Industrial Automation

CPG Plant Data: Your Secret Weapon to Boost Customer Loyalty

Peter Kneski
CPG companies need to change their manufacturing processes if they want to earn customer trust and bring loyalty back. It starts by tracking and making changes based on CPG plant data.


To win and keep the hearts of customers, CPG (consumer packaged goods) companies must find ways to become more competitive every day. Nearly seven in 10 consumers say they’re willing to leave a current brand if they find a higher-quality alternative elsewhere. Today, nearly 33% of U.S. customers say they aren’t loyal to brands at all—they’re willing to shop around.


How can you earn customer trust and bring loyalty back? (After all, loyal returning customers typically spend more than new customers!) Data may be your secret weapon.


The more information you can collect and analyze on the plant floor to improve back-end production, the more you can optimize efficiency, get products out to store shelves ahead of competitors and deliver higher-quality products to your customers faster.


Which CPG Plant Data Is Worth Using?

For most plants, data generation isn’t the problem. Your plant already produces plenty of data, whether you realize it or not:

  • EHS monitoring data
  • Employee and operator data
  • Engineering data
  • Machine alarm/notification data
  • Material tracking data
  • Production volume data
  • Safety data


The problem lies in determining which data to use. You don’t need to capture and analyze it all: The data that matters most is information that will help you keep your CPG plant running, optimize worker productivity and reduce costs.


When you understand how key plant-performance parameters impact production, you’ll be able to determine how they impact the quality of the products your plant produces, as well as how quickly products can move from the production line to the store shelf. This can be accomplished by delivering OT data to an analytics platform for aggregation, analysis, correlation and decision-making to unlock new opportunities.


For instance, capturing overall equipment effectiveness (OEE) data about machine and device usage can help prevent downtime.


This is an example of how it could work: OEE data can tell you when the equipment was installed, how long it’s been in operation, when failures occurred, why those failures happened, etc. This information can be aligned with the equipment manufacturer’s replacement recommendations to establish predictive maintenance processes that allow workers to proactively complete maintenance based on usage and need instead of reactively responding after equipment fails.


When your plant is ready, this data can be used to take maintenance to the next level: prescriptive maintenance.


Through machine learning, prescriptive maintenance enables equipment operating conditions and necessary maintenance to be adjusted based on desired outcomes. Work orders and alerts are automatically generated and sent to maintenance and storeroom personnel with details about which device failed, where it is, when it failed and what caused the failure.


Instead of relying on humans, a behind-the-scenes system analyzes data from different sources to keep tabs on operations.


Once a significant amount of data is collected, you can pinpoint and resolve common causes of downtime and performance problems, such as:

  • Unplanned maintenance requirements
  • Missing supplies
  • Electrical failure
  • Product changeover


Collecting CPG Plant Data without Wasting Resources

But acquiring, transmitting, orchestrating and managing this data isn’t always as easy as it seems. Information comes from different places and different teams. It’s stored in different locations. Disparate systems don’t support strong reporting capabilities, which can lead to a slow decision-making process that reduces productivity.


In some CPG plants, employees must use many different tools and search in many different places to find the data they need, which wastes time and effort and prevents them from focusing on high-value tasks.


In other cases, to uncover the insights they’re looking for, employees may attempt to synchronize business tools and systems. Or they may manually enter data from one system into another, which becomes tiring and creates opportunities for human error. Managing the same dataset in two different systems also leads to data inconsistencies and data leakage.


Collecting data without the ability to bring it together and conduct a proper analysis wastes resources and doesn’t provide a net gain when it comes to customer loyalty.


So how can you get over this hurdle? That’s where Belden comes in: to help you focus on resolving the issues that impact customer loyalty. We’ll guide you in building solid strategies to overcome whatever data acquisition, transmission, orchestration or management challenges you face, such as aggregating information from different systems—regardless of where it’s coming from. We can help you unite data so it can be integrated, analyzed and applied together.



Related resources: