Weighted Scoring Models in Excel: Decision Matrices, Normalisation & Sensitivity

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Turn the steering group's importance points into weights, repair a manager's weight set that adds up to 105% and add a validation rule that blocks any weight above 40% for Larkfield Outdoor's warehouse-software selection.
• Turn five vendors’ raw data (dollars, weeks, feature counts, star ratings) into comparable 0–10 scores, flipping the scale where lower is better.
• Weight the five normalised vendor scores, rank them, build a live leaderboard with SORTBY and measure how close the race is.
• Screen seven candidates for a data analyst role at Larkfield Outdoor: three must-have rules with AND, a gated final score with IF, the best qualifying candidate and a red highlight on everyone who is out.
• Derive weights for four project criteria from pairwise judgements: column sums, a normalised matrix, row averages and a consistency check.
• Vary the cost weight from 0% to 60% for the two leading vendors, chart the crossing lines and solve for the exact weight at which Palletra overtakes Binwise.
• Combine four evaluators’ scores for eight projects: mean, standard deviation, a "Discuss" flag and a yellow highlight on the score rows where the panel disagrees.
• Finish Larkfield Outdoor’s project prioritisation: weighted scores, ranks, a funding line that respects the $900k budget and a sorted bar chart for the steering committee.