Chapter 1: Introduction

Contents:

Introduction

By definition, a system is composed of interrelated parts. In systems theory, the degree of interrelationship is termed the "wholeness" of the system. If the operation of every part of a system is related to every other part, wholeness is said to be high. And in fact, an outcome measure taken from any part of such a system will represent the effectiveness of every part of the system to the extent that other parts enter into the outcome. Because all parts are interrelated, all of the outcome measures taken from this system will be complex measures reflecting the operation of every other part of the system and will be substantially intercorrelated.

For logical purposes, it is useful to contrast a system of high wholeness to a nonsystem in which no parts are interrelated. Measures of outcome would not reflect the operation of other parts measured and would not be Intercorrelated. This is so obvious that it seems silly. But the converse, stated above, is not so easily grasped: outcome measures from different parts of a system are correlated because those outcomes are jointly determined by common parts of the system.

What we hope is obvious is that the parts of the system themselves are interrelated but are theoretically independent in their unique operation. The only way to demonstrate this independence is to obtain less complex measures of outcome of that particular part of the system which are free of the effects of other parts of the system. As an example, the quality of the library staff would be one variable contributing to library size, A test of librarianship skills could be devised and administered to the library staff It would certainly be expected that the results of this test would be less correlated with university quality than would be library size. That is, the more molecular the measure, the less intimately it would be expected to he related to global indices of system functioning. However, more molecular measures would give more specific information about system functioning,

We believe the same holds true for mental ability. Certainly the human mind is a well‑integrated system having a high degree of wholeness. Wholeness is reflected in complex measures of human ability, which explains the high correlations between standard tests of intelligence. Simpler, more molecular measures should be individually less highly correlated with more complex measures but should provide more specific information about the operation of the system.

Management sciences have learned a great deal about organizations and how they work. Much of this learning has come from adopting the perspective that organizations are entities (systems, defined later), much like people, plants and animals. There are many benefits to leaders who adopt this systems view of their organizations.

Systems thinking has its foundation in the field of system dynamics, started in 1956 by MIT professor Jay Forrester. Professor Forrester recognized the need for a better way of testing new ideas about social systems, in the same way we can test ideas in engineering. Systems thinking allows people to make their understanding of social systems explicit and improve them in the same way that people can use engineering principles to improve their understanding of mechanical systems.

Systems thinking are fast becoming a powerful tool for decision-making and organizational change. All employees in a company should be equipped with the skills necessary for systems thinking. It is imperative to have some awareness of the origin of systems thinking and how it can be of benefit to various types of organizational change, such as reengineering, systems integration, process redesign, Total Quality Management, and teamwork. In order to apply systems thinking to challenges that occur in the work place, some of the tools and methodologies used in systems thinking should be taught. Some of the best known strategies used to implement systems thinking include systems modelling, simulations, causal loops, archetypes, and scenario planning. To meet the complex changes that are inevitable, systems thinking can no longer be esoteric knowledge held by few managers, but should be accessed by all.

The approach of systems thinking is fundamentally different from that of traditional forms of analysis. Traditional analysis focuses on separating the individual pieces of what is being studied; in fact, the word “analysis” actually comes from the root meaning “to break into constituent parts.” Systems thinking, in contrast, focuses on how the thing being studied interacts with the other constituents of the system – a set of elements that interact to produce behavior – of which it is a part.

The character of systems thinking makes it extremely effective on the most difficult types of problems to solve: those involving complex issues, those that depend a great deal on the past or on the actions of others, and those stemming from ineffective coordination among those involved. Examples of areas in which systems thinking has proven its value include:

§          Complex problems that involve helping many actors see the “big picture” and not just their part of it.

§          Recurring problems or those that have been made worse by past attempts to fix them.

§          Issues where an action affects (or is affected by) the environment surrounding the issue, either the natural environment or the competitive environment.

System Thinking stresses the systemic pattern of thinking (Systemic is the attribute of thinking derived from systems approach)

1.1 Basic Definitions

A system

• Is an object/process that has components bound by a mission and has a surrounding environment.
• Is an ordered, interdependent assemblage of components in a field that has boundaries defined by a clear mission.

Systems Thinking

• Is seeing through “the system’s structure generating changes and creating the problems.”
• Is a global way of thinking taking into considerations all factors bounded by the mission of the system.

1.2 What is a System?

Very simply, a system is a collection of parts (or subsystems) integrated to accomplish an overall goal (a system of people is an organization). Systems have input processes, outputs and outcomes, with ongoing feedback among these various parts. If one part of the system is removed, the nature of the system is changed.

