Data Science - A New Subject - Introduction and Courses Information

Data science includes  topics such as plotting, statistics and probability, Monte Carlo simulations, data modeling, clustering, and plotting. Data scientists are in high demand due to development of Big Data technologies and the adoption of these technologies by companies in both manufacturing and marketing as well as in surveillance and risk management.

https://en.wikipedia.org/wiki/Data_science

https://datascience.berkeley.edu/about/what-is-data-science/

1 Hour Data Science Course
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https://www.youtube.com/watch?v=-ETQ97mXXF0
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19 April 2015

https://www.coursebuffet.com/tag/Data%20Science

Data Science with R: Intro to Data Analysis
Date: Sept. 28th; Oct. 5th, 12th, 19th, 26th; 2014
http://nycdatascience.com/courses/r-programming-intensive-beginner/

Data Sciences - Moocs
http://www.coursetalk.com/courses/data-science



Updated 25 April 2020,   19 April 2015
1 August 2014

Big Use of Big Data in Market Research

Browse  Online MBA Management Theory Handbook

In Market Research or Marketing Research, the producers and marketers have to figure out what market wants currently and in future.  This requires data and interpretation of data. Interpretation is 60% and data collection is 40% of marketing research according to a marketing research expert.  The emergence of social media is making data available in real time about brands and their shortcoming. It also has desires of customers for future products. So emergence of Big Data that has the capability to analyze social media conversations and posts has made data available to data interpreters and thereby reduced the need for data collectors in marketing research activity.


Whereas earlier marketing research was based on small samples, Big Data based marketing research is able to take in millions of potential customers and buid insights on their conversations. The advantage is that marketing communications can be targeted to them and a better response rate can be achieved for products and services created according to their stated needs.




Big Data Use 2015

Analyzing customer behavior  65%
Bringing together different data sources  63%
Improving customer perception 59%
Enhancing responsiveness to market dynamics  51%
Generating reports faster  41%
Enhancing customer relationshipss  37%
Developing new products and services  33%
Identifying cost reduction opportunities  14%.


Article in Corporate Dossier on 15 May 2015
Interview with Richard Ingleton, CEO, TNS, Subsidiary of WPP Group and one of the largest market research firms in the world.



Updated on 25 April 2020
14 May 2015

Management of Research and Development - Jain, Triandis and Weick - Book Information and Summary

Published by John Wiley and Sons 2010

Ravi K Jain
Harry C. Triandis
Cunthia Wagner Weick

Table of Contents

Preface xiii

1 R&D Organizations and Research Categories

1.1 How Information can be Used,
1.2 A Perspective on R&D Management
1.3 What is Research and Development?
1.4 Research Categories,
1.5 What to Research,
1.6 Emphasis on Basic Versus Applied Research,
1.7 What is Unique About Managing R&D Organizations?
1.8 Summary,
1.9 Questions for Class Discussion,

2 Elements Needed for an R&D Organization 20

2.1 People, 20
2.2 Specialization, 22
2.3 Staffing, 23
2.4 Ideas, 24
2.5 Defects in Human Information Processing, 28
2.6 Fads in Science, 30
2.7 Communication Networks, 31
2.8 The Innovation Process, 34
2.9 Funds, 34
2.10 A Culture for R&D Organizations, 36
2.11 Not-Invented-Here Syndrome, 38
2.12 Fit of Person and Job, 40
2.13 Creative Tensions: Managing Antithesis and Ambiguity, 41
2.14 Develop a Climate of Participation, 44
2.15 Summary, 45
2.16 Questions for Class Discussion, 46

3 Creating a Productive and Effective R&D Organization 47

3.1 Organization Effectiveness, 47
3.2 Who are the Inventors and Innovators?, 52
3.3 Odd Characteristics of Inventors and Innovators, 58
3.4 Researcher’s Relationship with Management and Peers, 59
3.5 Formation of Teams, 60
3.6 Generating New Ideas, 64
3.7 Emphases on Aspects of Organizational Culture, 68
3.8 Ethos of A Scientific Community, 69
3.9 Summary, 71
3.10 Questions for Class Discussion, 71

4 Job Design and Organizational Effectiveness 72

4.1 Job Attributes, 73
4.2 Physical Location and Communication, 74
4.3 Career Paths, 76
4.4 Dual and Triple Hierarchies, 78
4.5 Centralization and Decentralization, 80
4.6 Keeping the Researcher at the Innovation Stage, 81
4.7 Job Design and Conflict, 83
4.8 Summary, 86
4.9 Questions for Class Discussion, 87

