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4.5. Value vs. Complexity Quadrant
4.5. Value vs. Complexity Quadrant
The quadrant is a prioritization tool based on a matrix. In a twoby-two grid, “Value” and “Complexity” are plotted against each other. For this architecture to function, each feature, update, correction,
or other product initiative must be quantified.
The value of a feature is determined by how much it benefits
your customers and your business. In other words, will the new
feature truly benefit consumers in the long run by alleviating their
everyday challenges and allowing them to complete their goals? The
impact of the feature on your company’s bottom line should also
be evaluated.
Complexity (or Effort) is what it takes for your organization to
deliver this feature. It’s not enough that we create a feature that our
customers love. The feature or product must also work for our business. Can you afford the cost of building and provisioning the feature? Operational costs, development time, skills, training, technology, and infrastructure costs are just some of the categories that you
must think about when estimating complexity.
Value
Complexity
Priority= .
The quadrants created by this matrix are (see Fig. 22):
1. Quick Wins (upper-left). Due to their high value and low
complexity, these features are the low-hanging-fruit opportunities in our business that we must execute with top priority.
2. Major Projects, Big Bets, or Potential Features (upper-right).
The initiatives that fall into this block are the big project releases that we know are valuable but are too risky to take on
because of the resources and costs involved with them.
3. Fill-Ins or Maybes (lower-left). In this quadrant are usually
positioned the “nice to have” features. Things like small improvements to the interface and one day, maybe ideas.
4. Time Sink Features (lower-right). Time sinks are the initiatives that we never want our team to be working on.
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4. Assessing innovative digital products
Fig. 22. Value vs. Complexity Matrix
21
The Value vs. Complexity Quadrant is a fantastic tool for product development teams. This framework is useful if you need to make
objective conclusions quickly due to its simplicity. In addition, if your
team is short on resources, the Value vs. Complexity Quadrant can
help you uncover low-hanging- fruit prospects. The Value vs. Complexity diagram has the disadvantage of being rather crowded if you’re
working on a highly developed product with a big list of features.
4.6. MoSCoW Prioritization Model
MoSCoW prioritization, also known as the MoSCoW approach or
MoSCoW analysis, is a prominent requirement management prioritization strategy. MoSCoW stands for four types of initiatives: must-have,
should-have, could-have, and won’t-have, or won’t have right now
(see Fig. 23). Some businesses interpret the “W” in MoSCoW to represent “wish”. While working at Oracle, software development expert
Dai Clegg developed the MoSCoW approach. He created the framework to assist his team in prioritizing tasks during product development.
Mu -have
Features or stories are critical for the product’s success. These
features represent the non-negotiables which, if not implemented
successfully, might put the product at risk of failing. For example,
21
Value vs. Complexity // ProductPlan : site. URL: https://www.productplan.
com/glossary/value-vs-complexity/ (date of access: 02.06.2023).
62
4.6. MoSCoW Prioritization Model
let’s say you are the PM of a university’s e-learning system. A musthave feature might be the assignment submission feature because it
serves a primary and essential need for both ideal customer profi les.
Fig. 23. MoSCoW model
22
Should-have
The next level down from “must-haves” are “should-have” projects. Although they are not important, they are necessary for the
product, project, or release. The project or product still works if left
out. The projects could, however, bring a lot of benefi t. In contrast
to “must-have” eff orts, “should-have” initiatives can be scheduled
for a future release without aff ecting the present one. Examples of
“should-have” projects include performance upgrades, minor bug
patches, or new features. The item still functions without them.
Could-have
Nice-to-have initiatives are another term for “could-have” projects. “Could-have” initiatives are not required for the product’s fundamental function. However, when compared to “should-have” initiatives, they have a far smaller impact on the outcome if they are
not implemented. As a result, projects in the “could-have” category
are frequently the fi rst to be deprioritized if a project in the “shouldhave” or “must-have” category proves to be larger than anticipated.
22
MoSCoW prioritization // ProductPlan : site. URL: https://www.product-
plan.com/glossary/moscow- prioritization/ (date of access: 03.06.2023).
63
4. Assessing innovative digital products
PD
Won’t-have
A requirement that will not be implemented in a current release
but may be included in a future stage of development. Such requirements usually do not affect the project success.
4.7. Decision Trees — Expected Commerce Value
A decision tree is a method you can use to help make good choices, especially decisions that involve high costs and risks. Decision
trees use a graphic approach to compare competing alternatives and
assign values to those alternatives by combining uncertainties, costs,
and payoffs into specific numerical values. By modeling the various
expected outcomes and their probabilities, businesses can then select
the decision that produces a favorable outcome.
