Reducing Barriers to Workforce Innovation

Introduction

Measurable processes for fostering inventive thinking are needed to get things done. The origin of innovation, from the Latin innovationem — in novo — is to renew, essentially the action taken to fix or replace something.  A contemporary definition by computer scientist and author Peter J. Denning has said that “innovation is the adoption of a new practice in a community.”

Innovation requires that people, as change agents, accept or reject the modification. Indifference or resistance to change may stifle innovation without explicit leadership intervention.  Just as an organization applies risk management techniques to minimize potential negative consequences, so, too, an organizational framework is needed to maximize potential positive change. Without a supportive structure and assessment measure, leaders can only intuitively infer their organizations’ commitment or impediment to innovation. An innovation index indicates an organization’s culture for encouraging, evaluating, processing and approving ideas for new or improved business products, services, processes and programs.

Innovation, typically associated with advances in technology, has become the new normal in the business community.  Commercial businesses operate in a highly competitive market environment that rewards good product and service differentiation with consumer revenue.  Commercial businesses in a competitive market embrace innovation to improve efficiency and productivity by lowering production costs and to create new, better and less expensive products for consumers.

Governmental organizations also have customers but, by contrast, exist to serve the public and do not compete for profit.  Generally, government agency missions are oriented toward accomplishing national laws and policies through administrative processes and regulatory compliance. Within the federal public sector, the innovation imperative may stem from national interests competing with other countries in areas such as defense security, energy sufficiency and food independence. To address these strategic challenges the Congressional Budget Office in 2014 reported: “The federal government influences innovation through two broad channels: spending and tax policies, and the legal and regulatory systems.” Federal laboratories, programs and grants support foundational research and development of new technologies at the frontiers of science and engineering. Federal agencies also provide public services directly to citizens, businesses and other organizations, in which the customer experience is becoming more demanding in the digital era of e-government.

Both commercial and government enterprises have goals of improving safety, responsiveness, satisfaction, efficiency, productivity, effectiveness and cost savings. The difference is the translation from goals to implementation—the desire and the ability to remain agile to allow adaptive changes necessary to improve the business model and customer service in the Information Age. Organizations that desire to remain viable encourage innovation. Implementing new and potentially disruptive practices requires leadership approval of the vision, tolerance for initiative uncertainty and commitment to transformation. The leader establishes the organizational culture that embraces the opportunity for innovation and calculated risk taking, while the line of business directors encourage commitment and facilitate change, as noted in 2012 by Harvard Business School Professor John Kotter. Leaders place great confidence in managers to oversee the day-to-day business and trust their judgement. As noted in a Prosci Change Management report, “…engagement with and support from middle management as a top contributor to change management success. In a separate study with 575 change leaders, 84 percent of participants ranked manager and supervisor involvement in change initiatives as ‘extremely important’ or ‘very important’ to the success of their project.”

Innovation Intervention

There are two fundamental reasons why there is so little actual innovation within organizations: (1) implementing innovation is not easy, and (2) an innovation environment is not an imperative. The former reason is a challenge as the path from good idea to successful process replacement is risky, demanding and barrier-prone. The latter reason is attributable to whether leaders and managers foster an innovative culture, which is a prerequisite for innovation opportunity. The challenge is not identifying and executing innovative concepts, but failing to overcome the inertia that status quo is good enough. Lack of innovation is found in organizations with passive/defensive cultures identified with behavioral characteristics including conformity, rigidity, lack of team member accountability and initiative, interaction that will not threaten their security, and with “fit in and meet expectations.” Employees adjust to whether organizational culture is receptive to thinking outside the box or just fulfilling the daily requirements.

Konosuke Matsushita recognized the significance of an innovative and creative workplace environment. Matsushita, an orphan raised in poverty, was an entrepreneur who started a business with three employees and about $50, based on an electric light socket he designed. Last year, Matsushita’s Panasonic Corp. employed about 330,000 people in 580 subsidiary companies with revenue of approximately $74.5 billion. Matsushita stated, “You [U.S. businesses] firmly believe that sound management means executives on one side and workers on the other, on one side men who think and on the other side men who can only work. For you, management is the art of smoothly transferring the executives’ ideas to the workers’ hands.” Matsushita relied on his employees for innovation, instilling a culture in which employee proposals receive impartial management evaluation and leadership adjudication.

Innovation Process

Managers are particularly important to innovation effectiveness in their intermediary role between senior leaders and employees, and their administration of the people who work for them. Managers receive ideas, consider merit and determine whether to continue developing the proposals. The management staff responsible for executing daily operations are the stakeholders whose buy-in is key to move an idea from concept to implementation. Every organization experiences internal competition for influence and resources to attain success. Innovation represents change, with the intention of improving or replacing certain processes. Change may be disruptive, perhaps with actual or perceived winners and losers. Managers especially have a stake in the outcome, not the least of which is that they risk having a failure that could affect their careers.

