Overall equipment effectiveness: is it still relevant in the industry 4.0 era?

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Industry 4.0 and the emergence of an unified physical-digital production ecosystem

Industry 4.0 builds upon the enterprise-wide automation stack (that characterized Industry 3.0), focusing on integrating more (and newer) technologies, like IIoT and data science, into the production environment. The goal is to blur the chasm between the physical (Operational Technology) and the digital (Information Technology) world – creating a fully connected, integrated, data-driven, and autonomous digital factory. A genuine digital factory generates enormous data each day, both at the OT and the IT layer, subsequently consumed to derive insights and power decisions. The holy grail for Industry 4.0 focusses on the following tenets:

  • Capture and digitize data from the physical manufacturing value chain (sensors, PLCs / HMIs, etc.), and relay it across IT systems (ERP, MES, WMS, Cloud, etc.)
  • Consume, analyze, and visualize real-time data (from multiple OT and IT sources), generate insights using advanced analytics technologies, and share them across the connected ecosystem
  • Leverage the insights (inputs to the IT systems) to drive physical manufacturing operations autonomously.

Thanks to the relatively mature IT infrastructure and emergence of connected OT devices (PLCs, sensors running on open source IIoT frameworks), the most significant impediment towards achieving the holy grail is not the availability of data but deriving actionable insights (both from real-time and historical data generated on the shop floor) and feeding those insights back to the IT systems for autonomous decision making.


The modern realities of manufacturing and how Industry 4.0 has furthered OEE as a key performance metric

Today, manufacturers are faced with an ever-increasing need to produce high-quality products while meeting the often-steep production targets and timelines, primarily attributed to growing consumer expectations, competitive landscape, and regulatory requirements. Also, Industry 4.0 technologies have furthered the move towards mass customization and unit batch size production, increasing the need to monitor, improve, and maintain productivity even further.

Manufacturing organizations leverage a range of performance metrics, like OEE, OOE, and TEEP, to effectively monitor the efficiency and productivity of their manufacturing operations. Overall equipment effectiveness, or OEE, is still one of the manufacturing industry’s oldest (dating to the 1950s) and ubiquitously used metrics. Not only is it still relevant, but its importance has amplified with the opportunities and challenges created by Industry 4.0 technologies and processes and the ongoing push to develop the Digital Factory of tomorrow. OEE benchmarks the percentage of production time that is genuinely productive. It incorporates availability (no downtime), performance (the speed of production), and quality (products up to standard). For example, an OEE score of 100% indicates that you are manufacturing only good parts with no downtime as fast as possible.

Thanks to Industry 4.0 technologies, today manufacturers are better positioned to accurately measure and monitor OEE across multiple levels of granularity, i.e., machines, assembly lines, factories, etc. Key contributing factors include:

  • Enhanced equipment integration and connectedness within and across the production facilities improves the value of the data collected for OEE calculations.
  • Data collection across multiple touchpoints (machine, assembly line, and overall facility) and higher data sampling rates – owing to the democratization and increasing adoption of sensor technologies, network infrastructure, and cloud computing.
  • Improved data quality and accuracy, owing to minimal manual intervention and the availability of modern data collection and processing systems.
  • Better contextualization and processing of OEE data (real-time and historical) to derive valuable insights enabled by advanced analytics solutions, machine learning, artificial intelligence, etc. Examples include virtual OEE simulation, predictive maintenance (as against scheduled maintenance), downtime analysis, quality analysis, to name a few.

In a nutshell, time and innovation haven’t lowered the significance of OEE in manufacturing. On the contrary, they have necessitated a change in how manufacturers perceive, measure, and achieve equipment efficiency and effectiveness. With the Industry 4.0 technologies available today, manufacturing leaders have more opportunities than ever to increase the effectiveness of their machines and improve production efficiency.


Harshad Prabhudesai

Senior Manager (DI Solutions), Lumin by Fosfor

Harshad brings over a decade of rich and diverse experience in manufacturing, heavy engineering, and technology. His core expertise lies in designing high-impact technology solutions for use cases across the manufacturing value chain. He holds an MBA from the Indian Institute of Management, Udaipur, and a bachelor’s degree in mechanical engineering from the College of Engineering, Pune. As a Senior Specialist at Lumin, Harshad is responsible for architecting augmented analytics solutions for manufacturing organizations and helping them accelerate their data-to-decisions journeys.

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