You have lower costs when you do not have to scrap off-specification product, or have to rework bad product. Product quality is not always a cost-benefit trade-off: it is beneficial to you in the long-term to improve your product quality, and for your customers as well.Īs we spoke about in the univariate review section, good quality products (low variability) actually boost your profits by lowering costs in the long term. There was historical thinking that quality is the equivalent of âgold-platingâ your product, but that has mostly fallen away. Most industries have now realized that product quality is not an option. Product development and product improvementģ.3. Applications of Process Improvement using Data Analysis of designed experiments using PLS models Variability explained with each component A mathematical/statistical interpretation of PLS Advantages of the projection to latent structures (PLS) method Introduction to Projection to Latent Structures (PLS) Visualization latent variable models with linking and brushing Using indicator variables in a latent variable model Determining the number of components to use in the model with cross-validation Algorithms to calculate (build) PCA models Preprocessing the data before building a model Interpreting loadings and scores together More about the direction vectors (loadings) Extended topics related to designed experiments Blocking and confounding for disturbances Highly fractionated designs: beyond half-fractions Generators: to determine confounding due to blocking Generating the complementary half-fraction Example: analysis of systems with 4 factors Assessing significance of main effects and interactions Example: design and analysis of a three-factor experiment Analysis of a factorial design: interaction effects Analysis of a factorial design: main effects

Changing one single variable at a time (COST) Experiments with a single variable at two levels Design and analysis of experiments in context Outliers: discrepancy, leverage, and influence of the observations

More than one variable: multiple linear regression (MLR) Summary of steps to build and investigate a linear model Least squares models with a single x-variable The industrial practice of process monitoring Statistical tables for the normal- and t-distribution The normal distribution and checking for normality General summary: revealing complex data graphically

Process monitor software