Chapter 2

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The Nature of Systems

“Finally, we shall place the Sun himself at the center of the Universe. All this is suggested by the systematic procession of events and the harmony of the whole Universe, if only we face the facts, as they say, ‘with both eyes open?’ ”

-- Nicholas Copernicus
De Revolutionibus Orbium Coelestium, 1543


  1. What the definition of a system is;
  2. The difference between natural systems and man-made systems;
  3. The 19 major subsystems found in all living systems;
  4. The 7 major reasons why some systems should not be automated;
  5. The 5 major components of a typical automated information system;
  6. The definition and characteristics of several specific types of systems; and
  7. The definitions of -- and 3 examples of -- general system principles.

We can’t say very much about systems analysis until we have a clear idea of what a system is; that is the purpose of this chapter. As we will see, there is an “official” dictionary definition of the term, which will seem rather abstract. But there are many common usages of the term that will be quite familiar to you, and there are many common types of systems that we come into contact with every day.

So what? It is important to be familiar with different kinds of systems for at least two reasons. First, even though your work as a systems analyst will probably focus on one kind of system -- an automated, computerized information system -- it will generally be a part of a larger system. Thus, you may be working on a payroll system, which is part of a larger “human resources” system, which in turn is part of an overall business organization (which is, in itself, a system), which is, in turn, part of a larger economic system, and so forth. Or you may be working on a process control system that is part of a chemical refinery, or an operating system that is part of a vendor-supplied “package” of system software. Thus, to make your system successful, you must understand the other systems with which it will interact.

Many of the computer systems that we build are replacements, or new implementations of, non-computerized systems that are already in existence; also, most computer systems interact with, or interface with, a variety of existing systems (some of which may be computerized and some which may not). If our new computer system is to be successful, we must understand, in reasonable detail, how the current system behaves.

Second, even though many types of systems appear to be quite different, they turn out to have many similarities; there are common principles and philosophies and theories that apply remarkably well to virtually all kinds of systems. Thus, we can often apply what we have learned about other systems -- based on our own day-to-day experience, as well as the experience of scientists and engineers in a variety of fields -- to systems that we build in the computer field. For example, one of the important systems principles first observed in the field of biology is known as the law of specialization: the more highly adapted an organism is to a specific environment, the more difficult it is for the organism to adapt to a different environment. This helps explain the disappearance of dinosaurs when the Earth’s climate changed dramatically<ref>Paleontologists are still arguing about this issue: some feel that dinosaurs vanished in a relatively brief period of time after a massive meteor hit the earth, creating such a dense dust cloud that most plant life died. Others argue that the change was much more gradual, occurring over a period of nearly a million years. In any case, dinosaurs were highly adapted to one kind of environment and eventually proved unable to adapt to a different one.</ref>; it also helps systems analysts understand that if they optimize a computerized system to take maximum advantage of a specific CPU, programming language, and database management system, they are likely to have great trouble adapting that system to run on a different CPU or with a different database management system.<ref>It can also help the systems analyst understand the phenomenon of a user whose current practices are so specialized that there is no way to change them even if they are computerized. And it reminds the systems analyst that if he or she develops a computer system that is highly specialized for the user’s current application, it will be difficult to adapt as the user’s requirements (and the external environment in which the user operates) change and evolve.</ref>

Thus, if we understand something of general systems theory, it can help us better understand computerized (automated) information systems. This is more and more important today, because we want to build stable, reliable systems that will function well in our complex society — and there are, of course, many examples of non-computer systems that have survived for millions of years: the humble cockroach is likely to outlast every computer system ever built, and all of humanity as well.

So, let us begin with a definition of the basic term system. Every textbook covering some aspect of systems contains such a definition; I have chosen Webster’s New Collegiate Dictionary.<ref>Webster’s New Collegiate Dictionary, Springfield, Mass.: G. & C. Merriam Company, 1977.</ref> It provides several definitions:

  1. a regularly interacting or interdependent group of items forming a unified whole <a number ~> as
    a. (1) a group of interacting bodies under the influence of related forces <a gravitational ~> (2) an assemblage of substances that is in or tends to equilibrium <a thermodynamic ~>
    b. (1) a group of body organs that together perform one or more vital functions <the digestive ~> (2) the body considered as a functional unit
    c. a group of related natural objects or forces <a river ~>
    d. a group of devices or an organization forming a network, especially for distributing something or serving a common purpose <a telephone ~> <a heating ~> <a highway ~> <a data processing ~>
  2. an organized set of doctrines, ideas, or principles, usually intended to explain the arrangements or working of a systematic whole <the Newtonian ~ of mechanics>
  3. an organized or established procedure <the touch ~ of typing> b. a manner of classifying, symbolizing, or schematizing <a taxonomic ~> <the decimal ~>
  4. harmonious arrangement or pattern: ORDER
  5. an organized society or social situation regarded as stultifying: ESTABLISHMENT


As we can see from the definition above, there are many different types of systems; indeed, virtually everything that we come into contact with during our day-to-day life is either a system or a component of a system (or both).

Does this mean that we should study all kinds of systems, or hope to become experts in social systems, biological systems, and computer systems? Not at all! However, it is useful to organize the many different kinds of systems into useful categories. Many different categorizations are possible; indeed, the dictionary definition at the beginning of the chapter shows one categorization. Since our ultimate focus is on computer systems, we divide all systems into two categories: natural systems and man-made systems.


The vast majority of systems are not made by people: they exist in nature and, by and large, serve their own purpose. It is convenient to divide natural systems into two basic subcategories: physical systems and living systems. Physical systems include such diverse examples as:

  • Stellar systems: galaxies, solar systems, and so on;
  • Geological systems: rivers, mountain ranges, and so on; and
  • Molecular systems: complex organizations of atoms.

