Process exploration and analysis for an attribute output can be tricky. As with all process improvements, we must implement change in a way that is sustainable, and be confident the change was successful. First, we must try to understand how the process currently functions by collecting and visualizing data on the process. Then we need to analyze the impact of each potential X (input) on Y (output). But does all of this work if we are counting things rather than measuring them?
In sessions 1 and 2 of this series, we focused on understanding and analyzing variable data. In this 90-minute session we will discuss historical attribute data and, using a case study, we will explore what the data does and does not tell us. We will introduce specific statistical and graphical tools that process improvement practitioners can utilize to provide insights into their baseline process and identify root causes.
This session will cover five key concepts that will enable you to effectively understand your attribute process output data and help you communicate the process performance to your organization:
Where to start with your data. When we get our data, one of the most helpful initial tasks is to determine that it is attribute data and that it is not possible or feasible to obtain process output in variable terms. Also knowing more about the root source of your data and confirming that it is attribute and not variable will help us embark on the correct journey. We will explore count data and how this differs from proportion data, introducing the Binomial and Poisson distributions. We can then produce simple summary tables to give us a sense of our process.
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 key charts that will help you to learn more about your process output. These are the:
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:
Y=f(x) is the basis of process knowledge. We may be unhappy with the performance of our process in terms of the output but until we understand the relationship between the inputs and the outputs, we cannot make considered changes to gain improvement. Exploring the relationship between the inputs and outputs is fundamental to our ability to conduct graphical and statistical analysis.
Identifying the “vital few” inputs. In the Analyze phase we begin the process of identifying the vital few inputs from the trivial many. We can use statistical analysis to uncover the main drivers of the process output. We will introduce the following to begin this journey:
Who should attend
Joanne Sauvey is an experienced Lean Six Sigma practitioner and trainer who is credited with developing BMGI's online Green Belt and Black Belt curricula. In her capacity as an eInstructor, Sauvey trains and mentors practitioners across North America, New Zealand, Europe and Asia in a wide variety of industries, including healthcare, manufacturing, utilities, financial services and technology. Sauvey also manages the Master Black Belt development program at BMGI, working with practitioners to expand their mastery of Lean Six Sigma. She holds a Bachelor's in Industrial and Systems Engineering from Ohio University, and is a certified Lean Six Sigma Master Black Belt.
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.
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90 minute course
This four-part series explores the data and analysis tools that enable today’s process excellence practitioner to achieve a deep understanding of their processes, leading to opportunities for sustained improvement within their organization. From Measure, where we explore our baseline process through to Analyze, where we uncover the root causes driving our performance, attendees will take a journey on how to capture validated data to investigate and improve their processes. Discounts available when you register for the entire series.
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This training session, presented in partnership with IndustryWeek, will be conducted using a slides-and-audio format. You will also have the ability to ask the instructor questions, and you can participate in polls the instructor pushes to the audience during the session.
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