5 Influencing People 88

5.1 Attitude, Attitude Change, 89
5.2 Findings from Attitude Research, 90
5.3 Behavioral Science Division Case, 92
5.4 Case Analysis, 94
5.5 Communication Alternatives and Outcomes, 95
5.6 Summary, 101
5.7 Questions for Class Discussion, 102

6 Motivation in R&D Organizations 103

6.1 A Model of Human Behavior, 104
6.2 Changing the Reward System to Support Technical
Careers, 112
6.3 Structuring the Organization for Optimal
Communication, 113
6.4 Rewards and Motivation, 114
6.5 Reward System Discussion, 116
6.6 Sense of Control and Community, 119
6.7 A Federal R&D Laboratory Case, 121
6.8 Summary, 122
6.9 Questions for Class Discussion, 122

7 Dealing with Diversity in R&D Organizations 123

7.1 Assimilation and Multiculturalism, 124
7.2 Understanding Culture, 126
7.3 Cultural Differences, 128
7.4 What Happens When People from Different Cultures Work Together?, 129
7.5 Cultural Distance, 130
7.6 Cultural Intelligence and Related Concepts, 130
7.7 A Model for Diversity in Groups, 132
7.8 The Status of Minorities in Work Groups, 135
7.9 Dealing with People from Different Disciplines,
Organizational Levels, and Functions, 136
7.10 Intercultural Training, 136
7.11 Summary, 139
7.12 Questions for Class Discussion, 139

8 Leadership in R&D Organizations 140

8.1 Identifying Your Leadership Style, 142
8.2 Theories of Leadership and Leadership Styles, 151
8.3 Leadership in R&D Organizations, 154
8.4 R&D Leadership: A Process of Mutual Influence, 157
8.5 A Leadership-Style Case, 158
8.6 Leadership in a Creative Research Environment, 160
8.7 Summary, 161
8.8 Questions for Class Discussion, 163

9 Managing Conflict in R&D Organizations 164

9.1 Conflict Within Individuals, 164
9.2 Conflict Between Individuals, 169
9.3 Conflict Between Groups, 171
9.4 Intercultural Conflict, 177
9.5 Personal Styles of Conflict Resolution, 179
9.6 Unique Issues of Conflict in R&D Organizations, 181
9.7 Ethics, 183
9.8 Summary, 183
9.9 Questions for Class Discussion, 184

10 Performance Appraisal—Employee Contribution—In R&D Organizations 185

10.1 Some Negative Connotations of Performance Appraisal, 185
10.2 Difficulties with Employee Appraisal, 187
10.3 Performance Appraisal and the Management System, 189
10.4 Performance Appraisal and Organizational Stages, 190
10.5 Performance Appraisal and Organization Productivity, 190
10.6 Goals of Engineers Versus Scientists, 191
10.7 Performance Appraisal and Monetary Rewards, 192
10.8 Performance Appraisal in Practice, 194
10.9 A University Department Case, 195
10.10 Implementation Strategy with Emphasis on Employee Contribution, 196
10.11 Summary, 203
10.12 Questions for Class Discussion, 203
10.13 Appendix: Argonne National Laboratory Performance
Review Information, 20

11 Technology Transfer 213

11.1 Technology Transfer Hypotheses, 214
11.2 Stages of Technology Transfer, 214
11.3 Approaches and Factors Affecting Technology Transfer, 216
11.4 Role of the User, 218
11.5 Characteristics of Innovation and its Diffusion, 220
11.6 Role of People, 222
11.7 Boundary Spanning, 223
11.8 Organizational Issues in Technology Transfer, 226
11.9 The Agricultural Extension Model, 227
11.10 NASA Technology Transfer Programs, 228
11.11 IBM Technology Transfer Cases, 229
11.12 Technology Transfer Strategy, 231
11.13 Summary, 236
11.14 Questions for Class Discussion, 237

12 Models for Implementing Incremental and Radical Innovation 238

12.1 Defining Innovation, 239
12.2 Strategic Choices in Technological Innovation, 242
12.3 Making Technological Innovation Operational, 244
12.4 The Market, Marketers, and Market Research in
Technological Innovation, 249
12.5 Leading Innovative Organizations, 253
12.6 Summary, 254
12.7 Questions for Class Discussion, 256