Physical systems are interesting to study because, as pesky humans, we sometimes want to modify them. We also develop a variety of man-made systems, including computer systems, that must interact harmoniously with physical systems; so it is often important to be able to model those systems to ensure that we understand them as fully as possible.

Living systems, of course, encompass all of the myriad animals and plants around us, as well as our own human race. And, as James Miller elaborates in his monumental work, Living Systems (Miller, 1978), this category also includes hierarchies of individual living organisms -- for example, herds, flocks, tribes, social groups, companies, and nations.

The study of living systems is a career in itself; a brief perusal of Miller’s work will show what a massive subject it is. The purpose of this book is not to study living systems per se; but some of the properties and characteristics of familiar living systems can be used to help illustrate and better understand man-made systems. We often use an analogy to better understand something unfamiliar; among the more eloquent examples of living systems as an analogy of business systems and organizational systems is Stafford Beer’s Brain of the Firm (Beer, 1972), and The Heart of Enterprise (Beer, 1978).

A more elaborate analogy can be drawn from Miller’s categorization of the 19 critical subsystems of all living systems. Miller argues that living systems, whether at the level of the cell, the organ, the organism, the group, the organization, the society, or the supranational system, all contain the following subsystems:

  • The reproducer, which is capable of giving rise to other systems similar to the one it is in. In a business organization, this might be a facilities planning division that makes new plants and builds new regional offices.
  • The boundary, which holds together the components that make up the system, protects them from environmental stresses, and excludes or permits entry to various sorts of matter-energy and information. In a business organization, this might consist of the physical plant (office building, factory, and so on) and the guards and other security personnel who prevent unwanted intrusion.
  • The ingestor, which brings matter-energy across the system boundary from its environment. In a business organization, this might be the receiving or the purchasing department, which brings in raw materials, office supplies, and the like. Or it might consist of the order entry department, which receives verbal and written orders for the organization’s products and services.
  • The distributor, which carries inputs from outside the system or outputs from its subsystems around the system to each component. In a business organization, this could be telephone lines, electronic mail, messengers, conveyor belts, and a variety of other mechanisms.
  • The converter, which changes certain inputs to the system into forms more useful for the special processes of that particular system. Again, one could imagine a number of examples of this in a typical business organization.
  • The producer, which forms stable associations that endure for significant periods among matter-energy inputs to the system or outputs from its converter, the materials synthesized being for growth, damage repair, or replacement of components of the system, or for providing energy for moving or constituting the system’s outputs of products or information markets to its suprasystem.
  • The matter-energy storage subsystem, which retains in the system, for different periods of time, deposits of various sorts of matter-energy.
  • The extruder, which transmits matter-energy out of the system in the form of products or wastes.
  • The motor, which moves the system or parts of it in relation to part or all of its environment or moves components of its environment in relation to each other.
  • The supporter, which maintains the proper spatial relationships among components of the system, so that they can interact without weighing each other down or crowding each other.
  • The input transducer, which brings markers bearing information into the system, changing them to other matter-energy forms suitable for transmission within it.
  • The internal transducer, which receives, from other subsystems or components within the system, markers bearing information about significant alterations in those subsystems or components, changing them to other matter-energy forms of a sort that can be transmitted within it.
  • The channel and net, which are composed of a single route in physical space, or multiple interconnected routes, by which markers bearing information are transmitted to all parts of the system.
  • The decoder, which alters the code of information input to it through the input transducer or internal transducer into a private code that can be used internally by the system.
  • The associator, which carries out the first stage of the learning process, forming enduring associations among items of information in the system.
  • The memory, which carries out the second stage of the learning process, storing various sorts of information in the system for different periods of time.
  • The decider, which receives information inputs from all other subsystems and transmits to them information outputs that control the entire system.
  • The encoder, which alters the code of information input to it from other information processing subsystems, from a private code used internally by the system into a public code that can be interpreted by other systems in its environment.
  • The output transducer, which puts out markers bearing information from the system, changing markers within the system into other matter-energy forms that can be transmitted over channels in the system’s environment.

Figure 2.1(a) and 2.1(b) show an example of the 19 major subsystems for the communications team in a modern ocean liner; Figure 2.2(a) and 2.2(b) show the major subsystems for the ocean liner itself; and Figure 2.3(a) and 2.3(b) show the major subsystems for the entire country of Holland. These are worth studying, for they illustrate that if you look at any system that has living components, the major subsystems can be found.

Keep in mind that many man-made systems (and automated systems) interact with living systems; for example, computerized pacemakers interact with the human heart. In some cases, automated systems are being designed to replace living systems; and in other cases, researchers are considering living systems (known as organic computers) as components of automated systems. See (Hall, 1983), (DeYoung, 1983), (Shrady, 1985), and (Olmos, 1984) for discussions of this viewpoint. Living systems and man-made systems are often part of a larger metasystem, and the more we understand about both, the better we will be as systems analysts.


As we saw from the definition at the beginning of the chapter, a number of systems are constructed, organized, and maintained by humans. These include such things as:

  • Social systems: organizations of laws, doctrines, customs, and so on.
  • An organized, disciplined collection of ideas: the Dewey decimal system for organizing books in libraries, the Weight-Watcher’s system for shedding ugly extra pounds, and so on.
  • Transportation systems: networks of highways, canals, airlines, ocean tankers, and the like.
  • Communication systems: telephone, telex, smoke signals, the hand signals used by stock market traders, and so on.
  • Manufacturing systems: factories, assembly lines, and so on.
  • Financial systems: accounting, inventory, general ledger, stock brokerage, and the like.

