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Lets Give More Emphasis to Opportunities in Complex Enterprise Environments Brian E. White, Ph.D. Director, Systems Engineering Process Office The MITRE.

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Presentation on theme: "Lets Give More Emphasis to Opportunities in Complex Enterprise Environments Brian E. White, Ph.D. Director, Systems Engineering Process Office The MITRE."— Presentation transcript:

1 Lets Give More Emphasis to Opportunities in Complex Enterprise Environments Brian E. White, Ph.D. Director, Systems Engineering Process Office The MITRE Corporation Conference on Advanced Risk Management and Its Applications to Systems Engineering 8-9 November 2007 Hosted by the Hampton Roads Area Chapter of International Council on Systems Engineering (HRA INCOSE) Omni Hotel, Newport News, VA ©2007 The MITRE Corporation. All rights reserved.

2 2 Takeaways Mandatory reading: N. N. Taleb, The Black SwanImpact of the HIGHLY IMPROBABLE, Random House, New York, 2007 Improbable/dp/1400063515/ref=pd_bbs_sr_1/102-6890811- 9526543?ie=UTF8&s=books&qid=1192905688&sr=8-1 n Complex systems include people, and we must bring more humility to engineering the environment of complex. systems, e.g., enterprises. n A complementary mindset is critical to furthering progress in systems engineering. n In complex enterprises, its all about opportunities because one is going off track all the time. The biggest risk is in not pursuing opportunities.

3 3 Introduction* n Many of us (in diverse fields) are concerned about risk management. –So much so that risks are identified early and often, and mitigation techniques are aggressively pursued. –In contrast one does not hear much about opportunity management, e.g., n [de Weck and Eckert, 2007], [Chang and Backus, 2007], and [Youssef, 2005] –We should ask ourselves n In our complex enterprise environments, do traditional methods of handling risk (and opportunity?) carry over, or n Should we be more concerned about potential changes in the way we view the problems? n Enterprise Opportunity and Risk (terminology can be confusing) –The impression from aerospace and/or military-industrial complex documentation and anecdotal evidence is that there is a disproportional emphasis on risk as opposed to opportunity, e.g., n [Edmondson, et al., 2005] and [Roberto, et al., 2006] n Presenter at 5 April 2007 complex system workshop: … there are lots of opportunities to do it wrong(!) –What about other commercial, etc., domains, e.g., venture capitalists? –What is the sense of this risk management/systems engineering group? The institutional biases are: Venture capital domain: Stop the program if seed money doesnt produce something useful. Department of Defense acquisition: Continue the program even though there are serious problems. ___________ * [White, 2007a]

4 4 Introduction (Concluded)* n We assert new perspectives on this topic for enterprises –There is duality in treating risks and opportunities. –Be aggressive with opportunity and accepting of risk. –The greatest enterprise risk may be in not pursuing enterprise opportunities. –Complexity theory can inform us about effective approaches to opportunity management. –An opportunity mindset can lead to emergent innovations. ___________ * [White, 2007a]

5 5 Relative Importance of Opportunity* System of Systems View Systems View Enterprise View Risk Opportunity UnknownUn-assessable Uncertainty The minimum goal of this talk is to raise your sensitivity level for proactively pursuing opportunities at all engineering views. See Notes Page ___________ * [White, 2007a] and [White, 2007]

6 6 {View} = {Scope, Granularity, Mindset, Timeframe}* Time A change in a minds focus results in a change of View! No View change can take one beyond this limit! ___________ * [White, 2007]

7 7 Risk/Opportunity Representation on Probability/Impact Grid* Low Medium High Medium High Low Positive Impact Benefit of Success B s Negative Impact Consequence of Failure C f Probability P o Probability Q o RisksOpportunities Attention Arrow See Notes Page ___________ * [White, 2007a] after [Hillson, 2004], p. 126