As stated by Bruce D. Fischer and Matthew Rohde in a 2013 article in the American Journal of Management: “Resistance to innovation by management generally occurs in two ways. It may be in the resistance to ideas and their approval, or it may be through resistance to the implementation of approved ideas. Resistance to the introduction of ideas may not be detected, as the ideas will be deterred before they have a chance to blossom. Resistance to implementation or ineptitude in the management of change will eventually become evident in a low percentage of successful implementations.” Whatever the rationale, managers filter ideas and worthy proposals may be screened out. A consistent and formal process with benchmark indicators is useful to overcome the deterrence that may inhibit an organization’s innovation effectiveness.

Employees who are familiar with the organization’s business processes and who use the tools to perform their jobs are excellent sources for identifying potential improvements. Whether the proposed change is small or large, the way in which management facilitates the contribution may determine whether there is an early success or failure. The ad hoc approach of an office suggestion box or informal conversations with supervisors are not enough to engage employees. An evidence-based practice removes personal bias that could undermine the evaluation process. A formal idea solicitation and evaluation policy provides uniform procedures and instills confidence that leadership is responsive to change. The procedures should encompass four basic stages as depicted in Figure 1, leading up to project initiation that would then apply program management criteria. The stages are:

• Concept: an initial idea to improve an existing process or product.

• Consideration: business case analysis of feasibility, cost and likelihood of success.

• Evaluation: determining the merits of an enterprise investment decision.

• Approval: decision to commit resources and appoint responsibilities.

• Initiation: creating a project.

Proceeding through the process, from idea conception to approval, the probability of initiating a proposal diminishes and is particularly susceptible in the selection zone that has historically relied on the subjective inclination of management. The International Organization for Standardization (ISO) is developing management standards of terminology, tools, methods and interactions between relevant parties to enable innovation (ISO TC 279). Establishing and adhering to innovation process standards will ensure that idea fruition is not dependent on personal predilections. Also, having an innovation index will provide a quantitative indicator to identify strengths and weaknesses at each process stage. With recurring use, the index data will more accurately tell the story of the organizational innovation performance.

Innovation Index - Measuring Innovation Culture

An organization may tacitly support—or, at a minimum, not stifle—innovation. But applying organizational indicators will enable performance assessment and drive change. Tsutomu Harada last year wrote that “innovation probability should be the unit of analysis in the face of uncertainty.” Research indicates that the oldest firms tend to exhibit lower innovative probabilities, and larger firms by virtue of size increase the probability of innovation. Consistent historical data do not yet exist to benchmark the innovation probabilities within the selection zone. Examining an innovation engagement technique will illustrate the value of the probability index concept. The example links effective management action to outcome on maximizing innovation probability.

Crowdsourcing is a proactive idea-generation strategy to inexpensively and efficiently solicit employee contributions to improve or solve organizational issues, an approach that may increase participation compared to passive ideation. The use of motivating activities such as crowdsourcing increase the probability that innovation will occur, as opposed to focusing solely on maintaining established business processes. Kira Furuici and Isabel Seidel have said that, in conducting crowdsourcing, offering the targeted audience an incentive will affect the response rate. One organization reported crowdsourcing response rates (P₁) between 5 percent and 12.3 percent, the latter based on an innovation crowdsourcing tournament to solicit ideas from clinicians about how to enhance the use of evidence-based practices within a large public behavioral health system (Rebecca E. Stewart, et al., in Implementation Science, 2019). Analyzing the Stewart, et al., crowdsourcing project as an innovation process example offers insight into the selection zone probabilities as listed in Table 1. For this particular structured event, the probabilities exhibit the level of workforce engagement and the subsequent management adjudication.

When measuring indicators are adopted, over time as more data are collected, the probabilities would more accurately reflect specified characteristics of the organization’s innovation culture. Resistance to ideas, consideration and approval leading to implementation will be evident in low probability index ratings. While there is no assurance that procedures and indicators would maximize innovation, a formal governance foundation supported by empirical data will provide open and fair consideration. Examining the influence from corporate entrepreneurship and intrapreneurship on white-collar workers’ employee innovation behavior, Bjorn Willy Amo of Nord University in Bode, Norway, in 2006 reported that “There was a substantial (0.64) and highly correlated (p<0.01) relationship between the organization’s desire for employee innovation behavior and the employee innovation behavior.” The approval rate of innovation projects is a call to action by commitment of resources, important for demonstrating more than perfunctory policy. The following basic quantified expressions are lag metrics to evaluate a culture of soliciting, formally reviewing and approving innovation projects.

• Consideration = P₂ = (# qualified ideas / # employees) per year

• Evaluation = P₃ = (# feasible ideas / # employees) per year

• Approval = P₄ = (# approved ideas / # employees) per year

Conclusions and Recommendations

An organizational innovation framework improves the opportunity for maximizing positive change from ad hoc to a systematic approach for success. Innovative index probabilities provide an objective measurement of an organization’s innovation culture. To begin determining if innovation is meaningful within organizations, I recommend the following actions:

• Create procedures for each process stage from concept to approval.