Most of these systems include computers today; indeed, many could not survive without computers. However, it is equally important to point out that such systems existed before there were computers; indeed, some of these systems are still completely non-computerized and may remain that way for many more decades. Others contain a computer as a component, but also include one or more non-computerized (or manual) components.

Consider, for example, the common phrase, “John has a system for doing this job” or “Mary sure does have a systematic way of going about her work.” Such phrases do not necessarily suggest that Mary has computerized her work or that John has used some of the formal modeling tools discussed in Chapter 9 and Chapter 10 to document (or model) how he proposes to do his job. But certainly the phrases imply that John and Mary have broken their work into a series of discrete steps, the cumulative sum of which will accomplish some overall purpose.

Whether or not a man-made system should be computerized is a question that we will discuss throughout this book; it is not something you should take for granted. As a systems analyst, you will naturally assume that every system that you come in contact with should be computerized; and the customer or user, (the owner of the system in question) with whom you interact will generally assume that you have such a bias. As we will see in later chapters, your primary job as a systems analyst will be to analyze, or study, the system to determine its essence: its required behavior independent of the technology used to implement the system.<ref>We will discuss the essence of a system and essential models in Chapter 17.</ref> In most cases, we will be in a position to determine whether it makes sense to use a computer to carry out the functions of the system only after modeling its essential behavior.

Why should some information processing systems not be automated? There may be many reasons, but here are some of the more common ones:

  • Cost -- it may be cheaper to continue carrying out the system functions and storing the system’s information manually. It’s not always true that computers are faster and cheaper than the “old-fashioned” way! This is particularly likely to be true if the system under consideration requires only a limited functionality.
  • Convenience -- an automated system may take up too much room, make too much noise, generate too much heat, or consume too much electricity, or, in general, it may be a pain in the neck to have around. This is becoming less true with the pervasive influence of microprocessors -- but it’s still a factor. And even a compact microprocessor-based system might not be considered “convenient” by everyone; notice, for example, how some people prefer a Palm Pilot (or other hand-held PDAs) while others strongly prefer the convenience of their old-fashioned Filofax.
  • Security -- if the information system is maintaining sensitive, confidential data, the user may not feel that an automated system is sufficiently secure. The user may want the ability to keep the information physically protected and locked up.
  • Maintainability -- the user might argue that a computerized information system would be cost-effective except that there is nobody on the staff that can maintain the computer hardware and/or software, so nobody would be able to repair the system if it broke down nor would anyone be able to make changes and enhancements.
  • Politics -- the user community may feel that computers threaten their jobs or make their jobs too boring and “mechanical,” or they may have a dozen other reasons that the system analyst may regard as irrational. But since it’s the users’ system, their feelings are paramount. If they don’t want an automated system, they will do their best to make it fail if it gets shoved down their throats.
  • Inability to articulate policy and procedures in a precise fashion -- the user community may have been carrying out the required behavior of a system for decades, or even centuries, without ever having documented the precise rules, data definitions, and other aspects of their behavior. Indeed, their behavior might be somewhat “fuzzy,” occasionally contradictory, somewhat error-prone in nature, and subject to personal interpretation. This is the kind of situation that drives computer professionals crazy, but it may be perfectly acceptable to the user community.
  • Inability to automate a system within the required amount of time -- sometimes, the prospect of an automated system is enticing, but the time required to perform the analysis, design, implementation, and testing may be unacceptable. This was often the case with Y2K projects at the end of the last decade; the inexorable deadline of December 31, 1999 meant that some of the aging legacy systems had to be maintained, because there was not enough time to develop a new, Y2K-compliant replacement system.


Most of this book will concentrate on automated systems, that is, man-made systems that interact with or are controlled by one or more computers. No doubt you have seen many different examples of automated systems in your day-to-day life: it seems that almost every aspect of our modern society is computerized. As a result, we can distinguish many different kinds of automated systems.

Figure 2.1(a): Subsystems for an ocean liner communications team

Even though there are many different kinds of automated systems, they all tend to have common components:

Figure 2.1(b): Subsystems for an ocean liner communications team

One way of categorizing automated systems is by application: manufacturing systems, accounting systems, military defense systems, and so on; however, this turns out not to be terribly useful, for the techniques that we will discuss in this book for analyzing, modeling, designing, and implementing automated systems are generally the same regardless of the application.<ref>However, each application does have its own vocabulary, and culture, and set of procedures. The user generally expects that the systems analyst will know something about the details and business policy and procedures of his or her application so that everything won’t have to be explained from the beginning. Thus, if you’re going to be a systems analyst in a bank, it will probably be very useful to learn as much as you can about the business of banking. This is not a one-way street: bankers are learning more about the technology of information systems each day.</ref> However, it may be useful to categorize a system by application if an organization intends to develop a system and then sell it to numerous clients within the same industry.

Figure 2.2(a): Major subsystems for an ocean liner; Figure 2.2(b): Major subsystems for an ocean liner; Figure 2.3(a): Major subsystems of the country of Holland; Figure 2.3(b): Major subsystems for the country of Holland

A more useful categorization of automated systems is as follows:

  • On-line systems
  • Real-time systems
  • Decision-support systems
  • Knowledge-based systems

We will examine each of these next.

On-line Systems

In an earlier book (Yourdon, 1972), I defined on-line systems in the following way: An on-line system is one which accepts input directly from the area where it is created. It is also a system in which the output, or results of computation, are returned directly to where they are required. This usually means that the computer system has a hardware architecture that looks like that in Figure 2.4.