13 Organizational Change in R&D Settings 257

13.1 Why Organizational Change?, 258
13.2 Steps in Organizational Change, 259
13.3 Problems and Action Steps, 259
13.4 Individual Change, 262
13.5 Group Change: Team Building, 264
13.6 Organizational Change, 267
13.7 Evaluating Organizational Change, 268
13.8 Case Study in Organizational Change, 270
13.9 Summary, 273
13.10 Questions for Class Discussion, 273

14 Managing the Network of Technological Innovation 274

14.1 Overall Trends Within and Between Sectors, 274
14.2 Trends in Research, Development, And Innovation in the Commercial Realm, 276
14.3 Trends in Research, Development and Innovation in the Federal Government, 279
14.4 Trends in Research, Development, and Innovation in Universities, 286
14.5 Open Innovation, Regional Economic Development, and the Global Innovation Network, 290
14.6 Summary, 294
14.7 Questions For Class Discussion, 295

15 Universities and Basic Research 296

15.1 Basis for University Research Activities, 297
15.2 Federal Support of University Research: An Entitlement or a Means to Achieve National Goals?, 298
15.3 Basic Research: Who Needs It?, 301
15.4 University–Industry Linkage, 309
15.5 Rethinking Investment in Basic Research, 311
15.6 Summary and Concluding Comments, 312
15.7 Questions for Class Discussion, 313

16 R&D Organizations and Strategy 315

16.1 What is Strategy?, 316
16.2 Strategy Levels and Perspectives, 319
16.3 Strategy Formulation and Implementation, 319
16.4 Strategy Evaluation, 321
16.5 Strategy and Innovation, 322
16.6 Technology and Strategy, 324
16.7 Applying a Strategy Process, 325
16.8 Summary, 330
16.9 Questions for Class Discussion, 330

17 Research, Development, and Science Policy 331

17.1 Relationship Between Science and Technology, 334
17.2 Technical Innovation and Economic Development, 336
17.3 Analysis of Investment in Basic Research, 339
17.4 R&D Expenditure, 340
17.5 R&D Productivity, 347
17.6 Global Perspectives on Innovation, 352
17.7 R&D Expenditure and Science Policy, 357
17.8 Summary, 362
17.9 Questions for Class Discussion, 363


Ways to improve the productivity of R&D organizations and foster excellence in R&D organizations.

Of the approximately 595,000 doctoral scientists and engineers employed in the United States as of 2003, approximately 372,000 work in R&D. Of the 372,000, it is estimated that about 60,000 work in management of R&D. In the  remaining doctoral scientists and engineers (223,000), substantial number teach (184,000). Some are involved in professional services and consulting. Consulting engineers and scientists undertake creative activities that are, in many ways, responsible for closing the loop between research and development and application.

Studies have clearly shown that where supervisors were rated highest in technical skills the research groups were most innovative. And where supervisors did not possess excellent technical skills (but had high-level administrative skills), the research groups were least innovative (Farris, 1982, p. 340). These findings point to a fundamental need for a supervisor in an R&D organization who possesses

excellent technical skills. Consequently, scientist have to be provided inputs in managing R&D organizations and they are to be made managers.

The National Science Foundation (NSF) classifies and defines research as follows
:
Basic Research. Basic research has as its objective “a more complete knowledge or understanding of the subject under study, without specific  applications in mind.” To take into account industrial goals, NSF modifies this definition for the industry sector to indicate that basic research advances scientific knowledge “but does not have specific immediate commercial objectives, although it may be in fields of present or potential commercial interest.”

Applied Research. Applied research is directed toward gaining “knowledge or understanding to determine the means by which a specific, recognized need may be met.” In industry, applied research includes investigations directed “to discovering new scientific knowledge that has specific commercial objectives with respect to products, processes, or services.”

Development. Development is the “systematic use of the knowledge or understanding gained from research, directed toward the production of useful materials, devices, systems or methods, including design and development of prototypes and processes.”

A two-tier model for identifying “what to research
The model includes an economic index model and a portfolio model.

