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2. Use case points - Wikipedia

   en.wikipedia.org/wiki/Use_Case_Points

   Once the Unadjusted Use Case Weight (UUCW), Unadjusted Actor Weight (UAW), Technical Complexity Factor (TCF) and Environmental Complexity Factor (ECF) has been determined, the Use Case Points (UCP) can be calculated with the following formula: UCP = (UUCW + UAW) x TCF x ECF For the Online Shopping System, UCP = (100 + 13) x 1.02 x 1.085 = 125.06

3. Green chemistry metrics - Wikipedia

   en.wikipedia.org/wiki/Green_chemistry_metrics

   The simplest metrics are based upon the mass of materials rather than their impact. Atom economy, E-factor, yield, reaction mass efficiency and effective mass efficiency are all metrics that compare the mass of desired product to the mass of waste. They do not differentiate between more harmful and less harmful wastes.

4. Kaiser–Meyer–Olkin test - Wikipedia

   en.wikipedia.org/wiki/Kaiser–Meyer–Olkin_test

   The Kaiser–Meyer–Olkin (KMO) test is a statistical measure to determine how suited data is for factor analysis. The test measures sampling adequacy for each variable in the model and the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance.

5. Performance indicator - Wikipedia

   en.wikipedia.org/wiki/Performance_indicator

   KPI information boards. A performance indicator or key performance indicator (KPI) is a type of performance measurement. [1] KPIs evaluate the success of an organization or of a particular activity (such as projects, programs, products and other initiatives) in which it engages. [2]

6. Factor analysis - Wikipedia

   en.wikipedia.org/wiki/Factor_analysis

   Canonical factor analysis, also called Rao's canonical factoring, is a different method of computing the same model as PCA, which uses the principal axis method. Canonical factor analysis seeks factors that have the highest canonical correlation with the observed variables. Canonical factor analysis is unaffected by arbitrary rescaling of the data.

7. Overall labor effectiveness - Wikipedia

   en.wikipedia.org/wiki/Overall_Labor_Effectiveness

   Overall labor effectiveness (OLE) is a key performance indicator (KPI) that measures the utilization, performance, and quality of the workforce and its impact on productivity. Similar to overall equipment effectiveness (OEE), OLE measures availability, performance, and quality.