8 8 What Are Consequences of Failure?* ___________ * [White, 2007a] from [Garvey, 2005], p. 7 Condition Present 1 Consequence Event 111 Risk Event 11 Consequence Event 311 Consequence Event 211 Consequence Event 411 Consequence Event 511 Root Cause CONDITION Event B IF this Risk Event A Occurs The region bounded by this space is Probability (A|B) Current test plans are focused on the components of the subsystem and not on the subsystem as a whole. Subsystem may not be fully tested when integrated into the system for full-up system-level testing. Consequences of failure are undesirable events that degrade the performance or capability of a system, SoS, or Enterprise. THEN these are the consequences Full-up system will reveal unanticipated performance shortfalls Subsystem will have to accommodate unanticipated changes in subsequent build hardware/software requirements which will affect development cost and schedules User will not accept delivery of subsystem hardware/software without fixes Subsystem will reveal unanticipated performance shortfalls Subsystem will have to incorporate late fixes to tested software baseline The Risk Statement: An Illustration of CONDITION-IF-THEN See Notes Page

9 9 What is Opportunity?* n Opportunities are events or occurrences that assist a program in achieving its cost, schedule, or technical performance objectives. n In the larger sense, explored opportunities can enhance or accomplish the entire mission. n Opportunity also is associated with uncertainty and impact. n There is a duality or parallelism to risk that can be applied. n For an opportunity, let Q o be the probability of occurrence, B s, the benefit of success, and E e, estimated enhancement. n We can pose the simple formula: E e = Q o × B s [This is expected benefit!] Probability = 0 < Q o < 1 Benefit = 0 < B s < Opportunity Assessment A o = {Q o, B s } An interpretation: No Gain Worthwhile Pain Golden Opportunity Windfall Euphoria See Notes Page ___________ * [White, 2007a]

10 10 Opportunity Classification Example* ___________ * [White, 2007a] after [Garvey, 2005], p. 8 BsBs QoQo Opportunity Opportunity Averse System Profile Opportunity Seeking System Profile See Notes Page

11 11 Thoughts About Opportunity and Risk Concerning TSE, SoS Engineering, and ESE or CSE* n Think about opportunity/risk with respect to a complex systems environment in addition to the system per se. –There may be many more opportunities in the systems environment. –The pursuit of these opportunities could reduce the systems stress. –Environmental risks seem less important than the opportunities. –Enterprise-view opportunity action and risk avoidance can be viewed with a philosophy of nothing ventured, nothing gained. –Downside risk is about not incurring damage that might stifle the aforementioned opportunities. n Compare and contrast TSE and CSE concepts. –A complex system (and enterprise) is open. –This suggests a predisposition for opportunities. –One should open the system further to create more emergent behavior. –Be more aggressive with identifying, exploring, and developing opportunities than in TSE. See Notes Page ___________ * [White, 2007a]

12 12 Thoughts About Opportunity and Risk Concerning TSE, SoS Engineering, and ESE or CSE (Concluded)* n Enterprise risks can be mitigated by creating a management process that has built-in abilities to –Quickly assess whether emergent behavior is desirable –Encourage desirable behavior –Discourage undesirable behavior –Encourage greater acceptance of risks n Stevens: Messy frontier –Political engineering (power, control…) –High risk, potentially high reward –Foster cooperative behavior n One may learn from researching what economists do about opportunity and risk at multi-views of analysis, i.e., macroeconomics and microeconomics.** n In summary –Opportunities for intervening in enterprise environments are great. –The greatest enterprise risk may be in allowing this process to atrophy. See Notes Page _________ ** [Kuras, 2004] ___________ * [White, 2007a]

13 13 Opportunities and Risks in Establish Rewards* n Suppose a suitable outcome space has been identified. n Autonomous agents will develop specific outcomes taking advantage of opportunities. n There is risk in developing products that –May not become outcomes –Become less desirable outcomes n These risks are either not rewarded or are rewarded less. n Because a reward is granted to many outcomes, agents may pursue opportunities more aggressively than mitigating the risks of not achieving outcomes. n Risk mitigation could be reduced to ordering outcomes according to rewards. n This ordering might be pursued in conjunction with other autonomous agents because rewards are granted only to targeted populations of agents. n The hypothesis that opportunities would be treated more aggressively than risks still needs validation. See Notes Page ___________ * [White, 2007a]

