What you will be able to do
- Design a catchment study with explicit observation units and sampling strata.
- Calculate anchored site scores and the preference threshold that reverses a ranking.
- Test operational viability and write an evidence-linked conditional recommendation.
Before you begin: Basic spreadsheet arithmetic, proportions and contribution margin.
Separate eligibility gates from scored preferences
Start with conditions that a location must satisfy before you compare its attractiveness. If the service requires reliable power and a site cannot supply it at a feasible cost, an attractive demand score should not compensate for that failure. Write each gate as a test, with an evidence source and an accountable reviewer. Then identify genuine trade-offs among eligible sites. A slightly longer journey may be acceptable if customers obtain better service or lower prices. This separation prevents an impressive weighted average from hiding a fatal constraint. Record exceptions explicitly. An unresolved permission issue belongs in a conditional decision, not inside a quietly reduced score that executives might never notice.
Anchor scores to explicit performance ranges
Our illustration assigns each site a score for demand, access and cost attractiveness. Higher always means better, including for cost, where a cheaper comparable operating arrangement receives a higher score. The numbers are useful only after you define their anchors. For example, specify what zero and one hundred mean and why intermediate performance deserves its position. Otherwise two assessors may attach different meanings to eighty. Avoid scoring the same benefit twice through correlated criteria such as footfall and forecast sales unless the model explicitly separates their contributions. Keep the raw measurements alongside the converted scores. When someone disputes the ranking, you can then distinguish a disagreement about observed performance from a disagreement about priorities.
Preferences and measurements need different tests
Weight sensitivity addresses preferences; it does not capture uncertainty in measured demand. Treat those as separate layers. Revisit footfall estimates, conversion assumptions and operating costs using defensible ranges linked to actual evidence quality. Then examine combinations that can occur together. A road disruption may reduce visits and raise delivery costs simultaneously, so independent one-variable changes could understate risk. Conversely, combining every imaginable worst case without a coherent mechanism can exaggerate it. For repeated field observations, consider resampling at the day or other relevant cluster level rather than individual counts. Report how often rankings change under the stated scenarios, but do not call that a probability of success unless you have justified a probability model.
Make the recommendation inspectable
Your decision memo should let a reviewer reconstruct the recommendation without rebuilding the entire study. Show which sites passed the eligibility gates, how the catchments were defined and when observations were collected. Present raw measures alongside scores, identify the agreed weights and display the switching threshold. Then attach the operating model with baseline and coherent downside cases. State a conditional recommendation in operational language: proceed only if a specified uncertainty is resolved, an acceptable lease condition is obtained, or a pilot meets the predefined requirement. Assign an owner and deadline to every condition. Preserve the data version and calculation file so that the next update changes the evidence transparently instead of silently rewriting the original decision.
Apply the method
Work through each task before opening the model answer. Compare your reasoning with the review criteria. Written answers stay on this page and clear when you leave or reload it.
Applied task 1
Show model answer and review criteria
A defensible plan observes comparable day/time strata at both sites, separates people/visits/purchases, records exceptional conditions, excludes sites failing indispensable gates and retains raw measures beside anchored scores. It explains how related criteria avoid counting the same benefit twice.
- Comparable sampling strata and explicit denominator
- Verifiable gates and anchored score definitions
- Documented handling of repeated visits and correlated criteria
Applied task 2
Show model answer and review criteria
B is preferred at the baseline weights, but A becomes preferred above a 65.91% demand weight. The illustrated operation has only GHS 1,680 baseline surplus and loses GHS 7,602 under the stated downside. A recommendation should therefore require a targeted conversion pilot or revised fixed commitments before an unconditional investment decision.
- Correct ranking and threshold
- Correct surplus and downside calculation
- Evidence-linked condition with an owner and decision rule
Method references
- UK Government — Multi-criteria analysis: a manual
Option appraisal, explicit criteria, preference weights and sensitivity analysis; all numerical cases are original hypothetical teaching examples.