How to Improve Your Progress Towards Predictive Maintenance


Preventative maintenance is a common practice for most companies, but predictive maintenance (PdM) is the Holy Grail. Whether it’s a power plant, hospital or manufacturing facility, implementing a predictive maintenance plan can prove critical in minimizing downtime and optimizing productivity. The problem is that many organizations feel that achieving predictive maintenance is simply out of reach.


The key is recognizing the challenges and roadblocks to move from reactive to preventive to predictive maintenance, and then putting the right technologies and processes in place to make progress. This means filling functionality gaps within Enterprise Asset Management (EAM) systems like SAP to replace cumbersome paperwork and Excel files with automation systems. Squeezing every last drop of value out of the data at your disposal is a cornerstone of predictive maintenance.


The good news is that there are tools, strategies, and tactics out there that can actually move you along this journey.



Challenges in Migrating to Predictive Maintenance


Building out a robust and resilient PdM strategy won’t happen overnight. You’ll need the right technology in addition to anticipating common business, data quality and process roadblocks. One of the first challenges that scares operations and technology leaders away from fully embracing PdM are concerns around the potential time investment, disruptions, and costs.


A functional gap analysis can be helpful in breaking down the specific areas that need to be addressed or improved to reduce reactive work to free up time for more preventive and predictive planning and implementation. Prioritizing the most critical gaps and investing overtime in the technologies and change management processes to close them can provide solid returns and set the stage for the next level of investments. PdM is a journey that can be implemented in stages to minimize disruptions and costs.



Data in Predictive Maintenance


A key differentiator between what you are likely doing now and making progress towards predictive maintenance is how you collect, curate, and manage data. More specifically, you need to look at the data you collect today and evaluate whether it includes the information you need for creating a PdM plan. This includes the information crafts people capture in the maintenance or repair of your critical assets.


Under a preventative maintenance model, seeds of a potential failure that arise in between scheduled maintenance intervals can often go undetected. Investing in IoT technologies such as sensors with internet connectivity will start to supply the data needed. A PdM plan merges your organization’s technology ecosystem and human resources to stay ahead of the curve.


In addition to evaluating and improving data capture, make sure you have a good baseline of key measures to monitor your progress. Overall Equipment Efficiency (OEE) is one of the main KPIs that predictive maintenance helps uplift, along with minimizing downtime and optimizing technician efficiency. Do you collect and analyze the data to monitor these KPIs? If not, this is another set of data gaps you need to close to further your journey.



Investing in Technology and Automation


As you make progress in data collection, you can look to the additional technologies and automation for PdM. Machine learning and artificial intelligence software, for example, can play a key role in structuring data for this plan. But to achieve predictive maintenance, you need to do more than just analyze data to build predictive models, you need to put the plan into action.


Adding automation to your implementation processes can improve the prioritization and scheduling of predictive maintenance tasks. It can also structure and capture the data to support KPI tracking and future predictive analysis.


Going from preventive to predictive maintenance is a huge sea change for many operations. But the benefits of reduced downtime and more effective technician usage are well worth the investment. Selecting the right technologies for better data structuring and further automation will help you implement a PdM plan more efficiently and move your maintenance operations into the future.



Find the Right Partner


As with all transformation processes, there are challenges in the journey towards predictive maintenance. The change impacts your people, processes, policies, and technologies. This is why you need a partner who understands the unique requirements of maintenance and repair operations. Sigga can help. We have 20 years of digitalization and mobilization experience in helping enterprise operations teams deploy automation solutions. Contact us to help you complete the evaluation of your data and process gaps to get started on the path to greater productivity, data accuracy and cost savings.


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