14 14 Opportunities in Characterize Continuously* n This CSE activity is the continual generation and refinement of complex-system characterizations. Continuous Characterization is crucial for autonomous agents to independently develop metrics to guide their local decision making to be congruent. n The specific outcomes used as the basis for Judging should be characterized, as should the rationale that eventually explains the subsequent Judging decisions. n Rewards (and perhaps Outcome Spaces) initially should be characterized with succinct bumper-sticker labels. The U.S. Army motivated a tremendous spurt forward with the visionary, Own the Night. n Pithiness encourages opportunities for inconsistencies in how Rewards (and Outcome Spaces) are interpreted. To the extent that consistency matters, however, a complex system will benefit from continually developing and espousing more detailed and complete characterizations. n However, in complex-system evolution, characterizations cannot be too refined. New Outcome Spaces may need to be added to the characterizations, or their new possibilities will not be explored. See Notes Page ___________ * [White, 2007a]

15 15 Comparing TSE and SoS Risk Management* ___________ * [White, 2007a] from [Garvey, 2005], p. 12 Figure 9 (edited). Three Color Comparative Assessment Scheme See Notes Page

16 16 Comparing TSE, and SoS and ESE Opportunity Management* G = green Y = yellow R = red See Notes Page ___________ * [White, 2007a]

17 17 Concluding Remarks* n The greatest enterprise risk may be in not pursuing enterprise opportunities. n There is duality –In treating risks and opportunities –Between systems and enterprises n Opportunity (as well as risk) management is a team sport. –But ESE is the big leagues for opportunity management. n Keep in mind there are unknowns and unknowables. n Opportunities in ESE abound! n Qualitative assessments of opportunity management –Tend to be more difficult for enterprises than for SoS or systems –Could easily change after learning more about ESE n Our principal hypothesis: In ESE, be aggressive with opportunity and accepting of risk. –This is just the opposite of what seems to be the case in TSE! –Nevertheless, validation from actual case studies should be sought. See Notes Page ___________ * [White, 2007a]

18 18 List of References [Brooks, 1995] Brooks, F. P., The Mythical Man-Month: Essays on Software Engineering, 20th Anniversary Edition (Paperback), Addison Wesley, 2nd (anniversary) expanded edition, 2nd corrected printing, 1995 1403359-6272017?%5Fencoding=UTF8&n=283155&s=books [Chang and Backus, 2007] Chang, S.-J. F., and G. R. Backus, Applying Real Options to Flexible Systems Engineering: A Proof-of-Concept Study on Capability Acquisition of Tactical Data Links, IEEE Systems Conference, Honolulu, HI, 9-12 April 2007 [Edmondson, et al., 2005] Edmondson, A. C., M. A. Roberto, R. M. J. Bohmer, E. M. Ferlins, and L. R. Feldman, The Recovery Window: Organizational Learning Following Ambiguous Threats, Chapter 12, Organization at the Limit: lessons from the Columbia disaster, Blackwell Publishing Ltd, Malden, MA, 2005, pp. 220-245 [Garvey, 2005] Garvey, P. R., System-of-Systems Risk Management: Perspectives on Emerging Process and Practice, MP 04B0000054, MITRE Product, The MITRE Corporation, 2005 [Haberfellner and de Weck, 2005] Haberfellner, R., and O. de Weck, Agile Systems-Engineering versus Agile-Systems Engineering, INCOSE Symposium, Rochester, NY, 10-15 July 2005 [Hillson, 2004] Hillson, D., Effective Opportunity Management for Projects, Risk Doctor & Partners, Petersfield, Hampshire, United Kingdom, Marcel Dekker, Inc., New York, 2004 [Kuras, 2004] Kuras, M. L., personal communication, 2004 [Kuras and White, 2005] Kuras, M. L., and B. E. White, Engineering Enterprises Using Complex-System Engineering, INCOSE Symposium, Rochester, NY, 10-15 July 2005 [Kuras and White, 2006] Kuras, M. L., and B. E. White, Complex Systems Engineering Position Paper: A Regimen for CSE, Conference on Systems Engineering Research (CSER), Los Angeles, CA, 7-8 April 2006

