The Story of Your Data -- Session 1: To Fix it, I Must Be Able to Measure It

Overview

Fixing long standing problems in a process is not easy. To ensure the solution is of value to the organization, we must implement the change in a way that is sustainable. But how will we know if the change worked? First and foremost, we must try to understand how the process currently functions. Key to gaining this understanding is to collect and visualize data on the process.

In this 90-minute online session, we will discuss historical variable data and, using a case study, explore what the data does and does not tell us. Additionally, we will introduce specific statistical and graphical tools that process improvement practitioners can utilize to provide insights into their baseline process.

Agenda topics:

This session will introduce four key concepts that will enable you to effectively understand your variable process output data and help you communicate the process performance to your organization:

Knowing where to start with your data. When we get our data, one of the most helpful initial tasks is to create a graphical summary. This not only helps us to visualize the historical data but also allows us to characterize the process output in terms of central tendency, spread and shape. Also knowing more about your data collection and confirming that it is variable and not attribute will help us embark on the correct journey.

Using graphs to know your process. Graphical analysis is a powerful tool and can be used not only to get insights into your process but also to share these insights with others. Some graphs help us to know our data and others help us to show our data. We will introduce three key charts that will help you to assess the stability of your process output and other features. These are the:

• Scatterplot
• Time series plot
• Box plot

Identifying the common distribution. Many processes have outputs that match common statistical probability distributions. One that is likely familiar is the normal probability distribution; however, there are others that may be more appropriate to your particular process output. Often, when we are looking at cycle time improvements, a normal distribution is simply not adequate so we will look at alternatives. We will discuss:

• How to assess if your data set is normally distributed.
• How to identify a more appropriate distribution if your data is non-normal.

Assessing your process capability. Ultimately one of the key questions we can ask is whether our processes deliver what our customers desire. Capability analysis takes our data and compares it with our customers’ specifications. Using common probability models we can not only create a snapshot of our performance but also predict our future ability to deliver to the customer specification. We will specifically focus on:

• Generating a capability report for normal data.
• Assessing capability of data that is not normally distributed.
• What to do if your data does not fit a common distribution.

Click here to register

Who should attend

• Process improvement specialists
• Lean and Lean Six Sigma practitioners
• Operations and quality leaders
• Data analysts and other data analysis professionals
• Process owners

Click here to register

Instructors

Renee Snell has more than 20 years of experience in developing and teaching continuous improvement techniques. A Lean Six Sigma Master Black Belt and statistical expert, she has mentored more than 1,000 practitioners in Lean and Six Sigma. In addition to her role as an instructor, Snell develops customized curriculum for organizations in both transactional and manufacturing industries. Snell holds a Master's degree in statistics and has completed post-graduate work in management science/statistics at the University of Tennessee, Knoxville.
 

As a Six Sigma Master Black Belt and instructor, Liz McArdle has more than 15 years of experience in process excellence and teaching Lean Six Sigma courses in classroom, eLearning and blended environments. In addition to instruction, she develops curriculum and facilitates problem solving workshops designed to identify and prioritize improvement opportunities. McArdle holds an Engineering Doctorate from the University of Warwick and a Master of Science from Cranfield University. She is also a Chartered Engineer.
 

Click here to register

Sign up for this online training course