• Identify five organizations that have a history of innovation or that have successfully implemented innovation projects; collect probability data for each process stage.

• Implement a 12-month pilot test at five organizations using the procedures and concurrently at five control organizations with no procedures. Collect probability data at all test organizations to determine if the procedures demonstrate improved ideation rate.

Published in Defense Acquisition: January-February 2020 (https://www.dau.edu/library/defense-atl/DATLFiles/Jan-Feb2020/Frum.pdf)

Sources and Suggested Reading List

Bjorn Willy Amo (2006), “The Influence From Corporate Entrepreneurship and Intrapreneurship on White-Collar Workers’ Employee Innovation Behaviour,” International Journal of Innovation and Learning.

Congressional Budget Office (2014), Federal Policies and Innovation.

Patrick J. Denning, (2006). “The Profession of IT-Infoglut,” Communications of the ACM-Association for Computing Machinery-CACM.

Bruce D. Fischer and Matthew Rohde (2013), “Management Resistance to Innovation,” American Journal of Management.

Kira Furuici and Isabel Seidel (2017), “In Search of Best Practices for Journalistic Crowdsourcing,” retrieved from https://studio.knightlab.com/results/crowdsourcing/crowdsourcing/

Bronwyn Hall, Francesca Lotti, and Jacques Mairesse (2009), “Innovation and Productivity in SMEs: Empirical Evidence for Italy,” Small Business Economics.

Tsutomu Harada, (2019), Economics of an Innovation System: Inside and Outside the Black Box, Routledge Company.

Elena Huergo and Jordi Jaumandreu (2004), “How Does Probability of Innovation Change With Firm Age?,” Small Business Economics.

John P. Kotter (2012), Leading Change, Harvard Business Press.

Konosuke Matsushita (1988), “The Secret is Shared,” Manufacturing Engineering.

Panasonic Corp., Annual Report 2019.

Prosci, Inc. (undated online), Manager/Supervisor’s Role in Change Management.

Rebecca E. Stewart, et al. (2019), “The Clinician Crowdsourcing Challenge: Using Participatory Design to Seed Implementation Strategies.” Implementation Science.

Moonshot

Moonshot is a term associated with a herculean undertaking of epic proportions and/or profound significant.  A moonshot typically requires marshalling vast resources sustained over time to achieve the result.  The 20th of July, 2019 commemorating the 50^th anniversary of mankind setting foot on the moon – and returning safely to Earth – was a moonshot.  Reviewing some of the resources required to accomplish man on the moon helps define a moonshot.

The National Air and Space Administration (NASA) achieved the goal for the United States to reach the moon.  The moonshot began in 1961 and, including the six Apollo space flights following the successful 1969 Apollo 11 mission, was concluded in 1972 as NASA “shifted emphasis from manned space exploration-typified by Apollo-to space activities focused on direct practical down-to-earth benefits…” (Fletcher, 1974 p. 4).  The NASA expense to accomplish the moonshot was about $25 billion, equating to about $146 billion in 2019 (Official Data Foundation).  NASA estimated that the Mercury, Gemini and Apollo U.S. astronaut missions “employed 400,000 Americans and required the support of over 20,000 industrial firms and universities” (NASA, 2008).  The apex Apollo mission comprised the three-stage Saturn V rocket with escape rocket and three spacecraft: Command Module Columbia, Service Module, and two-stage Lunar Module Eagle, with associated life support, propulsion, propellant, flight control, communication, experiments and support equipment operating successfully within the space environment lacking gravity, heat, and atmosphere.  There was no precedence and very little science upon which to base the new moonshot.  For example, creating the on-board compact and lightweight electronic instrumentation for complex and precise guidance, navigation and control instruction processing required handmade fabrication of integrated circuits.  All hardware, software and personnel required the highest standards for testing.  The enormity of the Apollo project and associated investment was highly controversial, which some opposition even labeled as a boondoggle.  In May 1961 the surveyed American indicated that just 33% of respondents believed that the Apollo program of sending a man to the Moon, was a good investment of the estimated cost (Gallup Organization, 1961).

There are many moonshot examples that have been slow to be embraced, though few have reached the level of resource commitment as did the Apollo program.  Economically self-sustaining nuclear fusion reactors propose to yield more energy output than sustaining input.  Quantum computation would rely use quantum-mechanical qubit superposition and entanglement to compute multiple possible concurrent combinations of 1 and 0 states, as compared to the serial process of conventional instruction computing, resulting in significantly increased problem solving and time savings advantages. Artificial intelligence would endow machines with the capability to make autonomous cognitive decisions, surpassing individual human problem solving ability, leading to independent from human forms of action rationalization.  The Allied Operation Overlord involving about 14 months of planning culminating with Operation Neptune on 6 June 1944 on a massive scale which committed about 156,000 Allied troops, 6,939 Allied vessels, and 11,590 Allied aircraft, resulting in about 4,413 dead and over 10,000 casualties (Rank, 2014).