19 19 List of References (Concluded) [Roberto, et al., 2006] Roberto, M. A., R. M. J. Bohmer, and A. C. Edmondson, Facing Ambiguous Threats, Harvard Business Review, November 2006, pp. 106-113 [Taleb, 2007] N. N. Taleb, The Black SwanImpact of the HIGHLY IMPROBABLE, Random House, New York, 2007 Improbable/dp/1400063515/ref=pd_bbs_sr_1/102-6890811- 9526543?ie=UTF8&s=books&qid=1192905688&sr=8-1 [de Weck and Eckert, 2007] de Weck, O., and C. Eckert, A Classification of Uncertainty for Early Product and System Design, ESD-WP-2007-10, Working Paper, Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, February 2007 [White, 2005] White, B. E., A Complementary Approach to Enterprise Systems Engineering, National Defense Industrial Association, 8th Annual Systems Engineering Conference, San Diego, CA, October 24-27, 2005 [White, 2006] White, B. E., Enterprise Opportunity and Risk, INCOSE Symposium, Orlando, FL, 9-13 July 2006 [White, 2006a] White, B. E., Fostering Intra-Organizational Communication of Enterprise Systems Engineering Practices, National Defense Industrial Association, 9th Annual Systems Engineering Conference, San Diego, CA, 23-26 October 2006 [White, 2007] White, B. E., On Interpreting Scale (or View) and Emergence in Complex Systems Engineering, 1st Annual IEEE Systems Conference, Honolulu, HI, 9-12 April 2007 [White, 2007a] White, B. E., Lets Talk More About Opportunities in Uncertainty Management!, Project Risk Symposium, San Francisco, CA, 16-18 May 2007 [Youssef, 2005] Youssef, J., The Upside of Risk Management, ARM Customer Conference, Arlington, VA, 19-20 September 2005

20 20 Back Up Charts

21 21 Some Quotes from The Black Swan n Definition –... a Black Swan... is an event … [1] it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. [2] it carries an extreme impact. [3] … human nature makes … it explainable and predictable. pp. xvii-xviii n Asymmetry –Black Swan logic makes what you don't know far more relevant than what you do know. Consider that many Black Swans can be caused and exacerbated by their being unexpected. p. xix –The Black Swan asymmetry allows you to be confident about what is wrong, not about what you believe is right. p. 192 n Risk –… owing to the shallowness of our intuitions, we formulate our risk assessments too quickly. p. 97 n Statistics –… principle: the rarer the event, the higher the error in our estimation of its probability … the Gaussian bell curve sucks randomness out of lifewhich is why it's so popular. We like it because it allows certainties! How? Through averaging... p. 237

22 22 Some Quotes from The Black Swan (Continued) n Narration –… the problem of silent evidence. … to the point of blindness to reality. It is why we fall for the problem of induction, why we confirm. … We respect what has happened, ignoring what could have happened. pp. 131-132 –There is a blind spot: when we think of tomorrow we do not frame it in terms of what we thought about yesterday or the day before yesterday. Because of this introspective defect we fail to learn about the difference between our past predictions and the subsequent outcomes. When we think of tomorrow, we just project it as another yesterday. p. 193 n Decisions –Don't cross a river if it is four feet deep on average. … The policies we need to make decisions on should depend far more on the range of possible outcomes than on the expected final number. p. 161 n Mindset –Know how to rank beliefs not according to their plausibility but by the harm they may cause. p. 201

