Data Analytics Is a Cornerstone of Overall Equipment Efficiency (OEE) for Manufacturers

This research highlight explores the burgeoning market for manufacturing data analytics centered on Overall Equipment Effectiveness (OEE), projected to grow from US$1.8 billion to nearly US$9.9 billion by 2034. The piece outlines the critical metrics of availability, performance, and quality that determine OEE scores, while discussing the vendor ecosystem and current adoption trends among large manufacturers and Small and Medium Enterprises (SMEs). The rise of Generative Artificial Intelligence (Gen AI) is also highlighted as a key factor in driving OEE solution adoption.

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Market Overview  

The manufacturing data analytics landscape for Overall Equipment Effectiveness (OEE) is currently valued at approximately US$1.8 billion. ABI Research projects it to expand at a Compound Annual Growth Rate (CAGR) of 18.4%, reaching nearly US$9.9 billion by 2034. This growth is primarily driven by the increasing adoption of necessary infrastructure among Small and Medium Enterprises (SMEs), including Internet of Things (IoT) devices, edge sensors, and data-generating applications like Manufacturing Execution Systems (MESs) and Enterprise Resource Planning (ERP) solutions.

Despite the optimistic growth trajectory, the OEE market faces challenges. A significant barrier to entry is the high skill level required to operate OEE applications, which often necessitates specialized data engineering teams. Many manufacturers with limited budgets are hesitant to invest in these resources. However, recent advancements, such as low-code and no-code platforms, alongside educational go-to-market strategies that provide continuous training, are helping to mitigate these barriers.

 

Understanding OEE Metrics

Data analytics providers assess three critical metrics to calculate an OEE percentage score: availability, performance, and quality. A manufacturer with an OEE score of 40% or lower is considerably below average, while those achieving 85% are recognized as top performers. Here’s a closer look at these metrics:

  • Availability: This metric measures both planned and unplanned stoppages of machines on the factory floor. The goal is to maintain continuous production with minimal interruptions.
  • Performance: This focuses on the efficiency of machine operation, assessing energy consumption and cycle times. The ideal scenario is achieving the fastest output of products with the least energy expenditure.
  • Quality: This measures the ratio of defect-free parts produced versus defective items, aiming for a 0% defect rate in production.

To qualify as an OEE solution, vendors must provide either a single application or a suite of applications that can effectively track these metrics on a percentage basis. The solution should be capable of gathering data from multiple sources, including machines, Programmable Logic Controllers (PLCs), edge devices, IoT sensors, and manufacturing software such as MESs and ERP systems.

Assessing the OEE Vendor Ecosystem

The OEE vendor ecosystem encompasses a variety of solutions, including pure-play OEE solutions, Computerized Maintenance Management Systems (CMMSs), Asset Performance Management (APM) solutions, and Enterprise Asset Management (EAM) platforms. While pure-play OEE solutions are available, manufacturers are often more inclined to adopt CMMS, APM, and EAM solutions. This preference stems from the added value these systems provide through prescriptive analytics, which extend beyond just monitoring production efficiency.

Identifying Current Adoption Trends

While SMEs are catching up, large manufacturers have already embraced OEE solutions to gain greater visibility into machine and factory operations. They have been quicker to adopt the necessary infrastructure, such as edge devices, cloud-based manufacturing software, and modern equipment that integrates seamlessly with OEE solutions. The introduction of affordable Software-as-a-Service (SaaS) offerings, along with OEE vendors providing IoT hardware sensors for real-time data collection, has made these solutions accessible to SMEs.

Furthermore, the rise of Generative Artificial Intelligence (Gen AI) is set to enhance the adoption of OEE solutions and data analytics platforms. Gen AI, grounded in real-time and historical factory floor data, represents a significant advancement. Tracking OEE is the foundational step for deploying Gen AI solutions, which can analyze historical production rates, compare them to real-time data, and offer actionable insights based on established Standard Operating Procedures (SOPs).

Distinctions between Discrete and Process Manufacturers’ Pain Points

Both discrete and process manufacturers face similar production challenges and prioritize quality, availability, and performance. However, slight differences exist in their OEE focus. Process industries place a higher emphasis on resolving quality issues (22% more) and enhancing performance (19% more) than their discrete counterparts. This heightened focus is due to the high costs associated with reworking recipes and formulas after production.

Key Companies

Conclusion

For a deeper dive into the world of manufacturing data analytics for OEE, additional insights can be found in the report Manufacturing Data Analytics for OEE Improvements from ABI Research.