With so many worthy moonshot programs competing for investment resources a qualifying justification prerequisite is the return on investment in terms of number of people benefited, impact on the environment, capital savings, quality and extension of life improvement. Consider curing cancer as one such worthy moonshot.  According to the National Cancer Institute (NCI, 2018), cancer is among the leading causes of death worldwide; in 2012, there were 14.1 million new cases and 8.2 million cancer-related deaths worldwide with the number of new cancer cases per year is expected to rise to 23.6 million by 2030; there will be an estimated 606,880 deaths in 2019.  Many of us have relatives or friends who are suffering with – or who have died from – cancer.  The cost in both human morality and economic outlay is not measurable.  Against this tide of tragedy, the total NCI appropriated funds spent on different cancer sites, cancer types, diseases related to cancer, and research totaled $5.74 billion in 2019.  The proposed federal budget request for fiscal year 2020 totals $4.746 trillion (White House, 2019).  Against phenomenal odds America rose to the challenge (Kennedy, 1962),

We choose to go to the Moon in this decade and do the other things, not because they are easy, but because they are hard; because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one we intend to win, and the others, too.

America’s next moonshot should focus on curing cancer.

References

Fletcher, J. (20 March 1973). 1974 NASA authorization, p. 4. Retrieved from (https://babel.hathitrust.org/cgi/pt?id=mdp.39015084762734;view=1up;seq=8)

Gallup Organization (17-22 May, 1961). Poll question: It has been estimated that it would cost the United States $40 billion -- or an average of about $225 per person -- to send a man to the moon. Would you like to see this amount spent for this purpose, or not? Retrieved from https://ropercenter.cornell.edu/sites/default/files/2018-07/55088.pdf

Kennedy, J. (12 September 12 1962). John F. Kennedy moon speech - Rice Stadium. Retrieved from https://er.jsc.nasa.gov/seh/ricetalk.htm

NASA, (22 April 2008). NASA Langley Research Center’s contributions to the Apollo program. Retrieved from https://www.nasa.gov/centers/langley/news/factsheets/Apollo.html

National Cancer Institute (27 April 2018). Cancer statistics. Retrieved from https://www.cancer.gov/about-cancer/understanding/statistics

Official Data Foundation (2019). U.S. dollar inflation calculator. Retrieved from http://www.in2013dollars.com

Rank, S. (2014). D-Day statistics: Normandy invasion by the numbers. Retrieved from https://www.historyonthenet.com/d-day-statistics

The White House (11 March 2019). A budget for a better America. Retrieved from https://www.whitehouse.gov/wp-content/uploads/2019/03/budget-fy2020.pdf

U.S. House of Representatives (1974). Hearings before the Committee on Science and Astronautics, Ninety-Third Congress, First Session on H.R. 4567 (superseded by H.R. 7528) (https://www.congress.gov/bill/93rd-congress/house-bill/4567?q=%7B%22search%22%3A%5B%2293rd+Congress+1973+4567%22%5D%7D&s=8&r=2)

Multidimensional Opportunity Analysis

Introduction

Identifying, evaluating and communicating opportunities should be a best practice for every business investment analysis.  Fundamentally, business investment decisions are initiated for two reasons – to increase the potential for gain or to reduce the possibility of loss.  Investments result in a range between two possible outcomes, incurring either a benefit or a loss; the break-even point, no net loss or gain, may be considered a positive result for preservation of resources.  Investment decisions, even when proactively seeking an improvement to add value, typically focus towards mitigating potential negative outcomes and, deliberately or not, apply a risk mitigation approach intended to reduce the possibility of loss.  The Project Management Institute (2017) defines project risk as, “an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives” (p. 307).  The positive effect may be classified as opportunity which could support value maximization – the return outweighs the cost (Dixit & Pindyck, 1995).

The Department of Defense (2017) states, “Opportunities are potential future benefits to the program’s cost, schedule, and/or performance baseline, usually achieved through proactive steps that include allocation of resources” (p. 43).  Kendrick (2015) also stated, “The primary meaning of opportunity related to a project involves the value anticipated from the project deliverable”.  In addition to project opportunity, Frederick, Novemsky, Wang, Dhar and Nowlis (2009) defined opportunity costs as, “The unrealized flow of utility from the alternatives a choice displaces” (p. 553).  Examining forgone potential opportunity Baratta (2007) stated, “The basic premise behind this metric is that as soon as we are aware that there is an opportunity to realize a defined business benefit by making some change in our business, then every moment that passes without that change being in effect, represents a lost benefit, a missed opportunity.  Therefore, we need to measure lost opportunity just as much as adherence to an estimate, which may actually be wrong.”

