Unplanned downtime in manufacturing is a serious business challenge. Many businesses face these at some point in their business journey. While there may be a million reasons for the unplanned downtime cause it gives an adverse effect on the revenue. It eventually paves the way for a negative brand image.
Also, most importantly these will result in losing customers which we don’t want to happen at any cost. But, predictive maintenance in the manufacturing industry, on the other hand, helps to control and manage this problem.
It is one of the popular IIoT applications that helps to tame this beast. Treat the cause not the symptom – Here are some of the strategies that help to reduce unplanned downtime.
Up-to-date IT infrastructure
Not all manufacturing companies built to adapt to a digital transformation. Your company must be technology-ready for the quick adaptation of sensible hardware and software updates.
Invest in software like “Enterprise Resource Planning“, CMMS Computerized maintenance management system. Maintenance software helps in tracking assets, generating periodic analysis report, overload alerts, historical data and so on.
An effective blend of Preventive Measures & Predictive Measures
Only through careful analysis, we can determine which assets need rigid 360-degree maintenance checkups and which ones can follow a flexible schedule.
This is achieved only through effective team collaboration. Talk with the operational head, head technicians and engineers who know a thorough knowledge of the product.
Respective team has an in-depth knowledge of each asset capability, optimal load quantity, and the possible bottlenecks if any. Predictive maintenance helps to avoid unplanned downtime, optimize performance and reduce unwanted repair costs.
Determine the Direct and Indirect Product Cost
TCO, Total Cost of Ownership helps to determine the product’s life span which analyzes the operational expenses, long term expenses like assets replacements, updates and computer hardware/software expenses like migration cost, installation services, and other workstation setups, etc.
In 2014, a consortium of companies created a standard known as IIC (Industrial Internet Consortium) to streamline the adoption of IIoT technology across industries effectively.
“Downtime costs in manufacturing give a loss of $22k per minute. Unexpected failures are one of the root causes and provide a negative impact due to reactive and unplanned maintenance action. A powerful predictive system before any malfunction occurs has a strong positive impact on machine availability, reduced downtime, and maintenance costs,” said Plethora IIoT team leader Javier Diaz.
NASA was the first to install Digital Twin in Apollo 13 then later developed for the entire space station. Digital Twins is, “a digital replica of the physical components which tracks the historical data for future analysis and predicting trends”.
These digital twins combined with the root cause analysis tools will exactly pinpoint the problematic area, any bottlenecks through external management software.
QA teams, process engineers will have access to the real burning issue and plan on a workaround.
For example, the digital twin in Jet manufacturing industry helps to track down the normal wear and tear. It helps to streamline the manufacturing process by withstanding stress and helps to build a robust model.
Providing a Transparent, real-time visibility
Like in many industries, outdated information of any product or process documentation may lead to unwanted chaos and affect productivity, especially in any unexpected downtime phase. Impact of any system downtime is not only expensive but also takes longer recovery time.
Predictive Maintenance helps to organize and keep track of all documents and up-to-date information of the product life-cycle without any error. As with any project, many components are interdependent and rely on each other data to get a clear picture of what’s the business challenge. It will help to rectify the problem in an accurate way without wasting any productive time.
Even diligent best-laid plans can go sometimes wrong. Hence,
must be imposed to get the unplanned downtime in control by incorporating proper predictive maintenance. With predictive systems monitoring the production lines, the machines health, its behaviours, data transparency for any possible outbreaks can be easily determined. With a team of experts and the right predictive technology, one can obsolete this unplanned downtime.