spring 2011 - Çgie398 - lecture 31 lecture 3 : systems and st this lecture introduces basic...

Spring 2011 - ÇG IE398 - lecture 3 1

lecture 3 : systems and ST

this lecture introduces basic concepts of systems and systems thinking :

• what is the method used in science and should OR/IE use the same method?

• subject – object duality• decision making in complex situations• efficacy – effectiveness - efficiency• what is a system?• what kinds of systems are there?


what method is used in science?Aristotle viewed the world as a living entity: parts of the world could only be understood in terms of their relationship with each other and with the whole

• this is a holistic (ie. systemic) and also a teleological view of the world (teleology is the doctrine that says there is a purpose to everything; it explains phenomena by the purpose they serve rather than by postulated causes)

• so teleological thinking dominated Western thought for more than 2000 years, even though it was not called systems thinking


• then came the Enligthenment which marked the beginning of modernity, or “the age of reason”

• modernity started with the emergence of modern science, and epecially empirical sciences, when teleology was replaced by Cartesian mechanism, based on the philosophy of René Descartes (1596-1650)

• Isaac Newton (1642-1727) developed this type of thinking with great success; his work shaped the modern world, it determined how we live and understand the world


• the Newtonian perspective of the world is reductive rather than holistic; it assumes that analysis is the means to gain knowledge

• reductionism is the reduction of all phenomena to simple, unidirectional causal relationships between variables rather than interactions that can only be explained in terms of the functioning of the whole

• the Oxford Dictionary says: reduction is the practice of analysing and describing a complex phenomenon, especially a mental, social, or biological phenomenon, in terms of its simple or fundamental constituents

• this Newtonian, or the mechanistic paradigm dominated science until the middle of the 20th century and is still alive, even in the everyday language we use

• the Oxford Dictionary says: paradigm is a world view underlying the theories and methodology of a particular scientific subject

• alternatively, a paradigm as the set of shared and formal assumptions about the nature of reality and our knowledge of it



• thus, as quoted in Jackson (1):“Each of us lives and works in organisations designed from Newtonian images of the universe. We manage by separating things into parts, we believe that influence occurs as a direct result of force exerted from one person to another, we engage in complex planning for a world that we keep expecting to be predictable, and we search continually for better methods of objectively perceiving the world”

• Newtonian thinking emphasises cause-and-effect thinking, in contrast to systems thinking

• it is a hard view of the world and is based on the assumption that objectivity is possible


subject-object duality• Newtonian science is founded on the possibility of

objective knowledge acquired through independent observation

• this belief is based on the assumption of subject-object dualism

• which says that the observer (subject) can be separated from from the observed (object); ie. if the observer makes observations independently of the observed, then observation will be objective

• positivism is strongly rooted in this assumption; it claims that scientific inquiry can be objective, ie. strictly free of personal preferences and value judgements and therefore able to provide true knowledge


• in the 20th century, positivism and the Newtonian paradigm came under criticism

• it was observed, for example, that: – in quantum mechanics subatomic particles do

not exist as independent things; they come into being and are observed only in relationship to something else; so observation influences the observed and subject-object duality fails

– in biology it was understood that organisms are adaptive and co-evolutionary rather than mechanistic

– in chemistry, disorder in dissipative systems is now seen as the source of new order, and growth is found in disequilibrium, not in balance


• this new awareness led a movement toward holism, toward understanding phenomena as a system and emphasising the relationships that exist among seemingly discrete parts

• when we view the world from this perspective, “we enter an entirely new landscape of connections, of phenomena that cannot be reduced to simple cause-and-effect, and of the constant flux of dynamic processes” (1)

• it means that in systems, the observer inevitably influences the observed since he must be part of the system; absolute objectivity is therefore impossible,

• furthermore most systems that are important for understanding the world are complex


increased complexity• despite these developments, the positivist –

objectivist - disciplinary approach persists in science

• by contrast, OR has had an interdisciplinary character from the start and became aware early on of the impossibility of value-free inquiry

• today even everyday decision making faces complexity:– “Today's world has thus increased in complexity and

interdependence to a point where the traditional methods of problem solving based on the cause-and-effect model cannot cope any longer.” (2)

• the following examples illustrate the complexity that causes unexpected and undesirable consequences that outweigh expected benefits


