Professional GE McKinsey Matrix Analysis Tool

Problem being solved

For companies managing multiple business lines, portfolio planning often needs more than the BCG matrix. BCG compresses strategy into market growth and relative market share, which can hide how different business units compete, earn, and grow. The GE/McKinsey matrix replaces those single axes with weighted, multi-factor views of industry attractiveness and business strength. This skill turns that framework into a structured analysis workflow: define the SBU, collect factor weights and scores, compute weighted totals, and map each unit to a nine-grid strategic zone.

How the skill works

The main inputs are analysis_object, business_units, industry_attractiveness_factors, and business_strength_factors, with optional include_hover and compare_bcg flags. It follows the standard GE scoring sequence: list internal and external factors, assign weights that sum to 1, score each factor from 1 to 5, calculate weighted attractiveness and strength scores, then position each SBU in the high/mid/low grid. The result is not just a label; it is a reviewable table showing why a business unit falls into invest/develop, maintain/harvest, or divest territory.

The skill also includes two extensions. The GE vs BCG comparison explains why a multi-factor matrix can be more useful for diversified enterprises than a two-variable plot. The Hofer product/market evolution matrix adds lifecycle stages—development, growth, consolidation, maturity, and decline—useful when the issue is not only portfolio ranking but where a specific business unit sits in its market lifecycle. For target-market selection cases, the skill can express the calculation as S = ∑ai × Si and Q = ∑bi × Qi, keeping the reasoning visible.

Boundaries

GE matrix analysis is best for comparing multiple business units and setting broad investment priorities. It is not a substitute for financial modeling, risk analysis, or strategic collaboration. Common misuse includes poorly defined SBU boundaries, highly correlated factors, arbitrary weights, and treating the green/yellow/red zones as final decisions. For emerging markets, attractiveness scores should be refreshed regularly; a static score can quickly become misleading. The skill’s value is to package the framework, scoring tables, nine-grid logic, and limitation checks into a repeatable analysis path.