Articles
Scarcity of technical labour market is an opportunity to innovate more
Rob Gülickers, Technology director at Sitech: ‘Industry 5.0 is the next step in the industrial evolution, in which the synergy between man and machine is central. It builds on previous revolutions, such as Industry 4.0, where automation and digital technologies dominated. Industry 5.0 aims at a collaboration between human skills and advanced technologies. This allows us to increase productivity and improve efficiency.
Sustainable Asset Management makes a difference
The industry is very aware of the need to become more sustainable. Manufacturing companies are already taking major steps to reduce greenhouse gas emissions and energy consumption. But there is still a gap to be filled in reaching the European climate and energy targets. Sustainable Asset Management could be the answer.
Turn complexity into simplicity
Condition monitoring of all critical powertrain components is a challenge for all industrial sectors. Maintenance engineers need scalable and holistic monitoring solutions that provide detailed insights into the condition of both mechanical parts, as well as the electrical components in rotating machines.
AI and Enterprise Asset Management : Artificial Intelligence will transform Enterprise Asset Management (EAM).
Integrating AI into EAM platforms could radically enhance capabilities, resulting in higher employee productivity and further maximizing asset performance, with all relevant data protection in place. By leveraging AI, future EAM toolsets will be more intuitive, accessible and predictive, therefore driving unprecedented efficiency and effectiveness in asset management practices. That is the exciting potential, for sure, but getting there means some obstacles still need to be overcome.
How Artificial Intelligence Improves Your Maintenance Processes
AI can be used in combination with a maintenance planning system to enhance and streamline the maintenance process. A maintenance planning system is designed to organize and schedule maintenance activities and optimize asset availability. AI can analyze data and predict when maintenance is needed, optimizing the planning process and minimizing asset downtime.
By utilizing machine learning techniques, AI can identify patterns in data and detect anomalies, allowing for the prediction of issues before they occur. This means that maintenance can be scheduled based on the actual needs of the assets, rather than a predetermined schedule.
Don’t Focus On Wrench Time to Justify Planning & Scheduling ROI
Maintenance organizations often have similar motivations for reassessing their planning and scheduling processes. These include addressing a lack of visibility into resource availability, managing out-of-control backlogs, or responding to management pressure to reduce maintenance budgets.
Currently, over 67% of business cases for planning and scheduling software focus primarily on improving wrench time through scheduling solutions and process reviews. It’s widely known that the average Hands on Tool Time (HOTT) for most companies ranges between 25% and 35%. However, companies employing best practices in planning and scheduling report wrench times of 60% and higher. What accounts for this significant difference?
Reducing costs through enhanced reliability
Unplanned downtime regularly results in high costs for industrial businesses. That’s according to an ABB survey of 3,215 plant maintenance leaders across various sectors, including energy, metals, utilities, food and beverage, and chemicals. In the energy and power generation sector alone, an hour of unexpected downtime can cost nearly USD180,000 and 69 percent of industrial businesses experiencing these shutdowns at least monthly.
Reliable equipment delivers clear advantages in reducing unplanned downtime, maximizing operational uptime, improving performance, and ensuring continuous equipment availability. However, despite the well-recognised benefits of reliability, current maintenance practices often fall short.
Is installing a sensor for predictive maintenance always the right answer: How to make the hype work?
The use of sensors in predictive maintenance is a hot topic in the discussion about asset performance. Although sensors are used widely today, their effectiveness is a subject of debate. One thing is certain: sensors are not the be all and end all. They only provide value when used in support of people (experts who know the machines) making informed and better decisions. Jonas Vancauwenberghe, an expert in the field, sets the tone.
Unleashing the Power of Digitalization in Asset Management: Lessons learned at Aquafin
Aquafin, a company dedicated to the transport and treatment of wastewater for over six million people in Flanders, is at the forefront of creating sustainable environmental solutions. “Our mission is to improve the quality of the rivers, and we take a wider view than wastewater alone. We are also looking at how we can play a role in creating a living environment in harmony with rainwater, next to our traditional core business, managing and treating waste water.” Van Eygen explains. This holistic approach to environmental management underpins Aquafin’s commitment to long-term sustainability and resilience.
Enhancing Predictive Maintenance at Nordic Sugar: Insights into Steam Dryer Optimization
Nordic Sugar, a key player in the sugar production industry and a part of the Nordzucker Group, has been pioneering innovative maintenance strategies at its Nakskov plant in Denmark. As part of their continuous improvement efforts, they have embarked on a significant project to implement predictive maintenance on their steam dryer, a crucial component in their sugar production process. This initiative, led by Anders Jørgensen-Juul, Head of Projects, is setting new benchmarks in the industry.