Sharif, A M

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Brunel University Research Archive

Can Systems Dynamics be effective in modellingdynamic business systems?Amir M. Sharif1Information Systems Evaluation and Integration Group (ISEing)School of Information Systems, Computing and Mathematics,Brunel University, Uxbridge, UB8 3PHThe  economist  and  statistician  George  P.E.  Box,  with  reference  to  statistical probability models, notably opined that “All models are wrong – but some models are useful”  (Box,  1979).  This  largely  misquoted  but  nonetheless  famous  statement, outlined Box’s view that decision makers sometimes have to make decisions with incomplete information, which may be only explained by the best available models. As such, any mental or conceptual model which attempts to encapsulate all possible facets of a given system or situation, may be limited in its expression due to real-world complexity or due to subjective over-simplification of the same reality, at the time that the model was formulated.  Box’s argument is that if the boundaries of a system can be defined as far as possible, then the resulting systems model, can also be restricted to be within realistic and quantifiable bounds. Achieving accuracy within a representation of the real world in this way, becomes increasingly  difficult,  as  the  level  of  complexity  of  the  model  increases  to accommodate an ever finer granularity of representation. Sterman suggests that any model of the real world should yield a fundamental kernel of knowledge at its core, about the context within which it exists. But at the same time, this model should allow us  to  understand its  imperfections  and the  limitations of  the world it  is  trying to represent,  through the inclusion of human, social,  systemic and process flows and other  impinging  factors  (Sterman,  2002).  Indeed the  inclusion  and use  of  human expert knowledge is the fulcrum upon which such representations pivot (Ford and Sterman, 1998). A  natural  consequence  of  this  approach,  means  that  if  better,  detailed  or  more complex representations of reality are defined, the accuracy of our predictions and quality of decisions should also increase. Any and every technique which can help BPM and MIS researchers, as well as industry practitioners, to uncover and relate tangible and intangible factors, should therefore be pursued. At the junction of the disciplines  of  management,  social,  behavioural,  political,  and  more  recently, information sciences, the field of Systems Dynamics aims to provide the researcher with such a tool to resolve these questions. Systems Dynamics (or Systems Thinking as it  is  otherwise known) has  existed in  the  academic  as  well  as  industrial  R&D community for  over  25 years  at  least  (Ackoff,  1974;  Bertalanffy,  1976),  and  has grown to become a standard modelling technique in the armoury of the inquisitive in 1 Visiting Research Fellow, email: ams@amir.demon.co.uk1that time. The methods and approaches that can be employed range from variations on flow charting and state transition and dependency diagrams; through to ‘bounding box’  models  (which  model  fixed  and  semi-fixed  imaginary  boundaries  around  a system); so-called ‘rich picture’ diagrams (characterised by the ‘Weltanschauung’ – world view – of Checkland’s Soft Systems Methodology – see  Checkland, 1981); and more fundamentally, system archetypes (which ultimately define systems which have pre-defined behaviour and outcomes – see Ackoff, 1974 and Kim, 1995). The question that I pose is whether or not such systems dynamics approaches allow and provide an answer to Box’s enigma of generating ‘complete’ models of the world around us. To begin to answer this, I  choose to view those systems which are of interest  to  readers  of  this  journal,  namely  business  process  and  information management systems. The only question to be asked perhaps, is to what level of detail and accuracy do such systems need to be modelled in order for those representations to be realistic? And it is precisely this question that I believe can help us determine whether or not systems dynamics is useful and appropriate for modelling dynamic business systems. The answer I suspect, that most if not all BPM and MIS researchers will provide is ‘any level of detail and accuracy will be sufficient as long as it is a representation that can  be  used  as  a  vehicle  for  communication  and  disseminating  knowledge  and understanding about the business system being analysed’. Zachman and Sowa (1992) take this  view too in  part,  in  terms of  modelling enterprise  IT/IS architecture for example. This is a valid and entirely sensible and rational answer, and one which I myself,  wholeheartedly  concur  and  agree  with.  