Root Cause and Failure Analysis of Quality Uncertainty

Abstract

The occurrence of quality uncertainty due to information asymmetry is a complex phenomenon. This chapter attempts to develop a simpler method for minimizing quality uncertainty. This chapter undertakes a root-cause analysis of quality uncertainty by using an affinity and Interrelationship Diagram (ID). The data generated through the ID is then used for the failure analysis of quality uncertainty. The feasibility of this failure analysis is demonstrated by using the case of ball bearing. The issues resulting from this analysis are then prioritized. The comparison of quality uncertainty between developing and developed nations is also unraveled. This mechanism can be used by focusing on the industry-specific factors.

Appendix B

5.1.1 Worksheets 3 and 4

5.1.1.1 Using Worksheet 3

Worksheet 3 refers to the first part of the computation for root cause analysis of quality uncertainty. Worksheet 3 is an interrelationship matrix. Entries in worksheet 3 are as follows:

• 1. At the first place identify the issues that cause quality uncertainty of a product or products of the company. Make a list of the factors for every identified issue.

• 2. Make a list of all such factors identified so far. Number the factors in A, B, C, D... format. Now each factor will become a member of the interrelationship matrix in worksheet 3 in A, B, C, D... format.

• 3. Ponder carefully on relationships of the factors with each other. Give a thought on every possible pair of factors. There may be relationships in some pairs of the factors and for some of the pairs no relationship may exist.

• 4. For every pair where a relationship exists, identify cause (the factor that is responsible) and effect (the factor that is outcome). Draw arrows from cause to effect in the interrelationship matrix for all such pairs. Do this exercise only once for each possible pair. Hence, only half of the matrix, along a diagonal, is filled.

• 5. Using past experience, attach a suitable weight to each factor.

• 6. Now, there are incoming and outgoing arrows for each factor. These are seen in the row as well as the column of the factor. Count the incoming and outgoing arrows for each factor from the row and the column. Multiply the counts by the weights of the factors and make the entries – In, out, and total in the last three columns.

5.1.1.2 Using Worksheet 4

Worksheet 4 takes us toward the solution to the problem of quality uncertainty. The entries of incoming arrows are important for knowing the failures of quality uncertainty whereas outgoing arrows indicate the causes. Incoming entries can be translated into probability of quality uncertainty (P _qu). Entries in worksheet 4 are as follows:

• 1. Put the identified issues in the first column of worksheet 4.

• 2. List down the related factors for every issue in the second column of worksheet 4.

• 3. Compute P _qu for each factor in the third column of worksheet 4. For example P _qu for the factor A is as given below:

  P _qu (A) = Incoming arrows for A (from worksheet 3)/total incoming arrows (from bottom of worksheet 3)

• 4. Similarly, compute P _qu (issue) for each issue in the fourth column of worksheet 4 by adding P _qu of the all related factors to the issue.

• 5. Develop a solution as a priority matrix which is based on P _qu (issue) in worksheet 4. The issue with highest P _qu is a highest priority issue with a rank 1 in priority matrix. Similarly, list up to five priorities in the priority matrix of worksheet 4.

• 6. Make an action plan for the priority issues to minimize the quality uncertainty.

5.1.2 Worksheet 3

5.1.3 Worksheet 4