Predicting the Unpredictable and Predictive Maintenance
‘Companies still see big data as just another toy,’ the NRC headline announces. ‘When is your organization truly successful at big data?’ Marketing facts wonders. And Emerce states: ‘The investments in the Internet of Things are decreasing’. A lot is being said and written about big data and the Internet of Things (IoT), but people often leave it at that. Companies use IoT and compile huge volumes of data, but fail to tap into its full potential. Such a waste! Let’s zoom in on service organizations. In service organizations, this valuable data can, for instance, be used for Predictive Maintenance. Unfortunately, a survey by PwC shows that only 11 percent of maintenance companies are already mature in this field. Why so few? Because Predictive Maintenance not only ensures more efficient business processes but also leads to higher customer satisfaction.
‘Tomorrow’s customers already know what they want today’; ‘Insight this month regarding the parts which need replacement the next month’; ‘100% uptime due to preventing downtime’. Operational excellence means that you fulfill your customer’s needs to the best of your ability. In order to be successful at that, it is paramount to first carefully listen to and think about the customer. Does the customer want air conditioning or good indoor climate? If the customer wants a good indoor climate, this means that you supply a total solution instead of a product.
By equipping the heating boiler with smart sensors, it is possible to predict when maintenance will be needed through Predictive Maintenance. The data collected by these smart sensors can be compiled and then analyzed automatically. The data can be used to generate accurate maintenance predictions. A good example of that is ThyssenKrupp, an elevator manufacturer. The company uses sensors to measure factors like engine temperature, the speed of the elevator cab, the performance of the door and so on. Analysis of all the data allows the company to perform the necessary maintenance before the elevator shuts down. This can prevent quite a few claustrophobic situations.
Data is the new gold
IoT is the enabler, while big data is what flows from that, but what then? First and foremost, it is important to determine the thresholds. The thresholds are certain values which will trigger an action when they are exceeded. When these thresholds are exceeded, automatic choices can be made as a consequence using Machine Learning. For instance: if an escalator generates data on the number of rotations, the speed, the total weight of the users and the air humidity, and there is a combination of low humidity and high weight, the system can automatically disperse oil on the rotation systems, or a work order can automatically be created to schedule a service technician.
It is important to pool all the collected data on one single platform: the cloud. Service companies are starting to see the benefits of one central data hub. In addition to the fact that data is processed quickly and efficiently using a cloud approach, all linked technology is also available more easily and can be adapted from the cloud. System upgrades and updates are always available to cloud users first. In order to be up-to-date and have access to your data from any location, working from the cloud is a huge asset.
One of our customers that have already made major strides in the field of Predictive Maintenance is Royal Brinkman, a global specialist in horticulture. Royal Brinkman wanted to have control over the use of fertilizer liquids and to optimize logistical processes. The implemented IoT project enabled them to collect data about e.g. current weather conditions, weather predictions, humidity, and temperature. The data is stored at a central hub, then compared across different sources, after which connections are made. That way Royal Brinkman knows the exact use of fertilizer liquids at that time and can predict use in the near future. Will the humidity be lower next week? Then more fertilizer liquid needs to be added and a tank will be empty sooner. Using this knowledge, Royal Brinkman employees do not need to fill the tanks every day, but they can see exactly when a tank will run dry. This has resulted in the optimization of logistical processes, thus reducing costs.
Previously, ‘service’ meant solving the customer’s problems. By now, IoT and big data have made it possible to change the meaning of this concept by ‘solving the customer’s problems before they have even occurred’. Predictive Maintenance is the key to boosting the service level, the internal processes, and customer satisfaction. Microsoft Dynamics 365 is the driving force behind these developments.