the

time to do all the above even if they could. Why hasn' t it been possible to build systems

which could meet these criteria in the past? Be- cause to do so requires that those elements of experience and judgement that distinguish out- standing managers be [partially] built in. As these types of knowledge are not usually algorithmic or easily reduced to formulae, there was not practical way to capture them.

4. ' lhe Solulion: lnlelligenl (~ompulers

An interesting phenomenon of the conventional tools is that their growing popularity results in a larger actual and potential user base. As that user base expands, the average managerial competence of the user decreases. By competence we mean the competence of the manager to act both as a detail analyst and model builder for his business problems, and to be computer familiar enough to con- struct his models in a modeling language. The user must also have the time available to work with the computer-based tools. This gap between managerial competence and analytic/computer competence has led to growing frustration on the part of users and potential users of D S S products.

Interestingly, they are false for both the manager and the specialist. For the manager, we think the reader will grant them to be false pr ima facie for the reasons discussed heretofore. For the specialist, they are false because of the general inexperience in the realities and subtleties of business. Let us focus on the manager as it is our contention that for management science to live up to its potential, it must speak directly to them. If

We have formulated a number of criteria which we believe an intelligent computer decision aiding system must meet to be of direct use to the broad range of management:

1. Require no training, user's manual or docu- mentation. An executive of average intelligence, and with little or no computer experience, or expertise in the domain (e.g., expertise in finance), is able to sit down and work with the System immediately. Thus, the system must be able to teach its users about its operation and the domain while being used. It also adjusts to velocity and sophistication of user interaction, as the user be- comes more proficient. The system now feels "na tura l" to the user.

2. Are ready to explain at any time. To establish credibility for its analysis and recommendations, the system explains itself at any time. The explanatory capability is far beyond the command listings or canned paragraphs that pass for "he lp" in conventional software.


A. Intelligent systems offer the user explanations of terminology. For example, the user might touch "help" and "net working capital" to see, first, a definition of net working capital and an explanation of why investment in net working capital is important in project analysis.

B. Explanations of the system's reasoning process. The user can ask "why" and have the system justify its conclusions, analytic approach, ques- tions, etc.

C. "Navigation". In conventional software the user moves about and controls the program by either menu selection or by issuing strings of commands. Menus are a pleasure to use the first time, and an incredible aggravation there- after. The problem with command language is that, to be facile, the user must learn the entire language and use it frequently, Neither menus nor command language is satisfactory for real world executives.

It is possible to develop a computer "roadmap" that lets the user navigate through the system as easily as dealing with the familiar "you are here" maps at the airport. Point at where you want to go and the system transports you there. All changes of control, data, graphics, etc. that are needed, as the user moves are managed by the system's intelligent control structure.

3. Require no customization - an expert system should learn from and adapt to it users. Customi- zation is the bete noire of any flexible software product. Yet the issue is an especially serious one for an expert systems. Occasionally companies attempt to overcome the problems talked about heretofore by creating "customized" models out of a general modeling language. The theory is that the "unsophisticated manager" can get powerful well thought out analysis without the burdens of becoming a specialist by just "filling in the blanks".

Such approaches are ultimately unsatisfying because: - Managers resent being forced into an analytic

mold. - Some analysts do not lend themselves to the

mold. - The manager cannot understand the methodol-

ogy, assumptions used, limits, etc., of the mold and is reluctant to assign credibility to its answers.

- It stifles creativity with its sterility. Conventional software systems assume the user can define his world. If that is true, then the software need not take any of the particular char- acteristics of the company into account because the user will specify them. If you believe the five tenets are false, the system must understand its environment before it has any utility to the user.

While basic finance concepts do not differ from company to company, companies differ from each other in many ways. They have different capital structures and costs, different tax rates, valuation methods, hurdle rates, depreciation and scores of other variables, both quantitative and qualitative. The truly useful expert system must take all these things into account. It cannot assume the user is capable of supplying this information.

To overcome the problems this paper addre- sses, one can use AI technology to build systems that incorporate elements of human experience and judgement. Further, such systems can learn from, and adapt to their users. Through carefully researched and framed dialogs, today's expert systems request and assimilate information about their operating environments from the designated teachers at the customer site. These "teachers" are not programmers. They are senior managers familiar with the specifics of the company's organization, finances, policies, standards and opportunities.

As the system learns from its owners, it inter- nally tailors and customizes itself. This process is on-going. The owners of the system can update or enhance its knowledge whenever conditions war- rant.

Another advantage possible with today's AI technology is that systems can be built with the ability to allow individuals to add to their knowledge. If the manager has a differing point of view from the system's methodology, or with supplied corporate information, the user may alter the information or methodology for his purposes. The system requires that the user provide an explanation for each change, then it proceeds. This system does not judge the validity of the manager's view. Should another executive review this case, the system will alert him to the changes, who generated them, and the reason why the changes were used.

