AAOM Handbook

performance of the process. This will assist in identifying the type of strategy outcomes required and the most appropriate options to focus on.

In the Operating Model, the processes of Measure Process Performance and Measure Work Management performance will provide the data for the historical Purpose and Theory (including Production Strategy Theory) performance of the process. This data will allow the effects and interactions of Utilisation, Throughput Rate, Quality, Schedule Effectiveness, Schedule Completion, etc. on Process performance to be analysed. Since all real world processes show variation in their performance over time, performance measures are provided in the form of Control Charts and Capability Histograms (performance distributions). There are four factors to be considered when gathering historical data analysis of Production strategy; • Process stability - by definition, if a process is not stable then it is not performing in a consistent way – instability occurs when the process either deviates from normal operation or suffers a failure. Consequently, data from stable and unstable periods should be separated and data from unstable periods only used for production strategy analysis when the reason for the instability is a understood deviation. • Operating level - the performance of a process will differ depending on the operating range within its performance/effort curve. viz since process performance curves typically follow the law of diminishing returns, performance distributions tend to be different at the bottom and top of the performance curve - see figure 1 above. Hence we need to understand where data sits in the process performance effort curve and why the process was at that operating level. • Sample size - the more data that is available the representative it will be. Small data sets tend to under represent the range of variation that can be expected from the process, hence allowance will need to be made in the analysis for the data set size. • Data Independence - the data we collect may include independent and dependent elements. This is particularly true for the Purpose measures of a process in a connected series of Value Chain elements, unless there are significant storage points that effectively isolate the process from its pre/proceeding elements. Since the distribution output from the Set Performance Targets process is for the forecast independent performance distribution we must also look at the historical independent measurement data. The Theory measures for a process are generally likely to have a high degree of independence but the issue should be considered when looking at the data.

© McAlear Management Consultants 2006

Operational Planning: Set Production Strategy

Updated: August 2018

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