Statistical Tolerance Analysis Training

Commitment 2 Days, 7-8 hours a day.
Language English
User Ratings Average User Rating 4.8 See what learners said
Delivery Options Instructor-Led Onsite, Online, and Classroom Live


Statistical Tolerance Analysis Training course is an intense training program focused on both root sum square and Monte Carlo tolerance analysis approaches. This Statistical Tolerance Analysis course includes important tolerance allocation approaches that will allow your organization to assign less stringent, higher quality, and more sensible tolerances to your designs. This Statistical Tolerance Analysis course thoroughly covers the theoretical aspects of these advanced tolerance analysis and allocation approaches. The Statistical Tolerance Analysis Training course includes numerous practical examples, exercises, and approaches for using Excel and Visual Basic for Applications (included in Excel) for making these determinations.

Statistical tolerance analysis identifies likely dimensional variation in mechanical designs. Similar to the more conventional worst-case tolerance analysis approach, statistical tolerance analysis more realistically assesses the effects of tolerance stack-ups to identify likely tolerance variations. Statistical tolerance analysis is a tool for intelligently allocating less-stringent tolerances and for assessing the likelihood of unacceptable tolerance combinations. Using this approach allows engineering organizations to create more producible and less expensive products.

  • 2 days of Statistical Tolerance Analysis Training with an expert instructor
  • Statistical Tolerance Analysis Electronic Course Guide
  • Certificate of Completion
  • 100% Satisfaction Guarantee


  • We can adapt this Statistical Tolerance Analysis Training course to your group’s background and work requirements at little to no added cost.
  • If you are familiar with some aspects of this Statistical Tolerance Analysis course, we can omit or shorten their discussion.
  • We can adjust the emphasis placed on the various topics or build the Statistical Tolerance Analysis course around the mix of technologies of interest to you (including technologies other than those included in this outline).
  • If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the Statistical Tolerance Analysis Training course in a manner understandable to lay audiences.

Upon completing this Statistical Tolerance Analysis Training course, learners will be able to meet these objectives:



The target audience for this Statistical Tolerance Analysis course:

  • All

The knowledge and skills that a learner must have before attending this Statistical Process Control Training course are:

  • While there are no formal course prerequisites, this Systems Engineering course assumes a couple of years of prior experience in designing and building systems, large or small.


Day 1: Fundamentals, Worst Cost, and Root Sum Square Approaches
  1. Tolerance Analysis Fundamentals: The nature of dimensioning and tolerancing. Tolerance analysis purposes. Tolerance analysis history. How tolerances are typically assigned? Tolerance analysis concepts. Worst-case tolerance analysis and worst-case tolerance analysis shortfalls. Statistical tolerance analysis and statistical tolerance analysis shortfalls. Differences between worst-case tolerance analysis and statistical tolerance analysis. Suggested tolerance analysis approach selection criteria.
  2. Basic Statistical Considerations: The nature of variability. The normal distribution. Means and standard deviations. Manufacturing process variability. Process capability, Cp, and Cpk. Tolerances and nominal dimensions versus process capability. Coefficient incorporation to address differences in design nominal and process nominal dimensions. Exercises.
  3. Statistical Tolerance Analysis Concepts: Statistical tolerance analysis purposes. Statistical tolerance analysis assumptions. The realism of statistical tolerance analysis. Maximum possible versus a maximum probable dimensional variation. Why statistical tolerance analyses predict less variation. The economics of worst-case tolerance analysis versus statistical tolerance analysis.
  4. Root Sum Square Statistical Tolerance Analysis: Dimension chains, positive versus negative directions, and converting to equal-bilateral format. Finding the root sum square of all tolerances. Knowing the manufacturing process and assembly shift, and incorporating adjustment coefficients. Applying statistical tolerance analysis findings for dimensional predictions. Using statistical tolerance analysis for relaxing component tolerances. Using Excel. Exercises.
Day 2: Monte Carlo and Advanced Concepts
  1. Monte Carlo Tolerance Analysis. The Monte Carlo approach. Differences in the Monte Carlo simulation approach. Applying uniform versus normal distributions in the simulation. Randomness and normal statistical variation. Monte Carlo simulations with Excel and VBA for Excel. Statistical tolerance analysis versus Monte Carlo tolerance analysis considerations. Exercises.
  2. Tolerance Allocation Approaches Typical tolerance assignment approaches. Tolerance allocation based on the worst case, root sum square, and Monte Carlo tolerance analysis. Tolerance allocation incorporates the tolerance analysis approach and component size, process capability, cost, and mean shift. Using Excel for tolerance allocation. Exercises.
  3. Assessing Statistical Tolerance Analysis Applicability: Number of tolerances. Production quantities. Process controls and process capability. Centered processes versus nominal dimensions. Design sensitivity. Interchangeability. Independent variables. Suggested guidelines.
  4. Quality and Economics Considerations: Costs and benefits associated with statistical tolerance analysis. Costs associated with tighter versus looser tolerances. Rejections as a result of statistical tolerance analysis approach. Using statistical tolerance analysis to predict assembly rejection rates. Targeting tolerance relaxation candidates.
  5. Other Considerations: Non-normal distributions. Factor weighting by individual tolerance. Risks and risk management.
Statistical Tolerance Analysis TrainingStatistical Tolerance Analysis Training Course Wrap-Up