Economic Index Model

Research needs is to improve the operation or manufacturing efficiency of the organization or the enterprise. The emphasis is on building a “better products and processes” to reduce the cost of doing

things. Inputs for such needs come from looking at competitive products and operation

Portfolio Model

Normative needs are those of the user (a user being the primary or follow-on beneficiary of the research product). Comparative needs relate to research needs derived from reviewing comparable organizations, competitive product lines, and related enterprises. Forecasted research needs focus on trend analysis in terms of consumer or organization needs derived from new requirements, changed consumer behavior, new technological developments, new regulations (e.g., environmental, health, and safety regulations), and new operational requirements.

https://books.google.co.in/books?id=nSgebaFKwvMC

Updated on 25 April 2020
First posted on 11 March 2015

Digital Internal and Supply Chains Operations - Digital Product Offerings

Both are different. Require different skills at the present moment. May require two different digital information officers.

Digital products require much more new knowledge and understanding and therefore involves new science, engineering and management.

https://mitsloan.mit.edu/ideas-made-to-matter/how-to-master-two-different-digital-transformations
3 March 2020

Modern Science of Management - New Scientific Management

Is Management Still a Science?
by David H. Freedman

DECISION MAKING
Is Management Still a Science?
by David H. Freedman
From the November–December 1992 Issue

The traditional scientific approach to management promised to provide managers with the capacity to analyze, predict, and control the behavior of the complex organizations they led. But the world most managers currently inhabit often appears to be unpredictable, uncertain, and even uncontrollable. 

In the face of this more dynamic and volatile business world, the traditional mechanisms developed so far out of the current paradigm of  “scientific management” seem to be inadequate.  

The managers now trying to cope with the volatility of the business environment. Scientists  also recognize now  the inherent volatility of nature and with the dynamics of unpredictable and unstable systems in the natural world. The new science emphasizes chaos and complexity. Today scientists are developing powerful descriptions of the ways complex systems—from swarms of mosquitoes to computer programs to futures traders in commodities markets—cope effectively with uncertainty and rapid change.

The new rules of complex behavior that cutting-edge scientific research describes have intriguing parallels with the organizational behavior issues.  

The Science Behind Scientific Management

In 1911, the turn-of-the-century industrial engineer Frederick Winslow Taylor published his magnum opus, The Principles of Scientific Management, laid out his ground rules for efficient industrial organization.

Taylor’s book was profoundly influenced by the concerns of the science of his time. In the nineteenth century, Newton’s laws of motion were first used to analyze the forces exerted on and by complex physical systems, allowing scientists to predict the behavior of those systems. Meanwhile, the principles of thermodynamics, elucidated in the second half of the nineteenth century, provided the one missing ingredient—heat interactions—needed to complete Newton’s conception of the physical world. Together these theories allowed scientists to calculate how machines could function with maximum efficiency.

Taylor was preoccupied with the problem of efficiency as it applies to organizations. According to Taylor, the fundamental cause of this waste of human effort was unscientific management. In other words, he thought managers focused too much on the output of work and not enough on the processes by which the work was done. 

Taylor’s solution was the development of science of production and work, that is machine work and man work. Managers have to take up the planning of work, that is process of work based on the science developed by them or specialists engaged for developing science of work.   By analyzing all of the steps in a work process and creating standardized procedures for each step, managers could identify the “one best method” for performing a task that would guarantee maximum efficiency. “The best management is a true science,” Taylor wrote, “resting upon clearly defined laws, rules, and principles as a foundation.” And those laws constituted an understandable, predictable, controllable system. “In the past the man has been first; in the future the system must be first.”

In effect, Taylor urged the individual manager to think of himself as a scientist who tries to discover  the fundamental laws of the system he is studying.  The manager has to be a better educated person in science and hence armed with a scientific predisposition to “search for general laws or rules,” who can understand the true science of work.

An important  part of The Principles of Scientific Management concerns “the accurate study of the motives which influence men.” 

Taylor believed in the ability of science to understand motives of men. He held and promoted the belief that “the motives which influence men” can be reduced through scientific analysis and control in the same way that the physical activities of cutting metal or shoveling iron can be. In discussing employee motivation, Taylor noted, “At first, it may appear that this is a matter for individual observation and judgment and is not a proper subject for exact scientific experiments.” But while psychological laws are more complicated and subject to exceptions, “owing to the fact that a very complex organism—the human being—is being experimented with,” Taylor maintained that “laws of this kind, which apply to a large majority of men, unquestionably exist and when clearly defined are of great value as a guide in dealing with men.”