After a model is constructed, it is important to find the expected commerce value (ECV) to evaluate which decision results in the
most favorable outcome. Considering that the decision trees provide
all the possible outcomes in comparison to the alternatives, by calculating the expected commerce value, we can observe the average outcomes of all decisions and then make an informed decision. To calculate the expected commerce value, we require the probability of each
outcome and the resulting value. The formula for the expected commerce value is as follows:
ECVPVI PC
CS TS
,
where PTS is the probability of technology success, PCS is the probability of communication success, D is the development costs remaining, C is the commercialization/launch costs, PVI is the present value of future earnings.
The Expected Commercial Value (ECV) formula evaluates the
potential profitability of a project or investment by considering both
the probability of success and failure, along with their respective payoffs. A positive ECV indicates that the project is expected to be profitable and worth pursuing. A negative ECV suggests that the project
64
4.8. Multi-Criteria Decision Making
is likely to result in a loss, and it may require re-evaluation or reconsideration. In spite of ECV’s benefits, its weakness lies in its heavy
reliance on financial and other quantitative data, along with potential errors in probability estimates. Thus, leading many experts to argue that estimating the probability of success can be highly subjective.
4.8. Multi-Criteria Decision Making
Multi-Criteria Decision Making (MCDM) or Multi-Criteria Decision Analysis (MCDA) is one of the most precise decision-making
techniques. Benjamin Franklin conducted one of the first research
papers on multi-criteria decision-making when he published his research on the moral algebra concept. Since the 1950s, several empirical and theoretical scientists have worked on MCDM approaches to
investigate their mathematical modeling capability to give a framework that can aid to organize decision-making problems and produce
preferences from alternatives. MCDM encompasses a variety of methods that differ in many ways (Fig. 24). This strategy considers many
qualitative and quantitative criteria that must be met to get the optimum answer. For example, cost or price and quality of the processes
are among the most common criteria in many decision-making problems. In addition, in these problems, expert groups provide different
weights to the criteria that are based on the importance of each criterion in that specific case. Several types of MCDM approaches have
been created or modified by various writers over the last several decades. The key distinctions between these methods are connected to
the complexity level of algorithms, the weighting methods for criteria, the style of representing preferences evaluation criteria, the potential of ambiguous data, and finally, the type of data aggregation.
Furthermore, each type of MCDM has distinct advantages and
disadvantages that must be explained specifically based on the approaches. For example, the Analytic Hierarchy Process (AHP) is simple to apply but has limitations due to the interdependence of criteria and options. Nonetheless, in Fuzzy Set Theory (FST), imprecise
input is conceivable; nonetheless, this method is difficult to create.
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4. Assessing innovative digital products
In general, all MCDM techniques have the advantage of considering the disproportionate and inconsistent eff ects of actions. On the
negative side, the solutions that are generated by these methods are
a compromise among several goals and this leads to not obtaining the
optimal point due to the nature of the problem.
Compensatory
Multi Objective
Decision Making
Non-
compensatory
Individual
Decision Making
Group
Decision Making
Multi Attribute
Decision Making
Non-tradeo-
based
Tradeo-based
Qualitative
Quantitative
Certain
Uncertain
Fig. 24. Diff erent classifi cation of MCDM
23
Taherdoost H., Madanchian M. Multi- Criteria Decision Making (MCDM)
MCDM
Types
Function-free
Models
Discriminant
Function
Outranking
Relations
Utility
Functions
23
Methods and Concepts // Encyclopedia. 2023. Vol. 3.
66
4.9. Data Envelopment Analysis
4.9. Data Envelopment Analysis
The Data Envelope Analysis method was proposed in 1978 by
American scientists A.Charnes, W.W. Cooper, E. Rhodes24, who were
based on the ideas of M.J.Farrell25. This method is successfully used
in the West to assess the effectiveness of the functioning of homogeneous objects in various socio-economic systems. Such objects can
be industrial and agricultural enterprises, banks, healthcare and education institutions, government, and justice bodies, etc. The DEA
method is constantly evolving and improving.
The DEA methodology uses the term “operational efficiency”. This
term reflects the efficiency with which the objects under study transform inputs into outputs. Depending on the scope of application of the
DEA method, this term may be given one or another specific meaning.
DEA is frequently used exclusively to determine the technological
efficiency of government services. A basic numerical example of the
DEA approach to determining technical efficiency is a sample of five
hospitals that employ two inputs: nurses and beds, to create one output: treated patients. The inputs and outputs of a real hospital are obviously far more complex, but this simplification may be a suitable
starting point for both actual and illustrative instances — for example, the input ‘beds’ may serve as a proxy for the quantity of capital
inputs utilized by the hospital. Because the hospitals are likely to be
of varying sizes, input levels must be adjusted to those required by
each hospital to create one treated case. The input and output data
are presented in the table below.
The five hospitals range in size from 200 to 1200 beds, and the
number of nurses, beds, treated patients, and nurses per treated case
and beds per treated case varies accordingly. Given the vast differences in the characteristics of the five hospitals, it is unclear how to
compare them or, if one is deemed to be less efficient, which other
hospital it should employ as a role model to enhance its operations.