1. the Aswan High Dam in Egypt– loss of fertile silt and a new need for

fertilisers– salinisation – loss of land and decline of sardine fisheries– increased incidence of schistosomiasis

2. deterioration of urban transport – suburbanisation and increased car

ownership– extension of the road network– reduced demand for public transport– increased fares, declining service quality– further shift toward private transportation– traffic congestion

3. assessment of unit production cost– assessing the performance of each

machine centre on the basis of average unit costs:

the lower the unit cost, the more efficient the machine centre

– this assessment works fine in simple plants with one machine centre

– in a complex set up with several centres working together it can lead to accumulation of work-in-process inventory and profit loss



a counterintutive production example

produce more of the high-margin item:3 units of A, 2 units of B;total profit = 3(90) + 2(60) = 390

produce more of the low-margin item:2 units of A, 4 units of Btotal profit = 2(90) + 4(60) = 420

a counterintutive experiment: the Hawthorne effect

– does better lighting improve productivity at the Hawthorne plant?

– a experiment designed with an experimental and a control group of workers

• productivity of the experimental group improved with better lighting

• but the productivity of the control group also improved

• productivity increased even further under poor lighting

– the Hawthorne effect is an example of what we call reactive effects



efficacy–effectiveness–efficiency

• efficacy : does the means work? it is the capacity to produce an effect; e.g. aspirin is efficacious against head-ache but not against a broken bone

• effectiveness : will long term goals be attained?• efficiency : is resource use minimised? ie.

maintaining effectiveness with fewer resources used

• concern about efficiency should not conflict with effectiveness (e.g. reducing inventories to save costs might cause loss of sales and revenue )

• efficiency should complement effectiveness


systems• systems are everywhere; some varieties are:

natural systems: e.g. the solar system, a frog, a girl, an ecosystem

abstract systems: e.g. an algebra, the number system

social, or human activity systems: e.g. a production system,

• the epistemological (inside us) view is often more useful than the ontological (out there) view ; e.g. – an electric power supply system may first appear to be out-

there,it includes generators, transmission and distribution lines, transformer stations etc.

– but does it also include rivers and the electricity pricing system?

• what the system will include or exclude will depend on how we define the system, on the purpose of inquiry

• this can change for the same person too; e.g. a river system is not the same thing to an engineer when he is working as it is when he is vacationing


Dällenbach defines a system as follows:1. A system is an organized assembly of components. 'Organized'

means that there exist special relationships between the components.

2. The system does something, ie. it exhibits behaviours that are unique to the system.

3. Each component contributes towards the behaviour of the system and its own behaviour is affected by being in the system. No component has an independent effect on the system. (A part that has an independent effect and is not affected by the system is an input.) The behaviour of the system is changed if any component is removed or leaves.

4. Groups of components within the system may by themselves have properties (1), (2) and (3), ie. they may form subsystems.

5. The system has an outside - an environment - which provides inputs into the system and receives outputs from the system.

6. The system has been identified by someone to be of special interest for a given purpose.


• so for each system we can identify a relevant environment that– provides inputs to a transformation

process– and receives outputs from the

transformation process

• inputs can be uncontrollable or control inputs (these are represented as decision variables in mathematical models)

• outputs include measures of performance

• identification of the relevant environment requires setting boundaries


some examples• a traffic system• a motorcar– all parts fit together and interact– the motorcar provides transportation, something

that none of its parts can– is this a system out-there?– it may look like that but transportation also

requires a driver, a purpose and a road network– furthermore cars can be different things to

different people: a means of transportation, a prestige symbol or a collection item

– so a car may be better understood as a conceptual system rather than a system out-there


Table 3-1 Three different systems views for a sawmill

Systems view Industrial engineer Owners MS analyst

Purpose of viewing study physical lay- assess financial study effect on costs

entity as a system out of equipment, return on investment of different cutting product handling, & patterns to meet diff. operating rules given demand

System components • buildings, yards, • subsystems. such • processing

equipment, as procurement of subsystems vehicles logs, production, • intermediate • operators warehousing, mar- product stocks • logs in yards keting, finance • intermediate • funds invested products

Activities of system • cutting operations • purchasing of logs • subsystem product

• moving of cuts • conversion of logs conversions • drying of cuts • storage of logs • storage of interme- • planing of cuts • sales of logs diate products • storage • control of funds

