Tool Documentation & Guides

Introduction to Sigma Exacta

Sigma Exacta is a powerful web-based tool designed specifically for automotive and quality engineers. It provides essential calculators and analysis tools to help you streamline your workflow and improve product quality.

Note: Sigma Exacta requires JavaScript to be enabled in your browser. All calculations are performed locally in your browser—your data never leaves your computer.

Tool Index

Process Capability (Cpk) Calculator

Purpose of the Tool

Use the Cpk Calculator to assess the capability of a manufacturing or business process. It calculates both short-term (Cp, Cpk) and long-term (Pp, Ppk) indices, performs normality tests, and generates distribution plots and control charts. This is essential for:

  • Validating process improvements (e.g., after a Kaizen event).
  • Monitoring ongoing process performance for stability and normality.
  • Fulfilling customer requirements for quality assurance, such as in PPAP submissions.
  • Distinguishing between short-term (within-subgroup) and long-term (overall) variation.
  • Making data-driven decisions about whether a process needs adjustment or overhaul.

Origin and Background

The journey of process capability begins with the father of statistical quality control, Dr. Walter A. Shewhart. In the 1920s, he laid the groundwork for statistical process control. However, it was the intense global competition of the 1980s that propelled capability indices to the forefront, championed by the U.S. automotive industry through the Automotive Industry Action Group (AIAG). They needed a standard way to measure supplier quality. Initially, the focus was on Cp (Process Capability), but it assumes the process is perfectly centered. This led to the widespread adoption of Cpk (Process Capability Index), a much stricter and more practical measure. Cpk accounts for both the spread and the centering of the process mean relative to specification limits. It became a universal language, with suppliers for companies like Toyota or Bosch required to demonstrate Cpk values of 1.33 or 1.67 for critical features.

How to Use the Cpk Calculator

The calculator is a comprehensive tool that handles multiple datasets for both short-term and long-term analysis. Follow these steps to get a complete process capability report:

  1. Step 1: Enter Process Data and Specifications

    Use the "Add Dataset" button to create one or more data groups. In the text area for each dataset, input the numerical values from your process samples, separated by commas or spaces. Then, enter the common engineering limits: Lower Specification Limit (LSL), Upper Specification Limit (USL), and the Target value.

  2. Step 2: Calculate and Analyze

    Click the "Calculate" button. The tool instantly processes your data and displays a multi-faceted analysis. The within-subgroup sigma method (Moving Range, R-bar/d₂, or S-bar/c₄) is selected automatically based on subgroup size and structure, matching the logic used by tools like Minitab and JASP.

  3. Step 3: Review the Results

    The output is split into two main sections:

    • Short-Term Results: For each individual dataset, the tool shows key metrics like Cp, Cpk, Cpm, mean, and standard deviation, each with its 95% confidence interval. It also performs normality tests (Shapiro-Wilk, Anderson-Darling), displays a process distribution plot, a Q-Q Plot for visual normality assessment, and an Individuals Control Chart (I-Chart). You can switch between datasets using the tabs.
    • Overall Performance (Long-Term): By combining all datasets, the tool calculates the overall indices Pp and Ppk with confidence intervals, providing insight into long-term process performance. This section also includes its own normality tests, distribution chart, and Q-Q Plot.

  4. Step 4: Export Your Analysis (Optional)

    Once the calculations are complete, you can click the Export to Excel button to download a spreadsheet containing a summary of all results and the raw data for your records.

Open Cpk Calculator

Interactive Control Plan Creator

Purpose of the Tool

The Control Plan is a living document that describes how to control critical processes and product characteristics to ensure quality and meet customer requirements. This tool follows the AIAG‑VDA 2019 approach and is fully aligned with the FMEA tool. Use it to:

  • Systematically detail the controls for product and process characteristics identified in a Process FMEA (PFMEA).
  • Automatically import failure modes, Severity (S), Occurrence (O), Detection (D) and recommended controls directly from an Excel file generated by our FMEA tool.
  • Calculate the Action Priority (AP) (High/Medium/Low) in real time using the official AIAG‑VDA lookup table (the same logic as in the FMEA tool).
  • Define measurement systems, sample sizes, frequencies, control methods, and reaction plans for each operation.
  • Ensure continuity of quality during prototype, pre‑launch, and production phases.
  • Validate that all mandatory header fields (Control Plan Number, dates, part information, supplier code, customer plant, core team) are filled before exporting – a requirement for PPAP submissions and IATF 16949 audits.
  • Save the complete control plan as a .json file (including revision history) and reload it at any time; export a professional PDF with formatted header, approvals section, and the full control plan table.

Origin and Background

The Control Plan is a key output required during Phase 4 (Product and Process Validation) of the AIAG's Advanced Product Quality Planning (APQP) framework. It is mandatory for PPAP (Production Part Approval Process) submission in the automotive industry. The modern standard (AIAG‑VDA 2019) replaces the old RPN with Action Priority (AP) to prioritise risks. This tool implements the same AP logic as our FMEA tool, ensuring seamless traceability from risk analysis (FMEA) to manufacturing controls (Control Plan).

How to Use the Control Plan Creator

The tool is organised into three tabs: Theory & Info, Control Plan Tool, and Results. The main workflow happens in the second tab.

  1. Step 1 – Fill the Control Plan Header

    Select the Control Plan Type (Prototype, Pre‑Launch, or Production). Complete the header information: Control Plan Number, FMEA Reference Number, Part Number / Change Level, Part Name / Description, Supplier / Plant, Supplier Code, Customer Name / Plant, Core Team, and approval blocks (Engineering, Quality, Supplier). Fields marked with an asterisk are mandatory – the export function will block the download if any are missing, preventing incomplete PPAP submissions.

  2. Step 2 – Import Data from FMEA (Optional but Recommended)

    Click Upload FMEA Excel and select the Excel file generated by our FMEA tool. The system will automatically populate process names, failure modes, Severity (S), Occurrence (O), Detection (D), and suggested control methods / reaction plans. The Action Priority (AP) column will be calculated on the fly using the same AIAG‑VDA 2019 logic as in the FMEA.

  3. Step 3 – Build or Edit the Control Plan Table

    The main table contains rows for each process step. For each row you can define:

    • Operation No., Process Name, Machine/Device, Characteristic No. – free text.
    • Characteristic Type – Product Characteristic or Process Parameter.
    • Characteristic – the measurable feature (e.g. “Hole Diameter”).
    • Special Characteristic – choose from SC ◆ (Special), CC △ (Critical), YS (Safety/Regulatory), YC (Government Regulatory), ★ (Key Product Characteristic).
    • Failure Mode – imported from FMEA (free text).
    • S, O, D – dropdowns 1‑10. The Action Priority (AP) (H/M/L) updates automatically and is colour‑coded (red=H, orange=M, green=L).
    • Specification / Tolerance – free text.
    • Evaluation Technique – dropdown with common methods (Visual Inspection, Caliper, CMM, etc.).
    • Sample Size & Sample Frequency – free text or dropdown (e.g. “5 pcs”, “1 pc / hour”).
    • Control Method – dropdown (SPC Chart, First‑off Inspection, 100% Inspection, etc.).
    • Reaction Plan – dropdown (Stop Process, Notify Supervisor, Containment, etc.).
    • Responsible – free text (Name / Position).

    Use the Add Row button to insert new lines, and the trash icon () to delete any row.

  4. Step 4 – Load an Example or Reset

    The Load Example button populates the table with realistic manufacturing data (stamping, CNC machining, welding) and fills the header with plausible values. The Reset All button clears all data and leaves a single empty row.

  5. Step 5 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage your document lifecycle:

    • Save – Downloads the complete Control Plan as a .json file, including the header, all table rows, and the revision history. Use this to reload or share the plan later.
    • Load – Opens a previously saved .json file and restores all fields in read-only mode.
    • Revise – Once a file is loaded, click Revise to register a new revision (date, description, author) and unlock all fields for editing. Revision details are saved to the JSON and appear in the exported PDF, ensuring full traceability for audits.

  6. Step 6 – View Results and Export

    Switch to the Results tab to see a clean, read‑only summary of the control plan. Click Export PDF – the tool will first validate that all mandatory header fields are completed. If any are missing, a modal dialog lists them and prevents the export. Once validated, a fully formatted PDF is downloaded, including the header, approvals, revision history, and the full control plan table, ready for PPAP submission or internal use.

Tip: For best results, first complete a Process FMEA using our Advanced FMEA tool, export it to Excel, and then import it directly into the Control Plan. This ensures full traceability from risk analysis to manufacturing controls and automatically populates the Action Priority (AP) based on the official AIAG‑VDA 2019 logic.
Open Control Plan Creator

Weibull Analysis (2P & 3P) – Professional Reliability Tool

Purpose of the Tool

This advanced Weibull analysis tool is designed for reliability engineers to model life data, predict failures, and support maintenance and warranty decisions. It supports both 2‑parameter and 3‑parameter Weibull distributions, with robust estimation methods and comprehensive diagnostics. Use it to:

  • Model time‑to‑failure data with exact, right‑censored (suspensions), and interval‑censored observations.
  • Choose between Maximum Likelihood Estimation (MLE) – recommended for censored data – and classical Linear Regression (probability plot).
  • Estimate the location parameter γ (3P Weibull) to identify a guaranteed failure‑free period.
  • Obtain key reliability metrics: β (shape), η (scale), γ (threshold), MTTF, B‑lives (B10, B50, B90).
  • Assess goodness‑of‑fit with Kolmogorov‑Smirnov and Anderson‑Darling tests (with traffic‑light interpretation).
  • Compute confidence intervals: Likelihood‑Ratio (LR) and asymptotic Fisher information, plus optional parametric bootstrap.
  • Perform likelihood‑ratio test to decide whether 3P significantly improves over 2P.
  • Visualise the Weibull probability plot, PDF, CDF, hazard function, and reliability curve.
  • Predict failure probability at a given time and evaluate warranty reliability targets.
  • Save the complete analysis as a .json file for later reuse, and export a fully formatted PDF report with parameters, confidence intervals, goodness-of-fit metrics, and all charts.

Origin and Background

The Weibull distribution was popularised by Swedish engineer Waloddi Weibull in the 1930s‑50s. Its flexibility – controlled by the shape parameter β – makes it the standard model in reliability engineering. The 2‑parameter form (β, η) is widely used; the 3‑parameter extension adds a location (threshold) parameter γ, which represents a minimum life before which no failures occur. This tool implements modern estimation techniques (MLE via Nelder‑Mead, profile likelihood CIs) and follows best practices from The New Weibull Handbook (Abernethy) and ISO 22514‑2.

How to Use the Weibull Analysis Tool

The tool is organised into four tabs: Theory & Info, Calculator, Results, and Formulas. The main workflow happens in the Calculator and Results tabs.

  1. Step 1 – Configure Analysis & Enter Data

    In the Calculator tab:

    • Toggle 3‑Parameter Weibull if you suspect a failure‑free period (requires ≥3 exact failures).
    • Select Calculation Method: MLE (handles all censoring types; recommended) or Linear Regression (classic probability plot; does not support interval data).
    • Choose Rank Method (for regression) and Confidence Level (90%, 95%, 99%).
    • Enter Failure Times (exact failures), Suspension Times (right‑censored), and Interval‑Censored Data (pairs like “100‑150”). Values can be separated by commas, spaces, or newlines.
    • Optionally set a Predict Time to obtain failure probability, and a Warranty Target for reliability KPI.
    • Enable Bootstrap CIs (MLE only) for robust percentile intervals – note this adds computation time.

