Key Metrics for an Automated Test Equipment Performance Based Logistics Sustainment Program

Abstract

A common long-term strategy for cost effective sustainment of Automated Test Equipment (ATE) is Performance Based Logistics (PBL). In PBL, the usual deliverables are performance outcomes which are defined by identified performance metrics for the ATE. The Product Support Integrator (PSI) delivers these performance outcomes to the Product Support Manager (PSM) usually at increased performance but at costs like those achieved previously under a non-PBL or transactional agreement for goods and services. In PBL execution, there are key metrics identified with performance outcomes. These key PBL metrics can be tied to incentives based on contract type, contract length, or incentive fees (or penalties). These incentives are achieved via threshold values versus objective values. Key PBL metrics for consideration are Fill Rate, Supply Down Time, and On Time Delivery. The threshold and objectives for these metrics can be determined using Spares Analysis. Spares analysis is an element of Logistics Support Analysis (LSA) which is an effective strategy of inventory control to meet the demand of spare parts from maintenance services. The Spares analysis is used to determine range and depth of spares to cover procurement lead time, repair cycle time, and transportation delays between the various nodes of the Supply Chain. Spares analysis helps determine the best possible performance at optimum cost by estimating how many spares to stock and confidence in stock level. With PBL providing higher margins to reward improvements such as better inventory management which includes the opportunity to reduce stock, these margins can be used to set goals in Spares Analysis/Model that will affect the desired metrics; Fill Rate, Supply Down Time, and On Time Delivery. A robust predictive system / Optimization Model with rigorous forecasting and long-term planning is essential to compressing the supply chain, optimizing repairs, and managing outcomes. An Optimization Model with multi-indenture, multi-echelon scenarios is needed for spares provisioning so the PSI profit can be maximized under the PBL contract.