Production Downtime and OEE: Why Machine Data is Valuable

by | Feb 17, 2019 | Business, Industry News, IoT, Strategy, Tips & Tricks

Production Downtime and OEE: Why Machine Data is Valuable

Smart Manufacturing is the use of one or more technologies including IoT, automation and robots, big data, artificial intelligence, and modeling to optimize manufacturing. An efficient manufacturing operation means, among other things:

  • Product flows through the plant seamlessly;
  • Machines have the highest asset utilization possible to drive as much production and revenue as possible; and
  • The plant’s machines, operators, and systems are providing data that is shared across the organization to enable visibility into the factory floor for making valuable decisions throughout the organization.

Optimizing of Manufacturing

Companies can optimize manufacturing in product concept generation, flexible operations for changing customer needs, manufacturing efficiencies, machines becoming self-optimizing and self-diagnosing, and creating a dynamic manufacturing supply chain.

Manufacturing Efficiencies

Manufacturers can work on developing more efficient and flexible operations. This greater efficiency and flexibility enables manufacturers to attain better asset utilization, production capacity, and lower overall costs of production. The higher production capacity and asset utilization, and flexible operations enable them to handle more dynamic customer needs and a dynamic supply chain. They can then dominate their market as a more flexible and consistent low-cost producer with solid quality and delivery to dominate their markets.

Need Data to Improve Company

To reach these levels of better production capacity and low-cost production the key is people must have access to data about machines and how they’re operating… uptime and downtime, asset utilization ratios, the productivity of the machine, quality of the products it creates, it’s running condition, how the operator is running the machine, etc. With this data, companies can then monitor production lines and pieces of equipment, find issues, and make improvements.

Production Downtime

Unplanned production downtime is the time a machine is down due to circumstances that weren’t planned ahead of time. This includes times when:

  • The machine is available but not running: the machine could be used and there is a product to produce but no operator is available, or there is an operator but no raw materials to produce a product.
  • The machine is not available: The machine is down because it had a failure and is under repair.

Production downtime is one of the biggest “killers” in production efficiency. It leads to material loss, resource loss, uptime or asset utilization loss, and capacity loss. Additionally, it reduces a company’s ability to deliver products to quality standards which leads to missing promised delivery dates and lost trust with a customer. In the end, it all results in higher costs and less revenue.

Benefits of Tracking Downtime

Tracking downtime gives you the data you need to both find root causes of downtime and make improvements to users, machines, and processes. When downtime improves then production capacity and asset utilization increase, costs of a product are reduced, revenue increases, and margins increase.

Downtime Study

In 2017, GE ServiceMax commissioned a study that surveyed 450 decision-makers across manufacturing and other industrial verticals. They found companies had issues as a result of unplanned downtime including:

      • 46% of companies couldn’t deliver services to customers
      • 37% lost production time on a critical asset
      • 29% were completely unable to service-specific equipment

They also found that production downtime in a factory affects 62% of its productivity with scheduled resources and equipment. These numbers indicate unplanned downtime is common and has a big impact on companies.

Downtime Fallacy

Companies who don’t track the production downtime of their machines may believe they know what the equipment downtime is. However, they are often off by as much as 50%. Without the actual data to measure and analyze, many companies are not seeing the whole picture, and taking all information into account when measuring downtime – and this can greatly impact their business.

This article is part of a series, if you found this information insightful, check out our next article on downtime and how to use the data.


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