  2. Step 2 – Run the Analysis

    Click the Analyze button in the top toolbar. The tool automatically switches to the Results tab and displays all outputs.

  3. Step 3 – Interpret the Results

    The Results tab is divided into several panels:

    • Weibull Parameters: β, η, γ (if 3P), MTTF, R² of the probability plot. Small‑sample bias correction (Ross 1994) can be toggled.
    • Goodness‑of‑Fit: Log‑Likelihood, AIC, BIC, KS and AD statistics with approximate p‑values. A traffic‑light indicator gives a qualitative fit assessment.
    • Failure Mode Analysis: Classifies β into infant mortality, random, wear‑out, or severe wear‑out, with practical recommendations.
    • B‑Life Percentiles: B10, B50 (median), B90 – times at which 10%, 50%, and 90% of the population have failed.
    • Confidence Intervals: Likelihood‑Ratio CIs for β, η, γ; asymptotic Fisher CIs; and, if requested, parametric bootstrap CIs.
    • LR Test (2P vs 3P): Indicates whether adding γ significantly improves the model (boundary‑corrected test).
    • Probability Plot & Distribution Charts: Interactive Weibull plot with fitted line, plus PDF, CDF, hazard, and reliability curves.
    • Descriptive Statistics: Sample size, mean, median, standard deviation, and a list of failure times with outlier detection.

  4. Step 4 – Save and Export

    Use the toolbar at the top of the page to persist your work. Click Save to download the current input parameters as a .json file — reload it later with the Load button to restore all settings instantly. Click Export PDF to generate and download a fully formatted PDF report covering the estimated parameters, confidence intervals, goodness-of-fit statistics, B-life percentiles, failure mode classification, and all distribution charts.

Advanced Features: The tool implements robust MLE optimisation, profile‑likelihood confidence intervals, Herd‑Johnson adjusted ranks for censored data, and parametric bootstrap. Outlier detection and mixture‑of‑failure‑modes warnings help identify data issues. The 3‑parameter MLE includes a grid search with golden‑section refinement to avoid local optima.
Open Weibull Analysis

PDCA Cycle (Deming Wheel)

Purpose of the Tool

PDCA (Plan-Do-Check-Act) is a continuous improvement model used to manage change and solve problems iteratively. Its purpose is to:

  • Provide a simple, repeatable, and scientific method for testing and implementing improvements.
  • Ensure changes are based on data and validated before full deployment.
  • Create a culture of experimentation and continuous learning within the organization.
  • Systematically drive a process from problem definition to standardization.

Origin and Background

The cycle was popularized by Dr. W. Edwards Deming in Japan after WWII, which is why it is often called the Deming Wheel or Deming Cycle. However, Deming credited his mentor, Walter Shewhart, with the original concept, known as the Shewhart Cycle. Deming emphasized that the cycle must be repeated continuously (spiraling upwards) for true improvement. It is the foundation of many quality and management systems, including Lean Manufacturing and Six Sigma, and forms the philosophical basis for standardization in ISO 9001.

How to Use the PDCA Cycle Tool

The tool is organised into three tabs: Start & Info, PDCA Tool, and Report. The main workflow happens in the second tab.

  1. Step 1 – Fill the Four Phases

    Go to the PDCA Tool tab. It is divided into four sub-tabs — one per phase. Navigate between them using the sub-tab buttons or the Next navigation arrows at the bottom of each panel:

    • 1. Plan: Document the Problem/Opportunity Statement, define the Goal/Hypothesis, and detail the Action Plan.
    • 2. Do: Describe what was executed and record any problems or unexpected observations encountered during the pilot implementation.
    • 3. Check: Enter the Data & Results collected and the Analysis of those results versus the planned goal.
    • 4. Act: Write your Conclusions & Learnings and define the Next Steps (standardize or re-plan).

  2. Step 2 – Review the Report

    Switch to the Report tab at any time. A summary table automatically aggregates all four phases into a single, print-ready view so you can review or share the complete PDCA at a glance.

  3. Step 3 – Save, Load, and Export

    Use the toolbar at the top of the page:

    • Save – Downloads a .json file containing all PDCA fields and the full revision history. Use this file to reload or share the analysis later.
    • Load – Opens a previously saved .json file and restores all fields in read-only mode.
    • Export PDF – Generates and downloads a fully formatted, print-ready PDF covering all four phases and the revision history.

  4. Step 4 – Manage Revisions

    Once a file is loaded (read-only mode), click the Revise button. A modal dialog prompts you to enter a revision date, description, and author. Once confirmed, all fields are unlocked for editing and the revision record is appended to the history, which is saved to the JSON and printed in the PDF — ensuring full traceability across multiple PDCA iterations.

Open PDCA Tool

Tolerance Stack-up Analysis

Purpose of the Tool

This tool is essential for design and manufacturing engineers to predict the final assembly variation of multiple components. Use it to:

  • Ensure parts will fit together correctly under all tolerance conditions.
  • Avoid costly redesigns by identifying potential interference or gap issues early.
  • Optimize component tolerances to reduce manufacturing costs without sacrificing quality.
  • Compare worst-case (Arithmetic), statistical (RSS), and simulated (Monte Carlo) outcomes.
  • Model realistic process behavior by selecting the appropriate statistical distribution for each manufacturing process (Normal for stable CNC operations, Uniform for Go/No-Go inspected parts, Triangular for manually centered processes, Weibull for skewed wear patterns, Lognormal for coating thickness, Beta for bounded percentage measurements, and Exponential for failure rate modeling).

Origin and Background

Tolerance analysis is linked to the industrial revolution and Eli Whitney's concept of interchangeable parts. The most basic method is Arithmetic (Worst-Case) analysis, which assumes all tolerances conspire to produce the worst outcome. While safe, it's often too costly. A more sophisticated approach is the Probabilistic (RSS) method, which recognizes that a worst-case scenario is statistically rare and provides a more realistic prediction of variation. The ultimate evolution came with modern computing and Monte Carlo simulation. An engineer can define specific statistical distributions for each dimension, and the software runs thousands of "virtual builds" to generate a rich histogram of all possible outcomes. This is indispensable for aerospace giants like Boeing and consumer electronics companies like Apple.

How to Use the Stack-up Analysis Tool

This interactive tool allows you to build a dimensional chain and analyze it using different statistical methods, automatically selecting between RSS and Monte Carlo based on your distribution choices.

  1. Step 1: Build Your Dimensional Chain

    Click "Add Dimension" for each component in your stack-up loop. A new row will appear for each dimension. You can also click "Load Example" to see a pre-configured analysis with mixed distributions.

  2. Step 2: Enter Details for Each Dimension

    For each dimension, provide the following information:

    • Dimension Name: A descriptive identifier (e.g., "Housing Width", "Shaft Length")
    • Nominal Value: The target dimension
    • Distribution Type: Select from Normal, Homogeneous (Uniform), Triangular, Weibull, Lognormal, Beta, or Exponential based on your manufacturing process characteristics
    • Distribution Parameters: Depending on the distribution selected, you'll define parameters such as:
      • For Normal: Choose to define by Tolerance (±Tol with separate USL/LSL), Standard Deviation (σ), or Cpk value. The tool stores your last values for each method.
      • For Homogeneous: Minimum (A) and Maximum (B) values
      • For Triangular: Minimum (A), Mode (C), and Maximum (B) values
      • For Weibull: Shape (β) and Scale (η) parameters
      • For Lognormal: μ (log mean) and σ (log standard deviation)
      • For Beta: α and β shape parameters, plus Min (a) and Max (b) bounds
      • For Exponential: λ (rate) parameter

  3. Step 3: Configure Monte Carlo Simulation

    Set the "Monte Carlo sample size" (default: 10,000). A higher number provides more accurate results but takes longer to compute. The simulation runs automatically when you calculate, regardless of distribution types.

  4. Step 4: Visualize the Stack-up

    Use the "Stack-up Visualization" section to see a graphical representation of your dimensional chain. Toggle between Horizontal and Vertical orientations. The visualization updates in real-time as you modify dimensions.

  5. Step 5: Calculate and Review Analysis

    Click "Calculate" to perform the analysis. The tool displays three sets of results:

    • Arithmetic Method (Worst-Case): Shows the absolute minimum and maximum possible assembly dimensions by summing all tolerances. This guarantees 100% assemblies meet specifications but leads to the tightest (most expensive) component tolerances.
    • Probabilistic Method: Automatically uses RSS (Root Sum of Squares) if all components are Normal distribution, or Monte Carlo simulation if any component uses a non-Normal distribution (Weibull, Triangular, Homogeneous, Lognormal, Beta, or Exponential). The method label clearly indicates which calculation was used.
    • Monte Carlo Results: Always displayed, showing the simulated distribution histogram, statistical metrics (mean, standard deviation, min, max), and a visual chart of the assembly outcome distribution. This provides the most comprehensive view of expected variation.

  6. Step 6: Save, Export, and Document Results

    After calculation, you can persist and share your work through several options:

    • Click List Results to view a detailed history of all calculations performed in the current session, including input parameters and all three analysis methods.
    • Click Export to Excel to download a comprehensive spreadsheet containing all calculation history, input dimensions, distribution parameters, and results for documentation and record-keeping.
    • Click Save to download the complete dimensional chain and all settings as a .json file — use Load at any time to restore your analysis instantly.
    • Click Export PDF to generate a formatted PDF report of the analysis, suitable for design reviews and engineering documentation.
Distribution Selection Guide: Choose Normal for well-controlled processes (CNC machining), Uniform for Go/No-Go inspected components, Triangular for manually centered operations, Weibull for wear-related or skewed processes, Lognormal for coating/deposition processes, Beta for bounded measurements (percentages, indices), and Exponential for reliability/failure rate modeling.
Open Stack-up Analysis

Taguchi Design of Experiments (DOE)

Purpose of the Tool

This tool helps you design robust products and processes that are insensitive to variation. Its purpose is to:

  • Efficiently identify the optimal settings for control factors in a process or design.
  • Minimize the effects of "noise" factors (like environmental variation or manufacturing inconsistencies) without eliminating them.
  • Reduce the number of experiments required compared to a full factorial design, saving time and resources.
  • Improve quality by designing it in, rather than inspecting it in.

Origin and Background

Dr. Genichi Taguchi, a Japanese engineer, developed a revolutionary philosophy known as Robust Design. He argued that quality is the "total loss to society" after a product ships. His key insight was that it's cheaper to make a product insensitive to "noise" (variation) than to control the noise itself. He introduced two powerful tools to achieve this: Orthogonal Arrays, which are highly efficient experimental designs, and the Signal-to-Noise (S/N) ratio, a metric to find parameter settings that maximize performance (signal) while minimizing variability (noise). His methods were famously adopted by Toyota and its suppliers and were later brought to the West by companies like Ford and Xerox.

How to Use the Taguchi Method Tool

The tool is organised into five tabs: Start & Info, Setup Experiment, Design & Data, Analysis & Report, and Formulas.