Though risk identification and associated risk management plans are often mandated in order to minimize loss, an opportunity model – to include opportunity costs and passed up potential opportunities – is typically not included in decision-making apart from possibly a lean component of SWOT analysis.  Formalizing the opportunity model would not only better inform investment decisions, but may also improve economic efficiency if selected opportunities result in resources more productively employed.  For example, the opportunity to reduce redundancies among business units might yield greater value than the potential return on alternative investment decisions taken by the individual business units.  An opportunity model should illuminate such strategic possibilities for decision maker consideration.  Strategic business decisions to further organizational objectives must go beyond narrowly considering the downside based on the traditional risk analysis paradigm.  Risk is characteristically evaluated as the relationship of the scales for two variables – probability and impact, represented as conventions (x) and (y) respectively.  Applying a similar approach for opportunity analysis, with the inclusion of a third variable scale (z) for investment resources, would enable more detailed decomposition into multidimensional components to identify potential options or reveal hidden value.

Traditional Approach

Investment decisions focusing on risk tolerance often base decisions on simplistic single-point green (low), yellow (medium) and red (high) visual indicators which are typically represented in the form of a five-cell-by-five-cell color-coded table derived from probability and impact scales.  Each of the 25 cells in the risk matrix heat map provides the measure of risk derived from associated qualitative translations of likelihood from “not likely” to “near certainty” and consequence ranging from “minimal impact” to “critical impact.”  As a business investment decision support tool, the color-coded representation is rudimentary for presenting opportunity and ineffective for articulating quantified risk.  Industry employs more complex models, often accompanied with statistical analysis; for example, weather forecasting calculates the likelihood for a significant number of variables to estimate impacts such as precipitation, temperature, and cloud cover (National Weather Service, n.d.).  Variables having linear relationships also allow for linear optimization programming models to minimize total risk while meeting goals (Lawrence & Pasternack, 2002).

Depending upon the type of investment – i.e., real estate, financial instruments, capital equipment, etc. – compound opportunity or risk statements should be normalized into single actionable events or conditions, and complex scenarios may be further decomposed into interrelated components.  Deconstructing opportunity and risk into constituent elements down to root cause(s) can be time consuming and may not be practical for every situation.  Subject matter experts (SME) are a good source for identifying and assigning accurate values to the opportunity decision variables, and recommending key variables to focus on.  The fundamental risk variables of probability and impact are important two-dimensional approach starting points, yet remain exiguous without correlation to investment constraints for informed allocation of resources.

Improving cost, schedule and/or performance is often equated to offsetting or decreasing negative impacts.  Risk mitigation is a reactive plan against potential loss or under performance while an opportunity strategy is proactive for possible gain or over performance in which – for the purpose of this review – the opportunity model axes boundaries separate mutually exclusive positive and negative return space.  Fundamentally, risk is based upon the likelihood of an event occurrence and the resulting impact of something bad.  Worst case risk outcomes occur in negative space, though scenarios could be evaluated that while remaining in positive space the results attain less than optimal goal outcomes.  In such situations, risk and opportunity overlap in the positive space.  For example, a risk might be stated as, “The probability that 1^st fiscal quarter earnings will not reach the 5 percent estimate.”  Earnings of less than zero would be negative (loss), while greater than zero but less than 5 percent – though below the target – would still be positive (gain).  Net positive values could be examined for opportunity.

Risk and opportunity are inverse – risk worsens and opportunity improves as probability and impact increase.  Euclidean three-dimensional cube space (x, y, z axes) affords an infinite number (n) of two dimensional plane values; incorporating time (t), which will not be presented here, could also provide a measure of change order.  While brainstorming often hypothesizes possibilities based upon unconstrained resources, the practical application of available investment resources for risk mitigation and opportunity exploitation, while remaining defined as the area within the three-dimensional space, limits the collection of useful points to a smaller range of numerical values narrowed by the resource scale.  Uncertainty (x), which exists for both risk and opportunity, is the probability expressed as a positive value between 0.0 (cannot occur) and 1.0 (certain occurrence); as risk is a tentative future event, 0 and 1 denote definitive results and are not included.  Impact (y) may have a negative (risk) or positive (opportunity) effect, calculated as an interval numerical value (which may be derived from an ordinal scale such as low, medium and high).  Available resources (z) further bounds opportunity within the positive impact space.

Opportunity Defined

Opportunity (O) represents the value placed upon a potential benefit, such as typical project control metrics that deliver net savings derived from improved cost or schedule results.  More difficult to specify are intangible benefits derived from faster decision-to-answer time or improved organizational performance in essential planning, organizing, directing and controlling operations.  However, reducing investment uncertainty for intangible benefits is possible as indicated by Hubbard (2014) and applied to an investment risk simulation example (Frum, 2017).  SME knowledge, supplemented with historical and industry statistics, may be a reliable source to both articulate the benefit variables such as cost/schedule/performance and also to accurately define the probability and impact variable numerical values.

Lowercase theta ᶿ from the Greek alphabet can represent the opportunity model, supported by the mathematical proof for the existence of an opportunity when defining the opportunity statement.  Calculating the quantitative value for an opportunity ᶿ – the opportunity score – permits plotting the x, y and z coordinates.  Though missed opportunity may also be quantitatively determined, as no actual loss to the balance sheet occurred, only the value of potential opportunity will be studied.  Achieving the potential opportunity may require either explicit (actual) cost, or implicit (tradeoff) equivalent cost had the resources been applied to alternate benefit options if available; implicit value would equate to (opportunity cost = selected option cost – not selected next best alternate option cost).