He insisted that productivity improvements based on scientific management be shared with workers in higher wages; otherwise they won’t cooperate in work reorganization. He gave some suggestions or ideas for dealing with men: Never deal with workers as a group, only deal with one individual at a time. “When men work in gangs, their individual efficiency falls almost invariably down to or below the level of the worst man in the gang.” Taylor’s solution was to have workers assigned individual tasks that they were to perform in the greatest possible isolation.

All of the techniques of scientific management—the planning department, time-and-motion study, standardization of methods and tools, and the like—are so many means to this end. But Taylor urged his readers not to confuse the techniques with the basic scientific principles. “It is only through enforced standardization of methods, enforced adoption of the best implements and working conditions, and enforced cooperation that this faster work can be assured. And the duty of enforcing the adoption of standards and enforcing this cooperation rests with the management alone.”

Frederick Taylor’s principles inaugurated a revolution in management and in the organization of work. In the decades after his book’s publication, Taylor’s ideas contributed to massive increases in productivity and the standard of living. Many management scholars contributed to the development of scientific theory of management activities in basic processes of management and functional areas of management.

However, the experience of the last 20 years has taught managers a different experience.  In fast-changing markets, the fragmentation of work, the separation of planning from execution, and the isolation of workers from each other create rigid organizations that can’t adapt quickly to change. As a result, managers must now rethink the fundamental elements of Taylor’s system: work organization, employee motivation, and the task of management.

The majority of new managerial ideas—like cross-functional teams, self-managed work groups, and the networked organization may be different from earlier ideas promoted by scientific management. Yet for all of the proliferation of specific techniques, the fundamental principles of a new managerial paradigm are far from clear.

Coping with Chaos and Complexity

Nineteenth-century physics, based on Newton’s laws of motion, posited a neat correspondence between cause and effect. Scientists were confident that they could reduce even the most complex behaviors to the interactions of a few simple laws and then calculate the exact behavior of any physical system far into the future. This conviction profoundly shaped Taylor’s analysis of organizations and of that “very complex organism,” the human being at work. But during the past few decades, more and more scientists have concluded that this and many other of science’s traditional assumptions about the way nature operates are fundamentally wrong. Far from being as predictable as clock-work, nature appears as random as a throw of the dice.

“Chaos theory” is the general term for this new model of how things work, and probably the best introduction to it is the best-selling book Chaos by science writer James Gleick. According to Gleick, the chief catalyst for chaos theory was the research of MIT meteorological scientist Edward Lorenz. In the early 1960s, Lorenz developed a computer program that simulated a weather system. By plugging in numbers representing the initial state of winds and temperatures, Lorenz’s program churned out the subsequent weather pattern as it evolved over time. Lorenz, like most scientists, assumed that small changes in the initial conditions he fed into the computer would result in correspondingly small changes in the evolution of the entire system. To his surprise, he discovered that even the most minuscule of changes caused drastic alterations in the weather pattern. In effect, a slight breeze in Idaho or a one-degree drop in temperature in Massachusetts could end up changing balmy weather in Florida into a hurricane a month later.

A good article on Chaos Theory: https://theconversation.com/explainer-what-is-chaos-theory-10620

Chaos and Fractals by Feldman (2012): https://books.google.co.in/books?id=rd4VDAAAQBAJ

The effect defied both intuition and what meteorologists had previously understood about their science. Intrigued by Lorenz’s puzzle, scientists from a wide variety of fields began experimenting with simulations of other physical systems, only to discover the identical phenomenon. An infinitesimal change in initial conditions could have a profound effect on the evolution of the entire system. Take the simple example of water dripping from a faucet. Speed up the rate of flow ever so slightly, and the pattern by which drops fall changes radically. Repeat the experiment again, and the pattern will be completely different. What’s more, the pattern of drip formation changes in ways that no one can model. Even the most powerful supercomputer can’t predict when the next drip will fall.  A small shift in the fertility rate doubles the population of a community of gypsy moths.

This basic insight—that minute changes can lead to radical deviations in the behavior of a natural system—has inaugurated an equally radical shift in how scientists see the world. Put simply, the nineteenth-century emphasis on predictability and control has given way to a late twentieth-century appreciation for the power of randomness and chance. For all practical purposes, the behavior of even relatively simple physical systems is fundamentally unpredictable.

