LSE Complexity Group BUSINESS PROCESS MODELLING & SYSTEM EVOLUTION 11 January 2001 Co-evolution of...

LSE Complexity Group

BUSINESS PROCESS MODELLING & SYSTEM EVOLUTION

11 January 2001

Co-evolution of the business process and IS development:

A complexity perspective

Eve Mitleton-KellyComplexity Research Programme

LSE

www.lse.ac.uk/lse/complex


Theories

Natural sciences

Dissipative structureschemistry-physics (Prigogine)

Autocatalytic setsevolutionary biology (Kauffman)

Autopoiesis (self-generation)biology/cognition (Maturana)

Chaos theory

Social sciences

Increasing returnseconomics (B. Arthur)

self-organisation

emergenceconnectivityinterdependencefeedback

far from equilibrium

space of possibilities

co-evolution

increasing returns

Genericcharacteristics

of complexadaptivesystems


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)


Familiar termsFractals

AttractorsParadoxes

Edge of chaosetc

CHAOS THEORY


Systems Theory• Emphasises the whole• and the inter-relationship of parts within that

whole• Emergent properties or qualities• Feedback• Connectivity & interdependence

Complexityenriches

builds on extends Systems Theory


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.


PRINCIPLES

• Co-evolution within a social ecosystem

• not just adaptation to the environment

• One domain changes in the context of the other.


Co-evolution within an ecosystem


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


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


Co-evolution in a Social Ecosystem

• A social ecosystem includes:social

culturaltechnical

geographiceconomic milieu

• In human systems, co-evolution places emphasis on the ‘evolution of interactions’– on the relationship between co-evolving entities


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?


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.


• 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.


Connectedness Diversity

Density Intensity Quality

of interactions between human agents

Determine network of relationships


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


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


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


A machine:

• Is a complicated system

• With many inter-related parts

• Relies on feedback

• Can be thought of as an object


• 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.


• Feedback in this context is taken to mean influence, 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


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’.


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]


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


Complex System

• 2 or more intelligent interacting agents

• Is capable of adaptation and evolution

• Can create new order• Its behaviour cannot be accurately

predicted


Complex System

• Can change its rules of interaction

• Can act on limited local knowledge

• Is self-repairing and

self-maintaining


Complexity thinkingChange of emphasis

from objects

to relationships between entities

from control

to enabling infrastructures


Why complexity thinking?

• Unstable evolving environmentsDynamic 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 & learningStrategic advantage lies in developing new useful knowledge

from the continuous stream of “unstructured, diverse, random, contradictory data” (Ogilvie 1998) swirling around firms.


Principles of complexity or generic characteristics

• Framework used as:

– a method of analysis

– to help identify and develop enabling infrastructures


• 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.


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.


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


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


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..


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


b) Organisation & management

• Personal career agendas in conflict with business objectives

• Management discontinuity:

projects not completed


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)


Reciprocal influences

Technical problems Organisational changes

impacted exacerbated

Organisational Technical

issues concerns


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]


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


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


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


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


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)


• Top-down design, &• single solution approaches • tend to constrain:

self-organisationemergenceinnovationco-evolution

• Looked for evidence of above – found a ‘natural experiment’ – identified enablers and inhibitors


Enabling Conditions in the

Natural Experiment

• Monthly meetings – enabled good networking, trust, a common language

mutual understanding

autonomystabilityco-location

integrated team effort‘interpreter’


Some inhibitors• Charging for system changes

• Management discontinuity projects 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


Some inhibitors

• No documentation with high interconnectivity and incremental growth

• Inaction when systems seen as “old but reliable”

• Contradiction of how legacy is perceived and what is being done about it


Enabling Infrastructure

Combination of cultural, social and technical conditions which facilitate ‘x’

Conditions

enable inhibit


Aerospace Project

Richer framework

• Enablers/facilitators

• Inhibitors/contraints

• Considered from a complexity perspective

• Implications & consequences

Individual level

Group level

Organisational level


Enabling Infrastructures

• Only part of a possible solution– depends on individuals, their inter-

relationships and the social ecosystem (organisational, cultural, etc milieu)

• Need to explore space of possibilities/alternatives – no single optimum solution – cannot copy – allow for emergence and self-organisation


Enabling Infrastructures

• Understand nature of organisations as CSS– e.g. distributed intelligence– inter-dependencies between

principles/characteristics

• Understand nature of change, adaptation, co-evolution within a social ecosystem.


Some questions

• How efficiently does information percolate through the organisation?

• What is the degree of connectivity?– Too much connectivity, could lead to ‘complexity

catastrophe’

• How varied is the repertoire of behaviours?

• Does the organisation evolve new structures to adapt to changes?

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