An interesting benefit of this learning/accoun- tability approach is that the system assures


management of two important things over which management currently has little or no control. One is that the proposal created with the assis- tance of the expert system is expertly done and takes into account senior management's preferred methods, criteria for analysis and specifications for the type of situation at hand. The other is assuring that these analyses are done in a con- sistent manner. In this way they are able to extend control much deeper into the organization because the system is representing their points of view as well as providing the most sophisticated analysis.

The local learning abilities of the system aid the manager in being creative, in teaching the system about special knowledge or conditions which apply to the problem at hand, or in advocating a different point of view. The system will tirelessly help the manager explore scores of different scenarios of his own generation, doing each one with a comprehensiveness and level of professionalism practically unattainable without a team of experts and specialists at his constant beck and call.

Expert systems can also accumulate their experience. The system collects and stores the analyses and conclusions of all the experiences it has had in working with managers throughout the company. In this way, the company's experience in many different situations is instantly available to any user of the system. Management can also track the problems facing the company or query the system for identification of common phenom- ena.

This experience survives the departure of any of the original users. Because of the expert systems' explanatory capabilities, anyone reviewing a case, or extending/modifying it, is privy to the original thinking which went into it.

4. Incorporate state-of-the-art analytic techniques. In every area of management there are new analytic concepts, models and techniques which are rarely used by practicing managers. In most cases the roadblocks for use are not complexity or lack of realism. The techniques are seen as relatively simple and practical when a human expert ts on hand to explain them. Moreover, their techniques are seen to address basic issues missed by conventional techniques. Yet the insight and power of these techniques, as provided by the expert, are lost when the expert goes home. Practicing managers cannot quickly absorb the knowledge

and acquire the experience necessary to use the new techniques with confidence.

Today's expert system technology can make state-of-the-art techniques available to practicing managers because the system has been taught to explain the techniques, to recognize situations when the techniques would have a material impact and also to refrain from suggesting techniques where they would only amount to fine-tuning. It is just as if the human expert who developed and explained the techniques were on hand 24 hours a day. The key to being able to accomplish this is the AI technology's ability to encode non-algorithmic experience based knowledge and employ programming methodologies (e.g.; goal oriented control mechanisms, constraint based programming, object oriented programming, etc. etc.) which facilitate the creation of programs designed to accommodate human methods of problem definition and solution.

Here is an example from the Palladian Soft- ware, Inc.'s Palladian Financial Advisor:

In some circumstances The Financial Advisor will suggest use of contingent claims analysis as an alternative to, or supplement for conventional discounted cash flow, payback or other tools of investment analysis. Contingent claims analysis is based on finance theory originally developed for puts and calls in options trading, but now ex- tended to many more complicated financial decisions.

The words "contingent claims" and "option pricing theory" suggest ivory tower stuff of no conceivable use to a practical manager. Indeed they are not practically useful as abstract concepts without an expert present. A conventional software product that crunched numbers according to option pricing theory would be likely to sell very few copies to corporate users.

But suppose that corporate user of The Finan- cial Advisor must choose between two production technologies for a new product. One technology employs standard machine tools; the other employs specialized, custom-built equipment. The specialized technology is more efficient, and scores higher on conventional discounted cash flow.


However, the experienced manager will recognize an intangible disadvantage of the specialized equipment - its lack of flexibifity if the product fails and is abandoned. The specialized equipment is just scrap, while standard tools can be sold or put to another use. That manager would be inter- ested in a technique that puts a dollar value on flexibility and in explanation, in common sense terms, how the dollar value was calculated.

The Palladian Financial Advisor enables the user to explore how the value of flexibility changes under alternative assumptions, and identifies the circumstances under which abandonment would make sense through dialog, graphics, and calculations presented in the manager's idiom. The manager would not care that the calculations were performed using contingent claims analysis as long as the calculations made sense to him and could be readily explained by the system to him, such that he could understand them well enough to explain him- self to his colleagues.

6. Conclusion

In summary, traditional management science has not been as much use to management as it could be. The mathematical underpinnings of management science have tended to overshadow the need for the experience and judgement of seasoned managers to temper them for appficabil- ity in the "real world"." Computer based tools for management science have only supported the mathematic portion of the science and in doing that, have been unable to bridge the gap to those who will not or cannot devote a lot of time to becoming facile programmers.

Artificial Intelligence, especially expert systems technologies, have advanced to the point that the limitations of the past need not prevent bringing management science to management. Not as a panacea for decision making, but to raise the height of the platform of analysis upon which managers of large corporations must stand to make their decisions in today's complex, numerically oriented, world.

Expert systems in management science

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