Notes on KPIs, Data Analysis, Problem Solving, Surveys, and Lean/Six Sigma

KPIs, Dashboards, and Scorecards

  • Key Performance Indicators (KPIs):
    • Definition: A measurable value tied to a specific business objective.
    • Types relevant to different stakeholders:
    • Financial KPIs: For investors to assess profitability and growth.
    • Operational KPIs: For managers focusing on efficiency and process improvement.
    • Customer Satisfaction KPIs: For customer service teams to gauge service effectiveness.
  • Balanced Scorecard:
    • Internal Section:
    • Measures internal processes such as operational efficiency and productivity.
    • Components of a Scorecard:
    • Financials, Customer Satisfaction, Internal Processes, Learning and Growth.
  • Dashboards:
    • Purpose: Provides a real-time visual summary of key metrics and performance indicators.
    • Comparison with Balanced Scorecard:
    • Scorecards track long-term strategy execution.
    • Dashboards monitor short-term operational performance.
  • Characteristics of KPIs:
    • Relevant, measurable, actionable, time-bound, clearly defined.

Data Analysis: Correlation, Regression, and Averages

  • Regression vs Correlation:
    • Regression: Shows causation and prediction.
    • Correlation: Indicates the strength and direction of a relationship, but is simpler and less informative.
  • Correlation Value Range:
    • Ranges from -1 to 1.
    • 0 = no correlation
    • 1 = perfect positive correlation
    • -1 = perfect negative correlation.
  • Moving Averages:
    • Purpose: Smooth out short-term fluctuations to highlight trends.

DMAIC and Data Quality

  • DMAIC Model:
    • Define, Measure, Analyze, Improve, Control; a Six Sigma framework for process improvement.
  • Data Quality Issues:
    • Common problems: Missing values, inconsistency, inaccuracy, etc.
  • Timeliness of Data:
    • Data must be up-to-date and available when needed.
  • High-Quality Data Attributes:
    • Accuracy, completeness, consistency, timeliness, validity.
  • Accuracy:
    • Definition: How close data is to the true value.
  • Data Quality Questions:
    • Address completeness and consistency of the dataset.

Surveys and Question Types

  • Loaded or Leading Questions:
    • Definition: Biased questions that suggest a particular answer.
  • Types of Surveys:
    • Cross-sectional, longitudinal, online, in-person, etc.
  • Data Collected by Survey Types:
    • Quantitative: rating scales
    • Qualitative: open-ended responses.
  • Rating Scale Questions:
    • Measure intensity of feelings or frequency of behavior.
  • Dichotomous Questions:
    • Measure binary responses (Yes/No, True/False).
  • Multiple Choice Questions:
    • Comprised of predefined options, useful for quantifiable responses.

Decision-Making Tools

  • Weighting Criteria:
    • Assign numerical weights to compare and prioritize options.
  • Decision-Making Tools:
    • Use T-charts or Decision Matrices when comparing multiple options.
  • Pair Y's Comparison:
    • Tool for prioritizing factors by comparing them in pairs.
  • Advantages of Using Ratios and Benchmarks:
    • Provides relative performance measures and industry comparisons.
  • Interpreting Index Values:
    • Show change over time relative to a base value; e.g., index of 110 signifies a 10% increase.
  • Interpreting Ratios:
    • Used to compare two quantities, e.g., profit margin = rac{ ext{net income}}{ ext{revenue}}.
  • Benchmarking Purpose:
    • Measure performance against industry standards or best practices.

Problem Solving and Analysis Tools

  • Problem Two-Pager:
    • Useful for concise summaries with problem, data, analysis, and solution.
  • Success Criteria/Symptoms:
    • Signs indicating whether a problem has been solved.
  • 5 Why's Technique:
    • Root cause analysis tool that asks 'Why?' five times to reach the root cause.
  • Benefits of a Problem Statement:
    • Provides clarity and focus for addressing specific issues.
  • Fishbone Diagram:
    • Visual tool for identifying possible causes of a problem (cause-and-effect diagram).
  • Advantages/Disadvantages of 5 Why's:
    • Simple and fast, but may lack depth if not applied systematically.
  • Fishbone Categories:
    • 6 M's: Man, Machine, Method, Material, Measurement, Mother Nature.
    • 6 P's: Policies, Procedures, People, Plant, Product, Process.

Types of Data and Excel Basics

  • Types of Data:
    • Nominal Data: Categories;
    • Ordinal Data: Ranked order.
  • Primary vs Secondary Data:
    • Primary Data: Collected firsthand;
    • Secondary Data: Collected by others.
  • Qualitative vs Quantitative:
    • Qualitative Data: Descriptive;
    • Quantitative Data: Numerical.
  • Identifying Data Types from Sources:
    • Determine if the source gives primary/secondary or qualitative/quantitative data.
  • Open-ended Questions:
    • Allow for detailed, free-text responses.
  • Data Types Breakdown:
    • Nominal: Categories;
    • Ordinal: Ranked;
    • Interval: Equal intervals, no true zero;
    • Ratio: Same as interval, plus has a true zero.
  • Excel Basics - Rows and Columns:
    • Row: Horizontal set of cells;
    • Column: Vertical set of cells.

Lean and Six Sigma

  • Six Sigma vs Lean:
    • Six Sigma: Focus on reducing variation.
    • Lean: Focus on eliminating waste.
  • Lean Focus:
    • Streamline processes to remove non-value-added steps.
  • SPIOC Diagram:
    • Supplier, Input, Process, Output, Customer - shows flow of a process.
  • Value Stream Mapping:
    • Visual map of all steps in a process to identify waste.
  • 8 Wastes of Six Sigma:
    • Defects, Overproduction, Waiting, Non-utilized talent, Transportation, Inventory, Motion, Extra-processing.
  • Five Steps of Six Sigma:
    • Define, Measure, Analyze, Improve, Control.