But this is not to say that chaotic systems don’t have any patterns. While the idea that nature is fundamentally random is counterintuitive, chaos theory’s second basic insight is even more so: that patterns do lurk beneath the seemingly random behavior of these systems. In fact, systems don’t end up just anywhere; certain paths apparently make more sense—or at least occur much more frequently—and chaos theorists call such paths “strange attractors.” Thus while meteorologists can’t say with certainty what the weather will be on a particular day in the future, they can estimate the probability of the kind of weather likely to occur. In other words, strange attractors allow scientists to determine within broad statistical parameters what a system is likely to do—but never exactly when a system is likely to do it. The cause-and-effect precision of traditional physics has been replaced by the statistical estimate of probabilities.

Many scientists now learn a  holistic approach to study systems. They focus increasingly on the dynamics of the overall system. Rather than attempting to explain how order is designed into the parts of a system, they now emphasize how order emerges from the interaction of those parts as a whole.

The quest to gain insight into and make use of the order that emerges from chaotic systems is the subject of Complexity, M. Mitchell Waldrop’s upcoming book. Waldrop, a contributing correspondent of Science magazine, describes some recent research from the Santa Fe Institute, a New Mexico think tank specializing in the analysis of “self-organizing” systems. The institute has brought together an eclectic group of scientists who focus on the ways that the simple actions of independent components can combine to produce extremely complex behaviors, even in the absence of any central intelligence or control. Santa Fe chemists, for example, are examining how molecules organize themselves into self-reproducing proteins. Biologists are determining how cells arrange themselves into immune systems. And economists are considering how the limited actions of individual buyers and sellers form complex markets, industries, and economies.

In the process, the Santa Fe researchers have developed some basic rules for  “complex adaptive systems.” These systems are among the most successful in nature. Some examples include the ecology of tropical rain forests, colonies of ants, and even the human brain.

Such systems have several characteristics in common. First, they are “self-managed”—that is, they consist of a network of “agents” that act independently of one another and without guidance from any central control. For example, each one of the brain’s roughly 100 billion neurons is a kind of miniature chemical computer that follows its own independent pattern of behavior. Take a neuron out of the brain, and it can still function. There is no “master neuron” or central area of the brain that controls what each neuron does.

Yet these agents are capable of engaging in cooperative behavior. They can form groups or “communities” that cooperate in producing higher-order behaviors that no single agent could accomplish on its own. In the brain, each neuron is connected to millions of others. Some communities of neurons, clustered in particular areas of the brain, specialize in functions such as language or visual recognition. It is precisely the interactions among neurons that produce human intelligence. For example, the structural difference between individual squid neurons and human neurons is relatively small. However, a human brain not only contains many more neurons than a squid’s but also the organization of its neurons is much more complex and interwoven.

A particular kind of feedback makes self-management possible. In a sense, self-organizing systems are learning systems but of a specific sort. Capable of “learning” through feedback from the external environment, they also “embed” that learning in their actual structure. For instance, the more a set of neurons is involved in some piece of mental work—like recognizing a face or solving a mathematical problem—the stronger the actual chemical connection among the neurons (and the easier for the brain to make the connection the next time). Indeed, the human brain is forever reconfiguring the connections between neurons in response to external and internal stimuli. In this way, self-organizing systems constantly rearrange themselves as the effects of previous actions or changes in external conditions ripple through the system. Information is embedded in structure. As external conditions change, the structure of the system automatically changes.

Finally, self-management and learning through feedback allow these systems to operate by “flexible specialization.” Self-organizing systems usually contain an array of specialized behavioral niches occupied by specific agents or groups of agents. However, old niches constantly disappear and new ones are created as the external environment changes. Therefore, agents aren’t permanently locked into previously useful behaviors that have since become obsolete, which helps the system as a whole adapt to change. Waldrop notes that self-organizing systems tend to change so rapidly and so completely that it becomes meaningless to talk about agents or groups of agents “optimizing” (a term redolent of the nineteenth-century focus on efficiency) their behavior. Rather, such systems are characterized by  “perpetual novelty.”

In general, the complex adaptive systems found in nature contain individual agents that network to create self-managed but highly organized behavior; respond to feedback from the environment and adjust their behavior accordingly; learn from experience and embed that learning in the very structure of the system; and reap the advantages of specialization without getting stuck in rigidity. If these characteristics sound familiar, it’s because they so closely match the new kind of organization many managers are struggling to create in order to cope with a more uncertain—and frequently chaotic—business environment.