Equivalent opportunity cost examination in which diverse opportunities are compared based upon some common value, at least at an exploratory order of magnitude, is necessary to more definitely determine that the selected opportunity is valued above any alternate opportunities.  For example, an investor examining certificates of deposit at various financial institutions may consider not only the interest rates but also 24/7 account access, customer support, length of deposit requirements, physical versus on-line only commerce, etc. – all factors evaluated against a common and consistent quantitative scoring method – in choosing one institution over another.  The opportunity model ᶿ assumes to be true that for each type of opportunity, whether comprised of one or more constituent criteria, there is only a mutually exclusive single best choice possible to maximize return value, the selection cannot be divided such as a linear programming feasibility solution for constrained optimization or as an investor choosing to place a combination of funds in two or more CDs.

Opportunity Model

After a potential opportunity has been identified, the opportunity must be correctly stated – concisely and accurately defined – to proceed to numerical evaluation.  Simply stating that the organization wants to increase production while becoming more efficient is a general goal too ambiguous for measurement.  The opportunity must provide a specific, well defined and assessable value.  Adding specificity such as increasing production by 10 percent while becoming 5 percent more efficient within 12 months is a quantified outcome objective.  If the organization pursues an opportunity, then accurate, relevant, practical and computable performance measures must be applied to quantified resources for input, process, output and outcome to evaluate expected results.  The organization’s strategy is an appropriate starting point in which mission priorities, objectives and initiatives – all predetermined to be important to the organization – can inform opportunity construction, leading to selecting the most promising from possible alternatives.

Should investment in some combination of each opportunity be possible, then a linear programming maximizing/minimizing function might be applied to derive the optimal mix of resources.  As stated by Martinich (1997), “Constrained optimization models are mathematical models that find the best solution with respect to some evaluation criterion from a set of alternative solutions” (p. B2).  Comparing opportunities may be further challenging when the units of measure (M) are not equivalent.  For example, an investment that leads to improving senior leader decision time may need to be compared against employee training that improves processing time; for the former, direct access to data may reduce decision time from days to hours by visualization of metrics, while the latter might improve customer support by faster turn-around time for product purchase requests.  The organization would benefit from each alternative but resource constraints allow for just one investment selection.

Decision Variables.  Opportunity options – primary and alternate, if applicable – may be dissimilar, making comparison and choice decisions more challenging.  To calculate opportunity cost among dissimilar options, the quantity of resources required for each possible selection becomes the basis for comparing alternative choices.  The options must first be reduced to their equivalent per unit basis, such as the miles per gallon gasoline equivalent (MPGe) used by the Environmental Protection Agency to compare electric to gas vehicle fuel economy and average distance traveled per unit of energy consumed.  Reduce the opportunity expression to equivalent terms.  For example, when selecting the best value (most cost effective purchase) for protein among beef, fish or chicken, the protein per 100 grams of each does not provide all the necessary information for a decision.  Setting aside all other factors, consider on average that per 100 grams, beef contains approximately 36 grams of protein, fish provides 26 grams, and chicken has 18 grams – the logical component among the variables is protein.  Then determining the equivalency based upon cost per gram of protein enables improved opportunity selection among multiple candidates, indicating beef would be the best value; such as:

Opportunity equivalency (O_e), where ó is defined as logically equivalent.

Protein equivalency A_n ó Opportunity B_n ó Opportunity C_n

·         per 100gm: meat_36g ó fish_26g ó chicken_18g

O_e, setting the national average prices per 454 grams (1 pound):

·         beef = $2.49 = $0.55 per 100gm with 36 gm protein = $0.015 per gm protein

·         fish = $3.99 = $0.88 per 100gm with 26 gm protein = $0.034 per gm protein

·         chicken = $3.18 = $0.70 per 100gm with 18 gm protein = $0.039 per gm protein

Opportunity cost (O_c) = (selected option cost) – (not selected next best alternate option cost).

·         O_c = | (beef at $0.015 per gm protein) – (fish at $0.034 per gm protein) |

·         O_c = $0.019 per gm protein; selected O = beef

Objective Function.  

·         Opportunity cost O_c = |O_c – !O_c|, where O := !O and the quantity n of unit of measurement M = n x [M] = n[M]

·         O ó !O, opportunity alternatives that may or may not be homogeneous but allow for like or equivalent comparison and analysis based upon a common equivalent unit of measurement

·         unit of measurement M, allows for the multiple n of cost M for the same base units

Constraints.  Examples of limits or restrictions include: How much can be afforded to invest; How much can be afforded to lose to attain the desired effect; Is the opportunity time-bound; How many units of each type can be processed per person per unit of time; Are there legal specifications?  Opportunity constraints for a business with billion dollar earnings are likely not the same as a small owner-operator business.  Constraints must be well documented to determine if opportunity equivalency comparisons are appropriate.  Opportunity identification should also establish threshold cost-benefit performance measures such as those taken from strategic objective targets, existing performance and practice measures, resource or capacity constraints, legal or policy standards, analysis of similar organizations, industry statistics or best practice benchmarks.  The threshold should define that point at which if the organization can do better, then how much better per unit invested?  The costs avoided or dollars gained by a program must be defined.

