AssetStudio

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.

This article provides a brief description of how availability is calculated, explains its limitations as a reliability metric, and introduces a useful alternative.

One of the objectives of RAM analysis is to measure the Production Loss due to equipment unreliability and maintenance downtime. This information is used to identify the gaps for improvement, and also a mean to track improvement programs and evaluate its effectiveness.

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.

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.

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?

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.

This illustration showcases projecting future failures within repairable inventory. The field data undergoes conversion into a suitable reliability data type tailored for Recurring Data Analysis, enabling the creation of an NHPP (Non-Homogeneous Poisson Process) model. This model, in turn, facilitates the estimation of the expected number of future events.

Recurring Data Analysis is suited for repairable items that undergo multiple failures throughout their operational lifespan. These items are typically intricate, exhibiting numerous failure modes.

Life Data Analysis is unsuitable for assessing the failure intervals of such items since these intervals don't conform to a consistent distribution. In this scenario, the example showcases the modeling of a repairable system employing both the NHPP model and compares these outcomes with the reliability digital twin simulation approach.