Some complexity researchers have applied the concepts of their emerging field to the specific organizational problems managers face. But one area of research at the Santa Fe Institute takes a step in that direction. Economists at the institute are creating computer simulations of economic transactions  to model complex market behaviors by constructing them from the interaction of a limited set of simple building blocks. The economy  would be seen  as something organic, adaptive, surprising, and alive.”

One program in the above research  simulates the stock market. It consists of agents that decide when to buy or sell stock. As in real stock markets, the actions of the computerized “traders” determine the price of the stock. At first traders made decisions randomly; but soon they came to buy and sell stock exactly as classical economic theory says they should—according to the stock’s fundamental value as set by its discount rate and dividend. Still later in the simulation, the agents “discovered” that by studying the history of a stock’s price performance, they could make money by bidding a stock above and below its actual value. The result: the computer system learned to simulate the same kinds of bubbles and crashes that occur in real markets. Much as chaos theory has revealed the shortcomings of traditional physicists’ mathematical models of the world, these simulations have pointed up the shortcomings of the elegant mathematical models of neoclassical economists.

Toward a New Managerial Science

Chaos theorists and complexity scientists and their perspective has already shaped recent managerial literature. The Fifth Discipline by MIT researcher Peter Senge uses chaos theory. If Taylor’s chief concern was inefficiency and waste, then Senge’s focus is the loss of purpose that frequently comes in organizations.  Most people, Senge argues, feel lost in the organizations of which they are a part. 

According to Senge, this systematic inability to cope with complexity is a direct result of traditional scientific approaches to management. From its opening sentences, The Fifth Discipline is an attack on the reductionism of nineteenth-century science. “From a very early age,” Senge notes, “we are taught to break apart problems, to fragment the world. This apparently makes complex tasks and subjects more manageable, but we pay a hidden, enormous price. We can no longer see the consequences of our actions; we lose our intrinsic sense of connection to a larger whole.”  (interesting)

The alternative is to stop seeing an organization as a machine with mechanism that can designed used kinematics and dynamics. Kinematics will determine the exact movement path of any point on the link of any mechanism and dynamics will determine the force transmitted by each link without breaking itself in the process. Organization has to be seen as a living organism with its constituents also being living organisms. This requires a holistic approach that reflects chaos theory’s focus on the overall behavior of a system. “Living systems have integrity,” and “Their character depends on the whole. The same is true for organizations; to understand the most challenging managerial issues requires seeing the whole system that generates the issues.”

“Systems thinking” is the fifth discipline of Senge’s title. As he portrays it, systems thinking is the ability to understand the key interrelationships that influence behavior in complex systems over time—and should give managers the capacity for “seeing wholes.”

In WonderTech case study, Senge traces the source of WonderTech’s failure to management’s ignorance of a few basic feedback processes. Put simply, high demand increased pressure on the company’s production capacity. Inadequate capacity meant large backlogs of orders and long delays in delivery. Customers became angry and dissatisfied, which caused sales to drop.

As Senge tells the story, senior managers did understand that as sales grew, the company needed to invest in capacity. But as their fixed investments in manufacturing increased, so did their need to keep sales up and their tendency to push sales and marketing to get more orders. Because the two sides of the organizational system—sales and manufacturing—were never in balance, the vicious circle of high growth, undercapacity, delayed delivery, and customer dissatisfaction repeated itself over and over again, continually growing worse.

Senge notes that there are a limited number of such feedback processes at work in any organization, what he calls “systems archetypes.” In a sense, they are the organizational equivalents of strange attractors in chaos theory: the basic patterns of behavior that occur in all organizations again and again.

The WonderTech story illustrates a number of these archetypes. Senge’s term for one of them is “limits to growth”—the idea that any growth process produces the conditions for its own collapse. The more WonderTech focused on sales, the more it created a capacity problem that retarded sales. Another archetype is “shifting the burden”—the idea that a short-term solution to a problem may actually make it worse by undermining an organization’s ability to implement a more fundamental, long-term solution. Managers at WonderTech  were never able to focus on the real solution to their problem: expanding production capacity to control delivery time.

Managers do not understand the systemic, automatic quality of these processes and feel they are “out of control” of their organizations. As they are ignorant of the systems archetypes, they end up always seeing only the part, never the whole. In contemporary organizations,  managers have to understand the systemic processes driving human behavior and to change them: “The art of systems thinking lies in seeing through complexity to the underlying structures generating change.”