A common characteristic of on-line systems is that data are entered into the computer system and received from the computer system remotely. That is, the users of the computer system typically interact with the computer from terminals<ref>The word “terminal” is so commonly used throughout society today that it scarecely needs to be defined. However, you should be aware that there are many synonyms: “screen,” “workstation,” “keyboard,” and “display unit” are among the more common ones. And there are common abbreviations used to describe the input/output device with which one communicates with the computer: “CRT” for “cathode ray tube,” “VDU” for “visual display unit,” and so on. These terms will be used interchangeably throughout the book.</ref> that may be located hundreds of miles from other terminals and from the computer itself.

Another characteristic of an on-line system is that its stored data, that is, its files or its database, are usually organized so that individual pieces of data (such as an individual airline reservation record or an individual personnel record) can be retrieved and/or modified (1) quickly and (2) without necessarily accessing any other piece of data in the system. This is in stark contrast to the batch systems, which were more common in the 1960s and 1970s. In a batch computer system, information is usually retrieved on a sequential basis, which means that the computer system reads through all the records in its database, processing and updating those records for which there is some activity. The difference between a batch computer system and an on-line system is analogous to the difference between finding a specific musical selection on a tape cassette and a musical selection on a CD player; one involves sequential access through all the tracks, while the other allows “random” access to any one of the tracks without listening to the others.

Because an on-line system interacts directly with people (i.e., human users at terminals), it is important for the systems analyst to carefully plan the human-computer interface.<ref>This is sometimes referred to as the “man-machine dialogue,” or the “man-machine interface.” More and more systems development organizations are changing to “human-computer interface,” or just “human interface,” to avoid any unnecessary bias.</ref> That is, the analyst must have a way of modeling all the possible messages that the human user can type on his or her terminal and all of the responses that the system can make -- and all of the responses that the human can make to the computer’s response, and so on. This is usually done by identifying all the states that the computer and the user can find themselves in and identifying all the state-changes. An example of a state that the computer in an automated bank-teller system might be in is “The user has inserted his credit card, and has identified himself, but has not yet told me his confidential password.” An example of a state-change is, “He’s told me his password, and now I can proceed to find out whether he wants to withdraw cash or display his current bank balance.” Another state-change might be, “He has tried unsuccessfully three times to enter his password, and now I am going to sound the alarm.” These states and changes of state are typically modeled with state-transition diagrams, which we will discuss in detail in Chapter 13.

Figure 2.4: An on-line system; source: File:Figure24.graffle

Because on-line systems usually need to retrieve data quickly (in order to respond to inquiries and commands from on-line terminals), it is usually very important to design the files and databases as efficiently as possible. Indeed, it is often true that the computations performed by an on-line system are relatively trivial, while the data (especially the structure and organization of the data maintained by the on-line system) are rather complex. Hence the data modeling tools discussed in Chapter 12 are of great importance to the systems analyst and systems designer.

The decision to make a system on-line or not is, in the context of this book, an implementation decision, that is, not something that ought to be determined by the systems analyst, but rather by the people implementing the system. However, since the decision has such an obvious impact on the user (the presence or absence of computer terminals, and so on), it is an implementation decision in which the users will generally want to participate. Indeed, it is part of the user implementation model, which we will discuss in Chapter 21.

Real-time systems

A real-time system is considered by many to be a variant of an on-line system; indeed, many people use the terms interchangeably. However, it is important to distinguish between the two; we will use the following definition from (Martin, 1967):

A real-time computer system may be defined as one which controls an environment by receiving data, processing them, and returning the results sufficiently quickly to affect the environment at that time.

The term “sufficiently quickly” is, of course, subject to many interpretations. And the issue of “quickness” is just one of the characteristics of a typical real-time system. As Paul Ward and Steve Mellor point out (Ward-Mellor, 1985), a more comprehensive list of characteristics would include the following:

The problem formulation vocabulary for real-time systems is drawn from science and engineering disciplines ... The environment of a real-time system often contains devices that act as the senses of the system. Real-time system often require concurrent processing of multiple inputs. The time scales of many real-time systems are fast by human standards.The precision of response required of real-time systems is greater than that required of other systems.

Certainly there are many on-line systems -- banking systems, airline reservation systems, stock brokerage systems -- that are expected to react within one or two seconds to a message typed on the terminal. However, in most real-time systems, the computer must react within milliseconds and sometimes within microseconds to inputs that it receives. This is characteristic of the following kinds of systems:

  • Process control systems -- the computer systems that are used to monitor and control oil refineries, chemical manufacturing processes, milling and machining operations are examples.
  • High-speed data acquisition systems -- computer systems that receive high-speed telemetry data from orbiting satellites, or computers that capture massive amounts of data from laboratory experiments are examples.
  • Missile guidance systems -- computer systems that must track the trajectory of a missile and make continuous adjustments to the orientation and thrust of the missile engines.
  • Telephone switching systems -- computer systems that monitor voice and data transmissions over thousands of telephone calls, detecting phone numbers being dialed, on-hook and off-hook conditions, and all of the other many conditions of the typical telephone network.
  • Patient monitoring systems -- computer systems that monitor various patient “vital signs” (e.g., temperature and pulse) and either adjust medication or sound an alarm if those vital signs stray outside some predetermined condition.

Besides speed, another characteristic differentiates real-time systems from on-line systems: on-line systems generally interact with people, while real-time systems interact with both people and an environment that is generally autonomous and often hostile. Indeed, the overriding concern of the real-time systems analyst is that, if the computer does not respond quickly enough, the environment will get out of control -- incoming data may be irrevocably lost, or a missile may stray so far from its trajectory that it cannot be recovered, or a manufacturing process may blow up.<ref>One of the more interesting examples of such a real-time situation involved a project team whose job was to attach a small computer to a nuclear bomb. When the bomb was detonated (as part of an underground testing program), the computer had only a very few microseconds to capture as much data as possible and transmit it to a remote computer system before the hardware and software vaporized as part of the explosion. Now that is a real-time processing requirement.</ref> By contrast, an on-line system that does not respond quickly enough will generally do nothing more than make its users impatient and grumpy. People may “explode” or “blow up” in a figurative sense if they have to wait more than three seconds to get an answer from an on-line system, but not in a literal sense. This is illustrated in Figure 2.5.

