Network & Optimisation · Decision

MCDM Selection Studio

Score four supplier/technology alternatives on cost, quality, lead time and sustainability. Set your criteria weights, switch between weighted-sum, TOPSIS and PROMETHEE, and watch the ranking - and rank reversal - respond. Press Run to play 20 rounds, or Step to go one round at a time.

Round 0 / 20

Ranked alternatives & weights

Decision-Logic Coach

Press Run or Step to play the first round.

Set the controls on the left first.

Round-by-round

No rounds yet. Each round adds a point to this chart and a line to the log.

Scoreboard & export

Par is the default-policy score. Tune the controls to beat it, export your run, or save a leaderboard best.

You (cum.)

Par (default)

Delta

Debrief

Assessment 2 · Technology selection report

Use this studio to justify the technology you select for your Assessment 2 report. Click Load A2 technology set to score four e-business technologies (Cloud ERP, Blockchain traceability, AI demand forecasting, RPA) on cost, strategic fit, implementation time and scalability, then:

List the 3–4 candidate technologies you are considering for your selected supply chain. The A2 set is a starting point; your report should use your own candidates and your own scores.
Derive your criteria weights from pairwise comparisons in the AHP Weight Builder and send them here.
Rank with TOPSIS, then test weight sensitivity and the rank-reversal toggle before you commit.
In your report (about one page), show: the candidates, the criteria and weights, the ranking result, and whether you followed the model's recommendation and why or why not.

Do not paste the tool's output into the report. Use it to justify why your chosen technology fits your supply chain, and acknowledge at least one limitation of the selection model. This supports the existing A2 requirements on technology selection and justification; it does not change the assessment task.

About this game

MCDM Selection Studio - weighted multi-criteria ranking

The concept

Multi-criteria decision analysis ranks alternatives on several conflicting criteria (cost, quality, lead time, sustainability) using explicit weights.

How it works

Criteria are normalised to a common benefit scale, then a method aggregates them: weighted-sum adds them, TOPSIS ranks by closeness to the ideal, PROMETHEE by pairwise net flow. Adding an alternative can reverse ranks.

Winning strategy

Test weight sensitivity and watch how close the top two are; a robust choice survives reasonable weight changes and does not flip under rank reversal.

TOPSIS: closeness = d⁻ / (d⁺ + d⁻)

MCDACriteria weightsNormalisationTOPSISPROMETHEERank reversal

Core idea: MCDA (AHP/TOPSIS)