When managers understand the dynamics of these archetypes and are able to make the deep connections between systems and behavior, they are in a position to effect real change. And just as chaos theory teaches that small changes can have big effects in physical systems, a crucial concept in systems theory is “leverage”: the idea that “small, well-focused actions can sometimes produce significant, enduring improvements.” In the WonderTech case, a simple commitment to rapid delivery—a strategy managerial experts have since enshrined in the rubric “competing on time”—would have done more to solve the company’s problems than all the salespeople in the world.

If managers master systems thinking and the other disciplines Senge describes, the result is “the learning organization.” The learning organization has characteristics remarkably similar to the complex adaptive systems that scientists are discovering in nature. It is a highly decentralized system in which any number of decision-making processes on the local level maintain order throughout and constantly adjust to change.

Senge’s prescriptions recommend  the organic control found in nature to be used in human organizations. Following him,   some organizations are creating the tools  to help managers develop the skills they need to make organic control work. Microworlds are computer-based simulations of complex business situations based on the principles of systems thinking. With microworlds, managers can experiment with their organizations to reveal the largely hidden dynamics of complex systems, much as scientists use simulations of the weather or water dripping from a faucet to learn how physical systems work.

At MIT’s Sloan School,  microworld was used to  simulate  the rise and fall of People Express airline. They explored the interrelated forces that, over a six-year period, caused People Express to lurch from one of the fastest growth rates in airline industry history to a sudden financial crisis and eventual purchase by a competitor.  Microworlds  allow managers to recognize those strange attractors that may underlie behavior in all organizations and thus to identify high-leverage strategies for change.

Experiments and laboratories are required.  Senge’s new manager is also a scientist as Taylor advised. But,  the scientific managers of today must be researchers of learning processes that make self-organization possible, the processes that are essential to effective performance in a world characterized by perpetual novelty and change.

David H. Freedman is a contributing editor of Discover magazine and is also a frequent contributor to Science, CIO, and the Boston Globe.

https://hbr.org/1992/11/is-management-still-a-science


Executive Guide to Business Success through Human-Centred Systems

Andrew Ainger, Rukesh Kaura, Richard Ennals
Springer Science & Business Media, 06-Dec-2012 -  181 pages

This book is about people and skilled work. There has been much turmoil in the business environment about how to best manage the balance between people and technology, at a time when pressures for cost reduction are ever greater. Our argument is that people are central to business success, and the appropriate use of technology should support their needs. This is not always easy in practice. We work in a period when change occurs in ever-shortening cycles. Black-and-white solutions may seem attractive, but the long-term consequences are rarely advantageous. A new system is required, build ing on lessons from the past. Human-centred systems build upon core skills of the workforce within a rich, emancipatory environment, utilising the benefits of tech nology. Change can be embraced to achieve competitive advantage and mutual benefit. The three authors are, respectively, engineering director of an inno vative international manufacturing company; analyst for an inter national merchant bank; and university business school professor. The book is intended to offer a new synthesis of theory and practical experience, derived from recent British and European collaborative pro grammes. We are grateful to our colleagues and families for their tolerance during the writing of this book. Even human-centred books impose pressures on busy people. Old Windsor, Brighton and Kingston, June 1995 A.A. R.K.
https://books.google.co.in/books?id=uBzoBwAAQBAJ

The Evolution of Management Models: A Neo-Schumpeterian Theory
Zlatko Bodrožić, Paul S. Adler
First Published April 7, 2017
https://journals.sagepub.com/doi/10.1177/0001839217704811
Open access article



Federal Council on Scientific Management. Nature 139, 147 (1937).
After the highly successful sixth International Congress for Scientific Management, held in London during July 1935, a committee was set up under the chairmanship of Dr. E. F. Armstrong.
https://www.nature.com/articles/139147c0

Holistic and Systems Approach to Management - Natty Gur

https://ongalaxies.com/blog/

6 rules that enable autonomy without anarchy.
Agile Management / By nattygur
Autonomy is one of the basic principles organizations can implement to improve the organization’s ability to deal with complexity and uncertainty. 6 rules to create boundaries that enable autonomy without anarchy.
https://ongalaxies.com/2020/04/09/6-rules-that-enable-autonomy-without-anarchy/

Natty Gur
Enterprise Architect on Enterprise Architecture
https://weblogs.asp.net/ngur