Because of this concern with instant response to system inputs, a systems analyst working with real-time systems is generally very concerned with the time-dependent behavior of the system. We will discuss tools for modeling time-dependent system behavior in Chapter 13.

Figure 2.5: A real-time system; source: File:Figure25.graffle

From an implementation point of view, real-time systems (as well as some on-line systems) are characterized by the following features:

  • Many processing activities that are taking place simultaneously;
  • Assignment of different priorities to different processing tasks -- some need to be serviced immediately, while others can be delayed for reasonable periods of time;
  • Interruption of one processing task before it is finished in order to begin servicing another, higher-priority task;
  • Extensive communication between tasks, especially since many of the tasks are working on different aspects of an overall process like controlling a manufacturing process;
  • Simultaneous access to common data, both in memory and on secondary storage, thus requiring elaborate “hand-shaking” and “semaphore” procedures to ensure that common data do not become corrupted; and
  • Dynamic use of and allocation of RAM memory in the computer system, since it is often uneconomical (even in today’s world of cheap memory) to assign enough fixed memory to handle high-volume peak situations

Decision-support Systems and Strategic Planning Systems

Most of the automated information systems that have been built in the United States during the past 30 years have been operational systems that help carry out the details of an organization’s day-to-day work. These systems, also known as transaction-processing systems, include such familiar examples as payroll systems, order entry systems, accounting systems, and manufacturing systems. In organizations around the United States, these operational systems have been developed slowly, painfully, and at great cost; since many of them were initially developed more than 20 years ago, they are on the verge of collapse; thus, new operational systems are continuously being built in major organizations around the world.

But to the extent that today’s operational systems continue to wobble along, many organizations are focusing their attention on a new kind of system: decision-support. As the term implies, these computer systems do not make decisions on their own, but instead help managers and other professional “knowledge workers” in an organization make intelligent, informed decisions about various aspects of the operation. Typically, the decision-support systems are passive in the sense that they do not operate on a regular basis: instead, they are used on an ad hoc basis, whenever needed.

There are a number of simple examples of decision-support systems: spreadsheet programs (e.g., Microsoft’s Excel, and Lotus 1-2-3), statistical analysis systems, marketing forecast programs, and others. Indeed, a common characteristic of the decision-support systems is that they not only retrieve and display data, but also perform a variety of mathematical and statistical analyses of the data; decision-support systems also have the capability, in most cases, of presenting information in a variety of graphic forms (tables, charts, etc.) as well as conventional reports. Figure 2.6 shows a typical financial spreadsheet that a manager might use to evaluate the profitability of a division within the organization; Figure 2.7 shows a typical chart showing the division’s revenues as compared to the industry average. Note that in both cases the output produced by this system does not “make” a decision, but rather provides relevant information in a useful format so that the manager can make the decision.

Strategic planning systems are used by senior management to evaluate and analyze the mission of the organization. Rather than providing advice about an isolated business decision, these systems provide broader, more general advice about the nature of the marketplace, the preferences of the customers, the behavior of competition, and so on. This is usually within the province of the Strategic Planning Department, or the Long Range Planning Department, though it may be a more informal activity carried out by one or two senior managers.

Strategic planning is a concept that became popular during World War II (though some organizations were obviously doing it long before that), and it is the subject of many books; see (Steiner, 1979), (Drucker, 1974), and (Ackoff, 1970). Strategic planning systems are not computer programs per se; they are a complex combination of activities and procedures, much of it carried out by humans using information gleaned from outside sources (market surveys and the like) and internal data from the organization’s operational systems and decision-support systems. Steiner points out that there can be many types of strategic planning systems, depending on the size and nature of the organization.

Two typical models are portrayed in Figures 2.9 and 2.10. The strategic planning system based on gap analysis tries to identify the discrepancy between an organization’s current position (in terms of revenues, profits, etc.) and the position desired by senior management, stockholders, and others.

Strategic planning systems are a subject in itself, and we will not cover them in detail in this book. Our emphasis will be primarily on operational and decision-support systems.

Note the relationship between the three different kinds of systems discussed in this section. As shown in Figure 2.11, the operational systems represent the foundation upon which the decision-support systems and strategic planning systems rest. The operational systems create the data required by the higher-level systems, and they continue to update those data on a continuous basis.

Figure 2.6: A typical spreadsheet

Figure 2.7: A typical chart produced by a decision-support system

The pyramid shape of Figure 2.11 represents another typical characteristic of information systems found in most organizations today: the size of the operational systems (measured in person-years, or millions of COBOL statements, etc.) vastly exceeds that of the decision-support systems and strategic planning systems. But we can expect this to change gradually over the next decade. As mentioned earlier, many organizations have spent the past thirty years building their operational systems: for the most part, the job is done.<ref>There are some exceptions: smaller organizations that have not yet computerized much of their day-to-day operation; old operational systems developed by Fortune 500 companies in the 1960s that are on the verge of collapse; and new operational systems required by mergers, acquisitions, and forays into new markets and products; and the defense community has an apparently never-ending list of new operational systems to be built. Overall, though, the trend is that of a slow movement away from operational systems and toward the decision support systems.</ref> Much of the work now being done in some of these large organizations is the development of decision-support systems and strategic planning systems.

