PrimeSupplier Cross-Program Impact Analysis and Supplier Stability Indicator Simulation Model
- Wednesday, 02 September 2009
This application has potential uses in supply-chain and enterprise-resource planning software.
PrimeSupplier, a supplier cross-program and element-impact simulation model, with supplier solvency indicator (SSI), has been developed so that the shuttle program can see early indicators of supplier and product line stability, while identifying the various elements and/or programs that have a particular supplier or product designed into the system. The model calculates two categories of benchmarks to determine the SSI, with one category focusing on agency programmatic data and the other focusing on a supplier’s financial liquidity.
To expand further on programmatic data, excessive time gaps between manufacturing, repair, and/or failure analysis requirements [and subsequent purchase orders supporting the logistics purchase requests, established by the project elements through Logistics Supportability Analysis (LSA), which are for flight hardware (personal property) only] focus on hardware meantime- between failure, manufacturing lead time, quantity per assembly, and system effectiveness. Procurement minimum buy quantities, Federal Acquisition Regulations (FARs), and International Traffic and Arms Regulation (ITAR) also influence, and sometimes extend, this time-gap exposure for manufacturing product, thus leaving the supplier and subsequent second- and third-tier suppliers vulnerable to financial instability. This results in either poor product quality, or in a supplier-induced product discontinuance.
To understand this time-gap exposure, the last supplier product or service requirement need date, and first expected need dates, are documented in number of days for all three supplier offerings of manufacturing, repair, or failure analysis. Each functional capability of manufacturing, repair, and failure analysis is weighted slightly differently, with manufacturing having a heavier weight than repair, and repair would have a heavier weight than failure analysis.
The programmatic data feeding into the weighting calculations are collected from internal logistics support analysis data, like Line Replaceable Unit Probability of Sufficiency (LRUPOS), mean-time-between-failure, and repair generation rate forecasts, as well as last supplier capability need dates and first capability need dates that are all reported from the NASA project elements to the Program Office. Supplier capabilities include: manufacturing, repair and sustaining engineering, failure analysis and teardown, and test and evaluation. Other programmatic weighting factors include: procurement data, contract value, a supplier’s percentage distribution of NASA business, and procurement-order time-gap exposure. The financial benchmarks include liquidity performance measures, such as net profit margin, current ratio (current assets over current liabilities), and price earnings ratio. These financial indicators are all compared against industry standards with various business-centric weighting factors, and could be automated with a real-time data feed.
PrimeSupplier was developed to help NASA smoothly transition design, manufacturing, and repair operations from the Shuttle program to the Constellation program, without disruption in the industrial supply base. Complicating this effort are today’s economic conditions that have created unprecedented volatility within the country’s industrial supply base, negatively affecting quality and ability to deliver. PrimeSupplier helps organizations identify at-risk suppliers by providing a holistic assessment of suppliers’ total economic stability accounting for programmatic and enterprise-wide demands and general economic conditions.