How to improve OEE, quality with advanced analytics

A webcast March 29 explains how today’s Smart Factory leverages out-of-the-box and cloud-based overall equipment effectiveness (OEE) capabilities to accelerate improvement programs. Analytics allow prescriptive asset management and can result in significant improvements.

By GE Digital March 29, 2022
Courtesy: GE Digital

 

Learning Objectives

  • Understand how to improve overall equipment effectiveness (OEE), quality with advanced analytics.
  • Examine manufacturing challenges and how analytics help.
  • Learn how industrial analytics are delivered and applied.

Traditional manufacturing systems focus on the management and monitoring of production processes, from raw materials to intermediate and/or finished goods. Analytics focus on process optimization.

A Smart Factory leverages out-of-the-box and cloud-based overall equipment effectiveness (OEE) capabilities to accelerate improvement programs. Use of analytics allows a prescriptive approach to assets and production. Analytics applied to OEE can result in significant improvements. When combined with manufacturing and other data, analytics can deliver huge value to operations.

Understanding how to apply industrial analytics

To apply analytics to industrial processes, it’s important to:

  • Understand the three components of OEE (quality, performance, and availability).
  • Explore traditional vs. Smart Factory approaches to improving OEE – including out-of-the-box and cloud OEE as well as analytics.
  • Discover the analytics maturity model and how you can move from a descriptive to a prescriptive mode.
  • Understand the steps required to further optimize OEE with analytics using manufacturing data.

A March 29 webcast with Cobus van Heerden, senior product manager – analytics and machine learning, GE Digital, will explain these concepts and use real case studies to describe how companies increased performance, throughput, and quality using analytics.

Gregory Dunn, senior staff technical product manager, GE Digital, helps van Heerden with the audience question and answer session after the presentation. Below is some information from the webcast.

Figure 1: To apply analytics to industrial processes, it’s important to understand the three components of OEE (quality, performance and availability). Courtesy: GE Digital

Figure 1: To apply analytics to industrial processes, it’s important to understand the three components of OEE (quality, performance and availability). Courtesy: GE Digital

Manufacturing challenges, how analytics help

Leading challenges from manufacturers face include productivity, cost reduction, safety and compliance, agility and adaptability, and continuity of service. Industrial software is complex, even in a simplified view. Improving OEE and quality is possible by:

  • Using the human-machine interface (HMI), supervisory control and data acquisition (SCADA) and manufacturing execution (MES) for management and monitoring of production processes while analytics focus on process optimization.
  • Applying information from an MES in a descriptive way, while analytics allow a more prescriptive approach.
  • Optimizing the three OEE components, quality, performance and availability, will result in significant improvements.
  • Using analytics to combine operational technology data with other relevant information and data sources to deliver extra value.

How industrial analytics are delivered, applied

Analytic capabilities can be delivered in a hybrid cloud strategy, providing enterprise-wide visibility and analytics. On premises capabilities provide critical functions, plant-level, real-time or closed-loop analytics, and in-plant scheduling. Cloud capabilities provide non-critical capabilities not in real time for the plant, across plants and enterprise wide, using enterprise-wide key performance indicators (KPIs), reporting, product data orchestration and other capabilities.

Figure 2: A March 29 Control Engineering webcast with Cobus van Heerden, senior product manager – analytics and machine learning, GE Digital, will explain these concepts and use real case studies to describe how companies increased performance, throughput, and quality using analytics. Gregory Dunn, senior staff technical product manager, GE Digital, helps van Heerden with the audience question and answer session after the presentation. Courtesy: GE Digital

Figure 2: A March 29 Control Engineering webcast with Cobus van Heerden, senior product manager – analytics and machine learning, GE Digital, will explain these concepts and use real case studies to describe how companies increased performance, throughput, and quality using analytics. Gregory Dunn, senior staff technical product manager, GE Digital, helps van Heerden with the audience question and answer session after the presentation. Courtesy: GE Digital

Industrial analytics software optimizes asset and process performance by more closing matching and optimizing KPIs.

Examples in the webcast show how. A question-and-answer session will explore more details related to audience experiences.

– Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com from information provided for the March 29 webcast, which will be archived for 1 year.

KEYWORDS: Industrial analytics, on premise analytics, cloud analytics

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