Case Study: US Bottler bolsters reliability program with AIC

“AIC automates the labor-intensive process of gathering extensive assembly and part-level reliability information for our planners.  We use its comprehensive reports to quickly address ‘Bad Actors’, improve maintenance efficiency, and make smarter decisions which increase asset uptime.  AIC is the single most impactful technology we’ve implemented in years.”

Coca-Cola, Reliability, maintenance, parts, planners, CMMS

A large bottling facility in the United States (ex. Bottles Inc.) started a reliability initiative to make ‘smarter decisions around maintenance and increase asset uptime.’  They planned to identify which areas of their production line were more likely to fail, and prevent downtime through preventative maintenance and engineering improvements.

Bottles Inc. hired reliability planners, evaluated incumbent maintenance processes, and began to compile and analyze a LOT of data.  In the world of reliability, data is King.  They soon found, however, that their CMMS did not provide them with all the data they needed.

The Gap                           in Reliability Data

Bad Actors are assets, assemblies, or parts with a history of failure.  By identifying where these Bad Actors reside, reliability planners can proactively fix problems in the production line and increase overall productivity.  The problem, however, is how difficult it is to distinguish which component (or ‘assembly’) of an asset is most likely to fail.

Bottles Inc. could pull information from historical Work Orders to find which assets were Bad Actors, but they were unable to see the information on an assembly or sub-assembly level.  For example, they could identify a conveyor belt as a Bad Actor, but they couldn’t see which section of the belt was causing the problem and understand why the issue was occurring.  In order to gather this information, they started an initiative to add Equipment Numbers to their relevant assemblies and sub-assemblies for their most important assets, which were to be included in a Work Orders field.

This proved to be a very labor-intensive process.  They needed to mount and maintain physical tags for every component of every piece of equipment in their facility, and record Equipment Numbers for many ‘untaggable’ sub-assemblies with a different system.  Before they knew it, planners were inundated with the maintenance of corroding tags and searching for the Equipment Numbers of untaggable components in disparate systems.  Bottles Inc.’s had a 2-year backlog of tag replacements within 5 years of implementation.

Bottles Inc. could no longer manage the labor-intensive process required to gather reliability data, so they turned to Metanoia for a robust solution.

Modern Architecture for Parts Management

When implementing Asset Information Center (AIC), Metanoia builds a complete graphical and interactive representation of parts catalogs for relevant assets.  AIC’s Interactive Parts Catalogs include a hierarchy of assemblies and sub-assemblies, so when parts are submitted to a Work Order their location is automatically known without the need for any real physical tags.

This ‘Virtual Tagging’ capability helps Bottles Inc. circumnavigate the ‘gap in reliability data’ by automating a flow of robust reliability information while streamlining the Work Order process.  Their planners use AIC to build comprehensive reliability reports on an asset, assembly and sub-assembly level, which is then used to make smarter, more informed, decisions in the maintenance process.

AIC is now a central source of data for reliability planners because it circumvented the labor-intensive process of gathering extensive part-level reliability data and by bridging the gap of data for Bad Actors.


See how a large bottling facility in the US uses Asset Information Center (AIC) to automate a flow of rich reliability information to their planners.