Monte Carlo simulation is an excellent quantitative method for determining the likelihood of a potential opportunity over a range of values.  The subject-matter expert (SME) plays an essential role in determining opportunity, uncertainty, impact and constraints within their areas.  Figure 1 illustrates a hypothetical business opportunity simulation, indicating that for 10,000 simulations there is a 90 percent likelihood that the annual ROI will exceed about $46 million and a 10 percent probability that the annual ROI will exceed about $50 million, with a median (50 percent likelihood) expected annual ROI of about $48 million.

Opportunity Triplet

Representing the opportunity theta ᶿ values as ordered triplets (x, y, z) consisting of a scenario that characterizes the business objective and numerates the variable quantities will permit mathematically solving the ordered triple in ways meaningful to real world applications.  For example, the organization might determine the risk associated with a phishing and social engineering attack as the average impact cost of $1.6 million (M) and the likelihood of a targeted user clicking on the malicious attachment or link at 0.02736, with the risk value = ($1.6M x 0.02736) = $43,776.  Examining potential opportunities to reduce or offset the attack risk may yield increased cost savings (spend less than $43,776 for training) and training benefits (more flexible training scheduling) for performing security awareness and training in-house rather than outsourcing; for example, where,

x = possibility expressed as a probability; based upon best empirical data.

y = impact requires an accurate, meaningful and quantitative measurement in order to answer the business impact question: How significant would the benefit or value of the opportunity be to the organization?  A basic impact scale similar to the risk likelihood criteria described by DoD (2017) in Figure 2 would assess opportunity based upon Likert scales ranging from ‘minimal impact’ to ‘critical impact’.  Likert scales are ordinal, meaning the importance can be ranked but not accurately interpreted mathematically and would require corresponding numerical (interval) levels ranging from 1 (minimal) to 5 (critical).  The organization should develop standard impact rating criteria for evaluation consistency.  The SMEs as domain knowledge authorities provide insight in assessing impact as they are typically more knowledgeable than others regarding consequences within their area.

z = investment resources in which the z axis represents the positive range of potential opportunity values based on assets available such as funds, personnel and time to pursue objectives.  As previously expressed, a common unit of measurement is required to determine opportunity value (opportunity cost = selected option cost – not selected next best alternative option cost).

The (x, y, z) ordered triplet of numbers in this model represents point P on the axes within the positive coordinate space (+,+,+) corresponding to the top-right-front first octant.  In Cartesian geometry with three mutually perpendicular axes, the area of positive z coordinates with equal probability would take the shape of a cube as depicted in Figure 3.  After establishing the ordered triplet variable values, determining the opportunity value is a relatively straightforward point computation as shown in Table 1; which shows that Opportunity 1 would require a higher investment but could yield a higher result with O_c = ($22,500 – $12,000) = $10,500.  Varying the value of z represents a range of opportunity values along the z coordinate axis.  As with the example illustrated in Figure 1, more detailed analysis and simulation could be performed to arrive at population mean, standard deviation, confidence intervals and similar evaluations.

  

Table 1
Comparing Two Different Opportunities with Cost as Unit of Measure
Variable	Opportunity 1	Opportunity 2
x (likelihood)	0.75	0.60
y (impact)	3	4
z (investment)	$10,000	$5,000
ᶿ = (x) x (y) x (z)	$22,500	$12,000
Note. Opportunity Cost Oc = (O1 – O2) = ($22,500 - $12,000) = $10,500

  

Opportunity Plane

Another consideration of positive opportunity space could be the property of opportunity aggregate group (g) in which each z coordinate, should more than one exist, would be elaborated to include differentiating detail(s) for further examination and exploitation.  The (x, y, z) ordered triple numbering convention would share the same z value with either or both x and y values, varying to produce differentiating characteristics of the specific individual opportunity.  Each initial triplet such as (x8, y8, z8) and additional triplet such as (x1, y3, z8) extending from the initial triplet will determine a straight line, and all differentiating characteristic triplets in which z remains constant (i.e., z = 8) will lie in the same unique (x, y) plane surface as spatially illustrated in Figure 4.  The range of an individual opportunity option would be the difference between the worth assigned to the highest and lowest triplet values for which z = constant as listed in Table 2.  For example, a gambler with a single $100 chip to wager must decide whether a win with one roll of the dice (O₁), one card draw (O₂) or one slot machine spin (O₃) would yield the best desired outcome.

