|
|
|
Adaptable execution
Summary
This page introduces some problems that make it hard for a
business to execute effectively.
It then presents a theory
of execution.
It describes what the theory says must be done
to execute effectively.
It reviews General
Electric's use of adaptive planning to support effective
execution.
Then it details the execution
requirements.
Introduction
Each operation in a business is responsible for delivering value
to its markets profitably. In a perfect world the product or service offered has reached its target
customers because each function has achieved its required goals and
commitments. Marketing has generated desire in the
minds of customers. Sales have turned the desires into
purchases. Product development has put the desired
features into the product or service. Product management
has identified the target market segments and identified how
customer needs can be provided via developed features in a way
that creates an advantage versus competitors. Management
has correctly matched the required commitment of resources with
the pre-conditions that implied the operation could deliver
successfully.
Sometimes this description is far from operational
reality. In dynamic growth markets, First describes the dynamic
nature of any complex adaptive system (CAS).
It then introduces the broad effects of change
which includes opportunities and risks/uncertainties.
As a CAS grows opportunities become undermined so they must be acted on
quickly.
Uncertainties are also transformed
and relayed by the dynamic network. In particular
the recombination of current and new ideas brought in from the
network is discussed.
segments can be hard to characterize and
operational methods may change significantly, so
identifying effective implementations of The drive to fulfill current customer requirements can result in
the innovator's dilemma.
While the customer interest can diminish typical requirements
databases continue to reflect the earlier desire.
Accurate modeling of the customer's roles and goals creates a
more predictive indicator. Close
relationships with sentinel customers for key target
segments help build the models.
Processes should also support the migration of product
and customers to the winning architecture in a positive
return market.
valuable
features and relating Agents can manage uncertainty by limiting
their commitments of resources until the environment contains signals strongly correlated with the
required scenario. This page explains how agents can use Shewhart cycles and SWOT processes to do this.
commitments
to pre-conditions can be difficult. Hence product
development may start to develop a product or service release
without a clear understanding of the customer's priorities, or
even a clear vision of who the customer is or how to transform
the requirements into an effective offer.
Executives and venture capitalists is venture capital, venture companies invest in startups with intangable assets
have to decide on which subset of proposals to bet capital is the sum total nonhuman assets that can be owned and exchanged on some market according to Piketty. Capital includes: real property, financial capital and professional capital. It is not immutable instead depending on the state of the society within which it exists. It can be owned by governments (public capital) and private individuals (private capital). needed to finance
the proposals from their operations. Complex adaptive
system ( This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is
outlined. Links to other key aspects of CAS theory
discussed at the site are presented.
CAS) theory highlights the
adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage. Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents. Evolution indicates fitness when an organism survives and reproduces. For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery. He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's. When an agent achieved an explicit goal they obtained a final reward. Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas. The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness'; In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary. nature of the
problems executives and their operations face and how they can
cope with an iterative approach to
development.
A CAS theory of
execution
Execution occurs through Plans are interpreted and implemented by agents. This page
discusses the properties of agents in a complex adaptive system
(CAS).
It then presents examples of agents in different CAS. The
examples include a computer program where modeling and actions
are performed by software agents. These software agents
are aggregates.
The participation of agents in flows is introduced and some
implications of this are outlined.
agents
responding to goals
and other Agents use sensors to detect events in their environment.
This page reviews how these events become signals associated
with beneficial responses in a complex adaptive system (CAS). CAS signals emerge from
the Darwinian information
model. Signals can indicate decision
summaries and level of uncertainty.
signals with effective
actions. In a complex changing This web page reviews opportunities to find and capture new
niches, based on studying fitness landscapes using complex
adaptive system (CAS) theory.
CAS SuperOrganisms are
able to capture rich niches. A variety of CAS are
included: chess, prokaryotes,
nation states, businesses, economies; along
with change mechanisms: evolution
and artificial
intelligence; agency
effects and environmental impacts.
Genetic algorithms supported by fitness functions are compared to
genetic operators.
Early evolution
of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of
state are considered.
environment
that is a significant challenge. As the situation alters
the goals may need to change too. The actions will be
effective when the agent's This page looks at schematic structures
and their uses. It discusses a number of examples:
- Schematic ideas are recombined in creativity.
- Similarly designers take ideas and
rules about materials and components and combine them.
- Schematic Recipes help to standardize operations.
- Modular components are combined into strategies
for use in business plans and business models.
