The Changing Landscape of Asset-Intensive Industries
Asset-intensive industries, such as chemicals, oil and gas, mining, metals, pulp and paper, and power production, have been grappling with the challenge of increasing the reliability and availability of their equipment while keeping maintenance costs under control. In their quest for solutions, these industries have been turning to new technologies, leveraging digital tools and advanced-analytics capabilities alongside traditional lean techniques.
The goal is clear – to predict and prevent equipment failures, increase labor productivity, and streamline the management of external contractors. When companies get all the requirements right, the results can be impressive. Leading players in a variety of heavy industries have used digital tools to dramatically reduce unplanned outages while boosting maintenance-labor productivity. Higher availability and a more-efficient workforce have increased profitability by 4 to 10 percent in some organizations.
However, these lighthouse examples are the exception rather than the rule. Many companies that have implemented digital maintenance and reliability solutions have seen little measurable impact. What separates the successful initiatives from the rest? Our research has identified three common characteristics shared by the most successful digital maintenance and reliability implementations.
The Three Keys to Successful Digital Maintenance and Reliability
1. A Holistic Vision for the Future of Maintenance and Reliability
The most successful companies take a holistic view of these new digital tools, building them into a clearly defined vision for the future of the maintenance and reliability function. They understand that simply implementing a technology solution is not enough – it must be part of a broader transformation of their maintenance and reliability processes.
2. Focus on Predictive Maintenance and Digital Work Management
These leading organizations focus on two domains that have proven to be truly effective in heavy industrial applications: predictive maintenance (PdM) and digital work management (DWM). By mastering these two areas, they unlock significant value and set the stage for further optimization.
3. Ensuring the Necessary Enablers are in Place
Finally, successful companies support their use of digital tools by ensuring the necessary enablers are in place, including optimized workflows, a robust data infrastructure, and the capabilities of their personnel. This holistic approach ensures that the technological investments are complemented by the right processes and people.
Mastering Digital Work Management (DWM)
Digital work management (DWM) encompasses a wide range of systems that enhance maintenance work, covering everything from work identification and planning through to scheduling, materials management, dispatching, execution, and closeout. Typically, DWM is built into or on top of the organization’s existing enterprise resource-planning (ERP) platform, leveraging the foundational data from the ERP system.
The DWM system then optimizes job prioritization, job planning, job assignment, permitting, scheduling, and dispatch, based on constraints such as the competencies of available maintenance personnel and the availability of parts. These systems also provide execution support in the field, with mobile devices or augmented-reality systems guiding staff as they complete their tasks, collecting detailed data on asset condition, and improving the accuracy and speed of work-order closeout and contractor payment.
Successful DWM systems deliver value by:
- Increasing the efficiency of internal and external maintenance labor: Optimizing work assignment and scheduling can lead to a 15-30% reduction in maintenance costs.
- Reducing planned downtime through shutdown and outage optimization: By better planning and coordinating maintenance activities, companies can minimize production disruptions.
- Providing the opportunity to upskill the maintenance workforce: With the support of digital tools, maintenance technicians can focus on more complex, value-added tasks.
One global chemicals company, for example, introduced a bolt-on DWM system that seamlessly interfaced with its ERP platform. Implemented in just five weeks, including user training, the new system transformed the company’s maintenance planning, scheduling, and work execution. Productivity in the maintenance planning and scheduling department increased by 30%, and the organization doubled the number of maintenance tasks that were completed on schedule.
To maximize the impact of their investment in DWM, successful organizations take a few essential actions:
- Redesign maintenance processes in parallel with the introduction of new technology: Redesigning the process to fully leverage DWM technology will almost invariably produce a more efficient outcome than simply digitizing the existing paper-based process.
- Adopt a value-centric and user-centric approach: While the overriding aim of DWM is to reduce waste and address pain points for the business, successful systems must also make life easier for users, such as maintenance and operations staff or external contractors.