23 23 Some Quotes from The Black Swan (Concluded) n Opportunity –… invest in preparedness, not in prediction. Remember that infinite vigilance is not possible. … Seize any opportunity, or anything that looks like opportunity. They are rare, much rarer than you think. … you need to be exposed to [positive Black Swans]. Many people do not realize that they are getting a lucky break in life when they get it.... Work hard … in chasing such opportunities and maximizing exposure to them.... casual chance discussions at cocktail parties usually lead to big breakthroughs … pp. 208, 209 –I worry less about embarrassment than about missing an opportunity. p. 296 n Uncertainty –… in order to make a decision you need to focus on the consequences (which you can know) rather than the probability (which you can't know) is the central idea of uncertainty. p. 211 n Humility –As events present themselves to us, we compare what we see to what we expected to see. It is usually a humbling process, particularly for someone aware of the narrative fallacy, to discover that history runs forward, not backward. p. 268

24 24 The Black Swan Landscape MediocristanExtremistan NonscalableScalable Mild or type 1 randomnessWild (even superwild) or type 2 randomness The most typical member is mediocreThe most "typical" is either giant or dwarf, i.e., there is no typical member Winners get a small segment of the total pieWinner-take-almost-all effects Example: audience of an opera singer before the gramaphoneToday's audience for an artist More likely to be found in our ancestral environmentMore likely to be round in our modern environment Impervious to the Black SwanVulnerable to the Black Swan Subject to gravityThere are no physical constraints on what a number can be Corresponds (generally) to physical quantities, i.e., heightCorresponds to numbers, say, wealth As close to utopian equality as reality can spontaneously deliverDominated by extreme winner-take-all inequality Total is not determined by a single instance or observationTotal will be determined by a small number of extreme events When you observe for a while you can get to know what's going onIt takes a long time to know what's going on Tyranny of the collectiveTyranny of the accidental Easy to predict from what you see and extend to what you do not see Hard to predict from past information History crawlsHistory makes jumps Events are distributed* according to the "bell curve" (the GIF) or its variations The distribution is either Mandelbrotian "gray" Swans (tractable scientifically) or totally intractable Black Swans * "What I call 'probability distribution' here is the model used to calculate the odds of different events, how they are distributed. When I say that an event is distributed according to the 'bell curve,' I mean that the Gaussian bell curve (after C. F. Gauss; more on him later) can help provide probabilities of various occurrences." N. N. Taleb, The Black SwanImpact of the HIGHLY IMPROBABLE, Random House, New York, 2007, Table 1, p. 36.

25 25 Complexity Terms: View, Complexity, Emergence* n View: A human conceptualization consisting of scope, granularity, mindset, and timeframe n Complexity: Description of the ultimate richness of an entity that –Continuously evolves dynamically through self-organization of internal relationships –Requires multi-view analysis to perceive different non-repeating patterns of its behavior –Defies methods of pre-specification, prediction, and control n Note: Complexity as really a continuum extending from its lowest degree, complication, say, to its higher degree, intended here. n Emergence: Something unexpected in the collective behavior of an entity within its environment, not attributable to any subset of its parts, that is present (and observed) in a given view and not present (or observed) in any other view. –Notes: Some people employ a broader definition where things that emerge can be expected as well as unexpected. Emergence can have benefits or consequences. ___________ * [White, 2006a] and [White, 2007]

26 26 System Terms: System and SoS* n System: An interacting mix of elements forming an intended whole greater than the sum of its parts. –Features: These elements may include people, cultures, organizations, policies, services, techniques, technologies, information/data, facilities, products, procedures, processes, and other human-made (or natural) entities. The whole is sufficiently cohesive to have an identity distinct from its environment. n System of Systems (SoS): A collection of systems that functions to achieve a purpose not generally achievable by the individual systems acting independently. –Features: Each system can operate independently (in the same environment as the SoS) and is managed primarily to accomplish its own separate purpose. ___________ * [White, 2006a]

27 27 System Terms (Concluded): Complex System, CAS, and Enterprise* n Complex System: An open system with continually cooperating and competing elements. –Features: Continually evolves and changes according to its own condition and external environment. Relationships among its elements are difficult to describe, understand, predict, manage, control, design, and/or change. n Notes: Here open means free, unobstructed by artificial means, and with unlimited participation by autonomous agents and interactions with the systems environment. n Complex Adaptive System (CAS): Identical to a complex system. n Enterprise: A complex system in a shared human endeavor that can exhibit relatively stable equilibria or behaviors (homeostasis) among many interdependent component systems. –Feature: An enterprise may be embedded in a more inclusive complex system. ___________ * [White, 2006a]

