BUSINESS PROCESS MODELLING & SYSTEM EVOLUTION 11 January 2001

1. BUSINESS PROCESS MODELLING & SYSTEM EVOLUTION 11 January 2001 Co-evolution of the business process and IS development: A complexity perspective Eve Mitleton-Kelly Complexity Research Programme LSE www.lse.ac.uk/lse/complex LSE Complexity Group

2. Theories Natural sciences Dissipative structures chemistry-physics (Prigogine) Autocatalytic sets evolutionary biology (Kauffman) Autopoiesis (self-generation) biology/cognition (Maturana) Chaos theory Social sciences Increasing returns economics (B. Arthur) self-organisation emergence connectivity interdependence feedback far from equilibrium space of possibilities co-evolution increasing returns Generic characteristics of complex adaptive systems LSE Complexity Group

3. Application to Human Systems • Generic characteristics of CAS used as a starting point • What is relevant and appropriate? • Use term CES(Complex Evolving System) or CSS(Complex Social System) LSE Complexity Group

4. Familiar terms Fractals Attractors Paradoxes Edge of chaos etc CHAOS THEORY LSE Complexity Group

5. Systems Theory • Emphasises the whole • and the inter-relationship of parts within that whole • Emergent properties or qualities • Feedback • Connectivity & interdependence Complexity enriches builds on extends Systems Theory LSE Complexity Group

6. Complexity • Articulates and clarifies the principles or characteristics not articulated by systems theory e.g self-organisation, far-from-equilibrium, co-evolution, exploration of the space of possibilities, increasing returns, etc. • Provides a different language and perspective or way of thinking. LSE Complexity Group

7. PRINCIPLES • Co-evolutionwithin a social ecosystem • not just adaptation to the environment • One domain changes in the context of the other. LSE Complexity Group

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9. Co-evolution within an ecosystem LSE Complexity Group

10. Co-evolution and time • Co-evolution, strictly speaking, takes place when entities change at the same time • Consider short-term adaptation and long-term co-evolution • Discussion with Prof. Uzzi Sandler, theoretical physicist LSE Complexity Group

11. Co-evolution takes place within an ecosystem • Ecosystem(in biology): “each kind of organism has, as parts of its environment, other organisms of the same and of different kinds” Kauffman 1993 LSE Complexity Group

12. Co-evolution in a Social Ecosystem • A social ecosystem includes: social cultural technical geographic economic milieu • In human systems, co-evolution places emphasis on the ‘evolution of interactions’ • on the relationship between co-evolving entities LSE Complexity Group

13. e.g. a firm • Each firm is seen as a fully participating agent which both influences and is influenced by the social ecosystem. • Question: what changes when an organisation evolves? or • What does evolution mean in a social context? LSE Complexity Group

14. Why are relationships important? • Medium through which information is passed • A healthy system is one in which information is flowing freely (??) • Relationships – key to continuous improvement • “Communications and relationships are a vital part of complexity theory”Tom Irons MD, Prof of Paediatrics, East Carolina University School of Medicine. LSE Complexity Group

15. A complex co-evolving ecosystem is one of intricate and multiple intertwined interactions and relationships. • Connectivity and interdependence propagate the effects of actions, decisions and behaviours throughout the ecosystem. • Depend on degree of connectedness. LSE Complexity Group

16. Connectedness Diversity Density Intensity Quality of interactions between human agents Determine network of relationships LSE Complexity Group

17. Connectivity & interdependence • In human ecosystems there are networks of relationships with different degrees of connectedness • strength of coupling • epistatic interactions i.e. the fitness contribution made by one individual will depend upon related individuals • Essential element of feedback LSE Complexity Group

18. 2 Feedback mechanisms • Reinforcing (amplifying) – a driver for change • Balancing (moderating or dampening) operates whenever there is goal-seeking behaviour - creates stability Processes not mechanisms • need time dimension LSE Complexity Group

19. Feedback Process not Mechanism to avoid the machine metaphor A machine is a system, which we can: • understand • design • plan its operation in detail • predict its behaviour and • control LSE Complexity Group

20. A machine: • Is a complicated system • With many inter-related parts • Relies on feedback • Can be thought of as an object LSE Complexity Group

21. Not sufficient to describe all the feedback processes in complex systems • “Multi-loop, multi-level feedback systems” • Lehmans’ VIIIth Law • Link the micro and macro processes • The microscopic events and the macroscopic emergent structures or patterns change and evolve and in so doing influence each other through feedback processes. LSE Complexity Group

22. Feedbackin this context is taken to meaninfluence, which changes potential action and behaviour. • Influence • Not uniform • It depends on the degree of connectedness • Actions and behaviours vary with different individuals • With time and context • Reciprocal LSE Complexity Group

23. Exploration of the space of possibilities • Exploration of new options, different ways of working and relating. • The search for a single 'optimum' strategy is neither possible nor desirable, in a turbulent environment.  • But variety alone is not enough. New connections or contributions also need to be ‘seen’. LSE Complexity Group

24. Examptation • Often not expensive R&D which produces major innovations, but ‘seeing’ a novel function, in a new light. • “Exaptation is the emergence of a novel function of a part in a new context. … Major innovations in evolution are all exaptations. Exaptations are not predictable”. [Kauffman] LSE Complexity Group

25. Self-organisation • Spontaneous ‘coming together’ • Not directed or designed by someone outside the group • The group decides what needs to be done, how, when • Can be a source of innovation e.g. new way of providing information in an aerospace company • Consider what facilitates self-organisation LSE Complexity Group