  1. Step 1 – Setup Experiment

    In the Setup Experiment tab, define your experiment’s metadata (name, response variable, and optimization goal) and select an Orthogonal Array: L4 (up to 3 factors, 2 levels), L8 (up to 7 factors, 2 levels), L9 (up to 4 factors, 3 levels), or L16 (up to 15 factors, 2 levels). For each column, assign a Control Factor name and define the level values to test (e.g., 100 °C, 150 °C).

  2. Step 2 – Design & Data

    The Design & Data tab generates the full orthogonal experimental plan. Perform the runs as specified and enter the response value for each run. Data can also be imported from a previously saved Excel file.

  3. Step 3 – Analysis & Report

    Click Run Analysis. The Analysis & Report tab displays the complete results:

    • S/N Ratios & Main Effects: The S/N ratio at every level is calculated and plotted for each factor. The level that maximises the S/N ratio is highlighted as the optimal setting, regardless of the optimization goal (Smaller-is-better, Larger-is-better, or Nominal-is-best).
    • ANOVA Table: Analysis of Variance determines whether each factor’s effect is statistically significant or random noise. Significant factors are flagged; a saturation warning appears when the design has no degrees of freedom for the error term.
    • Predicted Optimal Result: Once optimal levels are identified, the tool estimates the expected response value at those settings.

  4. Step 4 – Save, Load, and Export

    Save downloads the complete experiment as a .json file (factors, levels, data, and results); Load restores a saved session; Load Example pre-fills the tool with a process optimisation dataset. Export PDF generates a printable report with the array, S/N plots, ANOVA table, and optimal settings. An Export to Excel option is also available from the Analysis & Report tab.

Open Taguchi Method

FMEA: Failure Mode & Effects Analysis

Purpose of the Tool

Use this advanced FMEA tool to proactively identify and mitigate risks in a product design (DFMEA) or manufacturing process (PFMEA). Built to comply with the joint AIAG‑VDA FMEA Handbook (2019), the primary goals are to:

  • Systematically identify how a product or process could fail to meet its intended function.
  • Understand the consequences (effects) of those failures on the customer and system.
  • Pinpoint the root causes of each failure mode.
  • Prioritise risks using the official Action Priority (AP) metric (High / Medium / Low) as defined by AIAG‑VDA, replacing the ambiguous RPN.
  • Assign responsibility and due dates for corrective actions and re‑evaluate risk after implementation (S2/O2/D2/RPN2/AP2).
  • Visualise the system structure as an interactive network diagram and analyse the risk distribution through a Severity × Occurrence Heatmap.

Origin and Background

The FMEA methodology was forged in high‑stakes environments, originating with the U.S. military standard MIL‑P‑1629 in the late 1940s. It gained immense credibility at NASA during the Apollo programme. In the 1970s, Ford Motor Company championed FMEA for automotive applications, leading to the creation of Design and Process FMEAs. The method's power lies in its structured team approach: identify failure modes, effects, and causes, then rank each on a 1–10 scale for Severity (S), Occurrence (O), and Detection (D). The joint AIAG‑VDA FMEA Handbook (2019) introduced the Action Priority (AP) system, which replaces the traditional RPN (S×O×D) because RPN often produced misleading rankings (e.g., 10×8×1 = 80 vs 4×5×4 = 80). AP uses a decision matrix that guarantees high‑severity failures receive appropriate attention. This tool implements the official AIAG‑VDA 2019 logic, including the rule that a Detection score of 1 (certain detection) lowers the priority when combined with high Severity and moderate Occurrence.

Tool Structure: Six‑Tab Workflow

The tool is organised into six tabs that guide you from theory through to risk visualisation. Use the global Load Example button at any time to populate all tabs with a pre‑built Electric Vehicle Powertrain analysis.

  1. Tab 1 – Start & Info

    A built‑in reference guide covering the FMEA methodology, the AIAG‑VDA rating tables for Severity, Occurrence, and Detection, and the complete Action Priority (AP) decision logic.

  2. Tab 2 – Planning (Project Header)

    Fill in the administrative information: Project / System Name, FMEA Type (DFMEA or PFMEA), Date, Customer, and Team Members.

  3. Tab 3 – Structure (System Decomposition)

    Build the system model in three steps. Add Components (mark external elements like power supply or user). Define directional Contacts between components. Finally, assign Functions to groups of contacts, classifying each as Primary or Secondary. This three‑layer model becomes the foundation for both the network diagram and the FMEA table.

  4. Tab 4 – Visualise (Interactive Network Diagram)

    Displays an interactive graph of the system structure. Internal components appear as filled blue boxes, external as light grey boxes. Each function is rendered as a coloured, labelled arrow over its contacts. Nodes can be dragged; use Fit View to re‑centre and Export Chart to download as JPEG.

  5. Tab 5 – FMEA Results

    Click Generate FMEA Analysis to create a full AIAG‑VDA compliant FMEA table. Each function gets one pre‑populated row; additional failure modes can be added. Each row contains:

    • Failure Mode, Effects, Causes, Current Controls: Free‑text fields.
    • S / O / D ratings: Dropdowns with descriptive AIAG‑VDA labels. The Action Priority (AP) is recalculated automatically using the official lookup table and colour‑coded (red = High, orange = Medium, green = Low).
    • Recommended Actions: Add one or more actions per row, each with a description, owner, and due date.
    • Revised Risk (S2 / O2 / D2 / RPN2 / AP2): After actions are implemented, update the post‑action ratings to confirm the achieved risk reduction.

    Multiple analyses can be generated in one session within the same project.

  6. Tab 6 – Risk Heatmap

    Displays a 10×10 Severity vs. Occurrence matrix automatically populated from the latest analysis. Each cell shows the count of failure modes at that S/O combination, coloured green → yellow → red relative to the highest count. The heatmap updates in real time when ratings change. Export it as an Excel file or a JPEG image.

Tip: Click Load Example to instantly populate the tool with a complete Electric Vehicle Powertrain FMEA (8 components, 8 contacts, 4 functions) so you can explore all features before entering your own data.
Save & Export: Use the toolbar to Save the complete project (components, contacts, functions, all FMEA rows, and revision history) as a .json file. Load restores a saved file in read-only mode; click Revise to register a change and unlock editing. Export PDF generates a formatted report. The Risk Heatmap tab offers its own Export to Excel and Export as JPEG buttons for the S×O matrix.
Open Advanced FMEA

QFD (House of Quality) Builder

Purpose of the Tool

QFD (Quality Function Deployment) is a planning tool used in the design and development of products to ensure that customer desires are translated directly into design requirements and, eventually, manufacturing specifications. Its primary goals are to:

  • Translate the Voice of the Customer (VOC) into concrete, measurable engineering characteristics.
  • Identify and prioritize the most important technical requirements (the "Hows").
  • Understand the correlation (trade-offs) between technical characteristics (the "Roof" of the house).
  • Provide a clear, traceable link from customer satisfaction to final production steps.

Origin and Background

QFD was developed in Japan by Yoji Akao and Shigeru Mizuno in 1966 and was first used at the Mitsubishi Kobe Shipyard. It was famously adopted by Toyota for the design of the Aisin Seiki oil pump, which reduced warranty costs to zero. The core of QFD is the House of Quality matrix, so named because its structure resembles a house. This tool is a fundamental practice in the early phases of Advanced Product Quality Planning (APQP) and ensures that "design for quality" is prioritized before the product is launched. It moves quality planning from the end of the process to the beginning.

How to Use the QFD Tool

The tool is organised into three tabs: Start & Info, Input Data, and Results & Matrix. The Input Data tab contains four sub-tabs that map directly to the four sections of the House of Quality.

  1. Step 1 – Customer Needs (the “Whats”)

    In the Customer Needs sub-tab, add each customer requirement (e.g., “easy to clean”, “long battery life”) and assign it an importance weight on a 1–5 scale.

  2. Step 2 – Technical Specs (the “Hows”)

    In the Technical Specs sub-tab, list the measurable engineering characteristics (e.g., “surface roughness”, “battery capacity”) that address the customer needs.

  3. Step 3 – Relationships (the Body)

    In the Relationships sub-tab, score the relationship between each “What” and each “How” using a standard scale: Strong (9), Medium (3), or Weak (1). The tool uses these scores and the customer importance weights to compute weighted technical priorities automatically.

  4. Step 4 – Correlations (the “Roof”)

    In the Correlations sub-tab, define the trade-off relationships between pairs of technical specs using the classic roof notation: strong positive, weak positive, weak negative, or strong negative. This reveals conflicts and synergies between engineering decisions.

  5. Step 5 – Results & Matrix

    Switch to the Results & Matrix tab to see the complete rendered House of Quality, the calculated importance ranking of all technical requirements, and a priority bar chart. Use the toolbar to Save (JSON), Load, Revise (for traceable revision history), and Export PDF for a print-ready report.

Open QFD Builder

Pugh Matrix Concept Selection

Purpose of the Tool

The Pugh Matrix, or Decision Matrix, is used to systematically evaluate multiple design or process alternatives against a standard baseline. It is crucial for:

  • Removing emotional bias from concept selection during the design phase.
  • Combining quantitative criteria (weighting) with qualitative scoring (better/worse/same).
  • Identifying the strengths and weaknesses of each concept relative to a known baseline (datum).
  • Facilitating team consensus and documentation for the best path forward in development.

Origin and Background

Developed by Professor Stuart Pugh at the University of Strathclyde in the UK in the 1980s, the matrix became a core part of the structured design process. Unlike simple weighted scoring models, the Pugh matrix focuses on relative comparison. One concept is chosen as the datum (the baseline). All other alternatives are scored only relative to the datum using a simple scale: + (better), - (worse), or S (same). This comparative approach is highly effective in engineering design, where the team must quickly determine which concepts are promising, which need modification, and which should be discarded.

How to Use the Pugh Matrix Tool

The tool is organised into three tabs: Start & Info, Data Input & Matrix, and Results.

  1. Step 1 – Define Criteria and Concepts

    In the Data Input & Matrix tab, add your evaluation Criteria (e.g., “Manufacturing Cost”, “Reliability”, “Aesthetics”) and assign a numeric Weight to each one to reflect its relative importance. Then add all the Design Concepts to be evaluated as columns.

  2. Step 2 – Select the Datum (Baseline) and Score

    Choose one concept as the baseline (datum). For every criterion, score each non-datum concept as + (better), − (worse), or S (same) relative to the datum. The tool converts these symbols to numeric values (+1, −1, 0) and multiplies by the criterion weight to compute a Weighted Net Score for each concept in real time.

  3. Step 3 – Review Results

    Switch to the Results tab. The winning concept is highlighted automatically based on the highest net score. The table shows each concept's weighted positive total, weighted negative total, and final net score, making it easy to identify both the leading candidate and the improvement opportunities for the others.

  4. Step 4 – Save, Load, and Export

    Use the toolbar to Save (JSON), Load, Load Example, Revise (to append a traceable revision record), and Export PDF for a formatted report of the matrix and results, ready for design reviews or documentation.