28 28 Engineering Terms: Engineering, Enterprise Engineering, and Systems Engineering* n Engineering: Methodically conceiving and implementing viable solutions to existing problems. n Enterprise Engineering: Application of engineering efforts to an enterprise with emphasis on enhancing capabilities of the whole while attempting to better understand the relationships and interactive effects among the components of the enterprise and with its environment. n Systems Engineering: An iterative and interdisciplinary management and development process that defines and transforms requirements into an operational system. –Features: Typically, this process involves environmental, economic, political, social, and other non-technological aspects. Activities include conceiving, researching, architecting, utilizing, designing, developing, fabricating, producing, integrating, testing, deploying, operating, sustaining, and retiring system elements. ___________ * [White, 2006a]

29 29 Engineering Terms (Concluded): TSE, ESE, and Complex Systems Engineering* n Traditional Systems Engineering (TSE): Systems engineering but with limited attention to the non-technological and/or complex system aspects of the system. –Feature: In TSE there is emphasis on the process of selecting and synthesizing the application of the appropriate scientific and technical knowledge in order to translate system requirements into a system design. n Enterprise Systems Engineering (ESE): A regimen for engineering successful enterprises. –Feature: Rather than focusing on parts of the enterprise, the enterprise systems engineer concentrates on the enterprise as a whole and how its design, as applied, interacts with its environment. n Complex Systems Engineering (CSE): ESE that includes additional conscious attempts to further open an enterprise to create a less stable equilibrium among its interdependent component systems. –Feature: The deliberate and accelerated management of the natural processes that shape the development of complex systems. ___________ * [White, 2006a]

30 30 Example System Profiles* PoPo CfCf 0 1 1 positive slopes negative slopes ΔPoΔPo ΔPoΔPo ΔCfΔCf < ΔC f 1 2 3risk event number risk averse profile (decreasing slope) risk seeking profile (increasing slope) risk neutral profile (constant slope) See Notes Page ___________ * [White, 2007a]

31 31 What Can One Do to Engineer a Complex Systems Environment?* n Analyze and shape the environment: Guide the complex- system's self-directed development. This depends on the nature of the system and its environment. No portion of the environment can be directly controlled in a persistent fashion. n Tailor developmental methods to specific regimes and scales: Any complex-system operates in multiple regimes and at multiple scales. The operational regime is directly associated with the purposes or mission of the whole system. The developmental regime is associated with changes in the system. These two regimes cannot be sufficiently isolated for a complex-system. n Identify or define targeted outcome spaces: Outcome spaces are large sets of possible partial outcomes at specific scales and in specific regimes. The complex-system itself will choose the exact combinations of partial outcomes that it realizes. n Establish rewards (and penalties): Establish rewards (and penalties) that are intended to influence the behavior of individual (but not specific) autonomous agents at one or more scales and regimes to influence agent outcomes. ___________ * [Kuras and White, 2006] See Notes Page

32 32 What Can One Do to Engineer a Complex Systems Environment?* (Concluded) n Judge actual results and allocate rewards: Consider and judge the actual outcomes in many or all of the regimes and scales in terms of targeted outcome spaces. Then allocate rewards to the most responsible agents, whether they were pursuing those rewards or not. Do this in ways that preserve or even increase the opportunity for more new results. n Formulate and apply developmental stimulants: Use methods that increase the number of, or the intensity and persistence of, interactions among autonomous agents. Specific forms of this method depend on the phase of the developmental cycle of a capability that is being addressed. n Characterize continuously: Aim at gathering information at multiple scales and in multiple regimes pertinent to Outcome Spaces and making it available to the autonomous agents. n Formulate and enforce fitness regulations (policing): For example, initiate procedures aimed at detecting and screening changes so that fitness is maintained; that monitor characteristic periods; and that inhibit or negate changes that increase characteristic periods. ___________ * [Kuras and White, 2006] See Notes Page

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