Systems range from very simple to very complex. There are numerous types of simple systems. For example, there are biological systems (the heart, etc.), mechanical systems (thermostat, etc.), human/mechanical systems (riding a bicycle, etc.), ecological systems (predator/prey, etc.), social systems (groups, supply and demand, friendship, etc.) and psychological systems (memory, thinking… etc.).

Complex systems, such as social systems, are comprised of numerous subsystems, as well; These subsystems are arranged in hierarchies, and integrated to accomplish the goal of the system. Each subsystem has its own boundaries of sorts, and includes various inputs, processes, outputs, and outcomes geared to accomplish and overall goal for the subsystem.

A pile of sand is not a system. If one removes a sand particle, you have still got a pile of sand. However, a functioning car is a system. Remove the carburetor and you have no longer got a working car.

1.3 Basic Concept

and

Characteristics

1.3 Basic Concepts and Characteristics

Basic principles

Systems theory has identified numerous principles that are common to systems, many of which help us to better understand organizations.

Systems thinking reposes on basic principles:

1.4 Stages of Systemic Thinking

Input output principle forms

Applying system approach

Potential impact on people

Development

 Systems do

 not have to be complicated

Example

How brain system handled information

 Language, notation and mathematics

Some necessary properties

 Individual's  psychological resources

 Concept of entropy of living organisms

A closed system

1.4 Stages of Systemic Thinking 

The Input-Output technique developed by the American General Electric Company can be helpful. Although it need not be, its use has been restricted mainly to technical problems in which the input is energy, light, heat, electricity, etc. - with a desired output in some way dependent upon it. Whiting gives, for example, the problem of devising a fire warning system. The input is fire and the required output a warning that fire is present, with a number of constraints in between: the warning must be foolproof and continuously available; it must be quick-acting to minimize damage; and it must be discernible at points remote from the fire. The problem may not be solved in one step. A warning system requires several intermediate steps, starting with the fire itself and ending with some physical warning system. Whiting warns against trying to short-circuit any intermediate point – this is more likely to lead to a stereotyped solution, since it fails to consider the opportunities for branching into the alternative paths offered by multiple outputs generated at some stages.

The Input-Output principle forms much of what might be considered the heart of a ‘Systems’ approach. This removes the limitations of a problem defined in purely technical terms and extends the definition of input, output and constraints to include the whole situation – men, money, materials, machines and methods. It thereby provides an overall view and allows us to arrive at a more comprehensive, unified and long-lasting solution than any piecemeal approach can make possible.

Thus, in applying a system approach, say, to a problem involving the manufacture of a chemical, we would not be limited to the technicalities of the process, choice of materials of construction, design and performance of mechanical and electrical equipment and methods of measurement and control. We should, in addition, be involved with the problems of processing and handling raw materials, methods of transport, and use and disposal of finished products; with the recruitment, training and working conditions of the management and men needed to run the plant; with the effects of the product and its manufacture on the local environment – the noise, smell, smoke and general pollution produced; with the long-term effects of our presence as an employer and a source of opportunity. Even then the list is far from complete, but we are beginning to paint a fuller picture of the total situation and thereby identify more of the important variables having claim to consideration alongside those of technology.

Clearly, the more complex a problem and the greater its potential impact on people, the more appropriate a systems approach becomes. But it would surely be wise to consider all but the most narrowly defined technical problem in a context which includes the human element, if we wish to avoid unpleasant reactions and resistance to our solutions when we create them.

Jenkins suggests that there are four main stages in the systems approach: analysis, synthesis, implementation and operation.

A useful development of the Systems approach is given by Nadler. He suggests that if we can disengage our thoughts from the present situation when defining a complex problem and think instead of an ideal solution, that is, one which is not restricted by money, method or resources, then by keeping this ideal solution in mind, we will come nearer to it in practice than by trying to inch forward with the present as our reference point. Nadler describes three stages in the achievement of a workable solution: the Ultimate Ideal System, the Technologically Workable Ideal System and the Technologically Workable Ideal System Target. An Ultimate Idea System represents the best system likely to be achieved through the development of existing knowledge. But it is achievable, even though at a later date, and can be made a target for improvement in the future, giving a fixed aim point rather than a projection forward from the present situation. A Technologically Workable Ideal System is one based on technology which already exists, but which does not take into account real-life restrictions such as money, available skill, etc. By designing several systems to this criterion and selecting one as a guide, a recommended system, the Technologically Workable Ideal System Target, as a guide, can finally be described which does take into account all real-life restrictions.

