Presentation on theme: "SEARCHING FOR THE QUANTUM ORGANISATION: THE IT CIRCLE OF EXCELLENCE By René Pellissier Published by JUTA, July 2001."— Presentation transcript:
SEARCHING FOR THE QUANTUM ORGANISATION: THE IT CIRCLE OF EXCELLENCE By René Pellissier Published by JUTA, July 2001
CONTENTS 1. Why I did what I did 2. How I did what I did 3. What I did 4. What I got out of it ‘There is a theory that states that if ever anyone discovers exactly what the Universe is for and why it is here, it will instantly disappear & be replaced by something even more bizarre & inexplicable. There is another theory that states that that has already happened.’
“… choas is found in greatest abundance wherever order is being sought. It always defeats order, because it is better organized.” T. Pratchett Interesting times 1994: 12.
1. Why I did what I did Toffler’s & Kondratieff’s (K) waves, Epochs (growth, maturity, decline) Chaos ensuing from a collision of waves & shortening cycles Each wave constructs its own demise Critical mass for the 3rd wave = microchip Critical mass for the 4th wave = tech, info Mass customization market one size shorter cycle times more customer products
The radical (discontinuous) change paradigms irreconcilable with the chaotic dimensions of business & its environment Radical change mainly because of (evolution of) IT (exponential rate of change) reproductive principle of tech information availability and needs The problem with Newton man-as-machine organization-as-machine
2. How I did what I did - Inputs Research methodology & the limitations of that empirical research The natural and mathematical sciences applied to the world of business The quantum world, the quantum age (or age of unreason) and the quantum organization
2.1Ecclectic research strategy Adding to the existing body of knowledge through Mathematics, Statistics, Business sciences Chaordic in design - from chaos follows order 2. How I did what I did
2.2 Hawking’s scientific theory (1988): It must accurately describe a large class of observations on the basis of a model that contains only a few arbitrary elements It must accurately describe a large class of observations on the basis of a model that contains only a few arbitrary elements It must make definite predictions about results of future observations It must make definite predictions about results of future observations To disprove a theory, one has to find a single observation in contradiction. To disprove a theory, one has to find a single observation in contradiction. Any new theory is an extension of the previous theory. Any new theory is an extension of the previous theory. It must accurately describe a large class of observations on the basis of a model that contains only a few arbitrary elements It must accurately describe a large class of observations on the basis of a model that contains only a few arbitrary elements It must make definite predictions about results of future observations It must make definite predictions about results of future observations To disprove a theory, one has to find a single observation in contradiction. To disprove a theory, one has to find a single observation in contradiction. Any new theory is an extension of the previous theory. Any new theory is an extension of the previous theory.
2.3 The inability of Statistics to produce a grand theory Statistics: is unable to handle open-ended research questions Focuses on known aspects (a priori) Can handle specific quantitative research questions/hypotheses known through some other source Limitations of empirical research
2.4 Traditional Newtonian cause and effect (linear model) Emanates from clock-work/machine type universe The traditional cause & effect model is fundamentally flawed (Russel, 1992) should be replaced by the laws of change Use cause & effect, but both shortened infinitely, so that the direction at each moment can be studied (like differential equations for causal laws). However, it is not possible to observe infinitesimals in time & space.
2.5 The problem with Newtonian science Newton’s 3 laws of motion Rulebound discipline that requires data up front Sees the world as a well-behaved machine operating in a predictable universe Relationships between cause & effect are simple, clear & linear ‘If X, then Y follows’ - from Industrial Era with machine-people Cornerstone of Scientific Management Management of constants
2.6 Quantum world & the notion of uncertainty when dimensions of time & space become smaller Def of quantum mechanics: ‘quantum mechanics does not predict a single definite result for an observation. Instead, it predicts a number of different possible outcomes & tells exactly how likely each of these is. Thus, if one made the same measurement on a large number of similar systems, each of which started off in the same way, one would find that the result of the measurement would be A in a number of cases & B in a different number & so on.’
