Benchmarked calibrated replacement lives We developed benchmark calibrated lives by first using the repex model to calculate calibrated lives based on the replacement data from all NEM distributors. For model inputs we again used the average, third quartile (above average), and longest of the calibrated lives of all NEM distributors for each category. We discuss how we prepared this data in appendix E. When applied to the repex model, compared to using calibrated replacement lives based on United Energy’s data the: average benchmark and third quartile calibrated replacement lives produced higher outcomes longest benchmark calibrated replacement lives produced lower outcomes. When applied to the repex model, compared to United Energy’s proposed forecast repex for the modelled categories the: benchmark average calibrated lives produced a higher outcome benchmark third quartile and longest calibrated lives produced lower outcomes. The calibrated benchmark replacement may reflect to some extent the particular circumstances of a distributor and this may not be applicable to the business under review. These inputs provide us with a check that United Energy's calibrated replacement lives were reasonable against its peer service providers in the NEM. Further, these outcomes support our view that United Energy's forecast for the modelled categories is likely to be a reasonable estimate of business as usual repex. Benchmarked unit costs We developed industry benchmark unit costs using the data collected from all NEM distributors in the category analysis RINs. For model inputs we used the average, first quartile (below average), and lowest unit costs of all NEM distributors for each asset category. We discuss how we prepared this data in appendix E. When applied to the repex model, compared to using United Energy’s unit costs the: average benchmark unit costs produced a similar but slightly higher outcome first quartile and lowest benchmark unit costs produced lower outcomes. We consider the benchmark unit costs provide a useful comparison with the cost of other distributors in the NEM. United Energy’s predictive modelling United Energy submitted a report by Nuttall Consulting which we respond to in the remainder of this section. We reviewed the submissions of United Energy and Nuttall Consulting and continue to consider that our predictive modelling is a useful and important technique when assessing United Energy’s forecast of repex against the capex criteria. Our predictive modelling approach is well established having been used
Benchmarked calibrated replacement lives We developed benchmark calibrated lives by first using the repex model to calculate calibrated lives based on the replacement data from all NEM distributors. For model inputs we again used the average, third quartile (above average), and longest of the calibrated lives of all NEM distributors for each category. We discuss how we prepared this data in appendix E. When applied to the repex model, compared to using calibrated replacement lives based on United Energy’s data the: average benchmark and third quartile calibrated replacement lives produced higher outcomes longest benchmark calibrated replacement lives produced lower outcomes. When applied to the repex model, compared to United Energy’s proposed forecast repex for the modelled categories the: benchmark average calibrated lives produced a higher outcome benchmark third quartile and longest calibrated lives produced lower outcomes. The calibrated benchmark replacement may reflect to some extent the particular circumstances of a distributor and this may not be applicable to the business under review. These inputs provide us with a check that United Energy's calibrated replacement lives were reasonable against its peer service providers in the NEM. Further, these outcomes support our view that United Energy's forecast for the modelled categories is likely to be a reasonable estimate of business as usual repex. Benchmarked unit costs We developed industry benchmark unit costs using the data collected from all NEM distributors in the category analysis RINs. For model inputs we used the average, first quartile (below average), and lowest unit costs of all NEM distributors for each asset category. We discuss how we prepared this data in appendix E. When applied to the repex model, compared to using United Energy’s unit costs the: average benchmark unit costs produced a similar but slightly higher outcome first quartile and lowest benchmark unit costs produced lower outcomes. We consider the benchmark unit costs provide a useful comparison with the cost of other distributors in the NEM. United Energy’s predictive modelling United Energy submitted a report by Nuttall Consulting which we respond to in the remainder of this section. We reviewed the submissions of United Energy and Nuttall Consulting and continue to consider that our predictive modelling is a useful and important technique when assessing United Energy’s forecast of repex against the capex criteria. Our predictive modelling approach is well established having been used
AER%20-%20Preliminary%20decision%20United%20Energy%20distribution%20determination%20-%20Attachment%206%20-%20Capital%20expenditure%20-%20October%202015.DOCX