Overview

The main objective of this advanced course is to provide a set of useful tools for the analysis, optimization and evaluation of business processes. They allow the qualitative and quantitative study of processes, motivating sustained improvement initiatives, increasing the external competitiveness of the Portuguese business sector.

Learning outcomes

By the end of this course, the learner should be able to:

• Model processes and identify direct waste;
• Develop a detailed plan to solve a problem, prioritizing graphical/visual analysis;
• Evaluate the root causes of a problem according to its typology. For the quantitative study, data from case studies provided in class by the trainer and/or real data brought by student-workers about the processes that operate in their companies will be used. The tools to be explored will allow:
• Make a decision (inference) between two hypotheses, taking into account the observed data of an industrial or logistics process;
• Evaluate the influence that one or two factors produce on a variable of a process;
• Statistically evaluate the level of variability of industrial or logistics processes;
• Evaluate and classify a process, product, company, service (or others), based on a set of input and output variables that characterize it.

Content

Module 1 – Management, planning and qualitative analysis tools for processes

• Continuous Improvement
  • The 6-Sigma philosophy
  • Define-measure-analyze-improve-control (DMAIC) methodology
• Problem-solving methodologies
  • Plan-do-check-act (PDCA)
  • A3 Report
  • 8 disciplines (8D)
• Process definition/modeling methodologies
  • Value stream mapping (VSM)
  • Suppliers, inputs, process, outputs, customers (SIPOC)
  • Flow diagram
• Guidelines for analyzing the root causes of a problem
  • Interrelationship diagram
  • Pareto principle
  • Failure mode and effects analysis (FMEA)
  • Ishikawa diagram + 6Ms + 5 Whys

Module 2 – Statistical tools for process control and optimization (Excel)

• Hypothesis testing (HT) – p-value and hypotheses
  • TH for a population proportion
  • TH for the population mean and variance
  • TH for homogeneity of variances (F test)
  • TH for comparing two population means with equal variances
  • TH for comparing two population means with different variances
  • TH for comparing the differences between pairs of observations (T test)
• One-way and two-way analysis of variance
• Correlation and linear regression
  • Correlation versus Causality
  • Linear relationship – Pearson’s coefficient and test of its significance
  • Monotonic relationship – Spearman’s coefficient and test of its significance

Module 3 – Comparative evaluation techniques (Google Colab or Visual Studio Code)

• Data Envelopment Analysis – Mathematical model, applications and Python code.
• Multi-Directional Efficiency Analysis – Mathematical model, applications and Python code.