12 Jul 2024

How Software as a Service (SaaS) model can help manufacturers overcome their digital scepticism

Maintenance 4.0

Fernando Velloso, application engineer — digital solutions at WEG, the motors and drives specialist, explains how Software as a Service (SaaS) model can help manufacturers overcome their digital scepticism, and also spot new opportunities to improve total cost of ownership (TCO).

In order to properly implement predictive maintenance (PdM) 4.0, PwC recommends that plant managers must embrace strong centralised data platforms, analytics and Industry 4.0. However, many companies are still resisting digitalisation.

PwC’s Predictive Maintenance 4.0: Predict the unpredictable report recommends three foundational building blocks for PdM 4.0. First, a company must establish a comprehensive big data infrastructure to support the collection and processing of data from both internal and external sources. This includes considerations about in-house versus cloud-based storage solutions, with a focus on ensuring accessibility, speed, reliability and bandwidth of the communication network.

Second, an Internet of Things (IoT) infrastructure is crucial in connecting assets wirelessly to the maintenance data center. This requires thoughtful decisions on wireless protocols, data encryption and security. Additionally, choosing an integrated data analytics platform is emphasised as essential for efficient PdM 4.0 implementation, as existing enterprise resource planning (ERP) systems may lack the capabilities required for sophisticated data trends and analytic methods.

Despite these recommendations, many manufacturers remain hesitant about going digital. Research by Make UK found that, while 45 per cent of manufacturers have already introduced digital technologies, 15 per cent of companies had no plans to do so. The longer these manufacturers delay adopting Industry 4.0, the harder it will be for them to stay competitive. How can these latecomers be encouraged to get aboard the Industry 4.0 bandwagon? The answer lies in using PdM 4.0 and big data to improve TCO.

Advanced sensors

Some production managers are sceptical of whether new digital technologies can fit into their existing, and accepted, processes. This is understandable, as manufacturers should always be wary of any changes that invite risk. It is absolutely essential that Industry 4.0 integrates seamlessly into the manufacturers’ existing practices.

This scepticism is also behind many manufacturers’ reluctance to embrace SaaS models, and some are averse to monthly subscription fees when they are used to buying solutions up-front. How can these manufacturers be encouraged to make the change?

One major driver for change are the data-driven insights gleaned from advanced Industry 4.0 sensors. Manufacturers are realising that technologies offer unprecedented visibility into equipment health and operational efficiency, helping companies to make more informed decisions and unlock TCO benefits.

Let’s look at the example of one manufacturer, a leading food and salad producer, which decided to shift its plant maintenance from a manual system to a wholly digitalised PdM 4.0 model similar to that recommended in PwC’s report.

Predictive strategies

The food and salad manufacturer used to rely on traditional maintenance processes. Literally, an individual maintenance specialist would circle the company’s plant and inspect the equipment. Nevertheless, the company was aware of the potential repercussions of equipment breakdowns in its process-driven environments — that’s why the company sought a solution that would enhance operational efficiency while minimising costs.

Specifically, the manufacturer wanted to improve how it monitored the performance of a series of WEG W22 motors used to power a compressor for a packing machine. To achieve this, it chose WEG’s Motor Fleet Management (MFM) platform, and the decision would prove to be a game-changer. The MFM platform enables real-time monitoring of both vibration and temperature, providing crucial insights into the health of machinery. MFM, which WEG offers as a monthly SaaS model, is compatible with the REST API application programming interface, so manufacturers can integrate their data and reporting into their own preferred hardware or software.

WEG provided the customer with three WEGscan sensors, which were then affixed to three of the WEG W22 motors. Data from the sensors could then be gathered into MFM’s consolidated reports — and the results were revelatory. The sensors detected abnormal spikes in vibration in the motors, pre-empting a major breakdown that could occur months into the future and lead to costly downtime and repairs.

In response, the food and salad manufacturer strategically deployed additional sensors on other critical assets. This brought the total up to five sensors, fortifying the manufacturer’s digitalised PdM 4.0 strategy. Through this vigilant monitoring, the company also uncovered the critical — and unexpected — cause of the vibration spikes. The problem related to how machines were anchored to the floor.

The W22 motor was re-screwed to the plant floor to address the issue, a problem that might have gone unnoticed without MFM. Aside from gaining a more comprehensive understanding of asset health, the new PdM 4.0 approach allowed them to strategically plan maintenance routes and allocate resources more efficiently.

Uninterrupted flow

The food and salad manufacturer’s transition from traditional, time-based maintenance to a data-driven, condition-based approach has yielded substantial TCO benefits, not only in terms of cost savings but also helped ensure the uninterrupted flow of operations.

Crucial to this is MFM’s high-frequency vibration analysis capabilities, a formidable tool in PdM 4.0. High-frequency analysis is more effective in predicting plant equipment failures because it captures data at shorter intervals, offering finer insights into machine behaviour. This enables early detection of anomalies or degradation, while, in contrast, low-frequency analysis, with longer intervals between data points, may miss critical warning signs. Manual methods, often relying on periodic checks, are also limited by their lower frequency and may lead to delayed responses or potential failures.

MFM’s high-frequency vibration analysis is especially useful for ensuring the detection of early-stage anomalies in motors, such as bearing wear or misalignments, which are indicative of impending failures. That is why the food and salad producer was able to predict its equipment failure more than a month before the event was likely to happen.

PwC’s Predictive Maintenance 4.0 report underscores the importance of the organisational support structure in PdM 4.0 implementation. By identifying potential equipment failures in their nascent stages, maintenance teams can take proactive measures to rectify them, averting costly and disruptive breakdowns.

This proactive approach translates to lower TCO for equipment, as it minimises the need for costly emergency repairs and reduces downtime. With MFM, the proof is in the results — easing manufacturer’s Industry 4.0 adoption by optimising motor performance, reducing downtime and ensuring cost-effective operations.