As a working example it presents part of the contents and schematic
details from the Adaptive Web Framework (AWF)'s
operational plan.
Finally it includes a section presenting our formal
representation of schematic goals.
Each goal has a series of associated complex adaptive system (CAS) strategy strings.
These goals plus strings are detailed for various chess and business
examples.
schematic
plan associates the required goal with appropriate
strategies, and these are the ones selected by the agent to
perform. The initial plan will probably start off with
gaps and flawed assumptions too. Execution is when the
problems become evident. The agents must be ready and able
to Walter Shewhart's iterative development process is found in many
complex adaptive systems (CAS).
The mechanism is reviewed and its value in coping with random
events is explained.
understand the implications of the
feedback and adjust the plan.
Hence it is important to:
GE an
example of adaptive planning and effective execution
Each of these requirements is challenging. But they can be
managed. Jack
Welch famously transformed General
Electric (GE)'s management processes into such an
iterative adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage. Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents. Evolution indicates fitness when an organism survives and reproduces. For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery. He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's. When an agent achieved an explicit goal they obtained a final reward. Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas. The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness'; In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary.
infrastructure.
The requirements derived from This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is
outlined. Links to other key aspects of CAS theory
discussed at the site are presented.
CAS
theory correspond to parts of GE's
extensive management processes. The top GE
executives are hub
agents driving the adaptive actions of the network.
GE's main processes include the quarterly, two and a half day, Corporate
Executive Council (CEC), the annual Session C leadership now aims to develop plans and strategies which ensure effective coordination to improve the common good of the in-group. Pinker notes the evolved pressure of social rivalry associating power with leadership. Saposky observes the disconnect between power hierarchies and wisdom in apes. John Adair developed a modern leadership methodology based on the three-circles model. and
organization reviews; S- 1 and S- 2
strategy and operation reviews; and the annual Boca meeting
where operating managers meet to plan the coming year's
initiatives and re-launch current initiatives. Through
these processes the hub executives are able to promote alignment
of goals, models
and actions deep into GE's organization. GE's processes
are one way to instantiate the CAS goals outlined above and
discussed in more detail below.
GE's earnings misses during the global financial crisis pulled
the covers back from Imelt/Welch's strategies. While the
management processes contributed to the decades long success of
GE, it is clear that financial engineering based on the presence
of GE Capital was acting as an This page reviews the strategy of setting up an arms race. At its
core this strategy depends on being able to alter, or take
advantage of an alteration in, the genome
or equivalent. The situation is illustrated with examples
from biology, high tech and politics.
evolutionary
amplifier that increased the pressure on GE's many
competitors. A clear illustration of how hard it is to
accurately model and attribute value to particular strategies
and actions.
CAS execution
requirements
Hiring
agents who are recognized as being able to do the job
Recognizing which prospective team members have the required
skills to do the job is challenging at
the best of times. Hopefully some one close to the
hiring manger already has first hand knowledge of the candidate
and the demands of the role being recruited for. In This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is
outlined. Links to other key aspects of CAS theory
discussed at the site are presented.
CAS theory hiring is the
implementation of a Plans change in complex adaptive systems (CAS) due to the action of genetic
operations such as mutation, splitting and recombination.
The nature of the operations is described.
genetic operator.
Most real systems demonstrate diversity in the phenotypes is the system that results from the controlled expression of the genes. It is typically represented by a prokaryotic cell or the body of a multi-cell animal or plant. The point is that the genes provide the control surface and the abstract recipe that has been used to generate the cell. . A
particular expertise may be useful in the current situation but
other practices may be more useful at other Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not
include time:
- A present that is common throughout the universe does not exist
- Events are only partially ordered. The present is
localized
- The difference between past and future is not foundational.
It occurs because of state that through our blurring appears
particular to us
- Time passes at different speeds dependent on where we are and how fast we travel
- Time's rhythms are due to
the gravitational field
- Our quantized physics shows neither
space nor time, just processes transforming physical
variables.
- Fundamentally there is no time. The basic equations
evolve together with events, not things
Then he
explains how in a physical world without time its perception can
emerge:
- Our familiar time emerges
- Our interaction with the world is partial, blurred,
quantum indeterminate
- The ignorance determines the existence of thermal time
and entropy that quantifies our uncertainty
- Directionality of time is real
but perspectival. The entropy of the world in
relation to us increases with our thermal time. The
growth of entropy distinguishes past from future: resulting in
traces and memories
- Each human is a
unified being because: we reflect the world, we
formed an image of a unified entity by
interacting with our kind, and because of the perspective
of memory
- The variable time: is one
of the variables of the gravitational field.