Open Pugh Matrix

VAVE (Value Analysis / Engineering)

Purpose of the Tool

VAVE (Value Analysis/Value Engineering) is a structured, cross-functional approach to systematically analyze a product's function to achieve the necessary performance at the lowest life-cycle cost. Its core objective is to improve the Value ratio (Function / Cost). Use it to:

  • Identify components whose cost is high relative to their contribution to the product's function.
  • Focus the team on the true function of the product, rather than its existing form.
  • Generate creative and systematic cost-reduction ideas.
  • Improve profitability without sacrificing quality or performance.

Origin and Background

VAVE was invented by Lawrence D. Miles at General Electric during World War II when material shortages forced him to seek functional alternatives for components. Miles' key realization was that cost is only meaningful relative to the function it buys. The methodology differentiates between Value Engineering (applied to a new product/design phase) and Value Analysis (applied to an existing product). The method is driven by a simple functional equation: Value = Function / Cost. The tool typically utilizes a FAST Diagram (Function Analysis System Technique) to logically decompose the product's function.

How to Use the VAVE Tool

The tool is organised into four tabs: Start & Info, Define Structure, Visualize, and VAVE Analysis.

  1. Step 1 – Define Structure

    In the Define Structure tab, build the functional model of your product in three layers using the three side-by-side panels:

    • Components: Add all physical parts (mark external elements like the power supply or the user).
    • Contacts: Define directional interactions between components (From → To).
    • Functions: Assign a name and type (Primary / Secondary) to each function and link it to one or more contacts.

  2. Step 2 – Visualize

    The Visualize tab renders an interactive network diagram of the system structure. Internal components appear as filled boxes; external elements as light-grey boxes. Functions are drawn as labelled arrows over their assigned contacts. Drag nodes to rearrange the layout and use Export Chart as JPG to save the diagram for reports.

  3. Step 3 – VAVE Analysis

    Click Generate VAVE Analysis to move to the VAVE Analysis tab. For each function identified in the structure, assign:

    • Cost ($): The estimated cost associated with this function.
    • Value Score (1–10): How important this function is to the customer or system (1 = least, 10 = most).

    The tool calculates the Value Index = Value Score ÷ Cost. Functions are colour-coded by priority: those with a low Value Index are highlighted as cost-reduction candidates; high-index functions confirm strong value delivery. A summary panel shows total cost, average Value Index, and counts of low- and high-priority functions.

  4. Step 4 – Save, Load, and Export

    Use the toolbar to Save the complete project (all components, contacts, functions, and analysis data) as a .json file. Load restores a saved project in read-only mode; Revise registers a traceable revision and unlocks the fields for editing. Load Example pre-fills the tool with a sample system. Export PDF generates a formatted report of the structure and value analysis results.

Open VAVE Tool

Design Thinking Facilitator

Purpose of the Tool

Design Thinking is a human-centered, iterative process used for practical, creative problem-solving and innovation. Its goal is to create products, services, and processes that are desirable, feasible, and viable. Use it to:

  • Gain a deep, empathetic understanding of the end-user's needs, behaviors, and motivations.
  • Challenge assumptions and identify innovative solutions not visible through traditional analysis.
  • Rapidly test and refine ideas using low-cost prototypes.
  • Foster a collaborative and multidisciplinary approach to solving complex, ill-defined problems.

Origin and Background

Design Thinking evolved from practices used in industrial design and engineering. It was popularized and structured into a formal methodology by IDEO (a global design firm) and the Stanford d.school (Hasso Plattner Institute of Design) in the early 2000s. The process is defined by five key phases: Empathize, Define, Ideate, Prototype, and Test. Unlike purely analytical methods, Design Thinking starts with desirability (what people want) before moving to feasibility (what is possible) and viability (what is profitable). Companies like Google, IBM, and Airbnb have institutionalized Design Thinking as their primary innovation engine.

Tool Structure

The tool is organised into three main tabs: Start & Info, Design Thinking Tool, and Report. The second tab contains five phase sub-tabs navigable with Previous / Next buttons.

How to Use the Design Thinking Tool

  1. Phase 1 – Empathize

    Document your user research across three structured fields: User Research & Observations (interview notes, shadowing findings), User Personas (who your users are and their goals), and a User Journey Map (the step-by-step experience from the user's point of view). This phase ensures the team builds genuine empathy before moving forward.

  2. Phase 2 – Define

    Synthesize empathy findings into a clear, human-centered Point-of-View (POV) Statement following the format "[User] needs [need] because [insight]". Then capture the Key Insights — the surprising patterns and tensions distilled from research that will fuel ideation.

  3. Phase 3 – Ideate

    Reframe problems as "How Might We…" (HMW) questions to open the space for divergent thinking. Record all brainstormed Ideas & Concepts without judgment, then identify which ones the team selects to prototype.

  4. Phase 4 – Prototype

    Define the Prototype Type (paper mockup, storyboard, roleplay, cardboard model, etc.), describe the Core Features of the prototype, and list the Assumptions to Test — the riskiest beliefs the prototype must validate or invalidate before further investment.

  5. Phase 5 – Test

    Document the Testing Plan (who, where, when, how), record User Feedback collected from participants, define clear Success Metrics, and capture Iterations & Next Steps. The process is deliberately iterative: testing often leads back to Empathize or Define.

  6. Step 6 – Review the Report

    Switch to the Report tab at any time to see a read-friendly summary aggregating all five phases and the full revision history — ready to share with stakeholders.

  7. Step 7 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage your project lifecycle:

    • New — Clears all fields and starts a blank project.
    • Load — Opens a previously saved .json file in read-only mode, restoring all five phases and revision history.
    • Load Example — Pre-fills the tool with a complete sample project so you can explore all features before entering your own data.
    • Revise — Available when a file is loaded. Registers a revision date, description, and author; then unlocks all fields for editing. Revision details are saved to the JSON and appear in the exported PDF.
    • Save — Downloads the complete project as a .json file, including all five phases and the full revision history.
    • Export PDF — Generates a fully formatted PDF report covering all five phases, the project summary, and the revision history.
Open Design Thinking Tool

Kano Model Analysis

Purpose of the Tool

The Kano Model is a theory of product development and customer satisfaction that classifies customer preferences into five categories. Its goal is to prioritize which features to develop based on their impact on customer delight, moving beyond simply meeting functional requirements. It helps to:

  • Identify Attractive (Delight) needs that create a competitive advantage.
  • Distinguish between basic Must-Be needs (unspoken, expected) and One-Dimensional needs (linear relationship to satisfaction).
  • Optimize resource allocation by focusing on features that maximize satisfaction.
  • Predict when a feature will shift from a "delighter" to a "basic expectation" over time.

Origin and Background

Developed by Professor Noriaki Kano in the 1980s, the model's core insight is that customer satisfaction is not linear. For example, perfectly functioning brakes (a Must-Be quality) will not make a customer happy, but faulty brakes will cause extreme dissatisfaction. Conversely, an innovative feature like a self-parking system (Attractive quality) causes delight, but its absence causes no dissatisfaction. Kano's genius was in creating a simple two-question survey structure (functional and dysfunctional form) that allows product developers to quickly classify any feature.

Tool Structure

The tool is organised into three tabs: Theory & Info, Input & Edit, and Results. The main workflow is performed in the second tab.

How to Use the Kano Model Tool

  1. Step 1 – Design the Paired Survey Questions

    For each feature, create a Functional question ("How do you feel if this feature is present?") and a Dysfunctional question ("How do you feel if this feature is absent?"). Responses use a five-point scale: I like it, I expect it, I am neutral, I tolerate it, I dislike it.

  2. Step 2 – Input and Tally Responses

    In the Input & Edit tab, add each feature by name and enter the response counts for each of the 25 paired functional/dysfunctional combinations. Click the edit icon next to a feature row to modify entries at any time.

  3. Step 3 – Classify Features and Review the Kano Graph

    The tool automatically applies the Kano Evaluation Rules to classify each feature into: A (Attractive/Delighter), O (One-Dimensional/Performance), M (Must-Be/Basic), I (Indifferent), R (Reverse/Undesired), or Q (Questionable). Switch to the Results tab to see the interactive Kano Model Visualization graph, which plots all features on the classic satisfaction/functionality axes, giving an immediate visual overview of your product's feature portfolio.

  4. Step 4 – Interpret the Customer Satisfaction (CS) Index

    Below the graph, the tool calculates two quantitative coefficients for each feature:

    • CS+ (Satisfaction Index): How much implementing the feature will increase customer satisfaction. Values close to +1.0 indicate features that will delight customers.
    • CS− (Dissatisfaction Index): How much not implementing the feature will hurt satisfaction. Values close to −1.0 indicate features whose absence will cause significant dissatisfaction.

    Prioritize features with high CS+ and strongly negative CS− to maximize customer value per unit of effort.

  5. Step 5 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page:

    • New — Clears all features and starts a blank analysis.
    • Load — Opens a previously saved .json file in read-only mode.
    • Load Example — Pre-fills the tool with sample features so you can explore the graph and CS Index before entering your own data.
    • Revise — Registers a traceable revision (date, description, author) and unlocks all fields for editing. Revision details appear in the exported PDF.
    • Save — Downloads the complete analysis as a .json file, including all categorized features and the revision history.
    • Export PDF — Generates a formatted PDF containing the Kano Visualization graph, the CS Index table, the categorized feature list, and the revision history.
Open Kano Model

8D Problem-Solving Report

Purpose of the Tool

Use this tool to create a comprehensive and standardized report for solving complex problems, especially those raised by customers. The 8D process is designed to:

  • Provide a disciplined, step-by-step methodology ensuring no critical step is skipped.
  • Emphasize a team-based approach to leverage collective knowledge.
  • Force a clear distinction between short-term containment and long-term permanent corrective actions.
  • Drive deep into root cause analysis to prevent problem recurrence.
  • Create a formal record of the problem-solving process for customers and internal learning.

Origin and Background

The 8D (Eight Disciplines) process was developed by Ford Motor Company in the mid-1980s as "Team Oriented Problem Solving" (TOPS). Ford needed a standardized, data-driven process to ensure that significant problems were permanently eliminated, not just patched over. A key aspect is D3: Implement Containment Actions, which forces the team to immediately protect the customer. Its true power lies in D4: Identify and Verify Root Cause and D7: Prevent Recurrence, which institutionalizes the lessons learned. Because of its effectiveness, the 8D report became the required response format for suppliers to Ford, General Motors, and major Tier 1s like Bosch and ZF.

Tool Structure

The tool is organised into three tabs: Start & Info, 8D Report (with nine sub-tabs, one per discipline), and Report. Use the toolbar at the top to manage saves, loads, and revisions.

How to Use the 8D Tool

The tool provides a fully structured digital template guiding you through all nine disciplines (D0 to D8).

  1. D0 – Plan and Prepare

    Before forming the team, fill in the 8D Report ID / Revision, the ERA (Emergency Response Action) and customer notification date if applicable, a concise Problem Awareness description, the Urgency Level (from a dropdown: High / Medium / Low), Initial Data, preliminary objectives, and optional evidence attachment URLs. This pre-work ensures the team is properly scoped and resourced before the formal 8D begins.

  2. D1 – Form a Team

    Enter the Team Leader, Champion / Sponsor (name and position), all Team Members, and the report date. A cross-functional team is essential for effective root-cause analysis.

