Strategise method guide
Opportunity vs. Feasibility (OxF) Analysis
Opportunity vs. Feasibility (OxF) Analysis compares both dimensions while showing the plausible scoring range for each project.
Beginner-friendly guide · 5 min read
What Opportunity vs. Feasibility (OxF) Analysis does
Opportunity vs. Feasibility (OxF) Analysis compares both dimensions while showing the plausible scoring range for each project.
Opportunity-Feasibility analysis helps a team evaluate early-stage technology and innovation projects when the available information is incomplete. The method keeps two composite scores separate: Opportunity estimates the magnitude of the opportunity plausibly available to the organisation, while Feasibility reflects the organisation's competence to address it.
Mitchell, Phaal and Athanassopoulou recommend making uncertainty explicit. Instead of forcing one exact score, participants estimate minimum and maximum plausible values. Each project is then shown by two connected points: its minimum Opportunity and Feasibility scores, and its maximum Opportunity and Feasibility scores. The position shows potential; the length and direction of the line show how much uncertainty remains and where it sits.
The primary source is the 2018 University of Cambridge working paper by Mitchell, Phaal and Athanassopoulou. It describes a customised multi-factor scoring method, keeps Opportunity and Feasibility separate, and represents uncertainty by connecting each project's minimum-minimum and maximum-maximum scores on an Opportunity-Feasibility chart.[1][2][3]
From analysis to a living plan
How this connects to roadmapping
The Cambridge working paper notes that scoring and project selection often work with roadmapping. A roadmap can identify opportunities, provide strategic context and elaborate project options before they are evaluated.[1][2]
After evaluation, selected projects, evidence gaps and learning actions can be placed back into the roadmap. This keeps the portfolio decision connected to timing, dependencies, resources and the assumptions that still need to be resolved.[1][2]
Each project connects its minimum-minimum and maximum-maximum scores. Position shows potential, while line length and direction show the amount and location of uncertainty.
Opportunity: low to high
Higher FeasibilityLower Opportunity
Lower Feasibility
Feasibility: low to high
A longer connected range means more uncertainty. Its direction shows whether Opportunity, Feasibility or both need attention.
InnovationFlow explanatory schematic, synthesised from the method sources[1][2][3].
Understand the method
The parts in plain language
Opportunity
Build a composite view of the opportunity plausibly available to this organisation. Select factors appropriate to the decision, such as strategic fit, market attractiveness, potential reward or wider benefit, then define clear scaling statements rather than relying on vague labels.[1][2][3]
Illustrative example
A verified energy service may fit the installed base and create recurring revenue, but the size and timing of customer demand still need bounds.
Feasibility
Assess the organisation's competence to address the opportunity, including relevant technical, commercial, operational and resource factors. Feasibility is not simply whether something is theoretically possible.[1][2][3]
Illustrative example
The service technology exists, but customer data access and subscription operations may limit what the organisation can deliver.
Minimum and maximum plausible scores
For every factor, record the lowest and highest scores that remain plausible given current evidence. Aggregate these into minimum and maximum Opportunity and Feasibility scores instead of hiding uncertainty inside one average.[1][2][3]
Illustrative example
A weakly evidenced market may justify Opportunity scores from 3 to 7 rather than a single score of 5.
Connected range
Plot the minimum-minimum point and maximum-maximum point for each project and connect them. Position indicates the range of potential; line length shows uncertainty; line direction helps reveal whether Opportunity, Feasibility or both need more attention.[1][2][3]
Illustrative example
A long, mostly horizontal range indicates that Feasibility is much less certain than Opportunity.
When to use it
- When several early-stage technology or innovation projects compete for attention and resources.
- Before detailed business cases, portfolio selection or roadmap commitment.
A practical workflow
- 1
Define the decision and a set of genuinely comparable projects.
- 2
Choose relevant factors and clear scaling statements under separate Opportunity and Feasibility headings.
- 3
Score the minimum and maximum plausible value for each factor, using evidence and facilitated discussion to expose assumptions.
- 4
Calculate each project's aggregate Opportunity and Feasibility ranges, then plot its minimum-minimum and maximum-maximum points as connected endpoints.
- 5
Compare position, range length, threshold criteria and portfolio balance before deciding whether to pursue, learn, hold or shelve.
Fictional worked example
Example: three service projects
This example is illustrative rather than a reported case. A manufacturer scores three comparable early-stage projects on a 0 to 8 scale. The ranges are plausible bounds, not statistical confidence intervals.
Observation:The connected range runs from F5/O5 to F7/O7, remaining in the stronger part of the portfolio.
Implication:Prioritise a commercial pilot while testing the assumptions behind the remaining range.
Observation:The range runs from F2/O3 to F6/O7 and crosses a large part of the chart.
Implication:Resolve the market and integration assumptions before making a major commitment.
Observation:The range runs from F1/O2 to F3/O6, with low Feasibility throughout.
Implication:Focus learning on assurance, data and delivery capability rather than promising implementation.
From analysis to decision
How to interpret the result
- 1Compare projects at a similar type and stage, using the same agreed factors and scaling statements.
- 2Read the chart in two dimensions: where the range sits and how far it extends. A short range in the upper-right is different from a wide range that only reaches it under optimistic assumptions.
- 3Use the direction of the range to identify whether Opportunity, Feasibility or both need more evidence.
- 4Expect a project's range to narrow as learning progresses. Re-score when material evidence changes rather than preserving the first workshop judgement.
- 5Use thresholds and total scores as prompts for discussion alongside portfolio balance, resource constraints and strategic judgement.
The interpretation guidance is an InnovationFlow synthesis of[1][2][3].
What a useful output looks like
Common pitfalls
- Do not replace uncertainty with one apparently precise score.
- Use comparable factors and scaling statements, and actively manage anchoring, advocacy and other scoring biases.
- Treat an illustrative threshold as decision support, not as an automatic verdict.
References and method basis
- [1]Mitchell, R., Phaal, R. and Athanassopoulou, N. (2018). Scoring methods for evaluating and selecting early stage technology and innovation projects. Centre for Technology Management Working Paper Series, University of Cambridge. Source ↗Original method source
- [2]Phaal, R., Farrukh, C.J.P. and Probert, D.R. (2006). Technology management tools: concept, development and application. Technovation, 26(3), 336-344. Source ↗Peer-reviewed research
- [3]Kerr, C., Farrukh, C.J.P., Phaal, R. and Probert, D.R. (2013). Key principles for developing industrially relevant strategic technology management toolkits. Technological Forecasting and Social Change, 80(6), 1050-1070. Source ↗Peer-reviewed research
This guide synthesises the named sources into practical questions for strategy and innovation work. It does not claim that using a tool by itself produces a successful decision.