Knowledge-based Systems

Another popular term in the computer industry is that of “expert systems” or “knowledge-based systems.” Such systems are associated with the field of artificial intelligence, defined in the following way by Elaine Rich (Rich, 1984):

The goal of computer scientists working in the field of artificial intelligence is to produce programs that imitate human performance in a wide variety of “intelligent” tasks. For some expert systems, that goal is close to being attained; for others, although we do not yet know how to construct programs that perform well on their own, we can begin to build programs that significantly assist people in their performance of a task.

Figure 2.9: A strategic planning model centered on gap analysis; source: File:Figure29.graffle

Figure 2.10: A strategic planning model based on market strength; source: File:Figure210.graffle

Two eminent writers in the field of artificial intelligence, Feigenbaum and McCorduck, describe knowledge-based systems and expert systems in the following way (Feigenbaum and McCorduck, 1983):

Knowledge-based systems, to labor the obvious, contain large amounts of varied knowledge which they bring to bear on a given task. Expert systems are a species of knowledge-based system, though the two terms are often used interchangeably. Just what is an expert system? It is a computer program that has built into it the knowledge and capability that will allow it to operate at the expert’s level. Expert performance means, for example, the level of performance of M.D.’s doing diagnosis and therapeutics, or Ph.D.’s or very experienced people doing engineering, scientific, or managerial tasks. The expert system is a high-level intellectual support for the human expert, which explains its other name, intelligent assistant.
Expert systems are usually built to be able to explain the lines of reasoning that led to their decisions. Some of them can even explain why they rejected certain paths of reasoning and chose others. This transparency is a major feature of expert systems. Designers work hard to achieve it because they understand that the ultimate use of an expert system will depend on its credibility to the users, and the credibility will arise because the behavior is transparent, explainable.

Figure 2.11: The hierarchy of information processing systems; source: File:Figure211.graffle

Expert systems are still generally thought of as specialized systems, using special computer hardware, and such special programming languages as LISP and PROLOG. However, simple expert systems have begun appearing on standard-size personal computers, and expert system “shells” -- software frameworks for developing specific expert system applications -- have begun to appear in standard COBOL-based mainframe environments.

While expert systems are beyond the scope of this book, they will gradually become a more and more important component of the “typical” system that you work on as a systems analyst. Beginning in the late 1980s, researchers have begun studying the relationship between classical software development techniques and artificial intelligence; typical of this is (Jacob and Froscher, 1986). Keller (Keller, 1987) foresees a time in the near future when AI and expert systems will be part of the “normal” activity of systems analysis; others, such as (Barstow, 1987) and (Lubars and Harandi, 1987) expected that artificial intelligence would be useful for helping systems analysts document user requirements by the mid-1990s. Unfortunately, this prediction proved to be too optimistic; we will return to this point later.


All the examples given above have one thing in common: they are all systems. While they may be different in many ways, they also share many common characteristics. The study of these “common characteristics” is known as general systems theory, and it is a fascinating topic to explore. For an initial glimpse of the subject, read (Weinberg, 1976); for another, more formal view, consult (Bertalanffy, 1969); and for a more humorous view of the often perverse nature of systems, read Gall’s delightful Systemantics (Gall, 1977).

While the subject of general systems theory is beyond the scope of this book, there are a few “general” principles that are of particular interest to people building automated information systems. They include the following:

  1. The more specialized a system is, the less able it is to adapt to different circumstances. This is often used to describe biological systems (e.g., animals that have difficulty adapting to new environments), but it applies to computer systems, too. The more “general-purpose” a system is, the less “optimized” it is for any particular situation; but the more the system is optimized for a particular situation, the less adaptable it will be to new circumstances. This is a real problem for many real-time systems, which must be optimized in order to provide sufficiently fast responses to external stimuli; but the optimization process generally takes advantage of the idiosyncrasies of the special computer hardware and systems software used on the project, which means that it may be very difficult to transport the system to different hardware. This principle is also important for many business systems, which “mirror” the user’s policies, which might also be extremely specialized. The more specialized the user’s requirements for a payroll system, for example, the less likely that an off-the-shelf commercial package can be used.
  2. The larger a system is, the more of its resources that must be devoted to its everyday maintenance. Biology is, once again, the most familiar example of this principle: dinosaurs spent a major portion of their waking life stuffing food into their mouth in order to maintain their huge carcasses. But it applies to armies, companies, and a variety of other systems, too, including the automated systems that you will be studying in this book. A small “toy” system, the kind that you can develop in an afternoon, will usually involve very little “bureaucracy,” whereas a large system will require enormous effort in such “unproductive” areas as error-checking, editing, backup, maintenance, security, and documentation.<ref>Users often don’t appreciate this phenomenon, and it may be one of the reasons for the current fascination with fourth generation languages and prototyping tools. You can quickly build a system in a fourth generation language that does the main processing parts (and thus provide instant gratification for the user), but it takes a lot of work to put in the additional intelligence for error-checking, backup, maintenance, security, performance tuning, documentation, and so on. You must keep this point in mind or you are likely to be “railroaded” by the user into building a “quick and dirty” system that ultimately fails. To give you an idea of the extent of something as mundane as documentation, consider this statistic reported by Capers Jones in Programming Productivity (New York: McGraw-Hill, 1986): a large telecommunication system had 120 English words for each line of source code, totaling 30 million words and 60,000 pages; a large government system had 200 English words per line of source code, totaling 125 million words and 250,000 pages of documentation.</ref>
  3. Systems are always part of larger systems, and they can always be partitioned into smaller systems. This point is important for two reasons: first, it suggests an obvious way to organize a computer system that we are trying to develop -- by partitioning it into smaller systems (we will see much of this in later chapters of this book). More important, though, it suggests that the definition of the system we are trying to develop is arbitrary -- we could have chosen a slightly smaller system or a slightly larger system. Choosing the scope of a system and defining it carefully so that everyone knows what is inside the system and what is outside is an important activity; we discuss it in detail in Chapter 18. This is more difficult than it might seem: both users and systems analysts often think that the system boundary is fixed and immutable and that everything outside the boundary is not worth studying. I am indebted to Lavette Teague and Christopher Pidgeon (Teague and Pidgeon, 1985) for locating the following example of systems within systems, taken from (Eberhard, 1970):

    One anxiety inherent in design methods is the hierarchical nature of complexity. This anxiety moves in two directions, escalation and infinite regression. I will use a story, “The Warning of the Doorknob,” to illustrate the principle of escalation.