  3. D2 – Describe the Problem

    Write a precise Problem Statement and complete the extended IS / IS NOT matrix across five dimensions (What, Where, When, Who, How Many), comparing confirmed facts with what the problem is definitively not. This structured comparison focuses the investigation and avoids scope creep.

  4. D3 – Implement Containment Actions

    Document the immediate measures taken to protect the customer while the root cause is being investigated (e.g., 100% sorting, lot quarantine). Include the responsible person, deadline, and verification method for each containment action.

  5. D4 – Identify and Verify Root Cause

    List potential causes from brainstorming and the Ishikawa / 4M-6M diagram (an optional input field is provided). Drill down using the 5 Whys method to confirm the technical root cause (why the failure happened) and the escape root cause (why the system allowed it through to the customer). Record the verification evidence.

  6. D5 – Develop Permanent Corrective Actions

    Propose permanent solutions that directly address the verified root causes. For each Permanent Corrective Action (PCA), assign a responsible person and a target deadline.

  7. D6 – Implement and Validate PCAs

    Document the implementation of each PCA and provide validation data — e.g., a new Cpk study, trend charts, or audit results — that prove the actions were effective. Confirm and formally remove the containment actions from D3 once validated.

  8. D7 – Prevent Recurrence

    Update systems to institutionalise the fix: work instructions, PFMEAs, control plans, training records. Use checkboxes to confirm: WI Updated, Training Completed, Error-Proofing implemented, Horizontal Deployment verified (the fix applied to similar processes), PFMEA Updated, and Control Plan Updated. Reference the updated document IDs and assign the responsible person.

  9. D8 – Recognize the Team

    Document how the team's effort was formally acknowledged (award, recognition letter, etc.), record the Lessons Learned and Knowledge Sharing plan, enter the official Closure Date, and obtain the Champion Sign-off.

  10. Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage the full document lifecycle:

    • Load — Opens a previously saved .json file, restoring the complete 8D report (all disciplines and revision history) in read-only mode.
    • Load Example — Pre-fills all disciplines with a realistic manufacturing case (PCB short-circuit defect) so you can explore every field.
    • Revise — Registers a new revision (date, description, author) and unlocks all fields for editing. Revision records appear in the JSON and the exported PDF.
    • Save — Downloads the complete 8D report as a .json file, including all discipline data and the full revision history.
    • Export PDF — Generates a fully formatted, print-ready PDF containing all disciplines, the IS/IS NOT analysis, checklist confirmations, and the revision history.
Open 8D Tool

Ishikawa (Fishbone) Diagram Generator

Purpose of the Tool

Use this tool during team brainstorming sessions to systematically explore all potential root causes of a complex problem. Its primary purpose is to:

  • Provide a structured framework for root cause analysis.
  • Prevent teams from jumping to conclusions by forcing consideration of multiple categories.
  • Visually organize complex cause-and-effect relationships.
  • Serve as a powerful communication and documentation tool for problem-solving (e.g., for an 8D report).

Origin and Background

Created by Dr. Kaoru Ishikawa in the 1960s, this diagram was designed to support "Quality Circles" in Japan. Ishikawa believed quality was a task for the entire organization, and he needed a simple tool for structured brainstorming. The diagram's layout resembles a fish's skeleton, earning it the nickname "Fishbone Diagram." The problem or "effect" is the "head," and the main "bones" represent categories of causes. In manufacturing, these are famously the 6Ms: Manpower, Methods, Machines, Materials, Measurement, and Mother Nature. This structure encourages systematic thinking and has become one of the seven basic tools of quality, used worldwide by companies like Toyota.

Tool Structure

The tool is organised into three tabs: Start & Info, Input Tool (with six category sub-tabs), and Diagram. The professional-grade diagram is rendered automatically using JointJS.

How to Use the Ishikawa Tool

  1. Step 1 – Define the Problem (The "Effect")

    In the Input Tool tab, enter a clear and concise statement of the problem in the "Problem / Effect" field. This forms the "head" of the fishbone and anchors the entire analysis.

  2. Step 2 – Brainstorm Causes by Category

    Navigate through the six 6M category sub-tabs — Manpower, Methods, Machines, Materials, Measurement, and Mother Nature (Environment). For each category, add as many causes as the team brainstorms. Each cause can also have one or more sub-causes (second-level branches), allowing deeper root-cause decomposition directly within the diagram structure. Enter one cause or sub-cause per field and use the + Add Cause / + Add Sub-Cause buttons to expand the analysis.

  3. Step 3 – Generate the Automatic Diagram

    Click Generate Diagram. The tool renders a professional fishbone diagram via JointJS with all causes, sub-causes, and category labels laid out automatically. The diagram updates whenever you modify the inputs and regenerate.

  4. Step 4 – Export the Diagram and Data

    Once the diagram is generated, you can:

    • Export as JPG — Downloads a high-quality image of the fishbone diagram, ready to include in 8D reports, presentations, or quality documents.
    • Export to Excel (.xlsx) — Downloads a structured spreadsheet listing all categories, causes, and sub-causes, formatted for documentation and further analysis.

  5. Step 5 – Save, Load, and Manage Revisions

    The toolbar supports a full AIAG / IATF 16949–aligned revision lifecycle:

    • New — Clears all data and starts a blank diagram.
    • Load — Opens a previously saved .json file in read-only mode, restoring the full cause structure and revision history.
    • Load Example — Pre-fills all six categories with a sample manufacturing problem so you can explore the tool immediately.
    • Revise — Registers a traceable revision (date, description, author) and unlocks the fields for editing. Revision details are saved in the JSON and shown in the exported PDF.
    • Save — Downloads the complete analysis as a .json file, including all causes, sub-causes, and revision history.
    • Export PDF — Generates a fully formatted PDF report including the fishbone diagram image, the full cause list, and the revision history.
Open Ishikawa Diagram

TRIZ Inventive Problem Solving

Purpose of the Tool

TRIZ (Theory of Inventive Problem Solving) is a systematic, data-driven methodology for innovation that transforms complex technical problems into solvable engineering challenges by identifying and resolving contradictions. It moves beyond psychological brainstorming toward a logical, pattern-based method for invention. Its purpose is to:

  • Systematically resolve technical contradictions (improving one engineering parameter worsens another) using the 40 Inventive Principles and the Contradiction Matrix.
  • Resolve physical contradictions (a parameter must simultaneously have opposite values) using four Separation Strategies.
  • Apply the 8 Laws of Development of Technical Systems (LDST) to map the evolutionary trajectory of the system and identify strategic directions.
  • Define the Ideal Final Result (IFR) to break psychological inertia and focus effort on truly useful functions.
  • Explore innovation opportunities through the 9 Windows (time × system-level matrix).
  • Map all component interactions through Function Analysis to detect harmful, insufficient, and excessive functions.
  • Model substance–field interactions with Su-Field Analysis and apply the 76 Standard Solutions to address measurement, harmful-effect, and improvement problems.
  • Accelerate breakthrough innovation with a clear, repeatable structured path from problem to solution.

Origin and Background

TRIZ was developed by Genrich Altshuller beginning in 1946 while working as a patent examiner in the Soviet Navy. Analyzing hundreds of thousands of patents across diverse technical fields, he discovered that inventive problems, regardless of their domain, were solved using a surprisingly small set of universal principles and patterns. His revolutionary insight was that innovation follows predictable patterns rather than being random acts of genius. His team codified these patterns into the foundational TRIZ toolkit. The methodology was further developed by contributors including Boris Zlotin, Alla Zusman, Vladimir Petrov, Victor Fey, Eugene Rivin, Simon Litvin, Valeri Souchkov, and Michael Orloff, who expanded applications to include LDST analysis, physical contradiction resolution, and modern Su-Field modeling. Darell Mann's Matrix 2003 updated the classical 39×39 contradiction matrix to 48 parameters using analysis of over 2.5 million patents. Companies like Samsung, LG, Intel, Boeing, and General Electric use TRIZ to accelerate R&D and drive systematic innovation.

Tool Structure

The tool is organised into two main tabs: Start & Info (theory, references, and toolbar guide) and TRIZ Tools (the integrated problem-solving workspace). The workspace contains eight sequential sub-tabs, one for each analytical step. A Report view synthesizes all steps for export. Files opened via Load or Load Example open in read-only mode to prevent accidental overwrites; click Revise to register a new revision and unlock all fields.

Step-by-Step Workflow

  1. Step 1 – Define the Problem

    Describe three elements that anchor the entire analysis:

    • System description — what the system is and what it does (e.g., "Screwdriver bit holder").
    • Primary Useful Function — the main beneficial action the system must perform (e.g., "Hold the bit firmly during operation").
    • Main drawback / harmful effect — the problem to be solved (e.g., "Strong magnet holds well but makes extraction hard; weak magnet loses the bit").

    Keep definitions simple and precise. Avoid jumping to solutions at this stage — the problem statement is the lens through which all subsequent tools are focused.

  2. Step 2 – LDST Analysis (Laws of Development of Technical Systems)

    Select the system's current lifecycle stage (Birth, Growth, Maturity, Decline, or Transition) from the dropdown. The tool maps it to the relevant LDST laws and generates strategic guidance. The tool implements all 8 classical LDST laws as formalized by Altshuller:

    • Law 1 – System Completeness: A technical system must have all essential parts and minimum necessary connections (e.g., combustion engine requires fuel, air, spark, and cylinder).
    • Law 2 – Energy Conductivity: Energy must flow without obstacles through all parts. Design removes intermediaries and increases conductivity (e.g., copper wires, fiber optics).
    • Law 3 – Harmony: System parts must be coordinated in rhythm, frequency, and structure — synchronization, resonance, spatial-temporal coordination (e.g., synchronized 4-stroke engine).
    • Law 4 – Ideality: Systems evolve toward greater ideality — more functions with fewer resources, parts eliminated, self-service enabled (e.g., smartphone replacing multiple devices).
    • Law 5 – Uneven Development: Parts of a system develop at different rates, creating internal contradictions. Identify and level lagging subsystems (e.g., battery vs. processor speed in mobile devices).
    • Law 6 – Transition to Supersystem: Systems integrate into larger, more complex supersystems through integration and expansion (e.g., individual computers → the Internet).
    • Law 7 – Macro-Micro Transition: Development moves toward microstructural levels and field-based interactions (e.g., transistors → integrated circuits → nanochips).
    • Law 8 – Increasing Su-Field Complexity: Systems evolve toward greater complexity of fields and substances — mechanical → electrical → electromagnetic → quantum (e.g., simple hand tools → intelligent robotic systems).

    The tool also displays the Lifecycle–Principles Correlation Matrix, showing which TRIZ principles are most relevant at each evolutionary stage.

  3. Step 3 – Formulate the Ideal Final Result (IFR)

    The IFR defines the ultimate solution: the system performs its useful function perfectly without any drawbacks, costs, or harmful effects — ideally by itself, using existing resources. The IFR is expressed as: "[System element] performs [useful function] without [drawback], without additional resources or costs." The IFR breaks psychological inertia — it prevents the engineer from anchoring to incremental improvements and opens the search space to radically different solution concepts.

