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Data-driven improvement typically follows a familiar pattern: Define a Y metric, identify potential X variables, obtain data, and model Y=f(X) to determine optimal conditions. When that pattern was used to investigate critical factors in maintaining product availability at retailers, things broke down quickly in spite of having a large set of high-quality data. So what should a practitioner do when modeling X’s result in “Why?” instead of Y?
In this case, the Y metric for the project turned out not be the right Y to model – a lesson all Minitab users can learn from. But discovering the right Y was not as easy as it first seemed, and the presenter ultimately had to keep digging deeper until the right metric was uncovered. Adding to the challenge, asking “Why?” uncovered that the true controllable metric was censored – meaning that for many observations only a range of possible values was known. Fortunately Minitab comes armed with the tools for analyzing such data, and in the end Y=f(x) saved the day once again!
Attendees will learn advice for selecting an appropriate Y metric, giving a model a healthy reality check, and dealing with censored data.
Process mapping is an easy-to-use method that allows people to analyze and agree on the most efficient paths for improving or re-engineering processes. Process maps aid in identifying redundant and inefficient tasks, poor handoffs, and unclear decisions. Process maps can be used with virtually any process, but selecting the best process map tool often is not clear leading to delays and rework for the process improvement team.
This session will present guidelines for using process mapping as an improvement tool by describing:
This presentation will demonstrate process mapping tools using Companion software and will be beneficial to all attendees who are involved in process improvement.
This presentation will summarize the approach taken by a team which improved internal vision inspection yields from 65 to over 98 percent. A project was initiated to resolve customer returns due to open solder connections. The project team ultimately discovered a root cause unknown to the electronics industry. The tools used for determining the root cause were not new and ranged from simple graphical analysis to more complex statistical tools such as DOE’s.
In this presentation, we will also discuss the difficulties the team encountered to implement a solution when something new and unknown gets discovered, summarize the data-driven techniques used, and provide tips and lessons learned during the team’s journey.
My journey with Six Sigma at Crayola started with a project focused on improving product cost in the watercolor line. The green belt training, project mentoring and senior management project reviews combined with great support from operations personnel help insure project success. Through measurement and data analysis I learned the importance of developing a clearly defined problem statement. The problem statement was not provided and had to be discovered. Working with a team through the measure phase of the project to develop a clearly defined charter statement is a skill that I will use throughout my career at Crayola. Directly observing the process and speaking to operators was the starting point to brainstorming practical theories when entering the measure phase. Spending time speaking to the operators and observing the current state of the process drove all of the improvement efforts. Understanding the statistical methods and software tools allowed all decisions to be data driven. As a new engineer, the ability to make decisions solely on the data made changes easier to communicate to all impacted by the decision.
Currently I am enrolled in Black Belt training and certification with projects varying from supplier quality assessments, capability analysis, many designed experiments and measurement systems analysis. My projects have had great involvement and participation from operations personnel. We have worked to analyze top contributors of process downtime through Pareto chart analysis, conducted and designed various design of experiments, and built the capability of raw material suppliers to reduce process variation. All of these components are essential elements that are part of Crayola’s Lean Six Sigma Matrix and support the on-going efforts to reduce cost, sustain excellent quality and continually improve customer service levels.
In this session, we will introduce Classification and Regression Trees (CART). We will provide a detailed description of the CART algorithm, discuss advantages of using CART, learn how to interpret the model, and discuss how you can use CART to better understand your data and potentially improve your models. Nearly all of the concepts discussed will be explained via visual or animated examples.
Managing your CI program can be difficult but it doesn't have to be. We will discuss practical tips and tricks for managing your program for the best results and moving your company forward. In addition to project management, we will also discuss how we manage strategic initiatives and deal with global and site issues.