In this month’s newsletter, we are pleased to share the results of our 2020 customer satisfaction survey, which focuses on Support and Product for customers under support. The purpose of the survey is to strengthen our relationship with customers, identify their long-term needs and constantly improve our products and customer services.

It is often said that maintenance digitalization projects have a very low success rate. Often managed by IT, most CMMS or EAM projects result in systems that maybe work from a technical standpoint (users can login and access the various functions) but cannot be used in practice (data not accurate, no useful reports, inconvenient user interfaces). Obviously such system does not help the company achieve any industrial improvement.

This is the second article in a series on the hot topic of Predictive Maintenance, drawing on the practical experience of Siveco working with maintenance improvement in China and the Asia region in the past 20 years. As seen in the first article Predictions on Predictive Maintenance: it will fail… unless., ‘right data’ is better than ‘big data’.

In the era of industry 4.0, most energy projects in Asia still enter commercial operation without a maintenance system in place. Instead, the operation and maintenance (O&M) team, already busy with start-up issues, struggles to gather technical documentation, to define paper-based procedures and when time allows, years later, to implement computerized system.

The history of O&M best practices in western water companies can be traced back to Roman magistrate Sextus Julius Frontinus’s “De aquis urbis Romae”, nearly 2000 years ago. On the other hand, Chinese water companies tend to be considered laggards by international and domestic observers alike. Yet, they are moving faster than anyone had expected in terms of Smart Water.

Long-time participants in the industrial maintenance market know that predictive maintenance has been a hot topic since at least the 1980s. In recent years, a variety of new players has claimed to revolutionize predictive maintenance using IoT and AI. As a result, the subject of maintenance, previously considered tedious, complex and… unprofitable as a business, has become hot!