AN EASY WAY TO IDENTIFY IMPROVED MANUFACTURING PROCESS RELIABILITY!

The search for measurable data that can be utilised may seem hard.  Equally hard could be a high level understanding of when a process is reliable, and what specifically must a process exhibit to be deemed reliable?

Fortunately for many, the daily production data is often the most accurate data found in a manufacturing plant.  It is also easy to find a source for this data. After all, this one daily number represents the efforts of the entire team across a 24 hours total. With just this one number, your management represents the relative health of the manufacturing process to their peers.

But how do you convert this “number” into “reliability” and what does it show us?

For starters, we can calculate the “lost opportunity” costs due to process variation. We can also identify the causes and assign actionable tasks to reduce these losses. While care must be taken in the use of certain production numbers, generally speaking, once understood, the number your organisation tracks can be utilised to determine your process reliability.

A world class “reliable process” is one that is operated at or near its functional capacity with low variability. Day in and day out the process delivers as promised.

A manufacturing process that can routinely deliver the expected result is one that is considered “ reliability improvement”. The daily output number can be measured in output tons, widgets per hour, or really any other measurement that your organisation has adopted for production success.

The key to sustaining high levels of manufacturing process reliability is the ability to recognise when the asset is not fully healthy while it still may be producing “acceptable levels” of throughput. Next, you must learn methods to diagnose which portion of the asset or sub system is responsible for the degraded performance.

A well accepted process to analyse and visualise production reliability was first published by Paul Barringer. We will explore this process more thoroughly at the Reliability Summit on the Gold Coast where we will hear from the leading users of this method in Australia.

So, how reliable is your manufacturing process?  

We will use a plants daily production output to explore just enough of the process to tease you, but not enough to explain it fully, as that detailed explanation would take more space than this blog post allows.

Analysis

This plant is reliable only about 50% of the time. Within this time of “reliable operation”, you should expect the plant to produce between 70% and 95% of the process maximum capacity.

That is a profound statement!

Conversely stated, 50% of the time you should expect this process to result in less than 70% of the capacity.

Equally profound.

The plant exhibits a significant loss when it is operating in an “unreliable” fashion. In fact, almost 30% of the capacity of the plant is lost each year.

ANALYSIS Specifics

This (Red Arrow) straight line represents how your manufacturing process has performed over time.

The (Blue) dashed line estimates on the vertical axis the “Reliability” you should expect from your process over time.

The (Yellow) dashed lines indicate the “range of throughput capacity variance” one should expect when operating reliably (~35% of capacity variance is expected).

Figure 2 – Exploded View Plant A Manufacturing Process Reliability

The area shaded in (Pink) accounts for 3.6% of the capacity loss. This occurs when the process is not operating reliably due to special causes that can include significant breakdown events.

The area shaded (Blue) accounts for 26% of the annual lost capacity. This is the difference between the inherent reliability and the demonstrated process reliability.

The area shaded (Light Green) accounts for 7% of the annual lost capacity which is generally considered “unrecoverable” due to the inherent reliability of the process.

The important point is that over 30% of a hidden plant exists within the existing plant structure which is not being converted to sellable product annually.