SOLVING THE REMOTE BUYING TEAM CHALLENGE

Kim Overstreet, Senior Content Strategist, Alignment, PMMI Media Group

Predictive maintenance is the ability to monitor a machine, or machine component, and avoid unplanned downtime by foreseeing machine failure and allowing the opportunity to take preventative action. The possibility of machine failure shutting down a production line ranks high on most manufacturing managers’ list of worries, and report research showed that manufacturing managers at CPG companies consider their packaging machines to be more prone to downtime than the other types of machines they use.

According to PMMI’s Packaging and Predictive Maintenance report, most of the predictive maintenance solutions currently on the market are designed to monitor critical assets such as AC induction motors, pumps, and gearboxes; and they tend to be based on vibration sensor solutions.

The critical functions of packaging machinery, however, tend to be under servo control, even though many machines do employ standard AC motors. Servo technology does not lend itself to vibration monitoring, so OEMs are currently using thermal imaging to gather necessary data on servo systems. It is expected that in the future, predictive maintenance opportunities will be built directly into the servo drive, allowing for standard predictive maintenance solutions to be applied.

Some types of packaging machinery are more prone to downtime than others, and CPGs reported interesting results with regard to which are most likely to break down. In the “extremely likely” category, form/fill/seal (f/f/s) machines were reported in the lead—with 14.3% of manufacturing managers at CPG companies rating them as extremely likely to suffer downtime. Next reported in the “extremely likely” to fail category are labeling, decorating, and coding machines—which were placed in this category by 13.3% of respondents.

Interestingly though, when the three categories of “likely to fail” (extremely, moderately, and slightly) are aggregated, labeling, decorating, and coding machines comes out in the lead as the least reliable type of machine; while f/f/s machines only make it into third place.

There were seven causes reported by respondents at CPG companies as the most common cause of packaging machine downtime. Within these seven leading types of downtime, three rated higher: general wear and tear (26.3%), operator error (21.1%), and product changeover (22.1%). Of these three, the only one that clearly could not be addressed by predictive maintenance is operator error.

Machines that are used to package multiple types of items and can require a changeover of parts when switching between different items was also mentioned as a problem area for CPG respondents, and according to the report, “there is a clear and definite need for OEMs to work with predictive maintenance specialists to design bespoke predictive maintenance solutions that can monitor the product changeover process.”

One other specific area with a need for predictive maintenance solutions is in washdown areas. Packaging machines that are implementing predictive maintenance based on vibration sensing can become dislodged by high pressure water washdown processes, and companies who want to implement predictive maintenance in washdown areas need to ensure that they find a predictive maintenance partner who understands their specific needs. Download PMMI Business Intelligence’s Packaging and Predictive Maintenance report by visiting: oemgo.to/predictivestudy

Optimizing your company’s current investment in existing production equipment is a widely shared goal in the consumer products industry. PMMI’s OpX Leadership Network recently commissioned an Asset Reliability Solutions Group to undertake a work product on asset reliability in order to get CPGs and OEMs on the same page regarding definitions, key performance indicators (KPIs), calculations, and leadership guidance when developing an asset reliability initiative. To access this free download, visit: oemgo.to/asset Easily share this article with your peers