Introduces an unavoidable element of unpredictability/randomness into any science Outstandingly successful theory that underlies nearly all of modern science & tech Application to bus science still unfamiliar: Bus organizations display similar behaviour patterns as the universe to which it belongs Uncertainties in the universe are reflected in the uncertain outcomes of organizations when confronted with changes in the environment (tech & info) Heisenberg’s uncertainty principle: There are features in the universe that cannot be known with complete precision. Such uncertain aspects would become more severe as the distance & time scales become smaller
An uncertain universe - uncertain environment - uncertain business outcomes - chaos - subsequent order ??? The birth of the quantum era/age of unreason/collision of waves ‘The framework of laws governing the universe whose unfamiliar features such as uncertainty, quantum fluctuations & wave particle duality become most apparent on the microscopic scales of atoms & sub nuclear particles.’ Greene, 1999
2.7 Research strategy deployed Hybrid, multiple case study approach, since it provides the opportunity to explore uninhibitedly all dimensions of organizational life How? & Why? Direct & indirect observation without the constraints of structured design No need for proper design of experiment (continued, rapid environmental changes influence this anyway) proper random sample drawn from some predefined population The case study (like an exp), does not represent a statistical sample and the goal of the research is to expand and generalize theories Multiple case studies are analogous to multiple experiments
Disadvantages acknowledged: From the viewpoint of the theoretical scientist (especially wrt possible bias on the part of the investigator - also true when designing questionnaires) Generalization to a specific population is questionable, but.. case studies are generalizable to theoretical propositions rather than populations Throughout the book, a strategic rather than an operational perspective was maintained
The chaotic nature of IT- related research goes beyond the accustomed linear research methodologies in accordance with Newton’s (limitedly) flawed cause & effect thinking
2.8 Concepts & definitions Rapidly expanding field, makes it virtually impossible to find proper references & definitions - cloned/updated/borrowed from related fields, such as organizational strategy, systems thinking, Industrial Engineering, Economics Ex: Def of IT in terms of its evolution rather than a fixed state
2.9 The ideal state Plato (427-347BC) During his time, many struggles and wars Disappointed in the State and Socrates’s death - similar to society’s dissatisfaction with the world (made the more so through tech & info) = the repetitive nature of society’s needs The Republic: State based on sound principles (waves) - justice & equality State = an extended form of personal mastery the individual, the org, the State all strive towards a perfect state - in this instance, the workplace
‘Rough empirical generalisations have a definite place in science, in spite of not being exact or universal. They are the data for more exact laws and the grounds for believing that they are usually true are stronger than the grounds for believing that the more exact laws are always true.’ Russel, 1992: 312
3. What I did Chaos theory, catastrophe theory, change point analysis Jumping the curve Study of BPR, Marxism Solutions are temporary events, whereas BPR promises the ultimate solution The value of information, the evolution of IT ‘The spaceship hung silently in the air, exactly like bricks don’t’
3.1 Chaos theory Self organising theory Orderly disorder - rather than random disorder Order & chaos are not extremes no law abiding Newtonian universe = revolution in understanding how the world works emphasises uncertainty, open-endedness, plurality and change
3.2 Foundations of chaos theory Non-linearity: Small changes can induce large effects, having little semblance to their beginning - everything beyond short-term predictions are impossible Feedback: Output at every step in the system provides material for a new outcome, thus amplifying deviation & destabilizing the system even more, introducing new patterns Bifurcations: Cusp. Occurrence can be predicted, but not the outcomes Strange attractors: Inherent state of affairs/underlying order Scale: Interpretation depends upon the scale Fractals: Shows similar (not identical) patterns at successively greater magnitude Self-organizing principle: Ability to reorganize. Unstable combination of randomness & plan, broken by flashes of change.
3.5 Flawed def of BPR H&C themselves preach a re-eng of re- engineering: not about cost cutting but growth H&C published ‘What re-eng is not’ Only one key word: Process, rather than: fundamental, radical, process, dramatic. Business processes are organic & have personalities because they are made up of people, having different goals, values, needs, etc. Presupposes a perfect solution, wherein the ‘machine’ will comply with the new set of rules
3.6 Marx’s synthesis Revolution over evolution Holistic approach over fragmentation Business org - society Re-eng - revolution
BPR and IT “Reengineering involves the fundamental rethink and radical redesign of business processes to achieve dramatic improvements in critical, contemporary measures of performance such as cost, quality service and speed.” Hammer & Champy, 1990 THE EVOLUTION OF IT = MORE THAN AN ENABLER: Information value chain Technology dimension Information dimension ‘ It is time to stop the cow paths. Instead of embedding outdated processes in silicon & software, we should obliterate them & start over. We should use th power of modern IT to radically redesign our business processes..’