    This has been my experience in Washington when I had money to give away. If I gave a contract to a designer and said, “The doorknob to my office doesn’t have much imagination, much design content. Will you design me a new doorknob?” He would say “Yes,” and after we establish a price he goes away. A week later he comes back and says, “Mr. Eberhard, I’ve been thinking about that doorknob. First, we ought to ask ourselves whether a doorknob is the best way of opening and closing a door.” I say, “Fine, I believe in imagination, go to it.” He comes back later and says, “You know, I’ve been thinking about your problem, and the only reason you want a doorknob is you presume you want a door to your office. Are you sure that a door is the best way of controlling egress, exit, and privacy?” “No, I’m not sure at all.” “Well I want to worry about that problem.” He comes back a week later and says, “The only reason we have to worry about the aperture problem is that you insist on having four walls around your office. Are you sure that is the best way of organizing this space for the kind of work you do as a bureaucrat?” I say, “No, I’m not sure at all.” Well, this escalates until (and this has literally happened in two contracts, although not through this exact process) our physical designer comes back with a very serious face. “Mr. Eberhard, we have to decide whether capitalistic democracy is the best way to organize our country before I can possibly attack your problem.”

    On the other hand is the problem of infinite regression. If this man faced with the design of the doorknob had say, “Wait. Before I worry about the doorknob, I want to study the shape of man’s hand and what man is capable of doing with it,” I would say, “Fine.” He would come back and say, “The more I thought about it, there’s a fit problem. What I want to study first is how metal is formed, what the technologies are for making things with metal in order that I can know what the real parameters are for fitting the hand.” “Fine.” But then he says, “You know I’ve been looking at metal-forming and it all depends on metallurgical properties. I really want to spend three or four months looking at metallurgy so that I can understand the problem better.” “Fine.” After three months, he’ll come back and say, “Mr. Eberhard, the more I look at metallurgy, the more I realize that it is atomic structure that’s really at the heart of this problem.” And so, our physical designer is in atomic physics from the doorknob. That is one of our anxieties, the hierarchical nature of complexity.

  4. Systems grow. Of course, this could not really be true for all systems or it would violate a very familiar general systems principle, the law of conservation of energy. But many of the systems with which we are familiar do grow, and it is important to recognize this, because we often fail (as systems analysts and systems designers) to take it into account when we begin developing the system. A typical information system, for example, will grow to include more software than initially planned, more data, more functions, and more users. For example, Lientz and Swanson found in a classic survey of nearly 500 data processing organizations around the country (Lientz and Swanson, 1980), that the amount of code in an existing automated system increases by approximately 10% per year, and the size of the database increases by about 5% each year. You cannot assume that a system you build will remain static; the cost of expanding it over time should be included in your “cost-benefit” calculations, which we discuss in Chapter 5 and in Appendix C.
  5. The interactions between components of a system are often complex and subtle. A popular expression of this point, during much of the 1990s, was that the flapping of a butterfly’s wings in Tokyo could ultimately cause a hurricane in New York. Obviously, this is a somewhat exaggerated metaphor, but it illustrates the point that the “ripple-effect” interactions between one system component and another can be quite dramatic. Even in less dramatic situations, it’s often important to remember that a change in system component A can cause a change in B, which can “ripple” into component C. But even more important is the realization that the change in C can cause a “feedback” effect on the original component A. Such a feedback loop may not operate on an instantaneous basis; that is, days, weeks, or even years could elapse between the original activity in component A, and the feedback impact upon A caused by C. These concepts are part of a discipline known as “system dynamics,” and more detail can be found in such textbooks as (Abdel-Hamid and Madnick, 1991), (Richardson and Pugh,1981), (Senge, 1990), and (Randers, 1992).


Systems analysts in the data processing profession are often victims of the law of specialization discussed above: they become experts in their own field, without realizing that there are other kinds of “system builders” and that some general principles might apply. The primary purpose of this chapter has been to broaden your horizon and provide you with a larger perspective before we plunge more deeply into the study of automated information systems.

Obviously, you can’t be an expert in living systems, physical systems, and all forms of man-made systems in addition to automated information systems. But since the systems you are likely to build almost always interact with these other forms of system, it is important for you to be aware of them. By understanding that other systems obey many of the same general principles as the computer system you’re building, you’re likely to be more successful at developing interfaces between your system and the external world.


