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Toward the Intelligent Enterprise
Editor's Note: This is the second part of a 2-part series by Jack Ring. In this article Jack describes the Intelligent Enterprise as a goal-seeking system, while exploring the application of systems engineering concepts to the operation of enterprise in general. If you've wondered about the real role and values of enterprise modeling you should find here a new and unequivocal perspective. Jack concludes that you can't have a goal-seeking Intelligent Enterprise without an explicit, active, and evolving model of the enterprise providing the necessary system control. In the course of all of this he offers us some interesting models of enterprise as system, and some interesting perspectives and data on the role of people as operators rather than as "human resources".
Enterprise As System
Enterprises contain people. People are inherently capable, collaborative, and caring, yet fundamentally indeterminate. A "peopled" system presents special challenges to those who operate and evolve an enterprise, and these challenges can be met only by leaders who are adept at seeing their enterprise as a system.
A systems view of a business gives the leader a competitive edge over those who see a business as a functional organization or a set of processes. An appreciation for the competencies of the staff gives the leader superior efficiency without loss of innovation. And the astute application of automation, not only to the physical operations of the business, but also to the intellectual operations of the business, ensures that the business will always stay at least one step ahead of its competitors.
A system consists of a set of components and a set of relationships between the components and between the respective relationships . There can be a variety of component types and a variety of relationship types. Some of the relationships are tangible while other relationships are intangible or even subconscious.
When a system is presented with a stimulus it produces a response. The Stimulus<>Response (S<>R) characteristic is called the systems behavior. A simple stimulus may trigger a complex response that plays out over time. Similarly, a complex stimulus that extends over time may result in a single, simple response. More often than not, however, the enterprise must respond to a complex stimulus with a complex response, both of which play out over time.
The S<>R behavior of a system is influenced by the nature of the relationships more than by the capabilities of the components.
Depending on content and structure, systems come in various flavors such as fixed, homeostatic, adaptive, or goal-seeking. A business producing the same commodity parts each day is an example of a fixed system. A homeostatic business seeks to maintain its internal operating characteristics regardless of the variability of the stimuli placed on it. A good example, unfortunately, is the Department of Motor Vehicles, as designed and operated by most state governments. In contrast, an adaptive enterprise can accommodate a variety of stimuli and produce responses that respective customers find valuable. However, an adaptive enterprise may be successful only within a narrow range of product technology or market niche, and may not be able to sustain exemplary results over more than one or two generations of management. In contrast, the goal-seeking enterprise can pursue multiple objectives simultaneously (such as harmonizing customer satisfaction, investor satisfaction and employee satisfaction) and can adopt, and adapt to, new technologies, markets, modalities of stimuli, and standards for responses. The dual ability to adopt as well as adapt is the main distinction. The goal-seeking enterprise can change not only its responses to stimuli, but also its own content and structure, to better serve the multiple stimuli it intends to honor. More importantly, it is self-motivated to make such changes, and the motivation stems from the manner in which it allocates the rewards from appreciative customers.
Seeing the Enterprise as System
Figure 1 shows the basic Context and Content of a business enterprise. A Business facilitates commerce between its Marketplace and Suppliers . A business can be viewed as a set of Components which interact to accomplish Projects which produce Results. Projects include taking input from suppliers, adding value, and providing output to the marketplace. Results include: the value added by these activities, the customer satisfaction therefrom, and the growth in profit from the rewards garnered from the marketplace and shared with the suppliers. The amount of value added is not necessarily a function of effort. In an increasingly competitive market the actual street price reveals the real value added by the business -- which, of course, determines the reward garnered by the business.
Business performance can be evaluated with a set of Measures of Effectiveness -- MOEs. External MOEs are lagging indicators, and Management MOEs are generally leading indicators, unless too much latency exists in management practices.
Figure 2, using the key described in Figure 1, shows the variety of activities that a business must engage in every day. Each activity should have a leader, a plan, participants, milestones, deliverables and celebrations. Some activities may take only a few minutes per day, but all must be done.
