OEE: how to approach root cause analysis?

An interesting question was posted on the LinkedIn MES - Manufacturing Execution Systems group recently by Elisa Rocca of Siemens: "OEE: how to approach root cause analysis?" She goes on to say that "I was wondering how to best execute the root cause analysis for a filling & packaging line." and later in the discussion that:

"I would like to decouple the RCA investigation from levels 1 and 2 [PLCs and SCADA systems directly controlling the machines - FG], conducting the entire analysis into MES.

...  I'm wondering whether you see the RCA as an "online" analysis performed during the acquisition of OEE data, or as an "offline" analysis done on historical data using reporting tools"


My Response:

The challenges with root cause analysis of any live process metric, including OEE, are:

1. A priori you do not know what data you are going to require to solve the problems that exist, since you do not know what these problems are!

2. You will have a range of users for this RCA, from Operators and Supervisors, who's needs will be near real time, to Engineers, who will perform their analysis mainly historically.

3. You will probably need to combine 'hard' data from your equipment with judgement / big picture input from humans in the process.

4. You need to ensure that the effort required to perform the RCA is minimized: the more work required, the less likely it is to get done, and the greater the opportunity for confusion and arguments!

What does this suggest?

You are right to decouple your RCA from levels 1 & 2: they are great for machine management, but are too inflexible, machine focused and simplistic (analytically) to provide the solution you need. Similarly, ERP (in a manufacturing context) and MES systems are focused on dispatching, routing and recording production, rather than analysing the performance of the process.

Your requirements are the motivation for Enterprise Manufacturing Intelligence systems. I would suggest looking for EMI systems and rating them by the following attributes:

1. Flexibility -- could a Process Engineer configure their own analytics?

2. Scalability -- you will rapidly scale to large numbers of data sources and volumes of data. Is the system architected to cope with this?

3. Analytical Power -- you are going to need to analyse your process along many axes: machine, product, batch, shift, material, etc, and to perform sophisticated analyses to correlate causes and effects. Does the system provide the power you need in its tool set?

4. Ability to combine data from many sources, including friendly interfaces for human operators. Is there an easy way to capture human input and combine it with machine data?

5. Automation -- the system should do 80% of the analytical work automatically, especially the 'drudge' number crunching that does not require human judgement, otherwise this will not get done at all! How is this handled, and how long does the user have to wait for the results? Again, if the answer is >> a minute, the User is unlikely to wait, no matter how useful your data!

See here for the full discussion.