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 1st 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 (Oe),
where ó is defined as
logically equivalent.
Protein
equivalency An ó Opportunity Bn ó Opportunity Cn
·
per
100gm: meat36g ó fish26g ó chicken18g
Oe, 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 (Oc) = (selected
option cost) – (not selected next best alternate option cost).
·
Oc = | (beef at $0.015 per gm protein)
– (fish at $0.034 per gm protein) |
·
Oc = $0.019 per gm protein; selected O
= beef
Objective
Function.
·
Opportunity cost Oc = |Oc
– !Oc|, 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 Oc = ($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.
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