Supportability predictions may be made for a new system and its components through a BCS process. When a new piece of equipment or software is being planned, preliminary logistics data analysis can begin by comparing the new system or component with existing "fielded" items. Knowledge of the new equipment design, physical features and the item's intended use will help identify like and similar equipment. The designers may also have designed the new equipment around some existing hardware/software and made improvements to known deficiencies. A certain amount of risk is dependent upon the degree of similarity. It is very probable that like and similar equipment will be identified since there are very few designs that are truly revolutionary.

Historical data must be collected on the like and similar system or component. It is important to use a large data sample representative of mature equipment. At a minimum, the latest available complete year of data should be used to assure consideration of all seasons of a mature item. It is difficult to determine a maturity point since new or modified equipment may continuously be installed, and the turnover rate of maintenance personnel impacts equipment familiarization (hence removals). If some BCS equipment comes from equipment approaching wearout, it may be advisable to use data from several years previous for a more representative sample. Good judgment is required in analyzing historical data so as to be able to identify relevant data from inaccurate or misleading data.

After the data have...