24
Charnes A., CooperW. W., Rhodes E. Measuring the Efficiency of Decision-
Making Units // European Journal of Operational Research. 1978. Vol. 2. P. 429–444.
25
Farrell M. J.The Measurement of Productive Efficiency // Journal of The
Royal Statistical Society. 1957. Vol. 120. P. 253–281. (Series A (General), Part III).
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4. Assessing innovative digital products
When the statistics for nurses per treated case and beds per treated
case are presented in Fig. 25, where data are abstracted from diff erences in size, the answers to these concerns become evident.
Input and Output Data
Hospital Nurses Beds
Treated
cases
1 200 600 200 1 3
2 600 1200 300 2 4
3 200 200 100 2 2
4 600 300 200 3 1.5
5 500 200 100 5 2
Nurses per
treated case
Beds per
treated case
Fig. 25. Illustrative hospital input- output data
26
Which hospitals in the sample are the most effi cient or have the
best practices? Because hospitals 1, 3, and 4 are on the effi cient frontier, they are believed to be functioning at peak effi ciency. However,
26
Data Envelopment Analysis: A technique for measuring the effi ciency of government service delivery / Steering Committee for the Review of Commonwealth/
State Service Provision. Canberra: AGPS, 1997.
68
4.9. Data Envelopment Analysis
because hospitals 2 and 5 are located to the north-east of the border,
they are seen as less efficient. This is because they appear to be able to
lower their input utilization while maintaining their output level when
compared to best practice hospitals. For example, before reaching the
efficient frontier at point 2’, hospital 2 might lower its consumption
of both inputs by one-third. Similarly, its technical efficiency score
is represented by the ratio 02’/02, which in this case is equal to 67 %.
This is because the ‘hypothetical’ hospital 2’ has a nurse per treated
case value of 1.33 and a bed per treated case value of 2.67. In terms of
actual input levels, hospital 2 would have to reduce its nurse staff from
600 to 400 and its bed capacity from 1200 to 800. At the same time, it
would have to maintain its output of 300 treated cases before it would
match the performance of the hypothetical best practice hospital 2’.
But how is the hypothetical best practice hospital 2’ determined?
It is created by reducing the inputs of hospital 2 in equal quantities until the best practice frontier is reached. In this example, the frontier is
achieved between hospitals 1 and 3, hence the hypothetical hospital 2’
is a combination, or weighted average, of hospital 1 and 3 operations.
If hospital 2 is seeking for other hospitals to employ as role models
to increase performance, it should look at the operations of hospitals 1 and 3 because they are the most efficient hospitals. These role
models are referred to as the organization’s ‘peers’27 in DEA research.
The scenario at the other, less efficient hospital, hospital 5, is
different. It is north-east of the efficient frontier, but if its inputs are
contracted in equal proportions, it leads to the hypothetical hospital 5’, which is still to the right of hospital 4 on the piece of the frontier that was stretched parallel to the nurses per treated case axis.
Thus, hospital 4 is the only one in the peer group for hospital 5 since
it is the only one that supports the area of the border where the hypothetical 5’ is located. However, hospital 5’ is inefficient since the
number of nurses per treated case can be reduced while the number
of beds per treated case remains constant, resulting in a drop from
5’ to 4. That is, to maximize its efficiency given the data available,
27
The term ‘peers’ in DEA has a slightly different meaning to the common use
of the word peer. It refers to the group of best practice organizations with which arelatively less efficient organization is compared.
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4. Assessing innovative digital products
hospital 5 must minimize one input more than the other. In this sce
nario, a radial contraction of inputs indicates that the frontier has
been reached, although additional reduction of one of the inputs is
possible without a fall in output. This excess input reduction available is referred to as input ‘slack’ in DEA studies. Thus, it is important in DEA28 studies to check for the presence of slacks as well as the
size of the efficiency score.
Queions for Chapter 4
1. What is SWOT analysis and why is it important?
2. How does SWOT analysis help in strategic planning?
3. What are the key elements of a SWOT analysis?
4. In what scenarios is PESTEL analysis most effective for assessing innovative digital products?
5. How can the MoSCoW method be used to improve decisionmaking in product development?
6. Can you provide examples of real-world applications
of PESTEL analysis in the assessment of digital products?
7. Are there any emerging trends or best practices in the assessment of innovative digital products?
8. Can you share any case studies or success stories where these
methodologies have led to significant product improvements?
9. How does the HIIT Matrix contribute to the assessment of
digital products?
10. What are the advantages of using the HIIT Matrix over other assessment methodologies?
11. What steps should be taken after the assessment process using these methodologies to ensure successful implementation
and adoption of recommendations?
-
28
Data Envelopment Analysis: A technique for measuring the efficiency of government service delivery / Steering Committee for the Review of Commonwealth/
State Service Provision. Canberra: AGPS, 1997.
70
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