  

Table 2
Comparing Three Opportunities in One Group with Same z Value
Variable	Opportunity 1	Opportunity 2	Opportunity 3
x (likelihood)	0.75	0.60	0.40
y (impact)	3	4	5
Z (investment)	$100	$100	$100
ᶿ = (x) x (y) x (z)	$225	$240 (highest)	$200 (lowest)
Note. Opportunity Range = (O2 – O3) = ($240 - $200) = $40

  

Conclusions and Recommendations

Commercial enterprises in competitive markets continually seek opportunities to grow their businesses; both opportunity recognition and opportunity exploitation are positively associated with innovations (Kuckertz, Kollmann, Krell, & Stöckmann, 2017).  Opportunity is incorporated into the capital budgeting decision based upon economic analysis of investment projects such as cost-benefit analysis (Bierman & Smidt, 2012, p. 8).  The absence of market forces generally deprives the motivation to pursue opportunity and innovation which is often accompanied by change and disruption.  Opportunity as described by DoD (2017a) covers 5 pages including a single opportunity management vignette, whereas risk management covers 23 pages.  DoD acquisition program guidance states risk 212 times and opportunity 4 times (DoD, 2017,b).  The difference allotted to the two topics reveals the prominence placed on mitigating the downside and lack of emphasis on exploring and quantifiably articulating the upside, effectively incentivizing risk aversion, apart from offices specifically focused on innovation.

The basic high-level risk model typically depicts a 5x5 cell matrix in which the combination of likelihood and impact determine a color coded risk level for low (green), moderate (yellow), and high (red).  As a business investment decision support tool, the color-coded representation is ineffective for articulating quantified risk probability distributions for a range of possible outcomes for any meaningful choice of action.  Beyond signaling candidate areas for in-depth analysis, the same approach is also an insufficient opportunity framework, particularly to facilitate goal achievement.  Opportunity estimation must become more than a cursory mirror approach to risk management, communicated as abstract qualitative concepts represented in terms of three colors.

Multidimensional opportunity analysis offers an improved model to quantitatively explore initiatives that may yield potential improvements in cost, schedule reductions, and/or performance.  Measurable opportunity evaluation should become an integral part of Defense acquisition program management and systems engineering, elaborated in a range of policy, regulatory, and statutory directives.

To begin improving cost, schedule, and performance opportunity benefit analysis I recommend the following:

(1) Incorporate opportunity equivalency and opportunity cost in Department of Defense guides that address opportunity particularly in the Acquisition field;

(2) Require the opportunity theta model based upon ordered triplets in which x represents possibility expressed as a probability, y represents impact requiring an accurate, meaningful and quantitative measurement, and z represents investment resources for all potential investment decisions exceeding $1 million. 

Published in Defense Acquisition: July-August 2019 (http://www.dau.mil/library/defense-atl/DATLFiles/Jul_Aug2019/Frum.pdf)

References

Baratta, A. (2007). The value triple constraint: measuring the effectiveness of the project management paradigm. Paper presented at PMI® Global Congress 2007—North America, Atlanta, GA. Newtown Square, PA: Project Management Institute.

Bierman Jr, H., & Smidt, S. (2012). The capital budgeting decision: economic analysis of investment projects. Routledge.

Department of Defense (DoD). (2017a). Risk, issue, and opportunity management guide for defense acquisition programs. Retrieved from https://www.acq.osd.mil/se/docs/2017-rio.pdf

DoD. (2017b). Operation of the Defense acquisition system. Retrieved from http://acqnotes.com/wp-content/uploads/2014/09/DoD-Instruction-5000.02-The-Defense-Acquisition-System-10-Aug-17-Change-3.pdf

Dixit, A. K., & Pindyck, R. S. (1995). The options approach to capital investment. Real Options and Investment under Uncertainty-classical Readings and Recent Contributions. MIT Press, Cambridge, 6.

Frum, R. (2017, November-December). How to improve communication of information technology investments risks.  Defense AT&L, 44-48.

Frederick, S., Novemsky, N., Wang, J., Dhar, R., & Nowlis, S. (2009). Opportunity cost neglect. Journal of Consumer Research, 36(4), 553-561.

Hubbard, D. W. (2014). How to measure anything: Finding the value of intangibles in business. John Wiley & Sons.

Kendrick, T. (2015). Project opportunity: risk sink or risk source? Paper presented at PMI® Global Congress 2015—North America, Orlando, FL. Newtown Square, PA: Project Management Institute.

Kuckertz, A., Kollmann, T., Krell, P., & Stöckmann, C. (2017). Understanding, differentiating, and measuring opportunity recognition and opportunity exploitation. International Journal of Entrepreneurial Behavior & Research, 23(1), 78-97.

Lawrence, J. A., & Pasternack, B. A. (2002). Applied management science: modeling, spreadsheet analysis, and communication for decision making. New York: Wiley.

Martinich, Joseph S. 1997. Production and operations management: An applied modern approach. New York, New York: John Wiley & Sons, Incorporated

National Weather Service. (n.d.). Welcome to the statistical modeling branch. Retrieved from https://www.weather.gov/mdl/StatisticalModeling_home

Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK® Guide) - sixth edition. Retrieved from https://www.pmi.org/pmbok-guide-standards/foundational/pmbok#