According to the recent “Power of Industry 4.0 in Asset Management Report” by MPI Group, 58% of maintenance leaders believe Industry 4.0 is a competitive advantage. Add the additional 39% that say it will arrive in the near future, that figure spikes to a whopping 97%. And why is this?
The inability to share equipment information with professionals and applications is seen as the biggest challenge, with 62% of maintenance leaders reporting machine-to-enterprise IT systems communication needs to improve. Industry 4.0 capabilities can help. Here’s how.
The Intelligence Gap when it comes to Intelligent Assets
Many companies still approach the management of assets with a planned or preventative maintenance strategy – where maintenance is routinely performed on a piece of equipment to lessen the likelihood of it failing.
But for companies that need to squeeze every bit of cost efficiency out of their assets, “better safe than sorry” no longer cuts it.
Preventive maintenance, in fact has been calculated to consume nearly as much of a typical facility’s operating budget as utility costs – amounting to more than one-third of total operating expenses.
From Preventive Maintenance to Predictive or Prescriptive Models
But as the cost of sensors go down, we are designing and manufacturing smarter assets that can help us better manage the performance, location and throughput of everything from equipment on the production floor, to forklifts and robots in the warehouse, to trucks and vehicles on the road and even products at customer facilities.
We can capture data on equipment status and with the ability to analyze this data within the context of their businesses, companies can expect (or detect) the unexpected – predicting issues before they arise. This puts you in the position to take swift, preemptive, and cost-effective action to fix them. In other words, companies can now perform maintenance only when required. This maximizes the lifetime value of parts, optimizes technician time, and helps to deliver a better customer experience.
We also see examples of companies moving beyond simply predicating what will happen next. Leveraging machine learning and predictive analytics, companies can now produce outcome-based recommendations for the machine to follow. After the predictive analytics tells you that a problem is imminent, the prescriptive part kicks in to serve up a selection of actions and scenarios to choose from.
Intelligent Assets can Improve productivity and drive new business models
By building intelligence into the assets we use or manufacture, we can now capture and leverage industry 4.0 data to create a digital twin across the end-to-end supply chain. By creating this digital twin of a physical asset, we can monitor, analyze, optimize and maintain it throughout its lifecycle from design to decommission. It allows us to achieve the balancing-act between profitability, asset health and availability.
If designed correctly, intelligent assets can tell you:
- How they are performing.
- How are they being used and for how long.
- What maintenance strategies make the most sense.
- If they are operating in a sustainable way with regards to emissions.
- When they require maintenance.
- When they have broken down.
As a result, business benefits can be achieved across the supply chain via:
- Reduced downtime
- Predictive and prescriptive maintenance processes
- Increase in Overall Equipment Efficiency (OEE)
- Increase in lifetime of expensive equipment
- Increase in customer service levels
The Digital Thread to Digital Transformation
In a digital age, the key to innovation is information. By leveraging Industry 4.0 to capture real-time, accurate information across a product’s life cycle from design to decommission, we can see a digital thread leveraged by all constituents, from the customer to the purchasing team to the maintenance team and back to the engineering team. These valuable insights can be used to identify improvements to designs, create new features and entirely new business models.