Relationships • sequencing of • subsystem outputs • subsystem outputs

between components tasks become inputs to become inputs to • location of fixed other subsystems other subsystems equipment • communications • feasible cutting • feasible comb ina- between combinations tions of cutting subsystems • financial aspects patterns • financial aspects

Inputs from • types of logs • funds • log availabilities

environment • supplies (oil, fuel) • personnel • cost data • processing rates & • product demands • processing rates & capacities • commercial laws capacities • operating rules • pricing policy • operating rules • product demands

Outputs to • finished products projections for projections for total

environment • by-products (saw- • net profit operating costs to dust, off-cuts) • cash flows meet customer • processing • return on demands capacity investment • bottlenecks • market share • equipment cap. use

Transformati on logs into finished wealth and production capacity,

process of system products and opera- production capacity logs available, and ting statisti cs at time t into wealth customer demands and production into total operating capacity at time t+ I cost

a sawmill:different things to different people

• there exists a hierarchy of systems– the sawmill firm is embedded in a system of

regional sawmills all sharing the same forest resources

– the system of regional sawmills is embedded in the national wood processing industry etc.

• in general the containing system exercises some control over the contained system; by setting the objectives, monitoring performance and having control over crucial resources

• the controlling system is then referred to as – the wider system of interest,

while the contained system becomes – the narrow system ofinterest


• for example, if the sawmill cost-minimizing system is the narrow system of interest, then the sawmill profit-maximizing system and its environment are its wider system of interest

• the advantage of viewing two is that their relationships are shown in their correct context– it may show that improvements in the

performance of the narrow system requires action to be taken in the wider system

– similarly, the relationships between various inputs into the narrow system are clarified; for example all labour costs in the cost-minimisation system of the sawmill may depend on the union contract signed in the wider system



system 1: narrow system of interest:

cost minimising operations

system hierarchies

environment for system 1

system 2 : wider system of interest

marketing & sales

suppliers

customers

environment for system 2

irrelevant environment


• although hard systems thinking can sometimes regard systems as out-there, it is often more useful to regard systems as inside-us, as mental constructs

• system definitions are therefore necessarily subjective, because they are influenced by:– the purposes and the interests of the observer– the Weltanschauung of the individual (each

person interprets the world in terms of his/her own experiences and biases, ie. repeated patterns of experience lead to a complex set of beliefs and values through which we perceive the world; hence W is the taken-for-granted outlook of the world; a formalised W

shared by a group of people is a paradigm)


“Suppose, for example, you were asked by the International Olympic Committee (IOC) to conduct a broad systems study of the future of the Olympic Games (…) It would be quickly apparent that there is no single account of the Games as the "system of concern" which would be generally acceptable: that "system" would be very differently described (and hence so would system objectives) by the IOC itself, by the host city, by would-be host cities, by athletes, by athletes' coaches, by officials, by spectators, by hot-dog sellers, by sponsors, by television companies, by television viewers who have no interest in athletics(…) This list could go on and on, and this is what happens as soon as you move outside technically defined problem situations and into human problem situations. (…) [This] illustrates that multiple conflicting objectives from multiple stakeholders are the norm in human

situations.” Checkland and Holwell (2)


• in other words, reality can only be known through our perceptions which are necessarily shaped by our world view (ie. weltanschauung)

• this means that subject-object duality cannot be assumed to hold in social systems

• hence the only valid type of objectivity must be consensual, ie. socially decided

• this means that unlike the “scientific objectivity” claim of positivism, systems thinking accepts that truth and validity can only be decided dialectically through a process of negotiation


critical considerations in systems thinking

• in order for systems thinking to be effective we have to be careful when we decide:

1.where to set the boundarythis is called boundary setting, or a

boundary judgement

2.what level of detail, or scale to adoptthis is called scale setting or separation of

scales

• these are the only two devices that will help us deal with complexity

boundary setting• supposing we define the environment as that

part of the world that lies beyond our control, it is still not clear how far our control extends

• if the boundary is set too tight, there is the danger of leaving out important system parts and interrelations

• if the boundary is set too wide then there will be too many components and interrelations that will be difficult to handle and understand

• the best we can do is to set the boundary and make sure not to forget that such boundaries must later be widened or narrowed as necessary