Systems do not have to be complicated or unintelligible, or even dressed in jargon. A system is just an arrangement of circumstances that makes things happen in a certain way. The circumstances may be metal grids, electronic components, warm bodies, rules and regulations or anything else. In each case, what actually happens is determined by the nature of the system. One can take the function of the system for granted and become interested in how it is carried out.

If young children are asked to invent a potato‑peeling machine they draw a‑winding tube through which a string of potatoes is seen traveling towards a simple box with the explanatory note, 'In here the potatoes are peeled.' Another tube carries the peeled potatoes away. There is nothing mysterious about the box; it just performs the potato‑peeling function. One takes it for granted that is the function of the box and that somehow the function gets carried out. In some of the inventions the potatoes are then carried to a metal grid through which they are forced in order to make chips. The making of the chips is not taken for granted but explained, because it is explicable.

If you put water instead of oil into a frying pan you would not expect to be able to fry chips. If you were to use fat or oil you would get some ordinary chips. If you add a little water to the oil before you put the pan on the fire, then the temperature of the oil will rise more slowly and the chips will be soft on the inside and crisp on the outside ‑ much more so than if only the oil had been used. The nature of the system determines what happens.

The brain is a system in which things happen according to the nature of the system. What happens in the brain is information. And the way how it happens is thinking.

Since thinking in this broad sense determines what people do on any level from the most personal to the most international, it could be worth looking at some aspects of the brain system.

The first useful thing that can come out of knowledge of a system is the avoiding of those errors that arise through thinking the system to be something that it is not.

The second useful thing is awareness of the limitations of the system. No matter how good they may be at performing their best functions, most systems are rather poor when it comes to performing the opposite functions. One would no more go racing in a shopping car than shopping in a racing car. Where one can, one chooses the system to fit the purpose. More often there is no choice, and this means that a single system will perform certain functions well, but others not so well. For instance, the brain system is well suited to developing ideas but not always so good at generating them. Knowing about the limitations of a system does not by itself alter them. But by being aware of the nature of the system one can make deliberate adjustments.

The third way in which one could use knowledge of a system would be to make use of the characteristics of the system to improve its performance or to achieve some end.

Some understanding of how the brain system handled information could be very useful. It might then be possible to recognize some of the errors and faults inherent in this type of system, to show, for example, that there was a tendency to arbitrary and self-enhancing divisions which were extremely useful in most cases but could also be the source of a lot of trouble. Apart from becoming aware of the errors of the system, it might also be possible to make more effective use of it through understanding its nature in order to make the learning process easier and more economic. It might be possible to do something about communication.

Language, notation and mathematics are useful artificial aids to thinking. There may be other artificial aids which could be invented if one had sufficient understanding of the brain system. With new notation it might prove possible to generate ideas as easily as we now develop them once they have been generated. For instance, it might be possible to invent a new word which would be functional in nature like 'and', 'if', 'but' or 'not. The function of this new word would be to compensate for the inherent limitations of the information ‑ processing system in the brain and open up new ways of talking and thinking. The word would ultimately have to justify its usefulness in practice, but its invention may not have been possible without an understanding of the nature of the system.

There are a few necessary properties of systems that need to be stated before proceeding. The most basic of these is that systems exhibit some degree of stability, or constancy. If they do not, it would not be possible to identify them as the same system over time. A system may be closed, which means that it is a self‑contained, self‑regulating entity that is insulated from, and does not interact with, other systems. Or, it may be open, or interactive. For open systems to be stable they must exhibit equilibrium through negative, or compensating, feedback, because if they do not, their form would change and the necessary property of stability over time would be lost. The "hunting" of servo‑mechanisms and the homeostasis of vegetative biological functions in animals are examples of open systems maintaining equilibrium through negative feedback.

The individual's psychological resources for coping with the social world‑what we call in everyday terms the person‑can be construed as a system in this sense. Analogies with systems in the social sciences usually concentrate on the behavior of thermodynamic systems. The second law of thermodynamics states that all closed systems are subject to increasing entropy. The entropy of a system is the measure of unavailable energy; energy that still exists but which is lost for the purpose of doing work. In thermodynamics, of course, the energy referred to is heat and the law can be roughly understood as the idea that all (hot and cold) material within a thermally insulated area will eventually come to have the same temperature. The more general version of this principle is that all closed systems are subject to loss of differentiation. A correspondence has been established between the entropy of a system and the loss of information in that system in the sense of information theory. So, this principle can also be taken to mean that the information in a closed system diminishes over time.

