Stastistical process control – Basic

Scope

Statistical process control (SPC) is a method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum of waste. SPC can be applied to any process where the conforming product’s output can be measured.

By the end of this course, participants will be able to:

  • Understand of basic statistics
  • Understand importance of control charts & distinguish different types of control charts
  • Interpretation of control charts
  • Decision based on data
  • Lecture
  • Presentation through Audio- Video
  • Case Studies
  • Desk Top Exercises
  • Group Exercise
  • Games
  • Role Modeling
  • Basic concept of SPC
  • Goal post view of Quality Vs. Taguchi’s Target
  • oriented Quality
  • Basic Statistics
  • Control Charts
  • Process Capability

Stastistical process control – Advance

Scope

Statistical process control (SPC) is applied in order to monitor and control a process. SPC can be applied to any process where the conforming product’s output can be measured.

SPC-Advance course is a follow up of SPC-Basic course. This course will expand fundamental understanding of SPC by getting knowledge of new tools & methods, which will be covered in this course

By the end of this course, participants will be able to:

  • Understand of statistics concepts
  • Understand importance of control charts & distinguish different types of control charts
  • Interpretation of control charts
  • Decision based on data
  • Lecture
  • Presentation through Audio- Video
  • Case Studies
  • Desk Top Exercises
  • Group Exercise
  • Games
  • Role Modeling
  • Basic concept of SPC
  • Goal post view of Quality Vs. Taguchi’s Target oriented Quality
  • Basic statistics + overview of advance statistics method
  • Types of Control Charts with practical exercises
  • Process Capability analysis
  • Case Study

Problem Solving Techniques 7 QC Tools

Scope

The course is designed based on a proven problem solving model; known as PDCA (Plan-Do-Check-Act). 7 QC Tools are to be applied at different stages in the PDCA cycle. 7 QC Tools has been proven effective by Japanese Quality Guru Mr. Karou Ishikawa and he believed that more than 95% of the problem in a company can be solved by using 7 QC Tools. The Basic Seven Tools made statistical analysis less complicated for the average person. Good Visual Aids make statistical and quality control more comprehendible. QC tools are the means for collecting data, analyzing data, identifying root causes and measuring the result. Users have to develop the solution & implement.

By the end of this course, participants will be able to:

  • Collect information for improvement
  • Analyze the information collected
  • Use the analyzed information for improving the performance
  • Lecture
  • Presentation through Audio- Video
  • Case Studies
  • Desk Top Exercises
  • Group Exercise
  • Games
  • Role Modeling
  • 7 QC Tools :
  • Check sheets
  • Pareto Chart
  • Histograms
  • Cause and Effect Diagram
  • Control Charts
  • Stratification

Measurement System Analysis

Scope

A Measurement system analysis(MSA) is especially designed experiment that seeks to identify the component of variation in the measurement any time you measure the results of a process you will see some variation. This variation comes from two sources: one, there are always differences between parts made by any process, and two, any method of taking measurements is imperfect—thus, measuring the same part repeatedly does not result in identical measurements.

Measurement system analysis (MSA) is concerned with identifying sources of part-to-part variation, and reducing that variation as much as possible to get a more consistent product. But before you do any MSA analyses, you may want to check that the variation you observe is not overly due to errors in your measurement system.

MSA is an important tool of Six Sigma and other quality management system

By the end of this course, participants will be able to:

  • To evaluate if a measurement system is adequate for the purpose
  • Process control (to decide if process is to be adjusted or not) or
  • Product control (to decide if a lot is to be accepted or rejected at final inspection stage).
  • If Gauge R & R is found to be poor, root cause(s) is (are) to be investigated and consequently corrective actions to be taken.
  • Presentations through Audio – Video
  • Games
  • Case Studies
  • Discussion
  • Role Play / Activities
  • Desk Top Exercise
  • Learning by Doing
  • Measurement System
  • Source of Variation
  • Bias
  • Linearity
  • Stability
  • Repeatability
  • Reproducibility
  • How to conduct MSA Study

Failure Mode & Effect Analysis (FMEA)

Scope

Failure Mode and Effects Analysis (FMEA) is a tool that examines potential product or process failures, evaluates risk priorities, and helps determine remedial actions to avoid identified problems. An FMEA is a form of Brainstorming that generally follows a Cause and Effect Analysis or a Process mapping and it is usually followed by a Pareto Analysis. It is a granular analysis of a process, a system or a product design for the purpose of identifying possible deficiencies. It is generally conducted by a cross functional group with all the participants having a stake or knowledge about the process, system or product being assessed.

The FMEA analysis procedure is a tool that has been adapted in many different ways for many different purposes. It can contribute to improved designs, products and processes, resulting in:

  • Reducing the likelihood of customer complaints
  • Reducing the likelihood of changes
  • Reducing maintenance and warranty cost
  • Reducing the possibility of safety failure
  • Reducing the possibility of extended life or reliability failure
  • Reducing the likelihood of Product Liability claim
  • Presentations through Audio – Video
  • Games
  • Case Studies
  • Discussion
  • Role Play / Activities
  • Desk Top Exercise
  • Learning by Doing
  • Describe Product or Process
  • Define Functions
  • Identify Potential Failure Modes
  • Describe Effects of Failure
  • Determine Causes
  • Detection Methods / Current Controls
  • Calculate Risk
  • Take Action
  • Assess Results

DOE (Design of Experiments) – Basic

Scope

DOE is a structured statistical approach to improve a product/process performance. In DOE with a small number of experimental runs & very large data, significant improvements to process can be made.