- Build tight, two-way integration between DWM tools and other systems: This ensures that data captured in the field become part of the organization’s master data, and can be accessed and analyzed to aid OEE tracking, condition monitoring, and root-cause analysis of reliability issues.
Unlocking the Power of Predictive Maintenance (PdM)
Predictive maintenance (PdM) encompasses a wide variety of methods used to detect the early signs of problems in equipment, allowing operators and maintenance staff to take action before a failure occurs. In today’s heavy industry, PdM techniques have become commonplace, but most companies are using lower-maturity approaches, such as sensors on machines that trigger alarms when vibration or temperature thresholds are exceeded.
More advanced PdM approaches attempt to predict failures by analyzing sensor data to identify the “signatures” of known failure modes. At the highest level of maturity, PdM systems apply an array of machine-learning and advanced-analytics techniques to identify and categorize issues, and to provide actionable insights to operations and maintenance teams.
Successful PdM programs reduce the requirement for both planned and unplanned maintenance interventions, cutting maintenance costs and increasing production and throughput (especially valuable for assets that are capacity constrained). In addition, these programs can increase the useful life of high-cost components and reduce safety risks related to machine failure events.
One leading offshore oil and gas operator introduced a sophisticated PdM system across nine platforms in Africa and Latin America. Using data collected from 30 years of platform operations, the company identified critical assets that it wanted to immunize against failure. It then developed and refined its PdM approach on one platform before rolling it out across its fleet. The effort resulted in a 20% average reduction in downtime and production increases equivalent to more than 500,000 barrels of oil annually – across a fleet that was already in the top-quartile of performance for the sector.
These results, however, required a significant data-science effort. Over a period of two years, a team of ten to 15 data scientists built more than 500 advanced-analytics models, testing and refining each one to achieve an acceptable level of false positive warnings.
Although high-maturity PdM approaches have now been proven at scale, their complexity should not be underestimated. Successful implementations require a certain level of data history, sensor deployment, near-real-time data streaming, and a high enough value of downtime to provide an attractive ROI. This is the case in most upstream oil and gas facilities, large refineries, petrochemicals plants, and similar operations in power generation (traditional and renewables), paper mills, and mining.
For operations where PdM may not yet be economic, less data-intensive methods of PdM, such as anomaly detection, can be successful as a first step. These methods are faster to implement but provide significantly lower predictive power, with high numbers of alarms that are low priority or difficult to act upon.
Ultimately, the decision on where and at what level to implement predictive maintenance requires an asset-by-asset validation of the potential benefits and data availability. Companies with advanced PdM ambitions should also look for the right partners, as the approach typically requires more knowledge, data, and development investments than other digital and analytics use cases.
Integrating Predictive Maintenance and Digital Work Management
The most successful companies integrate predictive maintenance with their other operations and maintenance systems, ensuring a seamless flow of data and insights. Ideally, the link between PdM and DWM systems should be tight, with PdM outputs triggering work requests in the DWM systems, and data from the DWM used to refine and improve predictive models.
This integration allows organizations to capitalize on the synergies between these two powerful tools, maximizing the return on their digital investments. By combining the insights from PdM with the streamlined workflows and productivity gains of DWM, companies can unlock the full potential of IoT-enabled systems and drive significant improvements in their maintenance and reliability performance.
Conclusion: Embracing the Future of Maintenance and Reliability
Unlocking the potential of digital and analytics in maintenance and reliability is not easy, but leading players in heavy industries have enjoyed significant rewards for their efforts. By taking a holistic approach, focusing on the power of PdM and DWM, and ensuring the necessary enablers are in place, organizations can transform their maintenance and reliability functions, boosting profitability, productivity, and competitiveness.
As the IT Fix blog, we are dedicated to providing our readers with the practical tips, in-depth insights, and cutting-edge solutions that can help them navigate the rapidly evolving landscape of technology and IT. By understanding the latest trends and best practices in predictive maintenance and IoT-enabled systems, our readers can position their organizations for success in the years to come.
