What are the most important statistical techniques for process improvement?

Make sure to start by assessing the quality of your measurement system however. After all, you need to be able to trust the data you will be using. This is when the Measurement System Analysis comes into play. An MSA measures the accuracy and precision of a measure system. It is also known as a Gage R&R because it mainly measures how Repeatable (can you repeat the same measurement within a short period of time) and Reproducible (would a colleague measure the same number as you) a measurement system is. This is of critical importance to ensure that you are confident in the descriptive statistics you will be presenting.

Right tool for the right data and right kind of data….. this is important. And of course what is the correct inference of the test on the data - that’s important rather than prioritising the tool

Ultimately, the goal is to know the critical inputs to a process and what level those inputs need to be at to achieve the desired output of your process.

Inferential statistics aren't just about drawing conclusions; they're your crystal ball for predicting future process behavior. From hypothesis testing to regression analysis, they reveal insights and measure improvement. Don't overlook capability analysis and Gage R&R; they offer crucial insights into process performance and measurement tool reliability.

Statistical techniques are vital for process improvement. Key methods include Statistical Process Control (SPC), Design of Experiments (DOE), Root Cause Analysis (RCA), Regression Analysis, Hypothesis Testing, Process Capability Analysis, Control Plans, Failure Mode and Effects Analysis (FMEA), Six Sigma, Lean Tools, ANOVA, Trend Analysis, Simulation, and Pareto Analysis. These techniques help monitor, analyze, optimize, and control processes, ensuring quality, efficiency, and problem-solving. Choosing the right method depends on specific goals and process characteristics.

i believe Statistical techniques are vital for process improvement. In the beginning, you might not have robust measurement parameters and data. However, you shall start somewhere.

Something that is not thought enough when performing inferential statistics is the difference between statistical significance and practical significance. For example, a low p-value on a t test will allow to conclude to a statistically significant difference. However, the confidence interval on the difference between the two means, combined with process knowledge, will allow to assess if the difference can be significant in practice or not.

You can not improve what you don't measure. There is something call p PRE SPC. Just measure to get a picture of your process against tolerances. Then apply SPC, to control it. DOE should be used for optimization. In that order.

Several points to consider: 1. Check the process stability as part of process capability study. If not stable, the process is not capabile. 2. Special cause variation can help you to identify causes or solutions 3. A stable process can generate noncorfomities and could more difficult to fix than an instabile process 3. Special cause variation could be fixed by one specialist but a large variation ?n a stable process requires a team. Also : # a stable process = a process ?n control # the center( green) line is the mean and not the target

Control charts are critical for process improvements. These charts can give you a snapshot of how your process is performing. It is also quick to alert you if something is trending away from target. Control charts and +/-3-sigma tracking should be used as an early detection tool to help you find potential issues before they become large problems that could affect your customers. Like anything in life, early detection and correction of issues is the key to a healthy process!

Statistical techniques play a crucial role in process improvement across various fields, including manufacturing, healthcare, and service industries. These techniques help identify and address issues in processes, reduce variation, and enhance overall efficiency and quality. Some of the most important statistical techniques for process improvement include: Descriptive Statistics, Histograms, Process Mapping, Cause-and-Effect Diagrams (Fishbone Diagrams), Control Charts, Capability Analysis and so on.

Control charts are a major contributor to improving the system. Paper charts are good. Computer SPC systems are better and very cumbersome and expensive.

There are 7 situations where a process is deemed to be out of control, for example: 1) One or more data points fall outside the control limits; 2) Seven consecutive data points increasing or decreasing 3) Eight consecutive data points are on one side of average Knowing those rules helps you to know when to stop a process and take corrective actions in order to bring your process under control again.

Ultimately, the goal of a DOE is to know what the critical inputs to a process are and at what level those inputs need to be in order to achieve the desired output of your process.

Any process improvement should be based on statistics and data. By using them correctly, you can unlock hidden learnings and accelerate your progress.

Meaningful sample size and direct process variable selection are often prioritized characteristics of design of experiments for data collection, and with good reason. However a good diligent thought should be put on confirming indirect variables are in fact negligible to avoid being blindsided by erroneous assumptions. There are processes which yields behave seasonally, some due to weather conditions (temperature, humidity), others socially (demand fluctuation, turnover). Make sure your experiment scope is clear and your data is clean, for an expected result accuracy.

I'm a big fan of the champion vs challenger model. It allows to compare and contrast various tests running in parallel with flexibility on optimizing them to achieve desired results without hurting the BAU operations. Experiments need to be conducted thoroughly and all use-case scenarios should be well thought out before going live with the changes. This allows us to minimize risk, optimize cost, and think about scalability without hurting the end-user.

Understanding process capability is crucial to making improvements. You want to be able to understand Common Cause variation vs Special (assignable) Cause variation. Common Cause variation, you work to improve the process and reduce the common variation (standard deviations). Special Cause variation relies on a strong FMEA/PFMEA to guide you to preventive measures to reduce the chances of Special (assignable) Cause variation.

Six Sigma isn't just a method; it's a way of thinking. It helps companies improve by constantly looking for better ways to do things. By using Six Sigma, companies find and fix problems, make customers happier and save money. It's not just about fixing one thing—it's about making improvement part of everything we do.

Six sigma is a powerful tool that truly delivers results and can be invaluable when it comes to process improvement. The DMAIC methodology, in particular, provides a structured and data-driven approach to identify and eliminate defects, thus reducing variation, and enhancing process efficiency. Six Sigma principles can also help improve the quality of the products and/ or services resulting in possible cost savings and increased customer satisfaction.

Objectives of Six Sigma: a. Defect Reduction: Six Sigma aims to reduce defects and errors in a process to a level where they occur extremely infrequently. b. Customer Satisfaction: By improving processes and delivering consistent, high-quality products or services, customer satisfaction is increased. c. Cost and Time Savings: Efficient processes result in cost savings and reduced cycle times.

It is called Six Sigma as its vision is to search for perfection. Indeed, a process that performs at a six sigma level of performance (six standard deviations either side of the mean) only generates 3.4 defects per million of opportunities. To put that in every day language, that is akin to $2 debt over $1Bn of assets, 1 mispelled word in all of the books of a small library or 6 seconds per century.

A vital relatively recent (starting just over 5 years ago) is the ability to understand complex systems, and most systems, particularly mixtures and mixture-process situations, are complex. Combining space-filling designs and self-validating ensemble modeling (SVEM), a machine learning method, provides much more accurate predictive models. Conventional methods, such as response surface designs, are restricted in their attempts to fully describe the actual response space. Quadratics only bend so far. Most importantly, this approach does not require a PhD in statistics. It’s very accessible to scientists and engineers.

In conclusion, statistics and data should be at the heart of any process improvement. Used correctly, they have the power to unlock learnings that are hidden and speed up your improvements.

Before we let the team to explore these intermediate/advanced tools the team should acquire the basic training in 7 Basic QC Tools first. One may be surprised to see they actually require only this basic tools to tackle 95% of the issues they see in theur process. Otherwise they will be overwhelmed and their enthusiasm will die out quickly.

These statistical techniques provide powerful tools for understanding, analyzing, and improving processes. The choice of specific techniques depends on the nature of the problem, available data, and the goals of the improvement initiative.

Considerar que buscamos medir el proceso y realizar hipótesis muy certeras comprobadas por datos no por sentir de alguna persona, para lograr resolver los problemas reales