Decision analysis provides a structured way to compare options with different benefits, risks and uncertainties. An intuitive ranking can hide the trade-offs or assumptions that favour one choice. Marketways makes the objectives, evidence and consequences explicit and tests what could reverse the preferred option. Leaders gain a transparent basis for prioritisation while retaining responsibility for the judgement involved.
The decision this method supports
We use Decision Analysis & Prioritisation to help clients answer: Which option is preferred, why, and what new evidence could change the choice?
How the method works
Decision analysis separates alternatives, objectives, evidence, uncertainty and consequences. Prioritisation methods make trade-offs visible when no option dominates every criterion. The method supports judgement by showing why an option is preferred and what could reverse the choice.
A business example
A company choosing between three expansion locations may compare demand, investment, operating difficulty and downside risk. Sensitivity analysis can show whether one location remains preferred when uncertain assumptions change.
How the client uses the result
Decision analysis makes a difficult choice easier to examine and defend. It shows which objectives and assumptions favour each option, where judgement enters the choice and what new evidence could change the priority.
What we deliver
We produce forecast ranges, scenarios, risk distributions, prioritised alternatives or an optimised plan. A manager should understand which assumptions drive the result and what conditions would trigger a different action.
Limits and complementary methods
Decision analysis structures judgement and trade-offs; it does not make the decision or eliminate disagreement about values and evidence.
Selected methods and techniques
We select from these established methods according to the decision, evidence and operating conditions.
- Alternative generation: Generate plausible options or explanations beyond the initial shortlist, using the decision objective, stakeholder values, causal possibilities and explicit challenges as prompts. Record excluded possibilities and the reason for exclusion so the search boundary remains visible. The method improves reach; it does not rank the alternatives or prove that the set is complete.
- Assumption mapping: Identify the explicit and implicit premises needed for a claim or recommendation to hold, then link each premise to the conclusions and other assumptions that depend on it. Mark ownership, evidence, uncertainty and testability where available. The map exposes dependency; it does not by itself test whether an assumption is true.
- Asymmetric-loss analysis: Compare choices when the consequences of different errors or outcomes are unequal. Identify the affected parties, magnitude, timing and reversibility of each loss rather than treating false positives, false negatives or upside and downside as equivalent. The analysis informs the decision rule; it does not supply probabilities unless those are estimated separately.
- Bayesian decision analysis: Compare actions using both the current probabilities of possible outcomes and how valuable or costly those outcomes would be. Update the choice when new evidence changes those probabilities.
- Competing-hypothesis analysis: Compare several possible explanations against the same evidence. Pay particular attention to evidence that contradicts an explanation or would be more likely under one explanation than another.
- Consequence analysis: Identify the beneficial and harmful outcomes that could follow an action or event, including who bears them, their magnitude, timing, duration and reversibility. Keep consequence separate from probability so a low-probability catastrophic loss is not hidden by an average likelihood score. This method describes what is at stake; risk exposure analysis additionally identifies the assets or activities placed in harm's way.
- Country / market screening: Reduce a broad set of candidate countries or markets to a defensible shortlist before detailed entry work. Define the decision, comparable market unit and evidence date; apply hard eligibility or exclusion conditions first; then compare the remaining candidates using stated attractiveness, accessibility, capability-fit and risk criteria. Record missing data, source comparability, uncertainty and sensitivity to thresholds or weights. Screening is the overall filtering and comparison process; market-attractiveness scoring is one operation within it, the screening matrix records the populated comparison, and the ranking orders the result. Screening prioritises deeper investigation rather than proving feasibility or selecting an entry mode.
- Decision decomposition: Separate a bundled decision into distinct choices, objectives, owners, dependencies and decision dates so each can be framed and assessed at the right level. Preserve links between the parts and identify their required sequence. Decomposition resolves decision convolution; it does not imply that the resulting choices are independent.
- Decision provenance analysis: Trace material parts of a decision, such as its frame, assumptions, evidence, alternatives and recommendations, to their documented origins and subsequent changes. Use timestamps, version history and independent pre-exposure records where available, while marking mixed or unknown origins. The method establishes lineage and sequence; a claim that a source influenced judgement requires additional evidence.
- Decision-process tracing: Reconstruct the chronological path from the initial question to the final decision, including information received, alternatives raised, challenges, revisions, owners and approvals. The trace reveals where evidence was ignored, overwritten or introduced too late. It describes the process record; decision provenance analysis focuses more specifically on the origins of material frame elements and judgements.
- Decision-tree analysis: Construct a branching representation of sequential decisions, uncertain events and resulting consequences. Define the order and information available at each decision point, assign probabilities only where defensible, calculate or compare terminal outcomes, and test sensitivity to uncertain branches and values. The method evaluates choices that can adapt as information arrives. A tree does not discover missing options or make unsupported probabilities reliable, and an influence diagram is a related compact representation rather than the performed tree analysis itself.
- Discrimination Analysis: Check whether evidence helps choose the relevant possibility over credible rivals once those possibilities have been considered. This concerns choosing between explanations or options, rather than using “discrimination” as a generic model score.
Parent method family
Forecasting, Risk & Optimisation explains how this method connects to adjacent methods and relevant services.
Related service families
These service families contain business questions supported by this method. Service pages link to the wider method family so readers can understand the complete analytical approach.