Schrödinger, applying the concept of entropy to living organisms, writes: "Thus a living organism continually increases its entropy‑or, as you might say, produces positive entropy‑and thus tends to approach the dangerous state of maximum entropy, which is death. It can only keep aloof from it, i.e., alive, by continually drawing from its environment negative entropy".

A closed system, in thermodynamics or biology or whatever, like the concept of infinity in mathematics, is an ideal or pure state, unreal when applied to the physical world. But although closed systems are probably never perfectly realized in practice, the pure concept serves as a useful anchor for theories in the study of material things. The same is true of the concept of system applied to the social world. Consider, for instance the "ideal" but incredible notion of a person as a closed system, completely insulated from other systems, from the rest of the community. Such insulation would take the form of never talking with anyone and never doing anything (doing, that is, in the sense of acting and choosing as non‑automatic, non‑habitual justified performance). Borrowing from the thermodynamics analogy, one of the properties of such a closed person‑system would be its increasing entropy; the gradual decline in the harness able energy, or differentiation, or information, within it.

1.5 Understanding the Nature of the System in Organizations

Good judge

Good grasp

Progress measurement

Variables to understand the nature of the system

1. Casual

2. Intervening

3. End-results

Interrelation of variables

Understanding the nature of the system

Effective managers needs

Important decisions

Other casual relations

Effective leader‑managers have a common affinity for understanding the nature of the larger system within which they work. Whenever they take on new job assignments, they make a special effort to understand the inner workings of the larger system of which their work unit is a part. Realizing that the needed information can not be uncovered simply from printed documents; they are relentless in their probing. They observe, inquire, and integrate until they are satisfied that they have a valid conceptual model of the system.

John Dewey, the philosopher and educator, was astute in his portrayal of the "good judge." This is a person "who has a sense of the relative indicative or signifying values of the various features of the perplexing situation; knows what to let go of as of no account; what to eliminate as irrelevant; what to retain as conducive to the outcome; what to emphasize as a clue to the difficulty." In essence, this is a person who has a profound understanding of the larger system within which he or she works.

In The Human Organization, Rensis Likert stresses that the manager should have a good grasp of two aspects of the system: the nature of the system and the state of the system. In this regard, he likens the manager's job to that of the physician:

It is generally understood that measurement of progress is dependent on accurately assessing the state of the system at any point in time. It also must be understood that accurately assessing the state of the system is dependent on understanding the nature of the system

To understand the nature of the system, Likert stresses that the manager must grasp the relations between and among three types of variables:

As an illustration of how these three classes of variables interrelate, we can consider the example of the effect of leadership style on productivity. In many situations, it would be assumed that a participative leadership style would be more effective than an autocratic style. This premise can be tested by correlating leadership style (causal variable) with employee motivation (intervening variable), and then correlating employee motivation with productivity (end‑result variable). In this way it could be demonstrated that leadership style has an effect on productivity, but via employee motivation.

Given this framework for "understanding the nature of the system", we will illustrate the notion by considering the dollar flow in a for‑profit engineering firm. You may not have any particular interest in an engineering firm, but the principles elucidated here would apply to any type of organization.

The dollar flow of the illustrative firm is shown in Figure 1.1. We will consider the business volume to be the causal variable, the net income to be the end‑result variable, and everything else to be intervening variables.

The business volume is broken down into these categories: labour, use of equipment and service centres, all other project costs, overhead (engineering department overhead, general overhead, and cost of capital), and fee. The general overhead is apportioned as direct expenses of engineering operations (funds allocated to the engineering departments) and indirect expenses (funds used to operate the company as a whole).

Effective managers understand the causal relations in this financial system. For example, they realize that increasing labour (time on projects) by one percent can have at least a 10‑percent impact on net income. They realize that a two‑percent overrun in project losses can cause a 20‑percent decrease in net income. Further, they realize that a fee increase of three percent can have a 30‑percent impact on net income. These multiplier effects are indeed noteworthy, and they are ever‑present in the mind of the effective manager.

Figure 1.1: Dollar flow in an engineering firm

Managers who understand this financial system also realize that it presents them with a number of important decisions. For example, will the return‑on‑investment with the marketing and proposals funds be better in the industrial arena or in the government arena? With the funds allocated for internal research and development, is it better to invest in a small number of really good ideas or in a large number of possibly promising ideas? With the funds for associations and publications, which particular associations and publications should be pursued? These are important questions, and the answers generated will determine the success or failure of the manager.

There are numerous other examples of important causal relations in the system, but these will illustrate the point that for you to be able to measure your unit's progress, it is essential that you understand the inner workings of the larger system.