From , the components that comprise a business are:
From Components to a System of Systems
A business can be viewed as a policy and process system, a people system, an information and decision system, and a materiel handling system. Unfortunately, such systems cannot be bought. Only the components can be bought. Then the "system" must be evolved, in situ.
Measures of Effectiveness
Peter Drucker  says that the basic external measures of enterprise health are market standing, innovation, productivity, and liquidity, to which many leaders add image.
Note that all these are post-facto measures, whereas anticipatory measures are needed in a high performance, goal-seeking system. Accordingly, we have management MOEs as well. The most informative measures of management effectiveness are: cost of quality, business model fidelity, change proficiency, climate surveys and incidences of conflicting goals. Special attention should be given to conflicting goals, because they are clear signals that the strategy is naive, or that communication is inadequate or distorted.
Benchmarks are another way to measure management effectiveness, but are a last choice because they are a slower, more expensive way. Further, benchmarks are post-facto measures, as are best practices and maturity models.
Figure 2 represents only a static view of the business. More important is the dynamic, behavioral view. Significant and even mind-numbing complexity is added to the picture by the time-based flows of materiel, information, and decision which must occur during the formulation of the thousands of responses each day.
Because the context of the business changes in a series of stochastic shocks, good systems produce the right responses to stimuli, better systems improve themselves as stimulus:response pairs and the resulting rewards are experienced, and the best systems adapt themselves, to paraphrase an old civil war general, "firstest with the leastest," to new stimuli. Accordingly, the life expectancy of a business depends not only on current performance, but also on its ability to evolve fast enough to thrive in an environment of unpredictable change .
Any system structure represents a compromise between stability and maneuverability. A paperweight is highly stable but not very maneuverable. Likewise, Brownian motion is highly stable, because even if the particles are in random motion the random motion behavior persists over time. Even though the particles move to new locations, the behavior of the system, not the behavior of the particles, is stable.
Many would agree that teenagers exhibit a low level of stability and a low level of maneuverability (doing what they are told, the first time). In contrast, a motorcycle is highly maneuverable but not very stable, particularly at rest.
Some systems are different, exhibiting high levels of stability and maneuverability simultaneously. One example is an homeostatic system -- the type of system that seeks internal stability, such as the normal humans ability to maintain an internal body temperature of 98.6 F regardless of external temperature. Likewise, a helicopter is much more maneuverable than an airplane, but also stable, at least as long as all of its internal control systems are working. Also, contrary to engineering predictions, the hummingbird can fly, thanks to dynamic stability. Similarly, a rules-driven organization, such as a government contractor, is more stable and more maneuverable than an enterprise where everyone does what they please. But a rules-driven organization is neither as stable nor as maneuverable as a rules-making, goal-seeking, organization -- a requirement to successfully serve industrial and commercial markets.
The secret of helicopters, hummingbirds and goal-seeking organizations is called dynamic stability. Dynamic stability is achieved by the inclusion of a process control system in the basic system. As control becomes more effective the enterprise can achieve higher levels of stability and maneuverability simultaneously.
A process control system contains a model of the system to be controlled in our case a model of the business. The accuracy of control is limited by the fidelity of this model to the real world. That is why model fidelity is a measure of management effectiveness, and keeping the enterprise model up to date is a necessary daily activity.
The key attributes of a process control system are acuity, latency, and accuracy. Acuity means the degree to which the control system can sense or know about all the pertinent events and conditions throughout the enterprise and its context. Latency means the delay between the time an event happens in the real world and the time that the control system deals with the event. Accuracy means how closely the control system can adjust the actual conditions to the desired conditions. Accuracy is more concerned with the "taking action" part of control than the observing or deciding part of control.
Modes of Control
Different choices of acuity, latency and accuracy for a control system amount to different modes of control. As the mode of control progresses from oversight control (as in congressional oversight, where the latency can be months or in annual assessments of capability) to embedded control (as in peer reviews of work in progress) to systemic control (as in a quality ethic), one achieves what Peter Senge  called "control without controlling."
