Everything you need to know about Generative AI in Manufacturing

Downtime in Manufacturing

Glossary post
Glossary post

What is Downtime in Manufacturing

Manufacturing downtime is the term used to refer to any period during which production output is zero. The International Society of Automation estimates that downtime cuts your productive capacity by anywhere from 5% to 20%. Besides impacting production and revenue, downtime can also have other costs, such as idle time for employees. After all, everybody likes a break from time to time, but too much idle time can lead to frustration, not to mention overtime for those same employees once production is back up and running.

More importantly, most industrial facilities can’t accurately predict their total downtime expenditure, making it impossible to control or reduce these costs. While some downtime is inevitable, being able to predict production line downtime and even eliminate it wherever possible will help you save money no matter what industry you’re in.

What you need to know about manufacturing downtime

Downtime may be scheduled (planned) or unscheduled (unplanned).

Unplanned downtime causes include equipment failure, raw-material shortages, or staff shortages. Planned downtime causes include product changeovers or routine maintenance. While routine maintenance is important, it may be avoidable, and in some cases, smart technology can help plan better maintenance to avoid downtime.

On average, manufacturers experience over 15 hours of downtime per week—at a cost of $22,000 per minute for the average automobile manufacturer.

The obvious reason you probably hope to avoid downtime is that it cuts into your production capacity. Factory downtime has a direct cost: units per hour x profit per unit. Factory downtime can also directly lead to dissatisfied customers in two ways: First, because when your production line is down, you can’t meet customer demand, and second, because even once things are back up and running, production may be rushed, with product quality suffering as a result.

  • Downtime can have other impacts on your business as well:
  • Higher costs to repair and resolve emergencies at inconvenient times
  • Stressed employees who are frustrated by machine downtime
  • Safety issues that can arise during production line startup and shutdown
  • Wasted opportunities (You can’t make improvements and innovate if you’re running from one crisis to the next.)

As a manufacturer, you need to avoid production downtime wherever possible. But until now, that has been very challenging.

How can you avoid downtime?

Traditionally, there have been a few common approaches to eliminating downtime:

  • Schedule preventative equipment maintenance to eliminate unplanned incidents and failures.
  • Invest in employee training (so everyone is qualified to deal with issues as they arise).
  • Implement strategic approaches, such as a risk audit, that consider a range of physical and virtual factors that could impact your business; this helps you budget, prioritize, and plan ahead for resilience.

A newer approach to avoiding downtime is predictive analytics, which takes a proactive approach. By looking at data coming in that tracks current production performance, predictive analytics warns you when problems could occur, determines the root cause, and flags issues for immediate resolution.

Until recently, this approach was not practical to implement for a few reasons:

  • Latency. A typical production line generates a lot of data. To avoid downtime, predictive analytics must operate with minimum latency and real-time processing.
  • Learning curve. Typically, this type of sophisticated data analytics requires months of dedicated research and development work by specialists (data engineers).
  • Security. Maintaining control of your production line data is essential to minimize the risk to your business.

Now, cloud-based solutions make detection and prediction simpler than ever before. This helps you save on costs, keep employees working, and respond more quickly when failures do arise.

How predictive analytics can help eliminate manufacturing downtime

Cloud-based predictive analytics offers you deeper insight, often letting you eliminate or minimize an issue before it becomes a problem. 

Predictive analytics operates in two stages:

  • Data collection. The system collects real-time sensor data on metrics such as first pass yield (FPY)the percentage of completed units that do not require rework. (In an ideal world, this would be 100%, but in reality, it can vary, giving you a good picture of the health of your production line.)
  • AI prediction. Based on strategically collected data, the system determines whether a threshold has been crossed and alerts you to issues like a probable failure for various stages of the manufacturing process.

When you choose a cutting-edge cloud-based predictive analytics platform such as Vanti, you’ll get up and running fast, and never need specialized staff or engineering R&D to customize predictive analytics to your business’ needs. Plus, Vanti scales as your manufacturing needs do, so you can handle multiple production lines from the same easy-to-use dashboard. 

Let Vanti help you take control of downtime and keep up with customer demand using smart AI-based predictive analytics.