  4. Step 4 – 9 Windows Analysis

    The 9 Windows (also called the System Operator) is a structured thinking framework that analyzes the problem across a 3 × 3 matrix: three time dimensions (Past, Present, Future) by three system hierarchy levels (Sub-System, System, Super-System). Each of the nine cells prompts discovery of resources and solution concepts that are invisible when focusing only on the system in its current state. For each cell, describe what the system or its context looked like, looks like, or might look like. Innovation opportunities often emerge in the Super-System Future cell, where technological or environmental changes can make the problem disappear or become trivial.

  5. Step 5 – Function Analysis

    Function Analysis decomposes the system into its components and maps every interaction between them. The workflow has three layers:

    • Components: List all physical parts of the system (mark external elements — power source, user, environment — separately).
    • Contacts: Define directional interactions between component pairs (From → To), building an interaction matrix.
    • Functions: For each contact, classify the function the carrier performs on the target. Each function is rated by its category and performance:

    Function categories: Useful (beneficial action), Harmful (negative side effect). Performance levels: Normal, Insufficient (action exists but is too weak), Excessive (action is too strong or too frequent). The resulting Function Model highlights which interactions require improvement — these become the specific inputs for the Contradiction and Su-Field steps.

  6. Step 6 – Technical Contradiction & the Contradiction Matrix

    A technical contradiction occurs when improving one engineering parameter causes another to worsen. To resolve it, select the Improving Feature (the parameter you want to make better) and the Worsening Feature (the parameter that degrades as a result). The tool looks up the intersection of these two parameters in the Contradiction Matrix and returns a ranked list of recommended Inventive Principles.

    The tool implements two matrix versions — choose based on your context:

    • Classic Matrix (39 × 39 parameters) — Altshuller, 1950–1980: The original 39 engineering parameters cover the full range of physical and engineering trade-offs. Parameters include: Weight of moving/stationary object, Length, Area, Volume, Speed, Force, Stress/Pressure, Shape, Stability, Strength, Temperature, Illumination, Energy use, Power, Loss of Energy/Substance/Information/Time, Quantity of substance, Reliability, Measurement accuracy, Manufacturing precision, Adaptability, Device complexity, Ease of manufacture/operation/repair, Extent of automation, and Productivity.
    • Matrix 2003 (48 × 48 parameters) — Darell Mann, 2003: Updated through analysis of over 2.5 million patents. Adds 9 new parameters relevant to modern engineering: Amount of Information, Noise, Harmful Emissions, Compatibility / Connectability, Trainability / Operability / Controllability, Security, Safety / Vulnerability, Aesthetics / Appearance, and Control Complexity.

    Both matrices recommend from the same set of 40 Inventive Principles (numbered 1–40). For each recommended principle, the tool shows its name, a concise description, and a practical example:

    • P1 Segmentation — Divide into independent parts (modular furniture).
    • P2 Taking out — Separate an interfering part (noisy compressor outside).
    • P3 Local quality — Change structure from uniform to non-uniform (anti-slip socks).
    • P4 Asymmetry — Replace symmetrical form with asymmetrical (car tire sidewall).
    • P5 Merging — Bring together identical/similar objects (catamaran hulls).
    • P6 Universality — Make a part perform multiple functions (Swiss Army knife).
    • P7 Nested doll — Place one object inside another (telescopic antenna).
    • P8 Anti-weight — Compensate weight with lift (helium balloon camera).
    • P9 Preliminary anti-action — Pre-load counter-stresses (child-resistant caps).
    • P10 Preliminary action — Perform changes before needed (coffee capsules).
    • P11 Beforehand cushioning — Prepare emergency means (pre-stressed concrete).
    • P12 Equipotentiality — Limit position changes (canal locks).
    • P13 The other way round — Invert the action (mold turned inside-out).
    • P14 Spheroidality / Curvature — Use curvilinear forms (dome stadium).
    • P15 Dynamics — Allow characteristics to change optimally (adjustable chair).
    • P16 Partial or excessive actions — Do slightly less or more (spray then sand).
    • P17 Another dimension — Move into a new dimension (multi-story carpark).
    • P18 Mechanical vibration — Cause oscillation (jackhammer).
    • P19 Periodic action — Use pulsed instead of continuous action (piezoelectric lighter).
    • P20 Continuity of useful action — Work continuously (continuous steel casting).
    • P21 Skipping — Conduct process at high speed (flash photography).
    • P22 Blessing in disguise — Use harmful factors positively (waste heat for heating).
    • P23 Feedback — Introduce feedback to improve the process (thermostat).
    • P24 Intermediary — Use an intermediate carrier (optical fibre).
    • P25 Self-service — Make object serve itself (solar calculator).
    • P26 Copying — Use simpler inexpensive copies (crash test dummies).
    • P27 Cheap short-living objects — Replace expensive objects with multiples (paper cups).
    • P28 Mechanics substitution — Replace mechanical means with sensory (keyless entry).
    • P29 Pneumatics and hydraulics — Use gas/liquid parts (air suspension).
    • P30 Flexible shells and thin films — Use flexible shells (bubble wrap).
    • P31 Porous materials — Make object porous (foam padding).
    • P32 Color changes — Change color of object or environment (pH strips).
    • P33 Homogeneity — Make interacting objects of same material (biodegradable bags).
    • P34 Discarding and recovering — Reject/restore parts after function (rechargeable battery).
    • P35 Parameter changes — Change physical state (glue stick).
    • P36 Phase transitions — Use phase-change phenomena (shape-memory alloy).
    • P37 Thermal expansion — Use thermal expansion/contraction (heat-shrink tubing).
    • P38 Strong oxidants — Replace air with enriched oxygen (BOF steelmaking).
    • P39 Inert atmosphere — Replace normal environment with inert one (argon bulb).
    • P40 Composite materials — Change from uniform to composite (CFRP).

  7. Step 7 – Physical Contradiction & Separation Strategies

    A physical contradiction occurs when a single parameter must simultaneously have two opposite values (e.g., a magnetic field must be strong to hold the bit, and weak to release it). The tool walks through an intersection test to determine whether the conflicting demands occupy the same or different operational zones, then recommends one of six separation strategies (MATRIZ extended doctrine, Litvin et al.):

    • Strategy 1 – Separation in Space: Satisfy each demand in a different physical location. Key principles: P1 Segmentation, P3 Local quality, P4 Asymmetry, P7 Nested doll, P17 Another dimension, P30 Flexible shells. Example: Asymmetric USB plug forces correct insertion orientation — correct-fit and directional-constraint separated in space.
    • Strategy 2 – Separation in Time: Satisfy each demand at a different moment. Key principles: P9 Preliminary anti-action, P10 Preliminary action, P11 Beforehand cushioning, P15 Dynamics, P19 Periodic action, P34 Discarding and recovering. Example: Piezoelectric lighter — fuel flows and spark fires in overlapping but distinct temporal phases.
    • Strategy 3 – Separation in Relation (Condition): Satisfy each demand under different conditions or at different interfaces. Key principles: P31 Porous materials, P32 Color changes, P40 Composite materials. Example: Thermal insulation panel — dense outer skin under compression, foam core under tension, each layer operates under the condition most favourable to its function.
    • Strategy 4 – Separation between System and Parts: One demand is satisfied at the system level, the opposite at the subsystem (parts) level. Key principles: P1 Segmentation, P5 Merging, P7 Nested doll, P33 Homogeneity, P40 Composite materials. Example: Chainmail glove — each ring is rigid (subsystem), the whole glove is flexible (system).
    • Strategy 5 – Satisfy Both Demands Simultaneously via physical or chemical phenomena: Key principles: P28 Mechanics substitution, P35 Parameter changes, P36 Phase transitions, P37 Thermal expansion, P38 Strong oxidants, P39 Inert atmosphere. Example: Chemical cold pack satisfies both "needs no heat source" and "must deliver cold" via endothermic dissolution.
    • Strategy 6 – Bypass the Contradiction: Eliminate or redesign the conflicting requirement entirely by changing the operating principle. Illustrative principles: P6 Universality, P13 The other way round, P24 Intermediary, P25 Self-service. Example: Digital thermometer replaces mercury — the toxicity vs. precision contradiction is bypassed by changing the measurement principle altogether.

  8. Step 8 – Su-Field Analysis & 76 Standard Solutions

    Su-Field (Substance–Field) Analysis models system interactions as a triad: S1 (Object — what is acted upon), S2 (Tool — what performs the action), and F (Field — the type of energy or interaction). Available field types: Mechanical, Thermal, Electrical, Magnetic, Optical, Acoustic, Chemical, Gravitational, Nuclear, Biological. For each system defined, select the problem type to receive targeted Standard Solutions:

    • Insufficient: The action exists but is too weak. Add a new substance or field to enhance it.
    • Harmful: The action causes damage. Introduce a neutralizing substance, or modify S1/S2 to resist the harmful effect.
    • Difficult to measure: The interaction cannot be easily observed. Use a copy of the system or a detectable additive.
    • Missing: The desired action does not exist yet. Introduce a new substance or field to create it.
    • Excessive: The action is too strong. Reduce the field or introduce a counter-field.
    • Inefficient: The action is wasteful. Improve field efficiency or exploit resonance.

    The tool then recommends specific solutions from the 76 Standard Solutions organised across five classes:

    • Class 1 – Improving system with no/little change (13 solutions): Build or improve the Su-Field system. Includes strategies such as completing an incomplete system (1.1.1), introducing internal or external additive substances (1.1.2, 1.1.3), using environment as additive (1.1.4), controlling via surplus application/removal (1.1.6), introducing intermediary objects (1.1.7), eliminating harmful effects via S3 (1.2.1–1.2.3), and counteracting harmful fields with opposing fields (1.2.4, 1.2.5).
    • Class 2 – Improving system by changing the system (23 solutions): Evolve and develop the existing Su-Field system — transition to complex Su-Field chains (2.1.1–2.1.2), expansion, simplification, integration (2.1.3–2.1.5), transition to microlevel (2.1.6–2.1.7), field simplification (2.2.1), use of multiple fields (2.2.2), use of rhythm and resonance (2.3.x), and use of electromagnetic and physical fields (2.4.x).
    • Class 3 – System transitions (6 solutions): Transitions to higher-level systems and micro-level structures — integration of systems (3.1.x) and use of phase transitions and physical phenomena (3.2.x).
    • Class 4 – Detection and measurement (17 solutions): Solutions for measurement, detection, and control problems — using copies (4.1.x), physical effects for measurement (4.2.x), using resonance for detection (4.3.x), and making the immeasurable measurable by introducing tracers or additives (4.4.x).
    • Class 5 – Strategies for simplification and improvement (17 solutions): Simplify systems by eliminating auxiliary functions (5.1.x) — converting bi-systems and poly-systems (5.2.x) — and using phase transitions to simplify structure (5.3.x).

    Each Standard Solution is cross-referenced with the relevant Inventive Principles, and the tool displays a visual Su-Field diagram (coded in green for S1, blue for S2, orange for the field F) for each system you define.

  9. Generate Solutions & Report

    Click "Solve Problem & Generate Report" on the final sub-tab. The tool synthesizes all eight preceding steps and produces a consolidated report containing: the problem statement and IFR, the LDST law and lifecycle guidance, the 9 Windows map, Function Analysis results, Technical Contradiction with recommended Inventive Principles from the selected matrix, Physical Contradiction with the recommended Separation Strategy and specific principles, and Su-Field systems with their Standard Solution recommendations.