For  example,  I  could  conceivably model  a steel production facility, as a series of process flows, or as a number of engineering decision steps – or even as a visual ‘mind map’ of those dependencies and interactions connected with the smelting, rolling, stamping, quality assurance and delivery processes. However, this approach to using and applying a seemingly endless list  of  modelling  tools  (including  proprietary,  theoretical  as  well  as  heuristic  and empirical  models),  is  prone to  bias  and validity  risk overall,  in  my view for  two reasons.Firstly, because of the flexibility and simplicity with which such tools can be applied to modelling a system, it is quite possible to apply the wrong method to a given case; and thereby reach the wrong conclusions about the behaviour of that system. How would  you  know you were  wrong though?  The answer  to  this  belies  the  second reason. This is that modelling a system can easily be done at the wrong time also, and for  the  wrong reasons  with  the  wrong tools.  (Incidentally  when  I  state  the  word ‘wrong’ I do not necessarily mean incorrect, but rather mean inappropriate.) Thus, the intent must match the purpose, be it to represent, predict, monitor, optimise, or carry out a post-hoc evaluation of a dynamic system. So, in effect choosing a modelling methodology and tools is reliant upon choosing which tool to use and when to use it appropriately. This may seem an obvious statement, and in many ways it is – and leads  me  onto  a  key  solution  to  both  of  these  questions  raised  and  also  Box’s statement (so ‘here comes the science bit’). Representing business and IT/IS systems using systems dynamics / thinking tools and concepts can only be effective if such approaches  are  understood,  learnt,  employed  and  disseminated  by  the  industrial practitioner  community  (to  give  some  context  and  application  to  real  world situations);  and are also taught as a fundamental part of BPM and MIS syllabi in universities  and  colleges  also.  I  feel  that  systems  dynamics  as  a  field  has  great 2potential to be used more fervently within the business process community, and it is only being limited by a lack of understanding and knowledge about it presently, in my opinion. Those researchers and practitioners who consider themselves to be empirical purists, tend to shun systems dynamics / thinking as they view the inherent formalism involved in some of the methods to be leaning towards being more quantitative, and thus restrictive to modelling those ‘soft’ human issues. I too, held this view until I decided only recently to delve into this field in more detail. As such, and in the light of previous research efforts in terms of (fuzzy) cognitive mapping techniques (Kosko, 1991; Mentazemi and Conrath, 1986), I was particularly drawn  to  the  concepts  of  system  archetypes  (Ackoff,  1974)  and  morphological analysis (Zwicky, 1969). Whilst archetypes have their basis in ‘hard’ systems theory (Bertalanffy, 1950), some would argue that morphological analysis is not strictly a systems  method  at  all,  being  more  closely  aligned  with  forecasting  and  strategic planning approaches – however, I beg to differ on that). I was surprised to learn that these techniques have been used so very sparingly over the years, and in terms of the BPM  and  MIS  community,  have  rarely  been  used  if  at  all.  Upon  reading  and comparing these techniques to those that I know and use frequently, it struck me just how flexible and amenable both of these techniques may be.  In any case, I began to see the benefits that each approach had in viewing and modelling the IS evaluation process, for example. I was able to see how the investment appraisal decision-making process could potentially be viewed in terms of basic systems archetypes such as ‘Fixes  that  Fail’,  ‘Eroding Goals’,  ‘Attractiveness  Principle’,  and  ‘Tragedy of  the Commons’.  