Ken Feldman

How does your organization track and monitor organizational performance? Do you have key metrics that you frequently look at to make decisions? Using Key Performance Indicators (KPIs) is a common method for tracking your most important metrics. Let’s see how. A Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively a company […]

STOP THE PRESSES! Jidoka will immediately stop equipment upon discovering an abnormality in the process. Let’s explore this a little deeper.

The empirical rule is a simple way to understand the distribution of your data values if they come from a normal distribution. 

It is generally easier to understand what your data is telling you if you can visualize it rather than view it in rows and columns of numbers. A frequency plot is a great tool for doing that. A frequency plot is a graphical representation of the distribution of continuous data based on how often a […]

Enterprise Resource Planning (ERP) is a business management system that integrates all aspects of a company’s operations, including finance, human resources, manufacturing, supply chain management, and customer relationship management. ERP software allows businesses to automate and streamline their processes, creating a unified and efficient system that improves productivity and reduces errors. It provides a centralized […]

Manually changing parts in and out of a machine can be time-consuming and present a safety issue. Hanedashi solves that problem by automatically doing almost everything that the operator would do in a manual operation.

Since there is variation in all processes, how can you tell whether the variation is due to random expected variation, or something unexpected? The control chart is a tool for making that distinction. Let’s learn how that is done. 

You finally found the perfect spot to anchor your boat and catch all the best fish. But, the tide is strong and your boat starts to drift away from where you need it to be. The same can happen to your process. 

Degrees of Freedom (DF) can be thought of as the amount of information you have to compute certain statistics. The more you want to compute, the more data and information you need. Let’s learn more about how to compute and use DF. You can think of DF as statistical money that you can spend to […]

If you plot your data on a histogram, the resulting graphic will illustrate how your data is distributed. This distribution can provide a lot of information about your data and the process from which it came from. Let’s learn some more about distributions. Once you have collected your desired data, you can display the data […]

Wouldn’t it be great if your production process ran smoother? Maybe Heijunka is the solution. Let’s find out more about it.

DOE (Design of Experiments) can be defined as a set of statistical tools that deal with the planning, executing, analyzing, and interpretation of controlled tests to determine if there is a casual relationship between your process factors or variables and your process output. Overview: What is DOE?  Two common approaches to DOE are a full […]

A design of experiments (DOE) is a set of powerful designed experimentation statistical tools for understanding the impact that varying your process inputs will have on the outputs of your process. 

If you understand multiple linear regression, you already know a lot about the General Linear Model (GLM). Let’s learn some more about how and when to use the GLM. 

How much can you learn about your process sitting in your office? According to Genchi Gembutsu, you can’t learn much. In Japanese, genchi means “actual place,” and gembutsu means “actual thing.” In the Toyota Production System (TPS), this concept is interpreted as “to go and see.” Let’s explore this a little further. 

You walk by waste in your organization every day. The acronym DOTWIMP can help you identify and analyze your seven most common sources of waste. 

Directives are an important tool for organizations that seek to align employees with their goals, increase efficiency, and promote a culture of continuous improvement. Understanding Directives: Benefits, Best Practices, and Common Questions Directives are a critical tool for organizations seeking to improve their performance and achieve their goals. By providing clear guidance and expectations, directives […]

DMEDI stands for: Define, Measure, Explore, Develop, and Implement. This differs from the basic Six Sigma methodology of DMAIC which stands for: Define, Measure, Analyze, Improve, and Control. Both methodologies are designed to improve your processes and products. Whereas, DMAIC is designed for incremental improvements of existing processes, DMEDI focuses on more robust improvements for […]

The famed statistician, Dr. W. Edwards Deming, frequently talked about data and said, “Without data, you’re just another person with an opinion.” Let’s learn some more about data. Data refers to any information, facts, or statistics that can be collected, stored, and analyzed. It can be in the form of numbers, text, images, audio, or […]

Is quality free, as claimed by quality expert and author Philip Crosby? Or, is there a cost of quality (COQ) that your organization needs to understand and manage? 

If you look at the dashboard of your car, you will see a display of the current real-time status of important metrics. You can do the same thing with your business metrics.

Unfortunately, sometimes your process produces unacceptable output. These are usually referred to as defects or defectives. Let’s examine what a defective item is and contrast it with a defect. The Merriam-Webster dictionary defines a defective as: having a defect or flaw or imperfect in form, structure, or function. Simply put, a defect is a flaw […]

Would you expect the average of a sample to be the same as the average of the population from which it came? No. Why not? Because of randomness and sampling error. How should we handle that? Let’s find out.

When doing hypothesis testing, there is a risk of making two types of errors. One of them is the consumer’s risk, which is when the null hypothesis is rejected when it shouldn’t have been.