Modeling Every Failure Mode at Once
A Consumer-Products Maker Saves Billions

A global consumer-products maker runs extremely complicated production lines. Each line has hundreds of ways to stop, and the company collects mountains of stop data without knowing what it says. Reliability engineers and statisticians build models that handle every failure mode at once, then use them to decide what to fix, how to maintain it and how to run the line. By the company’s own figures, plant productivity rises up to 44% and savings pass $2 billion. It is the approach ReliaSim is built on.

The business

A global consumer-products maker · lines making consumer paper products, in plants worldwide

The question

With hundreds of failure modes on every line, which ones matter, and what should engineers do about them?

The model

Statistical reliability models of every failure mode together, built from the lines’ own stop data

What it is worth

Up to 44% more plant productivity; more than $2 billion saved, by the company’s figures

Source

Published accounts of the project

Data rich, knowledge poor

The company’s lines make consumer paper products. Their automated systems routinely collect loads of reliability data, far more than anyone can read. By the company’s own admission it is “data rich but knowledge poor”: overwhelmed by the volume, it can’t tell what the data contains.

The hard part is the number of ways a line can stop.

Looking at one failure mode at a time doesn’t answer the question. On a line, failure modes compete: while one has the machine down, the others aren’t wearing, and a stop on one machine idles the machines around it. What a fix is worth depends on all the others.

How it is modeled

The company’s reliability engineers work with a team of statisticians under a cooperative research agreement. Together they build reliability models that take in every failure mode on a line at the same time, fitted from the stop data the plants already collect. The models learn and adapt as new data comes in from running lines.

The work becomes a software tool for the plants. With it, engineers set up both the machines and their maintenance schedules around reliability. They can see product jams, component breakages and machine speed variations coming, and sometimes avoid them. In some cases they rearrange how product flows through the line.

What it is worth

By the company’s figures, the methods:

• increase plant productivity by up to 44%
• cut controllable costs by up to 33%
• improve equipment reliability by 30% to 40%
• cut line changeovers from as much as an hour to as little as six minutes
• make new equipment and product start-ups 60% to 70% faster
• save the company more than $2 billion

Where ReliaSim comes in

ReliaSim is built on the same idea: model every failure mode of every machine from the line’s own stop history, together, and let the simulation show what each fix is really worth. ReliaSim adds what a reliability model alone can’t show: the line itself, with its rates, buffers and converters, so stops on one machine starve and block the others the way they do on the floor. One of the reliability engineers on this work later checked ReliaSim’s approach against a real plant and found it matches measured OEE within 1%.

To see it on a line, play Beat the Bottleneck: 36 interrupts from a real line’s history, and one move to beat its line efficiency.

Where this work comes from

The project is described in a national laboratory’s news release, “From detonation to diapers”, on its computer codes in advanced manufacturing; in an ExtendSim case study; and in an interview with the project’s lead statistician in Statistical Science (“A Conversation With Harry Martz”), which gives the figures above as the company reported them.

Reliability models of every failure mode at once, built from the plants’ own stop data · sources: national laboratory news release, ExtendSim case study, Statistical Science interview.