• failure to do this will result in what Churchman (3) calls an environmental fallacy

• more specifically, an error committed by setting the boundary too tight is called an environmental fallacy, this is the most common and serious mistake made in IE/OR

• an environmental fallacy will be committed if we focus our attention on one part of the system only and forget about the larger system of which it is a part

• an environmental fallacy is often equivalent to confusing efficiency with effectiveness


• for example:– if we maximise the efficiency of each department of a

hospital, it does not necessarily follow that the hospital will work effectively

– a focus on minimising inventory costs will lead to a fallacy since inventories cannot be decided independently of production, of marketing, of procurement, of distribution, of maintenance etc.

• Ackoff (4) defines environmental fallacy as follows: “Recall Peter Drucker’s observation of the difference between doing things right and doing the right thing. This distinction is fundamental. The righter we do the wrong thing, the wronger we become. If we made an error doing the wrong thing and correct it, we become wronger. (…) It is much better to do the right thing wrong than the wrong thing right” Why?


separation of scalethe concept of “state”• this is a fundamental concept that cannot be defined

any more than the concept of a “set” can be defined in mathematics

• consider a physical system that transforms the single input represented by the time function v(t), into the single output represented by the time function y(t)

• if we know the structures and processes that make up this system, then complete knowledge of v(t) over the interval (-∞,t] is sufficient to determine y(t) over the same time interval

• however if the input is known only over the time interval [to,t] then we need information at some time t1, where to ≤ t1 < t, in order to determine the output y(t) over the time interval [to,t]

• this information constitutes the state of the system at time t1 ; it consists of the levels of all (structural and process) variables, or the state variables at time t1 , (remember the state variables in dynamic programming; or the number of customers in a B&D queue)

• in this sense, the state of the system is related to the memory of the system


• for another example of system-state, consider the solution of a linear differential equation with constant coefficients for t ≥ to

• once the form of the complete solution is obtained in terms of arbitrary constants, these constants can be determined by the fact that the system must satisfy boundary conditions at time to; no other information is required

• these boundary conditions can be termed the state of the system at time to


• intuitively, the state of a system separates its future from the past, so the state contains alI the relevant information concerning the past

• the state of a system is represented by a vector showing the values of all state variables at time t

• hence if the state vector is given for time t, then we have all the information there is to know about the system


• for an example consider a system of display consisting of 7x100 light bulbs that can allow 20 letters to be written by lighting some of the bulbs

• this system will have 2700 ≈ 10210 different states (note that the number of atoms in the universe ≈ 1073 , which is infinitesimally smaller)

• fortunately not all the states of a system need be relevant for decision making, in many cases knowledge of aggregates or averages is sufficient


• for example, – when studying traffic problems, we don’t need to

know the exact position and speed of all vehicles that are on the road-network at all times; often a knowledge of time-averages is good enough

– a civil engineer designing a bridge does not need to know the state vector of all molecules that make up the bridge, knowing aggregate properties such as tensile and ductile strength etc. is sufficient

• this reduction in the number of relevant system states is achived by separation of scales

• that is, by deciding what level of detail is relevant for decision making



large or coarse scale

high level

small or fine scalelow level


• sometimes, in extreme cases the scale can be so large that the system is defined as a black box when,– not all the detail of the transformation process

is needed, and – a black box representation is sufficient– which shows only the i/p and the o/p e.g.

logs T sales revenue

emergence• a good alternative definition of a system can be

given in terms of emergence:

a system is a set of interrelated components with emergent properties

• an emergent property normally appears at higher levels of scale as a result of interactions at lower levels

• human activity systems are often established in order to produce desired emergent properties (eg. a car-and-road network transportation)

• though they can also produce undesired emergent properties (eg. the same network noise and pollution)



references1. M.C.Jackson (2000) “Systems approaches to

management” Kluwer ( available as an e-book in METU Library)

2. M. Pidd (2004) “Systems modelling” Wiley3. W. Churchman (1979) “The systems approach

and its enemies” Basic Books4. R. Ackoff; J. Pourdehnad (2001) “On misdirected

systems” Systems research and behavioral science, 18, pp:199-205

spring 2011 - Çgie398 - lecture 31 lecture 3 : systems and st this lecture introduces basic...

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