We collect enormous amounts of data, but the question is how we can do something with it in practice. With the arrival of the Internet of Things (IoT) the flow of data is increasing. One of the fields where this offers many opportunities is Field Service. In this context, data can be translated into concrete recommendations for predictive maintenance for greater efficiency and higher customer satisfaction.

From data analysis to customer service

Rarely a day goes by without new stories about the opportunities offered by big data. You probably collect colossal amounts of data, but how do you find the needle in the haystack that really makes a difference to your customers? An important starting point is defining which data requires action, and which values should be actioned. Software then helps you with automatic triggers based on the data that you receive via IoT. In order for this analysis to take place properly, it is important to bring all this data together in one central location. This is possible in the cloud, for example. Storing data in the cloud offers multiple advantages including rapid processing and analysis of data. Another important advantage is that analysis systems are also directly accessible via the cloud. Based on data analyses, you can determine which (combinations of) deviating measurements lead to which problems. This allows you to get to the solution before a problem even arises. Now that is service!

Predictive Maintenance in practice: improving logistics processes

A good example of predictive maintenance in practice is a project by Royal Brinkman, a world-class specialist in horticulture. As part of their IoT project, Royal Brinkman collect weather data, including weather forecasts, current conditions, humidity and so on. This helps Royal Brinkman to estimate the amount of fertiliser fluids required during a specific period and the associated logistic processes. For example, increases in humidity result in a greater requirement for fertiliser which means that tanks have to be refilled more frequently. By translating data into concrete instructions, employees can accurately predict when tanks need to be refilled, thus avoiding unnecessary service runs. The result? Ideal logistics processes and cost reduction.

Getting started with predictive maintenance

Service has traditionally been thought of as reactive. The customer experiences a problem which has to be solved. But service today has evolved and there are greater expectations on service providers. The combination of IoT, big data and cloud make it possible to solve problems before the customer suffers. Predictive Maintenance is the key to future service success. It helps you to improve your service level, optimise your internal processes and increase your customer satisfaction levels! Microsoft Dynamics 365 can help you get started with predictive maintenance. Would you like to find out what Microsoft Dynamics 365, Power BI and the Azure IoT platform can future-proof your service organisation? Contact us!