Maintaining an interactive collaborative partnership with inter- and intra-departmental groups and outside groups is essential if a maintenance department is to be successful in delivering asset reliability and availability at a level suitable to achieve production throughput and quality targets. To achieve this, a maintenance department must understand the difference between “what it manages” and “what it controls.”

As we move towards empowering maintenance with AI and other learning tools, connecting to the growing sources of data is an important consideration. Add this to the plethora of MES systems that are available to us, there is much to explore.

If you search the Internet for information on asset management, the Internet and Industrial Internet of Things, digitalization, business trends and business reengineering, you’ll find a considerable increase in the number of articles with headlines heralding or promising significant and “disruption” or “disruptive” change. It’s wiser to focus on how to extract value from what you have and spend time creating the vision of where you want to go on the digital journey.

Inspite of the automation, work orders were created manually and assigned manually to maintenance engineers. The automation provided by software tools relied on humans inputting asset, maintenance and people related information in the software. Over the 5 to 7 years, the basic concept of entering information is becoming redundant. Most modern machines can now monitor their own condition and send out information to maintenance teams if their operating parameters have breached. Industrial Internet of Things is changing how maintenance work orders are created, executed and completed.

Many experienced maintenance professionals are at or near retirement age. With fewer replacements on the horizon, manufacturers are increasingly looking to industrial technology to maximize worker productivity to unlock capacity and improve overall equipment effectiveness (OEE). Better uptime starts with better data. These four technologies help provide better data quality and access to the teams that keep plants up and running.

Asset performance data fuels predictive, condition-based and reliability-centered maintenance strategies. Capturing run-time readings, energy consumption, condition and qualitative data in the EAM (Enterprise Asset Management)system on a real-time basis can save time, reduce maintenance costs and improve asset reliability. Obtaining quick and efficient access to asset performance data should be mission critical for every asset management operation.

While data quality may seem both simple and obvious, many companies overlook this area due to a misunderstanding on how the CMMS and data are used. In some cases, companies are well aware of their data quality issues, yet don’t understand the affects that poor data can have on their organization. In other cases, companies believe that an expensive new CMMS will be the answer to all of their problems, only to realize six months down the road that they cannot conduct basic part searches and transactional reports.