When systemic control exists in each business component new kinds of business enterprises can be envisioned. Stability vs. Maneuverability no longer needs to be a tradeoff. Businesses can enjoy high levels of dynamic stability just as do hummingbirds and helicopters. And resistance to change disappears, thus obviating the expense and trauma of management of change.
Overcoming Size, Complexity and Confusion
As a business grows, the number of projects, people and locations increase, and a pattern of confusion and inefficiency sets in. Part of this is due to the workload that increases the number of relationships (R) faster than the work contribution from the increase in the number of components (N). Another part is due to management competency or lack thereof.
Typically growth adds echelons in an organization chart. The more layers the more complexity and the more misinformation that gets passed. This manifests as uncertainty in the board room, and as increasing apathy down through the echelons. Little wonder why organizations get in trouble if they do not shift their mode of operation at staff sizes of approximately 15, 65, 250, and so forth.
Viewing a business as a system reveals the futility of the long standing management dilemma of organizing by function or by project or by the "least worst" alternative -- matrix management. The ten Principles of Agile System Design  indicate that a business should be highly modular and have a web topology, all within a common framework that is consistent with the nature of the stimuli placed by the context to which the business intends to respond.
Note that top down has no meaning, because no one can tell which facet of the system, the chairperson, the customers, the investors, the employees, etc., is the top.
In peopled systems the interpersonal and learning styles of the individuals involved must be taken into account, managed, and often times mitigated . Interrelationships are extremely important. This is demonstrated when a synergistic team of lesser capable people outperforms a group of non-cooperating experts. The importance of this factor has been quantified  and the general idea is shown in Figure 3.
The relative achievement of a system such as a workgroup is plotted against the size of the workgroup (number of components in the system). As the number, N, of people increases, the number of relationships increases as N(N-1)/2, and the relative effectiveness declines toward an asymptote of 50%. The bad news is that another factor can drag the relative achievement down below 50%, to 10% or even lower. The degrading factor is the style of interpersonal relationships practiced within the workgroup. If the style is encouraging but admonishing then relative achievement is maximum. If the style is anxious and insincere then the relative achievement is degraded another 50%. And if the style is critical and destructive then the relative achievement degrades even further, and can reach zero if the workgroup spontaneously disbands.
Critical Success Factors
The MOEs will show satisfactory results when all business plans and decisions pursue the triple maximum of: a) Quality, b) 1/(cycle time), and c) return on resources.
Return on resources is measured in two dimensions. One concerns the return on financial capital invested, and is measured by the amount of real value added and the time rate of adding value, divided by the amount of resources required and the time rate of capital. A second, more future oriented, dimension of return on resources concerns the people-capital invested, and is measured by the magnitude and rate of learning and knowledge transfer that the people achieve, divided by the amount of time and effort they invested.
The Goal-seeking framework
A specific system archetype, a goal-seeking system (GSS), is the framework for an intelligent enterprise. A GSS has a goal. It is triggered by a stimulus. It has energy with which to operate, and it has competency which guides the operation. Further, a GSS has statusing, the ability to objectively determine how well the operation is meeting the goal. Finally, it has feedforward, which orients and adapts the competencies and energies to reduce any gap between system status and its goal. This in-built statusing and adjusting is what gives the GSS its pursuit capability. With robust control the GSS can zero-in on its goal even as the goal is changing.
This GSS framework provides for inputs (stimulus, time, and resources, as well as disturbances). It provides for outputs (responses, in-process status reports, and learning). Another aspect of GSS context is the enterprise culture, which may constrain or enthuse the GSS. Measures of GSS effectiveness mirror the enterprise MOEs and dynamic stability physics with degree of goal achievement, dynamic range, and integrity limits.
