Toward Zero Blog for Digital Transformation Best Practices

Data, particularly machine data, is a foundational component of every smart factory or OEE initiative. It fuels analytics, triggers actions ahead of problems or shutdowns, and provides insight for continuous improvement. Many companies start the smart factory journey with an automated OEE system because it’s a natural progression with a set of metrics they’re already familiar with. Despite advances in automated data collection and production analytics, a lot of manufacturing executives are still at odds about exactly which machine data is required to calculate OEE. Unfortunately, a lot of projects get sidetracked early on as stakeholders try to utilize every piece of machine-generated data from the multitude available. A more pragmatic approach is to apply machine data based on priorities around business goals. To that end, capturing the machine data required for OEE is a critical early activity for most smart factory initiatives.

Legacy factory equipment (manufacturing machines with no built-in data collection mechanism) presents a significant challenge when it comes to manufacturing data, particularly for companies that want to calculate OEE. That most basic of all manufacturing metrics isn’t the only reason manufacturing companies are eager to make older equipment IoT-compatible. Capturing the right data can transform manufacturing operations: it eases the disconnect between the factory and business processes, eliminates the lag time for management to access, analyze, and act on data, and resolves problems with planning, inventory control, the supply chain, and meeting customer expectations.

An overwhelming number of manufacturing execution system (MES) deployments fall short of the finish line; in their wake, they leave partially implemented and, therefore, ineffective solutions. A typical outcome: stalled digital transformation and a budgetary freeze for additional IIoT projects.  It’s not an uncommon scenario for MES projects, yet most manufacturing companies seem unaware that many MES deployments fail to hit expectations.

Data, particularly machine data, is a foundational component of every smart factory or OEE initiative. It fuels analytics, triggers actions ahead of problems or shutdowns, and provides insight for continuous improvement. Many companies start the smart factory journey with an automated OEE system because it’s a natural progression with a set of metrics they’re already familiar with. Despite advances in automated data collection and production analytics, a lot of manufacturing executives are still at odds about exactly which machine data is required to calculate OEE. Unfortunately, a lot of projects get sidetracked early on as stakeholders try to utilize every piece of machine-generated data from the multitude available. A more pragmatic approach is to apply machine data based on priorities around business goals. To that end, capturing the machine data required for OEE is a critical early activity for most smart factory initiatives.

Legacy factory equipment (manufacturing machines with no built-in data collection mechanism) presents a significant challenge when it comes to manufacturing data, particularly for companies that want to calculate OEE. That most basic of all manufacturing metrics isn’t the only reason manufacturing companies are eager to make older equipment IoT-compatible. Capturing the right data can transform manufacturing operations: it eases the disconnect between the factory and business processes, eliminates the lag time for management to access, analyze, and act on data, and resolves problems with planning, inventory control, the supply chain, and meeting customer expectations.

An overwhelming number of manufacturing execution system (MES) deployments fall short of the finish line; in their wake, they leave partially implemented and, therefore, ineffective solutions. A typical outcome: stalled digital transformation and a budgetary freeze for additional IIoT projects.  It’s not an uncommon scenario for MES projects, yet most manufacturing companies seem unaware that many MES deployments fail to hit expectations.