With our scale we don't
register quantum fluctuations, making space-time
appear determined. At our speed we don't perceive
differences in time of different clocks, so we experience
a single time: universal, uniform, ordered; which is
helpful to our decisions
times, and agents have evolved
strategies to support partners that will reciprocate. Once
the team is in place and becomes This page reviews the inhibiting effect of the value delivery system on the
expression of new phenotypic
effects within an agent.
aligned
with the rest of the business network it can become very
difficult to alter the phenotypic attributes expressed. If
execution is failing badly it may be necessary to actively remove barriers to change.
Hub agents
control execution
Hub agents leverage This page discusses the effect of the network on the agents participating in a complex
adaptive system (CAS). Small
world and scale free networks are considered.
network effects
to interact with large numbers of the other agents. Just
as E. O. Wilson & Bert Holldobler illustrate how bundled cooperative strategies can
take hold. Various social insects have developed
strategies which have allowed them to capture the most valuable
available niches. Like humans they invest in
specialization and cooperate to subdue larger, well equipped
competitors.
Queen ants utilize this This page reviews the catalytic
impact of infrastructure on the expression of phenotypic effects by an
agent. The infrastructure
reduces the cost the agent must pay to perform the selected
action. The catalysis is enhanced by positive returns.
infrastructure amplification to
distribute signals that E. O. Wilson & Bert Holldobler illustrate how bundled cooperative strategies can
take hold. Various social insects have developed
strategies which have allowed them to capture the most valuable
available niches. Like humans they invest in
specialization and cooperate to subdue larger, well equipped
competitors.
influence and
coordinate the behavior of rest of the nest, managers must
push desired
ideas to the other members of the group, building team
coherence. If done poorly the group members will reject
'the manipulation', or never align.
Judging
how well agents are modeling and learning from the situation
Judging success and failure of an action is often
difficult. Which agent was responsible? Was
the strategy selected the best one? When a success or
failure becomes apparent it is likely to be the result of many
intermediate actions. Arthur
Samuel used a checkers game to research the selection of
appropriate models, strategies and valuations of effects.
He found that simple agents could use model based reasoning,
including look ahead and predictive modeling of other agents, to
learn effectively. However, he found that sampling limitations
reduced the value of particular predictive events. It was
The agents in complex adaptive
systems (CAS) must model their
environment to respond effectively to it. Evolution's
schematic operators and Samuel
modeling together support the indirect recording of past
successes and their strategic use by the current agent to learn
how to succeed in the proximate environment.
much safer to learn by iteratively
adjusting weights by small increments, converging the
valuations of early strategies that had been part of the
execution towards later ones. Hence the trend in major
corporations towards skewing rewards to high performers for
immediate success seems inappropriate!
GE's
top executives Corporate Executive Council (CEC) meetings review
broad aspects of their businesses and the external environment,
looking for problems and opportunities, and sharing
strengths. The The page describes the SWOT
process. That includes:
- The classification
of each event into strength weakness opportunity and
threat.
- The clustering
process for grouping the classified events into goals.
- How the clusters
can support planning and execution.
Operational SWOT matrices and clusters from the Adaptive Web
Framework (AWF) are included as examples.
SWOT technique
forces the effective categorization of environmental signals and
helps generate focused This page looks at schematic structures
and their uses. It discusses a number of examples:
- Schematic ideas are recombined in creativity.
- Similarly designers take ideas and
rules about materials and components and combine them.
- Schematic Recipes help to standardize operations.
- Modular components are combined into strategies
for use in business plans and business models.
As a working example it presents part of the contents and schematic
details from the Adaptive Web Framework (AWF)'s
operational plan.
Finally it includes a section presenting our formal
representation of schematic goals.
Each goal has a series of associated complex adaptive system (CAS) strategy strings.
These goals plus strings are detailed for various chess and business
examples.
action plans.
GE's yearly S-1 strategy process allows the top executives to
check the alignment of their own and the business units' goals, valuations and
actions. The later S-2 process checks the effectiveness of
the operations actions relative to the expectations of the
goals.
Combining
appropriate agents in an execution network
When the senior executives are agreed on an adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage. Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents. Evolution indicates fitness when an organism survives and reproduces. For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery. He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's. When an agent achieved an explicit goal they obtained a final reward. Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas. The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness'; In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary. execution plan
they need to cascade the goals into a network
of appropriate agents. GE's
Boca meeting ensures the senior executives have an agreed plan
to execute. GE's session C process allows the top
executives to effectively initiate and then manage this cascade.
In an open progressive organization the feedback will be broadly
shared. Changes will be absorbed by the execution teams
where this is possible. Some problems will have such large
impact as to move beyond the current mission.
In This page describes the organizational forces that limit change.
It explains how to overcome
them when necessary.
other styles of organization change
may be resisted, or used to demand more resources or Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not
include time:
- A present that is common throughout the universe does not exist
- Events are only partially ordered. The present is
localized
- The difference between past and future is not foundational.
It occurs because of state that through our blurring appears
particular to us
- Time passes at different speeds dependent on where we are and how fast we travel
- Time's rhythms are due to
the gravitational field
- Our quantized physics shows neither
space nor time, just processes transforming physical
variables.
- Fundamentally there is no time. The basic equations
evolve together with events, not things
Then he
explains how in a physical world without time its perception can
emerge:
- Our familiar time emerges
- Our interaction with the world is partial, blurred,
quantum indeterminate
- The ignorance determines the existence of thermal time
and entropy that quantifies our uncertainty
- Directionality of time is real
but perspectival. The entropy of the world in
relation to us increases with our thermal time. The
growth of entropy distinguishes past from future: resulting in
traces and memories
- Each human is a
unified being because: we reflect the world, we
formed an image of a unified entity by
interacting with our kind, and because of the perspective
of memory
- The variable time: is one
of the variables of the gravitational field.
With our scale we don't
register quantum fluctuations, making space-time
appear determined. At our speed we don't perceive
differences in time of different clocks, so we experience
a single time: universal, uniform, ordered; which is
helpful to our decisions
time. Any President of the US is the United States of America. faces particularly
challenging execution conflicts within their
administration. "Half of a president's suggestions which
theoretically carry the weight of orders can be safely forgotten
by the cabinet member. If the president asks about a
suggestion a second time he can be told its being
investigated. If he asks a third time, a wise cabinet
officer will give him at least part of what he
wants. But only occasionally except about the most
important matters do presidents ever get around to asking three
times" from Richard Neustadt's Presidential
Power.
True understanding of the nature of problems being worked on by
a development program only occurs with the presence of feedback
from the market and the channels to it. When the feedback
arrives it must be prioritized and turned into adjustments to
the day to day activities of the business. A This page uses an example to illustrate how:
- A business can gain focus from targeting key customers,
- Business planning activities performed by the whole
organization can build awareness, empowerment and
coherence.
- A program approach can ensure strategic alignment.
program approach can help drive the
adoption of iterative processes, alignment and sharing of
signals by agents in a large functional organization.
The presence of reactionary and hierarchic
agents' intent on limiting change will be destructive to a
development of the nascent execution network. Kim and Mauborgne's tipping point process
can transform such organizations into progressive ones.
Network conflicts
The The complex adaptive system (CAS)
nature of a value delivery system is first introduced. It's a network
of agents acting as relays.
The critical nature of hub agents and the difficulty of altering
an aligned network is reviewed.
The nature of and exceptional opportunities created by platforms are discussed.
Finally an example of aligning a VDS is presented.
network of agents must extend all
the way out to the target customer. Typically the
customer will be reached via a sales channel. Since the
customer may be interested in a variety of products that the
sales channel can provide the sales agents can/should choose
which products to actively sell. If a product's business
managers fail to align the sales agents' goals with their
product, few sales or even customer interactions will
occur. The conflict is typical of a problem that agents
with multiple network partners and goals have.
HP's HP9000 computer hardware business needed solutions to offer to its customers. Some of
these solutions were produced by HP's software business but most
were developed and sold by partners. The sales force was
aligned with the hardware business and so was encouraged to sell
partner solutions maximizing the opportunity to sell computer
systems.
Any execution network's weakest links must be found and aligned
as part of a successful execution activity. If the network
adjustments undermine some other business within a corporation
the corporate strategy must be examined. The conflict may
require the removal of one of the businesses from the
corporation to maximize overall value generation. HP's
software businesses often struggled to get product through the
computer hardware sales channel and were often candidates for
sale.
.
 Politics, Economics & Evolutionary Psychology |
Business Physics Nature and nurture drive the business eco-system Human nature Emerging structure and dynamic forces of adaptation |
 |
integrating quality appropriate for each market |
|
 |