Enter predictive maintenance. Working with Microsoft Azure, Tetra Pak Services introduced the Industrial Internet of Things (IIoT) to their machine line. Tiny sensors in each machine can monitor them for various factors such as heat, usage, vibration, pressure, overclocking, and electricity consumption. The data gathered is fed to a statistical model that can predict when machines will break down. Clients schedule service requests well before any event occurs. Tetra Pak operates over 5000 such connected filling machines lines globally.
With real-time data and the right analytics teams, businesses can cut maintenance by 20 to 50%, boost equipment uptime by 10 to 20%, and reduce maintenance costs by up to 10%. Controlling these costs can support differentiation. By allowing for greater awareness and control, predictive maintenance ensures better product quality, strongly impacting customer satisfaction and brand differentiation.

Analyzing the data and optimizing the predictive algorithms requires having access to hundreds of experienced data scientists and researchers on tap, which is a tall order for most companies today. Instead, smart companies turn to open innovation challenges. An open talent approach can revitalise their innovative streak and solve grand problems at scale.

A balanced predictive maintenance strategy allows for fast identification of issues and precise diagnoses. By simplifying the monitoring process, companies can reduce troubleshooting time and repair inventory quickly to increase the asset’s lifespan. Predictive maintenance lets companies order parts ahead of time. Maintenance can be scheduled effectively without incurring overtime costs. Planning repairs around downtime activities help keep the just-in-time inventory and supply chain management moving smoothly.