The Collaboration Projects
An intelligent enterprise, including a virtual enterprise, consists of nine collaborative projects. These projects, within the GSS framework, simplify and streamline all enterprise activities, and ensure pursuit of the triple maximum. Each project is instantiated as a goal-seeking system in its own right, so that each provides the standardization needed to facilitate the triple maximum across the enterprise components, while simultaneously adapting collaboratively to the stochastic shocks of inevitable change. Note the purposeful absence of terminology such as marketing, sales, engineering, manufacturing, etc. in the nine projects:
Summary and Key Point
Success in most enterprises today requires dynamic stability the ability to pursue a goal even when the goal is changing. The best dynamic stability is achieved when control is systemic when each participant understands the enterprise goal, plan, success factors, and status; is clear on how their individual behavior and value added relates to the enterprise value added and consequent rewards; and is motivated to achieve personal, team, and enterprise success. Individual understanding is created best by involving everyone in modeling the enterprise, and in maintaining the fidelity of the model. This modeling activity also serves to enthuse the participants such that change is pulled by local forces rather than being pushed by expensive and often futile corporate change programs.
The enterprise that is sufficiently intelligent to adapt itself "firstest with the leastest" to its evolving problem space will be a winner. To adapt itself through goal-seeking pursuit, an enterprise must model itself, and the modeling must be a participative, collaborative endeavor acting locally to win globally.
 Weinberg, Gerald (1975), An Introduction to General
Systems Thinking, John Wiley and Sons.
Jack Ring, Innovation Management
|Would you like to offer some thoughts or add to the dialog? Responses of general interest may be posted below. Send your comment to . IMPORTANT: Make sure the subject line of your message contains: Comment on Guest Speaker 8/99.|
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From: (Rick Dove) Date: Thu, 19 Aug 1999
Jack - some work I'm engaged in at the moment has resonated with some of your thoughts above -- in a slightly different direction. It appears to me that the vision-mission-strategy (v-m-s) picture a company develops is actually an attempt on the part of the enterprise to "model" the customer/marketplace/opportunity (c/m/o) - though I've never heard it expressed that way before. Which makes me want to think about how modeling tools, knowledge and technique can be used to fashion v-m-s, and keep it current.
In your article you speak of process control as working upon an embedded model of the real process (enterprise) it is controlling - and therefore the importance of fidelity, and the need to update the model as fast as the real enterprise changes. With v-m-s as a model of the c/m/o environment, what are we controlling with this model -- different, that is, than the enterprise which we are controlling with the enterprise model? In business sectors where c/m/o changes rapidly the need to maintain fidelity of v-m-s implies the need to change v-m-s rapidly. I've just talked myself into a patently obvious statement - but it seems to have implications on the need to revisit v and m that I've not considered before.
MIT's Rodney Brooks, the robot control-system designer, takes issue with the AI people who want to drive a robot around according to some embedded world model - claiming the model never has enough fidelity - so his robots interact directly with the real world, learning and building their own constantly modified world model according to what they sense.
The eCommerce fringe is out there with automated methods to watch web surfers and capture customer/prospect profiles and preferences by seeing what they do on the web and feeding that back into product and service feature and delivery strategy. This seems to combine aspects of the Brooks approach with aspects of the v-m-s as model idea.
I don't know where this is going. And you can ignore it if you don't either.
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So how does an enterprise announce which stimuli it will honor? With a
[good] mission statement. Crisp and clear. Often saying what the enterprise will not do as
well as what it will do. Further, its strategy says what type of response it will produce.
Not how it will produce the response but what type (e.g. if mission = help win war then
strategy = bullets or guns or soldiers or survival gear or training or espionage, or etc.)
and in what competitive scenario (head on, flanking, infiltration, absorption -- then cut
the supply line, etc.). The strategy statement is also crisp, clear, and may describe what
Is not as well as what is.
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Conflict, as defined as "incompatible activities"; (not opposing interests) is natural in a process oriented system. Management should be evaluated on how they deal with these conflicting goals, (the impact on the customer, speed, and effectiveness) not whether conflicting goals exist.
Management can not be totally prescient and assuage all potentially conflicting goals before they arrive. Management must be aware if a conflict of goals can happen, and be able to recognize the conflict and act on it in the customers'; best interest.
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Given so many root causes, all of which can be precluded, I am interested in why you are concerned and why you think conflicts in activities are natural in a process oriented system. The only reasons I can think of are either the process is inadequately articulated (as are most BPR results) or the system situation is exceeding the limits of its controller.
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