Question

1.         How difficult did it appear to be to find the “root cause” of the errors? What do you think contributed to the difficulty?

This is a question of the Case Mentioned Below: I do NOT think you have to read the entire Case to answer one question

Case: The PIVOT Initiative at Midwest Bank – Phase 2

This case is a continuation of PIVOT Initiative at Midwest Bank in Chapter 7. You should review that case for background on the project and a description of the Define stage of the DMAIC approach. In this case, the focus is on the remaining steps—Measure, Analyze, Improve, and Control.

The Measure stage demanded an intense data collection effort by the PIVOT team. They used a tool called an XY matrix (see example in Table 1), designed to rank factors for potential error causes (Xs) and for customer outputs (Ys). The team gathered data and studied departmental process flows, seeking to find root causes of the problem, and to identify and agree on key CTQs that impact the customer. During the process, it was difficult for everyone, especially the subject matter experts (SMEs), to ignore their perceptions in speculating about possible causes of errors, which became known as “tribal knowledge.” Six Sigma theory strongly discourages any attempt to let unproven assumptions creep into recommendations. All factors must be statistically proven through in-depth analysis to justify recommendations.

For CPD, the principal customer outputs selected were risk mitigation, error reduction, and reducing dollar loss, which were then stratified against potential error causes. The matrix was then used to calculate an overall ranking to guide the team toward the most probable causes of errors. After deciding to focus on the top seven potential causes, the CPD department’s staff began the task of gathering data for each of them to verify their impact on errors in the process.

The Six Sigma analyst stratified the data collected across the potential error categories. During the Analysis stage, analysis of an extensive array of graphs developed from the data permitted the team to see trends in the process and to begin seeking strategic solutions. Construction of the graphs required more than 48 hours of team effort. Trends pointed to problems with the manual strapping process. However, the tribal knowledge suggested that errors were due to insufficient staffing, but the initial analysis of the data did not match this hypothesis. As a result, the team began to search for a way to prove or disprove the tribal knowledge.

It was suggested that the data were not properly stratified with regard to staffing and that analysis should be applied across a longer timeframe. CPD set out to collect more data from past months, and the Six Sigma analyst began developing the graphs needed to examine the new data. Over the next week, some 100 different graphs were created, depicting data in single strands and also paired with variables that interacted with one another. The team’s Six Sigma training had emphasized the importance of fully exploring all data interactions, using graphs to illustrate relationships between variables. Despite the team’s best efforts to find a relationship, staffing and volume did not appear to affect strapping errors. This finding disproved the tribal knowledge, while providing a multitude of additional graphs for analysis.

Answer 1

mn 1. It is very difficult to find root 1. Cause of the errons because, the difficulty is caused by lis the team gathered into on principal customer out puts such as risk mitigation, error reduction and leducing dollar loss. But unificatica and analyzing of data, to Tribal Knowledge whether to accept or not. And to evaluate loo's of graph is not that easy. - so collectively the Six signa considerations, the team's graph Integration over sixmonths all led to this difficulty of finding the root cause & error.