AAOM Handbook

INTERNAL

AI.19 Model/Test to Validate or Reject Potential Actions Context Brainstorming may result in the identification of many potential control actions for an issue. Even after these have been organised and rationalised there may still be a number of potential actions. There is a natural tendency at this point to want to rush into action, that is, implement some of the control actions we have listed. However, at this time none of these potential control actions has been proven. An inappropriate action will generally make the process perform worse, not better. Before any control action is taken we need to validate that it will have a positive and a significant contribution to the process issue we are investigating. We ranked the potential control actions to help reduce the workload required to model/test and validate them. We start the validation from the highest ranked action and proceed until we have confidently identified suitable control action(s). The validation must be based on data, hence this task is to specify, model and or test the control actions to collect the data necessary to validate or reject a control action as practical or not. Purpose To specify the method of validating each potential control action. Quantity One specification for modelling/testing each high potential control action, to confirm that it is (or is not) the most practical. One set of validation data for each high potential control action. Quality Consider the following;  Type of data to be collected (is it a measured value, a characteristic, a cause of variation),  The decision that is required from the data (what is the impact of the action on a cause and on the KPI of interest),  Characteristics of the data,  Potential sources of the data,  For measured values the quantisation level, based on the minimum value of change that is significant for the variable to be measured.  The data sampling/measurement frequency, based on the rate of variation that is likely for the variable to be measured.  For concurrent causes of variation, the ranking order for causes and the identification of primary and secondary causes. Once completed, collect all necessary data.

© McAlear Management Consultants 2007

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