26. Complex System • 2 or more intelligent interacting agents • Is capable of adaptation and evolution • Can create new order • Its behaviour cannot be accurately predicted LSE Complexity Group

27. Complex System • Can change its rules of interaction • Can act on limited local knowledge • Is self-repairing and self-maintaining LSE Complexity Group

28. Complexity thinking Change of emphasis from objects to relationships between entities from control to enabling infrastructures LSE Complexity Group

29. Why complexity thinking? • Unstable evolving environments Dynamic ill structured environments and learning opportunities become the basis of competitive advantage if firms early in their industry can recognise the new patterns as they are emerging. • New knowledge & learning Strategic advantage lies in developing new useful knowledge from the continuous stream of “unstructured, diverse, random, contradictory data” (Ogilvie 1998) swirling around firms. LSE Complexity Group

30. Principles of complexity or generic characteristics • Framework used as: • a method of analysis • to help identify and develop enabling infrastructures LSE Complexity Group

31. Two applications: • Facilitating co-evolution between the business process and IS development. • Creation of an inter-organisational trusting environment in IPTs (integrated project teams) in the Aerospace industry. LSE Complexity Group

32. IT Legacy Systems Project:The Bank Case Study IT legacy systems: do not support the changing business process Studied the co-evolution between the changing business process and information systems development – incl. the impact of legacy systems. LSE Complexity Group

33. Bank case study Change in one domain induced change in related domains. Some factors which introduced change, in the Bank’s socio-technical ecosystem: a) Business and market b) Organisation and management c) Technology LSE Complexity Group

34. a) Business & market Changes in business processes, products and services impacted the bank’s technological infrastructure How? • New applications built on old technology • Or, incremental functionality was added onto the existing system LSE Complexity Group

35. Consequence: Increased interconnectivity and interdependence e.g. among system components and applications The Bank customised or engineered solutions into its systems and changed their coded components. Over time a layered system infrastructure was created, which was tailored to service many different customers Exacerbated the legacy problem. . LSE Complexity Group

36. b) Organisation & management Factors which contributed to the problem: • Communication gap between developer and user communities • Lack of skills to maintain the legacy systems • Lack of training: organisation’s attitude in supporting change LSE Complexity Group

37. b) Organisation & management • Personal career agendas in conflict with business objectives • Management discontinuity: projects not completed LSE Complexity Group

38. c) Technology factors • Rapid technological change exerts pressure on management – offset against cost • Existing technological infrastructure fails to meet emerging expectations and changing business requirements • Interface between existing and new technology (new platforms, hardware, software and processes) LSE Complexity Group

39. Reciprocal influences Technical problems Organisational changes impacted exacerbated Organisational Technical issues concerns LSE Complexity Group

40. Complexity perspective: Macro-micro interaction “One of the most important problems in evolutionary theory is the eventual feedback between macroscopic structures and microscopic events: macroscopic structures emerging from microscopic events would in turn lead to a modification of the microscopic mechanisms.”[Prigogine and Stengers] LSE Complexity Group

41. Macro-micro interaction • Technology and organisational changes at the firm level affected the social ecosystem • Created change at the macro level • Affected individual organisations • Affected the various micro levels within the organisation incl. IT systems LSE Complexity Group

42. Technical and Socio-cultural reciprocal influences • Desire to offer standard banking to international customers • Organisational restructuring (socio-cultural) changed the systems’ architecture (technical aspect) • Centralisation of technology(technical aspect) affected the ways of working and organisational issues (socio-cultural) • Both changed the relationship with the customers – but created unintended problems LSE Complexity Group

43. Organisational restructuring (socio-cultural aspect)changed the systems’ architecture (technical aspect) • Late 70s, early 80s, each country branch in Europe had its own system ‘a bank in a box’ – it run all the local bank’s operations. • Mid 80s, centralisation brought the h/w and s/w into central service centres – branches run remotely LSE Complexity Group

44. Centralisation of technology(technical aspect) affected the ways of working and organisational issues (socio-cultural) and created unintended problems e.g. multi-ownership of common components - issue did not arise with systems that were managed and owned locally in a single country • achieved standardisation of customer accounts - but, loss of local technical knowledge - degradation of personal relationships with customers LSE Complexity Group

45. Complexity perspective • Adaptation – of the social to the technical and vice versa • Increasing connectivity & interdependence • Multiple feedback processes • Imposition of a single solution (e.g.centralisation) • No local exploration of possibilities and development of local optima • Loss of variability (technical), skills (social), modes of interaction with the customers (socio-cultural) LSE Complexity Group

46. Top-down design, & • single solution approaches • tend to constrain: self-organisation emergence innovation co-evolution • Looked for evidence of above – found a ‘natural experiment’ – identified enablers and inhibitors LSE Complexity Group

47. Enabling Conditions in the Natural Experiment • Monthly meetings – enabledgood networking, trust, a common language mutual understanding autonomy stability co-location integrated team effort ‘interpreter’ LSE Complexity Group

48. Some inhibitors • Chargingfor system changes • Management discontinuityprojects not completed • Differing perceptions – e.g. improving legacy infrastructure seen as a cost by business managers • Loss of system expertise, through restructuring, downsizing, outsourcing, etc LSE Complexity Group

49. Some inhibitors • No documentation with high interconnectivity and incremental growth • Inactionwhen systems seen as “old but reliable” • Contradictionof how legacy is perceived and what is being done about it LSE Complexity Group

50. Enabling Infrastructure Combination of cultural, social and technical conditions which facilitate ‘x’ Conditions enableinhibit LSE Complexity Group