What Is Overall Equipment Effectiveness?
Overall Equipment Effectiveness (OEE) is a critical metric manufacturers use to measure and optimize production efficiency. It evaluates three key factors: availability, performance, and quality, helping to pinpoint bottlenecks, downtime, and inefficiencies in the production process.
The benefits of tracking OEE include:
Optimize machine utilization and reduce wasteImprove production efficiency with real-time insights
Identify bottlenecks and minimize downtime
Having a real-time snapshot of how well equipment is functioning helps drive continuous improvement and operational transparency.
Small and Medium Enterprises (SMEs) have historically been slow to adopt OEE due to technology gaps, cost concerns, and skillset shortcomings. However, SMEs have expanded their technological apparatus, and employee skillset challenges are being addressed via low/no-code platforms.
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How Overall Equipment Effectiveness Is Calculated
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 metric 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, Internet of Things (IoT) sensors, and manufacturing software such as Manufacturing Execution Systems (MESs) and Enterprise Resource Planning (ERP) platforms.
Identifying Current OEE Adoption Trends
While SMEs are catching up, large manufacturers have already embraced OEE software solutions to gain greater visibility into machine and factory operations. They have been quicker in adopting the prerequisite 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 vendors providing IoT hardware sensors for real-time data collection, has made OEE solutions accessible to SMEs as well.
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 of reworking recipes and formulas after production.
A US$6.3 Billion Market Finding Its Ground
The market for OEE is currently valued at approximately US$1.8 billion. ABI Research projects that it will expand at a Compound Annual Growth Rate (CAGR) of 13.2%, reaching nearly US$6.3 billion by 2034. The industries investing the most in OEE solutions include automotive, food manufacturing, metals, machinery, electronics, and chemicals.
The uptake in OEE is primarily driven by the increasing adoption of necessary infrastructure among SMEs, including IoT devices, edge sensors, and data-generating applications like MESs and 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.
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 software 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.
ABI Research recently ranked the following companies as the top up-and-coming OEE data analytics providers:
- Litmus Automation
- MachineMetrics
- Augury
- Crosser Technologies
- Itanta Analytics
For a deeper dive into the world of manufacturing data analytics for OEE, additional insights can be found by downloading our whitepaper: Breaking Down The $6.3B Overall Equipment Effectiveness (OEE) Market.