Similarly  by using morphological  analysis,  I  could at  once detail  and define all possible known variables of the ISE process and step through numerous (and perhaps  albeit,  fanciful)  combinations  and outcomes,  of  different  technology management and investment scenarios (as outlined by Ritchey, 1997).So I believe that there is a strong case for starting to apply systems dynamics methods more openly in the BPM and MIS research fields, as I feel the tools and techniques available  are  vastly  under-rated  in  terms  of  their  applicability  and  capability  to provide novel representations of real world situations. Because these techniques may not  be  understood  or  even  taught  sufficiently  well,  I  also  think  their  use  as complementary methods to other forms of modelling (such as cognitive mapping, strategic planning etc), is also non-existent. However, the use of such methods could yield  significant  and  interesting  results  within  the  given  research  communities (although I have yet to find or see research which supports or employs this approach). By increasing and improving upon our experiences in modelling real world situations in this way, maybe we can one day falsify Box’s epithet and stand to claim: ‘All models are wrong, but system dynamics models tend to be the most useful’.Amir M. SharifDisclaimerThe author wishes to assert that the views and opinions expressed in this article are solely those of the author.3ReferencesAckoff, R.L. (1974). Redesigning the Future: A Systems Approach to Societal Problems. New York : John Wiley and Sons. Axelrod, R. (1976). Structure of Decision: the cognitive maps of political elites, Princeton University Press.Bertalanffy, L. (1950). An outline of General Systems Theory,  British Journal for the Philosophy of  Science, 1 : 139 - 164 Bertalanffy, L. (1976). General Systems Theory. George Braziller, New York.Box, G.E.P. (1979). Robustness in the Strategy of Scientific Model building. In (Eds. R.L. Launer and G. N. Wilkinson). Robustness in Statistics. New York : NY, Academic Press, pp. 202.Checkland, P. (1981).  Systems Thinking, Systems Practice. London, John Wiley & Sons.Daniel K. (1995). Archetypes as Dynamic Theories. The Systems Thinker, June/July, 1995.Ford, D.N., and, Sterman. J. D. (1998) Expert knowledge elicitation to improve mental and formal models, System Dynamics Review, 14 (4) : 309 - 340.Fowler, M., and Scott, K. (1998). UML Distilled - Applying the Standard Object Modelling Language. Addison Wesley Longman Inc..Kosko, B. (1991).  Neural Networks and Fuzzy Systems, Prentice-Hall, Inc., Upper Saddle River, NJ, USA.Mentazemi, A., and Conrath, D. (1986). The use of cognitive mapping for information requirement analysis. Management Information Systems Quarterly, March 1986, pp.45 – 56.Ritchey, T. (1997). Scenario Development and Risk Management using Morphological Field Analysis. Proceedings  of  the  5th  European  Conference  on  Information  Systems,  Cork,  Cork  Publishing Company, pp. 1053 - 1059.Sterman, J.D. (2002). Reflections on becoming a systems scientist. Systems Dynamics Review, 19 (4) : 501 – 531.Zachman, J.A.,  and Sowa, J.F.  (1992).  Extending and Formalizing the Framework for Information Systems Architecture, IBM Systems Journal. 31 (3): 590 – 616.Zwicky,  F.  (1969).  Discovery,  Invention,  Research  -  Through  the  Morphological  Approach.  The Macmillan Company, Toronto.4

An outline of General Systems Theory,

Archetypes as Dynamic Theories. The Systems Thinker,

Discovery, Invention, Research - Through the Morphological Approach.

Expert knowledge elicitation to improve mental and formal models,

Extending and Formalizing the Framework for Information Systems Architecture,

Neural Networks and Fuzzy Systems, Prentice-Hall, Inc., Upper Saddle River,

Redesigning the Future: A Systems Approach to Societal Problems.

Reflections on becoming a systems scientist.

Robustness in the Strategy of Scientific Model building. In

Scenario Development and Risk Management using Morphological Field Analysis.

Structure of Decision: the cognitive maps of political elites,

Systems Thinking, Systems Practice.

The use of cognitive mapping for information requirement analysis. Management Information Systems Quarterly,

UML Distilled - Applying the Standard Object Modelling Language.

Can systems dynamics be effective in modelling dynamic business systems?

Can systems dynamics be effective in modelling dynamic business systems?

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