Digital transformation has been disrupting job functions and industries steadily for the last few years. For the maintenance and asset management space, this means connecting every aspect of the business with IoT products providing an explosion of data and analytics in its wake. How are asset-intensive companies addressing this shift? What critical areas of focus are they investing in and where do they see opportunities for success?
The annual SAP-Centric EAM research project took the conference team across North America in a series of focus groups. After meeting with over 100 professionals who operate in maintenance, reliability and asset management, the following top five themes were uncovered as the most impactful in 2018. Familiar topics–master data, planning & scheduling–are still top of mind and perhaps even more essential as maintenance continues to innovate into a digital business function.
Master Data
As technology continues to evolve, the quality of an organization’s master data becomes even more integral to its success. Maintenance departments are receiving an increased amount of pressure to run efficient plants and must rely on data to help optimize costs. However, most struggle to trust their master data which often contains manual data entry errors and inaccurate records. Organizations are ultimately looking to create a corporate culture of accountability around master data standards and integrity so that data can be a reliable and strategic tool for the business.
“If they [organizations] don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.” Digitalist Mag, Data Analysts And Scientists More Important Than Ever For The Enterprise
Planning & Scheduling
It comes as no surprise why planning & scheduling continues to be a hot topic for maintenance and asset management professionals. Effective and efficient planning & scheduling can improve a company’s ROI and help maximize productivity. Hence, why it is more critical now than ever for organizations to develop a clear preventative maintenance plan to drive successful planning & scheduling.
Mobility
Whether it be in our personal lives or in the workplace, mobility is central to the way we operate. In 2018, organizations are reevaluating how they use mobility and are investigating solutions to incorporate into their overall technology strategy. Maintenance and asset management professionals are also challenged with how information is communicated beyond the plant, and how this impacts their system, user experience and data.
User Experience
User Experience (UX) was quickly identified as one of the top issues. Organizations are seeking ways to improve productivity by creating an intuitive user experience while successfully managing the change in process. Organizations are operating in an ever-changing technology landscape and need to gain fast user adoption while implementing robust training programs. New technologies in this area are fundamentally changing the user’s interaction with systems and facilitating collaboration across asset owners.
Internet of Things (IoT) & Predictive Maintenance
The ever-increasing amount of highly actionable data from assets, machines, mobile workers and operational processes creates a new opportunity for the world of asset management. IoT is causing a shift in maintenance from tactical execution and processing work orders towards asset analytics and predictive maintenance. Many organizations are already using sensor data for operations, however, this data isn’t always feeding into their asset management systems. The focus on improving the direct connection between asset sensors and the maintenance organization is a key focus for asset-intensive organizations in 2018.
Intel’s A Guide to the Internet of Things infographic illustrates the projected growth of connected devices from 2006 to 2020. Most IoT smart devices (40.2%) are currently in businesses and manufacturing creating real-time analytics of supply chains and equipment, robotic machinery.