  10. Save, Load, and Export

    The toolbar at the top of the page supports the full project lifecycle:

    • Load — Opens a previously saved .json file and fully restores all TRIZ tool data: Function Analysis components & interactions, Su-Field systems, and all analysis states, in read-only mode.
    • Load Example — Pre-fills all eight steps with the Screwdriver Bit Holder case study (Oliver Gerundt & Jochen Wessner, 2019), demonstrating a complete TRIZ workflow from problem definition to Su-Field resolution.
    • Revise — Registers a new revision (date, description, author) and unlocks all fields for editing. Revision records appear in the JSON and the exported PDF.
    • Save — Downloads a .json file containing all fields, Function Analysis data, Su-Field systems, and revision history.
    • Export PDF — Generates a multi-page, print-ready PDF of the full report: problem definition, IFR, LDST guidance, 9 Windows, Function Analysis, Technical & Physical Contradictions, and Su-Field Analysis with Standard Solutions.
    • Export to Excel — Available from the Report tab; exports all analysis steps, results, recommended principles, and Standard Solutions in spreadsheet format for further processing or archiving.
Technical reference: The Classic Contradiction Matrix (39 parameters) follows Altshuller (1950–1980). The Matrix 2003 (48 parameters) follows Darell Mann's Systematic Innovation (2003). The 76 Standard Solutions classification follows Domb, Terninko, Miller & MacGran. Physical Contradiction separation strategies follow MATRIZ extended doctrine (Litvin et al., 2000s). The LDST laws follow Altshuller's Creativity as an Exact Science (1984). Mapping of lifecycle stages to specific LDST laws is an educational heuristic, not classical Altshuller doctrine. Cross-referencing Standard Solutions with Inventive Principles is an informal analogy for educational purposes.
Open TRIZ Tool

Eisenhower Matrix Task Prioritizer

Purpose of the Tool

The Eisenhower Matrix, also known as the Urgent-Important Matrix, is a simple, yet highly effective time management and prioritization tool. Its purpose is to:

  • Help teams and individuals focus on the tasks that contribute to long-term strategic goals.
  • Clearly separate Urgency (requires immediate attention) from Importance (contributes to your mission).
  • Reduce reactive work by encouraging planning and delegation.
  • Force a clear decision on every task: Do, Schedule, Delegate, or Eliminate.

Origin and Background

The method is based on a quote attributed to the 34th U.S. President, Dwight D. Eisenhower: "I have two kinds of problems, the urgent and the important. The urgent are not important, and the important are never urgent." Eisenhower, a 5-star general and President, needed a system to prioritize the monumental number of tasks he faced. The matrix formalizes this thinking into four quadrants: Do, Schedule, Delegate, and Eliminate. It is a cornerstone of modern productivity training and is often associated with the principles taught by Stephen Covey.

Tool Structure

The tool is organised into two tabs: Start & Info (theory and toolbar guide) and Build & Edit (the live four-quadrant workspace). Files opened via Load or Load Example open in read-only mode; click Revise to register a new revision and unlock all tasks for editing.

How to Use the Eisenhower Matrix Tool

  1. Step 1 – Add Tasks

    In the Build & Edit tab, type the task description in the input field, select the target quadrant from the dropdown, and press Enter or click Add Task. Tasks appear immediately inside the chosen quadrant. There is no limit to the number of tasks per quadrant.

  2. Step 2 – Place Tasks in the Right Quadrant

    For each task, apply the two-question test — Is it Urgent? Is it Important? — and assign it to the correct quadrant:

    • Q1 – Do (Urgent & Important): Crises, deadlines, emergencies. Act immediately — these are the fires that must be extinguished today.
    • Q2 – Decide / Schedule (Not Urgent & Important): Strategic planning, prevention, relationship building, skill development. Block dedicated time on the calendar — this quadrant is the engine of long-term success.
    • Q3 – Delegate (Urgent & Not Important): Interruptions, reactive requests, certain meetings. Assign to someone else — urgency does not equal importance.
    • Q4 – Delete / Eliminate (Not Urgent & Not Important): Time wasters, trivial activities, endless scrolling. Remove from the list — these consume energy without returning value.

  3. Step 3 – Edit and Reorganise Tasks

    Click the pencil icon on any task to edit its description inline, or the trash icon to remove it. Tasks can be re-assigned to a different quadrant at any time by deleting and re-adding in the correct quadrant, or by using the quadrant dropdown during inline editing.

  4. Step 4 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage the full task-list lifecycle:

    • New — Clears all tasks from all four quadrants and starts a blank matrix.
    • Load — Opens a previously saved .json file and restores all tasks across all quadrants in read-only mode.
    • Load Example — Pre-fills all four quadrants with a realistic sample task set so you can explore the tool before entering your own work. Also opens in read-only mode.
    • Revise — Registers a new revision (date, description, author) and unlocks all tasks for editing. Revision details are saved to the JSON and appear in the exported PDF.
    • Save — Downloads the complete matrix as a .json file, including all tasks, their quadrant assignments, and the full revision history.
    • Export PDF — Generates a formatted PDF showing all four quadrants with their tasks and the revision history, ready for team meetings or personal review.
Open Eisenhower Matrix

APQP & PPAP Project Planner

Purpose of the Tool

APQP (Advanced Product Quality Planning) is a structured method defining the steps necessary to ensure a product satisfies the customer. PPAP (Production Part Approval Process) is the final submission step. The Planner is essential for:

  • Managing the product development cycle from concept initiation to full production launch.
  • Ensuring all key quality and engineering outputs (FMEA, Control Plan, Flow Chart) are completed on time.
  • Providing a roadmap for cross‑functional communication and approval.
  • Generating the final PPAP package – the evidence that the supplier can consistently meet requirements.

Origin and Background

APQP was developed by the Automotive Industry Action Group (AIAG) in the 1980s. The latest framework aligns with the AIAG‑VDA FMEA Handbook (2019), which introduced the Action Priority (AP) system and a harmonised 7‑step FMEA process. PPAP remains the mandatory submission requirement for automotive suppliers (IATF 16949). This tool reflects the current industry standard, where FMEA results (AP) directly influence the Control Plan and the overall APQP milestones.

Tool Structure

The tool is organised into several sub-tabs accessible from the main toolbar: Project Setup, five APQP Phase sub-tabs (Phase 1–5), a PPAP Checklist sub-tab, a Gantt Chart view, and an Action Plan view. A Verify AIAG button runs an automated compliance check at any time.

How to Use the APQP & PPAP Planner

  1. Step 1 – Project Setup

    Go to the Project Setup sub-tab and fill in the project header: customer name, part name and number, part revision, model year, PPAP submission level (1–5), and key milestone dates. The submission level controls which PPAP elements require physical documentation vs. records retained at the supplier. This information populates the Gantt chart header and the PSW.

  2. Step 2 – Plan the Five APQP Phases

    Use the Phase 1–5 sub-tabs to add and track deliverables. Each task has a responsible person, start date, end date, and completion status. The five phases are:

    • Phase 1 – Plan and Define Program: Voice of the Customer, design goals, reliability targets, preliminary BOM, preliminary process flow, preliminary list of special characteristics.
    • Phase 2 – Product Design & Development: Design FMEA, design verification plan, engineering drawings and specifications, prototype build control plan.
    • Phase 3 – Process Design & Development: Process flow chart, Process FMEA (PFMEA), pre-launch control plan, MSA plan, floor plan layout, packaging standards.
    • Phase 4 – Product & Process Validation: Production trial run, MSA study (%GRR), process capability study (Ppk/Cpk), production control plan, PSW, Run-at-Rate confirmation.
    • Phase 5 – Feedback, Assessment & Corrective Action: Lessons learned, reduced variation, customer satisfaction results, delivery and service performance review.

  3. Step 3 – Manage the PPAP Checklist

    In the PPAP Checklist sub-tab, review the 18 standard PPAP elements. For each element, assign a responsible person, update the status (Not Started / In Progress / Completed / N/A), and record quantitative data where applicable: Ppk/Cpk values for the Dimensional Results element and %GRR for the Measurement System Analysis (MSA) element. The tool validates AIAG sequencing — the Part Submission Warrant (PSW) cannot be approved until all other applicable elements are marked Completed or N/A.

  4. Step 4 – Visualise Progress with the Gantt Chart

    The Gantt Chart sub-tab renders automatically from the task dates entered in Phases 1–5. It displays each task as a horizontal bar spanning its start-to-end date range, colour-coded by completion status (completed / in progress / not started), with month markers along the timeline. The Gantt updates every time you modify a task date.

  5. Step 5 – Track Open Actions

    The Action Plan sub-tab automatically aggregates all incomplete tasks from Phases 1–5 and the PPAP Checklist into a consolidated open-actions register, sorted by due date. Use it for cross-functional team meetings and customer status reviews.

  6. Step 6 – Complete the Part Submission Warrant (PSW)

    Once all PPAP elements are finalised, go to the PSW sub-section of the PPAP Checklist. Enter the submission reason, declared submission level, and authorized signatures. Click Approve PSW — the tool validates completeness and, if all elements are satisfied, marks the PSW as approved and locks it for export.

  7. Step 7 – Verify AIAG Compliance and Export

    Click Verify AIAG at any time to run an automated compliance check against the AIAG APQP/PPAP standard. The checker flags any mandatory elements that are missing, incomplete, or out of sequence. Then use the toolbar to manage the project lifecycle:

    • New — Clears all data and starts a blank project, taking you directly to the Project Setup tab.
    • Load — Opens a previously saved .json file and restores the full project state (all phases, tasks, PPAP checklist, PSW, and revision history) in read-only mode.
    • Load Example — Pre-fills the entire planner with a realistic automotive component project, showing how the Gantt and Action Plan work. Also opens in read-only mode.
    • Save — Downloads the complete project as a .json file, including all phases, tasks, PPAP elements, PSW data, and revision history.
    • Export PDF — Generates a print-ready PDF containing the project information, all five APQP phases with their tasks, the rendered Gantt chart, the PPAP checklist with status and data fields, and the revision history.
Integration with Sigma Exacta tools: The FMEA and Control Plan tools are fully aligned with the AIAG‑VDA 2019 standard. Use them to populate the required APQP deliverables, then track your progress here. The Verify AIAG button cross-checks that FMEA action priorities are reflected in the Control Plan before PSW approval.
Open APQP Planner

Strategy Scorecard

Purpose of the Tool

The Balanced Scorecard (BSC) is a strategic performance management tool that helps organizations translate their vision and strategy into a set of measurable actions. Its purpose is to:

  • Provide a "balanced" view of performance beyond just financial metrics.
  • Align departmental and individual goals with the overarching corporate strategy.
  • Monitor performance across four critical organizational perspectives.
  • Communicate the strategy simply and effectively throughout the organization.

Origin and Background

The Balanced Scorecard was developed by Dr. Robert Kaplan and Dr. David Norton in the early 1990s in response to the recognized shortcomings of purely financial-focused performance management systems. They proposed that a modern organization must be measured across four main perspectives, which collectively link operational activities to strategic outcomes: Financial, Customer, Internal Process, and Learning & Growth. The BSC is a core tool in modern strategic management, used by governments, non-profits, and Fortune 500 companies alike to execute strategy.

