Training Seminar: Reliability Analysis for Repairable System

  • Date: 21 to 24 Nov 2022 (4 days)
  • Venue: Bogor, Indonesia

Using Work-Order (WO) captured in CMMS system (E.g., SAP) as reliability data source

Using WO as reliability data source to calculate equipment MTBF is a common practice in many big organizations. Meanwhile there are also many software vendors exploiting this demand by providing applications that extract WO data to generate MTBF values for the corresponding equipment.

This presentation describes the flaws in using WO captured in CMMS system to derive MTBF of equipment.

RAM Analysis for an Offshore Platform

In the last presentation (Equipment Production Loss Contribution), production loss due to equipment is estimated using its associated downtime. Production loss of a standby sub-system in a network is more complicated as a failure may or may not cause any loss. This presentation shows how to rank standby sub-system performance in terms of production loss.

Inventory Management - Spare Restock Triggering Policy

When the spare inventory reaches a minimum level, a reordering is initiated. Since there is a lead time for the items to be replenished, the current quantity should meet the demand until ordered items arrive. This article presents a simulation approach for assessing the production downtime impact due to spare restock -triggering policy, and lead-time constraint.

Inventory Management - Spare Inventory Optimization

A company warehouse maintains and supplies an item for a large scale, round the clock production.

In the current policy, when the stock level reaches 6 units, a restock order is initiated such that the inventory level is maintain at 10 units. I.e., when the stock level is 6 units, the CMMS will initial a restock order with a quantity of 4 units.

Is there any quantitative approach to figure out whether it is possible to reduce the inventory, by reducing both the restock upper limit (currently 10 units), and the restock trigger level, taking the production cost impact into consideration?

Non-Repairable Spares Forecast

A reliable failure events forcast allows managers to:

  • Fulfill objectives of the business (increase productions, minimize cost)
  • Preparing the budget
  • Taking management decision
  • Evaluating performance

This example leverages on the past historical reliability data to forecast spare inventory. Field data is converted to appropriate reliability data type for Life Data Analysis to obtain a distribution model for the item. The model is used to derive conditional probability of failure and hence estimates the spares required.

Repairable Spare (Rotable Spares) Forecast

This example demonstrates how to project the future failures of repairable inventory. Field data is converted to appropriate reliability data type for Recurring Data Analysis to obtain an NHPP (Non-Homogeneous-Poisson-Process) model. The model is used to derive the expected number of events in the future.

A discussion on Recurrent Data Analysis using Non-Homogeneous Poisson Process with Power Law

Recurrent Data Analysis applies to repairable item that experience multiple failures throughout its operational life. These items are generally complex with many failure modes. Life Data Analysis does not apply to the failure intervals of such items because these intervals do not follow the same distribution. This example demonstrates how to model a repairable system using both NHPP model, and compares the results with System Reliability Simulation approach.