Asset Reliability Modeling and Simulation

Traditionally in manufacturing discrete event simulation, delay occurrence and duration are represented by static distributions. For many problems and industries, this assumption may be appropriate, rendering them essentially independent. The manufacturing of soft, disposable consumer products, however, may involve the assembly of multi-component products with flexible materials that pose significant challenges to process reliability and thus make the independence assumption invalid. In fact, these non-linear interactions between coupled-delays can result in substantial financial opportunities from seemingly minor contributors. We describe an asset reliability modeling and simulation (ARMS) framework developed at Kimberly-Clark that uses discrete event simulation and dynamic reliability modeling of assets¿ process event databases. Models are essentially built ¿on the fly¿ and survival simulations are validated against asset process history. Analytics and reports provide a means for identifying the biggest, overall improvement opportunities in performance metrics including production, delays, uptime, and waste.