Chapter 3


  1. Edward Yourdon, Design of On-Line Computer Systems. Englewood Cliffs, N.J.: Prentice-Hall, 1972, page 4.
  2. James Martin, Design of Real-Time Computer Systems'. Englewood Cliffs, N.J.: Prentice-Hall, 1967.
  3. James Grier Miller, Living Systems. New York: McGraw-Hill, 1978.
  4. George Steiner, Strategic Planning. New York: Free Press, 1979.
  5. Peter Drucker, Management: Tasks, Responsibilities, Practices. New York: Harper & Row, 1974.
  6. Russell L. Ackoff, A Concept of Corporate Planning. New York: Wiley, 1970.
  7. Stafford Beer, Brain of the Firm. New York: Wiley, 1972.
  8. Stafford Beer, The Heart of Enterprise. New York: Wiley, 1978.
  9. Stephen Hall, “Biochips,” High Technology, December 1983.
  10. H. Garrett DeYoung, “Biosensors,” High Technology, November 1983.
  11. Nicholas Shrady, “Molecular Computing,” Forbes, July 29, 1985.
  12. David Olmos, “DOD Finances Case Western Biochip Research Center,” Computerworld, September 3, 1984.
  13. Elaine Rich, “The Gradual Expansion of Artificial Intelligence,” IEEE Computer, May 1984.
  14. Edward Feigenbaum and Pamela McCorduck, The Fifth Generation. Reading, Mass.: Addison-Wesley, 1983.
  15. R.J.K. Jacob and J.N. Froscher, “Software Engineering for Rule-Based Software Systems,” Proceedings of the 1986 Fall Joint Computer Conference. Washington, D.C.: IEEE Computer Society Press, 1986.
  16. Robert E. Keller, Expert Systems Technology: Development and Application. Englewood Cliffs, N.J.: Prentice-Hall, 1987.
  17. Robert Alloway and Judith Quillard, “User Managers’ Systems Needs,” CISR Working Paper 86. Cambridge, Mass.: MIT Sloan School Center for Information Systems Research, April 1982.
  18. Ludwig von Bertalanffy, General Systems Theory. New York: George Braziller, 1969.
  19. Gerald Weinberg, An Introduction to General Systems Thinking. New York: Wiley, 1976.
  20. John Gall, Systemantics. New York: Quadrangle/The New York Times Book Company, 1977.
  21. D. Barstow, “Artificial Intelligence and Software Engineering,” Proceedings of the 9th International Software Engineering Conference, April 1987.
  22. M.D. Lubars and M.T. Harandi, “Knowledge-Based Software Design Using Design Schemas,” Proceedings of the 9th International Software Engineering Conference, April 1987.
  23. Bennet P. Lientz and E. Burton Swanson, Software Maintenance Management. Reading, Mass.: Addison-Wesley, 1980.
  24. Lavette Teague and Christopher Pidgeon, Structured Analysis Methods for Computer Information Systems. Chicago: Science Research Associates, 1985.
  25. John P. Eberhard, “We Ought to Know the Difference,” Engineering Methods in Environmental Design and Planning, Gary T. Moore, ed. Cambridge, Mass.: MIT Press, 1970, pp. 364-365.
  26. Paul T. Ward and Stephen J. Mellor, Structured Development for Real-Time Systems. Englewood Cliffs, NJ: YOURDON Press/Prentice-Hall, 1985.
  27. Tarek Abdel-Hamid and Stuart E. Madnick, Software Project Dynamics: An Integrated Approach. Englewood Cliffs, NJ: Prentice-Hall, 1991.
  28. George P. Richardson, and G.L. Pugh III, Introduction to Systems Dynamics Modeling with Dynamo. Cambridge, MA: Productivity Press, 1981.
  29. Senge, Peter M, The Fifth Discipline: The Art and Practice of the Learning Organization. (paperback edition), New York: Doubleday, 2006.
  30. Jorgen Randers (editor), Elements of the System Dynamics Method. Cambridge, MA: Productivity Press, 1992.


  1. Give two examples of each of the definitions of a system provided by Webster’s dictionary in the beginning of Chapter 2.
  2. Give five examples of systems that have lasted for at least 1 million years and that are still in existence today.
  3. Give five examples of man-made systems that have lasted for more than 1000 years. For each case, give a brief description of why they have lasted and whether they can be expected to continue surviving over the next thousand years.
  4. Give five examples of non-man-made systems that have failed in your lifetime. Why did they fail?
  5. Give five examples of man-made systems that have failed in your lifetime. Why did they fail?
  6. Research Project: read Miller’s Living Systems and provide a book report.
  7. Research Project: read Beers’ Brain of the Firm and provide a book report for your colleagues.
  8. Research Project: read Beers’ The Heart of Enterprise and provide a book report for your colleagues.
  9. From Section 2.3; give an example of a man-made system that, in your opinion, should not be automated. Why do you think it should not be automated? What would go wrong?
  10. Give an example of a non-automated system that, in your opinion, should be automated. Why do you think it should be automated? What would the benefits be? What would the costs be? How confident are you of the benefits and costs?
  11. Give examples of Miller’s 19 subsystems for the following kinds of automated systems: (a) payroll, (b) inventory control, (c) the telephone system.
  12. Pick a small organization that you are relatively familiar with, or a department or division of a large organization. For the organization that you have chosen, conduct an inventory of the systems that it uses. How many of them are operational systems? How many are decision-support systems? How many are strategic planning systems? Are there any other useful categories of systems? To help you focus on this area, consult (Alloway and Quillard, 1982).
  13. Give five examples from your own experience of (a) real-time systems, (b) on-line systems, (c) decision-support systems, (d) strategic planning systems, and (e) expert systems.
  14. Figure 2.4 shows a typical hardware configuration for an on-line system. Draw a diagram for a reasonable different hardware configuration. Does it make sense to have some of the system’s data physically located at the terminals? When, in the development of the system, should this be discussed with the user?
  15. Give an example of a commercial system that is described either as an “artificial intelligence” or a “knowledge-based” system that, in your opinion, is not honestly or accurately described. Why do you think the description is misleading?
  16. Could the stimulus-response model shown in Figure 2.5 apply to systems other than real-time systems? Don’t all systems respond to stimuli? What is special about real-time systems?
  17. Can a decision-support system actually make decisions? If not, why not? What could be done to change the typical decision-support system so that it could make decisions? Would this be desirable? What are the trade-offs?



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