Tool Structure

The tool is organised into three tabs: Start & Info, Scorecard Tool (the main workspace with four perspective sub-tabs), and Report. A live Strategy Map diagram renders automatically as objectives are entered.

How to Use the Balanced Scorecard Tool

  1. Step 1 – Define Vision and Mission

    In the Scorecard Tool tab, enter the organisation's Vision (the long-term aspiration) and Mission (the day-to-day purpose). These statements anchor all four perspectives and are displayed at the top of the exported report.

  2. Step 2 – Define Objectives and KPIs by Perspective

    Navigate through the four perspective sub-tabs and populate each one. For every strategic objective, define:

    • Financial Perspective: How do we look to shareholders? Objectives focus on revenue growth, profitability, cost reduction, and return on investment. Example KPI: Operating Margin %, target ≥ 18%.
    • Customer Perspective: How do customers see us? Objectives cover satisfaction, retention, acquisition, and market share. Example KPI: Net Promoter Score, target ≥ 60.
    • Internal Process Perspective: At what must we excel? Objectives address operational efficiency, quality, cycle time, and innovation. Example KPI: On-Time Delivery Rate, target ≥ 98%.
    • Learning & Growth Perspective: Can we continue to improve and create value? Objectives cover employee skills, systems capabilities, culture, and knowledge management. Example KPI: Training Hours per Employee, target ≥ 40 h/year.

    For each objective, record: the KPI name, unit of measure, baseline value, target value, and the Strategic Initiative (the specific project or action that will close the gap). All fields update the Report tab in real time.

  3. Step 3 – Review the Strategy Map

    The Strategy Map renders automatically as a visual cause-and-effect diagram, stacking the four perspectives vertically with objectives linked by arrows. This illustrates the fundamental BSC hypothesis: investing in Learning & Growth improves Internal Processes, which drives Customer satisfaction, which produces Financial results. Use the map in strategy workshops to validate the logical chain between objectives before committing to initiatives.

  4. Step 4 – Monitor Performance in the Report Tab

    Switch to the Report tab to see a consolidated scorecard table grouping all perspectives, objectives, KPIs, baselines, targets, and initiatives. Use this view for management review meetings and quarterly strategy check-ins.

  5. Step 5 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage the scorecard lifecycle:

    • New — Clears all perspectives and starts a blank scorecard.
    • Load — Opens a previously saved .json file and restores all four perspectives, objectives, KPIs, and revision history in read-only mode.
    • Load Example — Pre-fills all four perspectives with a sample manufacturing-company scorecard so you can explore the tool immediately.
    • Revise — Registers a new revision (date, description, author) and unlocks all fields for editing. Revision details appear in the JSON and the exported PDF.
    • Save — Downloads the complete scorecard as a .json file, including all perspectives, objectives, KPIs, initiatives, and revision history.
    • Export PDF — Generates a fully formatted PDF containing the Vision & Mission, the Strategy Map, the complete scorecard table across all four perspectives, and the revision history.
Open Strategy Scorecard

SWOT Analysis Matrix

Purpose of the Tool

SWOT (Strengths, Weaknesses, Opportunities, Threats) Analysis is a foundational strategic planning technique used to evaluate the internal and external factors that could affect a project or organization. Its goals are to:

  • Identify Internal Strengths and Weaknesses that an organization can control.
  • Identify External Opportunities and Threats that the organization cannot control but must respond to.
  • Develop actionable strategies by combining the four elements (e.g., S-O strategies leverage Strengths to maximize Opportunities).
  • Facilitate focused team discussion on core competencies and key risks.

Origin and Background

The origins of the formal SWOT concept are generally credited to Albert Humphrey at the Stanford Research Institute during the 1960s. Its enduring popularity stems from its simplicity and comprehensive nature. The critical distinction in SWOT is between the two internal quadrants (Strengths and Weaknesses) and the two external quadrants (Opportunities and Threats). A common mistake is listing a weakness as an external threat. The matrix forces a structured analysis that is invaluable for launching new products, assessing competitors, or developing a five-year business plan.

Tool Structure

The tool is organised into three tabs: Start & Info, SWOT Matrix (data entry), and Results & Strategies (cross-analysis output). All cross-analysis strategies update automatically as you add or remove items.

How to Use the SWOT Analysis Tool

  1. Step 1 – Fill the SWOT Matrix

    Go to the SWOT Matrix tab. Type each item in the input field of the relevant quadrant and press Enter or click Add to add it to the list. Items can be edited inline or deleted at any time. Aim for 3–7 items per quadrant for a focused, actionable analysis:

    • Strengths (Internal — Positive): Capabilities, resources, and advantages the organisation controls. Examples: proprietary technology, strong brand equity, low cost structure, expert team.
    • Weaknesses (Internal — Negative): Limitations and deficiencies the organisation controls and must address. Examples: outdated equipment, high employee turnover, limited capital, slow decision-making.
    • Opportunities (External — Positive): Favourable trends and conditions in the environment. Examples: growing market segment, regulatory change, emerging technology, competitor withdrawal.
    • Threats (External — Negative): Unfavourable external forces that could harm the organisation. Examples: new competitor entry, rising raw material costs, changing customer preferences, economic downturn.

  2. Step 2 – Review the Automatically Generated Cross-Analysis Strategies (TOWS)

    Switch to the Results & Strategies tab. The tool automatically generates four strategic action types by crossing the four quadrants — the TOWS Matrix:

    • SO – Maxi-Maxi (Strengths × Opportunities): Use your strongest internal capabilities to capture the most attractive external opportunities. These are your growth and attack strategies.
    • ST – Maxi-Mini (Strengths × Threats): Deploy your strengths to neutralise or reduce the impact of external threats. These are your defensive differentiation strategies.
    • WO – Mini-Maxi (Weaknesses × Opportunities): Address internal weaknesses so the organisation can exploit available opportunities. These are your investment and turnaround strategies.
    • WT – Mini-Mini (Weaknesses × Threats): Minimise exposure by reducing weaknesses and avoiding threats simultaneously. These are your survival and contingency strategies.

    Each strategy card lists the specific Strength/Weakness paired with the specific Opportunity/Threat that generated it, giving immediately actionable insights rather than generic recommendations.

  3. Step 3 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage the analysis lifecycle:

    • New — Clears all SWOT items and starts a blank analysis.
    • Load — Opens a previously saved .json file and restores all SWOT items and revision history in read-only mode.
    • Load Example — Pre-fills all four quadrants with a realistic sample so you can explore the cross-analysis strategies immediately. Also opens in read-only mode.
    • Revise — Registers a new revision (date, description, author) and unlocks all fields for editing. Revision details are saved to the JSON and appear in the exported PDF.
    • Save — Downloads the complete analysis as a .json file, including all SWOT items and revision history.
    • Export PDF — Generates a fully formatted PDF report containing the SWOT matrix, the four cross-analysis strategy tables (SO, ST, WO, WT), and the revision history.
Open SWOT Analysis

Business Excellence Assessment

Purpose of the Tool

The EFQM (European Foundation for Quality Management) Model is a globally recognized non-prescriptive management framework used to help organizations achieve and sustain outstanding results. Its purpose is to:

  • Provide a comprehensive framework for self-assessment of organizational maturity.
  • Identify the relationships between what an organization Does (Enablers) and the Results it achieves.
  • Drive continuous improvement by identifying gaps and best practices across all areas of the business.
  • Serve as a model for organizational learning and transformation.

Origin and Background

The EFQM Model was created in 1988 by the European Foundation for Quality Management, founded by 14 leading European companies. It is the framework behind the prestigious European Quality Award. Unlike prescriptive standards (like ISO 9001), the EFQM Model is a tool for self-reflection and diagnosis. It is built around three core sections—Direction, Execution, and Results—with a total of seven criteria. The model uses the powerful RADAR logic (Results, Approaches, Deployment, Assessment, Review) for evaluating performance. It is widely adopted across Europe, particularly by companies like BMW, Siemens, and Xerox.

Tool Structure

The tool is organised into two tabs: Start & Info and Assessment Tool. The Assessment tab contains two sub-tabs: Assessment (scoring workspace) and Results (live dashboard with radar chart and maturity level). Scores and the radar chart update in real time as sliders are adjusted.

How to Use the Organisational Maturity Assessment Tool

  1. Step 1 – Score the Seven Criteria

    In the Assessment sub-tab, click on each criterion accordion to expand it and score its sub-criteria using the 0–100% sliders. Each criterion represents a pillar of organisational performance:

    • Criterion 1 – Leadership: Score three sub-criteria: leadership provides clear direction and purpose; a robust strategy is in place and effectively communicated; leaders foster a culture of excellence and continuous improvement.
    • Criterion 2 – People: The organisation attracts, develops, and retains talent; the workplace culture is positive, inclusive, and empowering; employee well-being and engagement are actively managed.
    • Criterion 3 – Stakeholder Partnerships: Key stakeholder needs are understood and anticipated; strong, value-driven relationships are built; stakeholder feedback is systematically gathered and acted upon.
    • Criterion 4 – Processes: Value-adding processes are designed, managed, and optimised; quality and efficiency are systematically improved; risk and opportunity are effectively managed within processes.
    • Criterion 5 – Innovation & Transformation: Innovation is encouraged and managed to create new value; digital technologies are leveraged to improve performance; the organisation is agile and adapts to change effectively.
    • Criterion 6 – Stakeholder Perception Results: Consistently high levels of customer satisfaction; positive results related to employee engagement; the organisation is viewed positively by partners and society.
    • Criterion 7 – Strategic & Business Results: The organisation is achieving its key strategic goals; key operational and performance targets are being met; financial health is strong and sustainable.

  2. Step 2 – Read the Live Results Dashboard

    Switch to the Results sub-tab at any time to see the live output:

    • Score Summary Cards: The weighted score for each of the seven criteria and the overall total score (0–100%).
    • Maturity Level: The tool maps the total score to a descriptive maturity band — from Initial (reactive, ad-hoc) through Managed, Defined, Measured, up to Optimising (world-class, benchmark-driven).
    • Radar Chart: A seven-axis spider chart visualising the balance of performance across all criteria, instantly highlighting which pillars are strong and which need investment.
    • Detailed Score Breakdown: A table listing each criterion, its sub-criteria scores, and their weighted contribution to the total, providing the evidence base for the improvement plan.

  3. Step 3 – Save, Load, and Manage Revisions

    Use the toolbar at the top of the page to manage the assessment lifecycle:

    • New — Resets all sliders, scores, and notes to zero and starts a blank assessment.
    • Load — Opens a previously saved .json file and restores all slider positions, scores, notes, and revision history in read-only mode.
    • Load Example — Pre-fills all seven criteria with example scores so you can explore the radar chart and analysis sections immediately. Also opens in read-only mode.
    • Revise — Registers a new assessment revision (date, description, assessor name) and unlocks all sliders for editing. Comparing revisions over time shows the maturity improvement journey.
    • Save — Downloads the complete assessment as a .json file, including all scores, notes, and the full revision history.
    • Export PDF — Generates a fully formatted PDF containing the score summary cards, the radar chart, the automated maturity analysis, the detailed score breakdown by criterion and sub-criterion, and the revision history.
Open EFQM Tool