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Process Improvement

Blog posts and articles about the use of data analysis and statistics to improve processes in business and industry.

I left off last with a post outlining how the Six Sigma students at Rose-Hulman were working on a project to reduce the amount of recycling thrown in the normal trash cans in all of the academic buildings at the institution. Using the DMAIC methodology for completing improvement projects, they had already defined the problem at hand: how could the amount of recycling that’s thrown in the normal trash... Continue Reading
I typically attend a few Lean Six Sigma conferences each year, and at each there is at least one session about compensating belts. Any number of ideas exist out there, but they commonly include systems that provide a percentage of savings as a portion of pay or provide a bonus for meeting target project savings. There are always issues with these pay schemes, including the fact that... Continue Reading
As someone who has collected and analyzed real data for a living, the idea of using simulated data for a Monte Carlo simulation sounds a bit odd. How can you improve a real product with simulated data? In this post, I’ll help you understand the methods behind Monte Carlo simulation and walk you through a simulation example using Devize. What is Devize, you ask? Devize is Minitab's exciting new,... Continue Reading
In my recent meetings with people from various companies in the service industries, I realized that one of the problems they face is that they were collecting large amounts of "qualitative" data: types of product, customer profiles, different subsidiaries, several customer requirements, etc. As I discussed in my previous post, one way to look at qualitative data is to use different types of... Continue Reading
In several previous blogs, I have discussed the use of statistics for quality improvement in the service sector. Understandably, services account for a very large part of the economy. Lately, when meeting with several people from financial companies, I realized that one of the problems they faced was that they were collecting large amounts of "qualitative" data: types of product, customer... Continue Reading
Choosing the correct linear regression model can be difficult. After all, the world and how it works is complex. Trying to model it with only a sample doesn’t make it any easier. In this post, I'll review some common statistical methods for selecting models, complications you may face, and provide some practical advice for choosing the best regression model. It starts when a researcher wants to... Continue Reading
As a member of Minitab's Technical Support team, I get the opportunity to work with many people creating control charts. They know the importance of monitoring their processes with control charts, but many don’t realize that they themselves could play a vital role in improving the effectiveness of the control charts.   In this post I will show you how to take control of your charts by using Minitab... Continue Reading
Stepwise regression and best subsets regression are both automatic tools that help you identify useful predictors during the exploratory stages of model building for linear regression. These two procedures use different methods and present you with different output. An obvious question arises. Does one procedure pick the true model more often than the other? I’ll tackle that question in this post. Fi... Continue Reading
Last week, thanks to the collective effort from many people, we held very successful events in Guadalajara and Mexico City, which gave us a unique opportunity to meet with over 300 Spanish-speaking Minitab users. They represented many different industries, including automotive, textile, pharmaceutical, medical devices, oil and gas, electronics, and mining, as well as academic institutions and... Continue Reading
by Jasmin Wong, guest blogger   Part 1 of this two-part blog post discusses the issues and challenges in injection moulding and suggests using simulation software and the statistical method called Design of Experiments (DOE) to speed development and boost quality. This part presents a case study that illustrates this approach.  Preliminary Fill and Designed Experiment This case study considers the... Continue Reading
by Jasmin Wong, guest blogger The combination of statistical methods and injection moulding simulation software gives manufacturers a powerful way to predict moulding defects and to develop a robust moulding process at the part design phase.  CAE (computer-aided engineering) is widely used in the injection moulding industry today to improve product and mould designs as well as to resolve or... Continue Reading
Here's a shocking finding from the most recent ASQ Global State of Quality report: The higher you rise in your organization's leadership, the less often you receive reports about quality metrics. Only 2% of senior executives get daily quality reports, compared to 33% of front-line staff members.   A quarter of the senior executives reported getting quality metrics on a monthly basis, at least. But... Continue Reading
Using statistical techniques to optimize manufacturing processes is quite common now, but using the same approach on social topics is still an innovative approach. For example, if our objective is to improve student academic performances, should we increase teachers wages or would it be better to reduce the number of students in a class? Many social topics (the effect of increasing the minimum... Continue Reading
by The Discrete Sharer, guest blogger As Minitab users, many of us have found staged control charts to be an effective tool to quantify and demonstrate the “before and after” state of our process improvement activities. However, have you ever considered using them to demonstrate the effects of changes to compensation/incentive plans for your employees?  Here's an example of how a mid-sized... Continue Reading
Screening experimental designs allow you to study a very large number of factors in a very limited number of runs. The objective is to focus on the few factors that have a real effect and eliminate the effects that are not significant. This is often the initial typical objective of any experimenter when a DOE (design of experiments) is performed. Table of Factorial Designs Consider the table below.... Continue Reading
If you’re already a strong user of Minitab Statistical Software, then you’re probably familiar with how to use bar charts to show means, medians, sums, and other statistics. Bar charts are excellent tools, but traditionally used when you want all of your categorical variables to have different sections on the chart. When you want to plot statistics with groups that flow directly from one category... Continue Reading
Via Christi Health, the largest provider of healthcare in Kansas, operates a Center for Clinical Excellence that's made up of a team of quality practitioners, all who have Lean and Six Sigma training. I recently had the opportunity to talk with the team about the types of projects they're working on. I learned not only about the areas of patient care where they are targeting improvements, but about... Continue Reading
The Six Sigma students at Rose-Hulman Institute of Technology are at it again! A few months back, we blogged about the Six Sigma project they did to reduce food waste at the on-campus dining center. This time, the students—lead by Dr. Diane Evans, Six Sigma black belt and associate professor of mathematics at Rose-Hulman—are performing a Lean Six Sigma project to reduce the amount of recycling... Continue Reading
Control charts are some of the most useful tools in statistical science. They track process statistics over time and detect when the mean or standard deviation change from what they have been. The signals that control charts send about special causes can help you zero in on the fastest ways to improve any process, whether you’re making tires, turbines, or trying to improve patient care. I’ve menti... Continue Reading
There is more than just the p value in a probability plot—the overall graphical pattern also provides a great deal of useful information. Probability plots are a powerful tool to better understand your data. In this post, I intend to present the main principles of probability plots and focus on their visual interpretation using some real data. In probability plots, the data density distribution... Continue Reading