4. What I got out of it - outputs Principle of self-organization (- renewal) Chaord, chaordic.. enterprise,leadership Holonic enterprise Identification of the cusp (bifurcation point,..) - mathematically, inferentially, through cusp models. Mathematical models for jumping the curve Technology Change model Information delivery matrix Critique of BPR - reasons for failure De-engineering to clean up after re-engineering Cyclical nature of the world, synchronicity
A LANDSCAPE OF INFORMATION DELIVERIES SYSTEM APPLICATIONS (OLTP, ESPECIALLY ERP) EXAMPLES: SAP, PEOPLESOFT, BAAN, JD EDWARDS, SSA, RUBICO BUSINESS INTELLIGENCE (DATA WAREHOUSING AND MINING) EXAMPLES: SAS HYPERION, COGNOS, BUSINESS OBJECTS DATABASE APPLICATIONS EXAMPLES: ORACLE, EXCEL, SYBASE, INFORMIX, MS ACCESS, LOTUS NOTES, DBASE, SOTWARE AG OFFICE AUTOMATION EXAMPLES: WORD PROCESSING, GENERAL ACCOUNTING, E- MAIL, DESKTOP PUB,SPREADSHEETS,FAX TRANSMISSIONS, VIDEO CONFERENCING
PROPOSED CLASSIFICATION OF PRODUCTS Mature products: ERP Growth products: ERP extension, e-commerce Growth products: Data warehousing, Data mining, OLAP Mature products: Desktop DBMS, Enterprise DBMS Growth products: Object relational DBMS. Mature products: Word processing, Spreadsheets, e- mail, General Accounting, DTP, Facsimile Growth products:Voice recognition, Groupware, Work flow, EDMS, Video Conferencing
EXTENDED PROPOSED LANDSCAPE business intelligence (data warehousing & mining) Examples: sas, Hyperion, Cognos, Business Objects, Seagate, Oracle, MS Olap. office automation Examples: Groupware, EDMS, Workflow, Spreadsheets, e-mail, desktop publishing, word processing, facsimile transmissions, video conferencing, Voice recognition. SYSTEMS APPLICATIONS (oltp – especially ERP) Examples: SAP AG, Peoplesoft, Baan, JD Edwards, SSA. database applications Examples: Oracle, Excel, Sybase, Informix, MS Access, Lotus Notes, DBase, Software ag ERP extensions
The cusp Definitions That point at which two curves meet Passing or change from one state/condition/pl ace to another
Self organising principle of an organisation ‘Build the organisation around the software and the software around the customer’ Organisation = collective knowledge & intelligence of its people Principle of renewal = f(openness, intelligence, learning)
The notion of a Chaord Bounded equilibrium Does not follow traditional (linear) organisational model One small change can accomplish substantial and unpredictable changes Chaos & order are not opposites
The chaordic enterprise Characteristics of the future organisation: org as living entity no single org structure knowledge workers information-based decentralised but densely linked through tech agile and adaptable creative, collaborative flatter, self controlling, open, trust customer value Shamrock Federal Triple I Virtual Self- organisation Holonic Hypertext Quantum
DEENGINEERING THE CORPORATION Revolutionary - Marxist Reengineering’s failures Organisation as machine Change programme works against people’s tendencies What if the reengineering does not work? Self organising pattern for leaders & workers Organisation as living entity Natural order and patterns exist C 4 I From chaos to well- ordered, efficient organisations
“It was our fault, and our very great fault – and now we must turn it to use. We have forty million reasons for failure, But not a single excuse, So the more we work and the less we talk The better results we shall get. We have had an imperial lesson; it may mold us an Empire yet!” Rudyard Kipling: The Lesson. IS THERE A PERFECT SOLUTION???
Conclusion ‘Great innovations when they appear, seem muddled and strange. They are only half understood by their discoverer and remain a mystery to everyone else. But, if an idea does not appear bizarre, there is no hope for it.’