  • Understand of Why design of experiments (DOE) are more efficient and effective than one-at-a-time
  • Understand of Operational Acceptance Testing(OAT) experimentation
  • How to use the major terms used in designed experiments mean
  • Types of designed experiments and when they are best used
  • How to use basic tests of significance
  • How to plan a designed experiment.
  • Presentations through Audio – Video
  • Games
  • Case Studies
  • Discussion
  • Role Play / Activities
  • Desk Top Exercise
  • Learning by Doing
  • What is DOE?
  • Objective of DOE
  • Principle of DOE
  • Common Statistical Designs(Including Full Factorial & Fractional Factorial designs)
  • Taguchi Methods
  • Planning for Experiment

DOE (Design of Experiments) – Advance

Scope

DOE is a structured statistical approach to improve a product/process performance. In DOE with a small number of experimental runs & very large data, significant improvements to process can be made.

DOE aids in improving product performance, develops efficient processes, quickly solves manufacturing problems, and assists in breakthrough discoveries by applying powerful statistical methods. A must for Six Sigma, DOE helps you find product and process performance levels where all process requirements are met at minimal cost. For additional information on Process Technologies’ approach to DOE.

  • Understand of Why design of experiments (DOE) are more efficient and effective than one-at-a-time
  • Understand of Operational Acceptance Testing(OAT) experimentation
  • How to use the major terms used in designed experiments mean
  • Types of designed experiments and when they are best used
  • How to use basic tests of significance
  • How to plan a designed experiment.
  • Presentations through Audio – Video
  • Games
  • Case Studies
  • Discussion
  • Role Play / Activities
  • Desk Top Exercise
  • Learning by Doing

Module 1: Why DOE?

  • Limitations of OATs(one-at-a-time) experimentation
  • How designed experiments overcome the limitations of OATs and are more effective and efficient way to characterize and improve processes and products.

Module 2: DOE Terminology

  • An explanation of the key terms used in designed experiments.

Module 3: Types of Designed Experiments

  • Full Factorials
  • Fractional Factorials
  • Screening Experiments
  • Mixture Experiments

What’s Included?

  • Specialized manual and course materials
  • Instruction by an expert facilitator
  • Small interactive classes

Module:4 Tests of Significance

  • ANOVA (Analysis of Variance)
  • Alpha and Beta Risks
  • Degrees of Freedom
  • Hypothesis Tests
  • t-Tests
  • F-Tests

Module 5: Setting Up a Designed Experiment

  • Design & Communicate the Objective
  • Define the Process
  • Select a Response and Measurement System
  • Select Factors to be Studied
  • Select the Experimental Design
  • Set Factor Levels
  • Final Design Considerations

Module 6: Experiment and Test & DOE Challenge

  • DOE Simulation with Game
  • An assessment of the learner’s progress

Reliability Engineering

Scope

The probability that a component or an equipment or a system performs its intended function for a stated period of time under specified operating conditions.

In today’s competitive world, reliability of equipment is extremely important to maintain quality and delivery deadlines. This is achieved by using proper maintenance and design changes for unreliable subsystems and components of a complex system. It is significant to develop a strategy for maintenance, replacement and design changes related to those subsystems and components. An analysis of down time along with causes is essential to identify the unreliable components and subsystems

  • Favorable company reputation
  • Improved Customer satisfaction (unreliable product would severely affect customer satisfaction)
  • Reduced Warranty costs and unwanted Negative attention
  • Repeat business
  • Lower life time cost (as the product would require fewer repairs or less maintenance)
  • Competitive advantage
  • Extended equipment life
  • Presentations through Audio – Video
  • Games
  • Case Studies
  • Discussion
  • Role Play / Activities
  • Desk Top Exercise
  • Learning by Doing
  • Overview of Reliability Engineering
  • Interrelationship between Quality & Reliability
  • Reliability in Product & Process Development
  • Definition of Failure
  • Reliability Program

Root Cause analysis

Scope

This course will enable participants to understand root cause analysis as a procedure for ascertaining and analyzing the causes of problems in an effort to determine what can be done to solve or prevent them. Consisting of lectures, practice, and role-playing, this course is designed to provide attendees with an in-depth understanding of how to analyze a system to identify the root causes of problems.

  • Introduction
  • why complete Route cause Analysis ?
  • CBEPL approach to RCA
  • Practical Guide to carry out an RCA
  • RCA tools & Techniques
  • Look up table for completing RCA
  • Examples RCA
  • Hints & Tips RCA
  • Pitfalls of RCA
  • Presentations through Audio – Video
  • Games
  • Case Studies
  • Discussion
  • Role Play / Activities
  • Desk Top Exercise
  • Learning by Doing
  • Introduction
  • why complete Route cause Analysis ?
  • CBEPL approach to RCA
  • Practical Guide to carry out an RCA
  • RCA tools & Techniques
  • Look up table for completing RCA
  • Examples RCA
  • Hints & Tips RCA
  • Pitfalls of RCA
  • Who Should Attend ?

Quality, safety, risk, and reliability managers, process engineers, technicians, operations supervisors and personnel, process owners, occurrence investigators, analysts, maintenance directors, reliability professionals, and anyone who wants to improve their ability to solve recurring problems should attend this training.