Supporting Your Journey into Predictive Maintenance


Creating a predictive maintenance (PdM) plan is only the first step in moving your operations into the future. As you navigate through the Fourth Industrial Revolution -- with industry driven by data and automation -- now is the time to implement PdM. With the right support partners, you can install the right technologies and create business processes that help you in this journey.


A PdM plan always seems promising from inception, but most organizations will face challenges like systems integration, data structuring and change management. Since PdM requires a lot of moving parts to operate -- from data collecting sensors to giving technicians the right data -- you’ll need to know the in’s and out’s of effective implementation.


The good news is that with the right knowledge and technology partners, you can implement steps towards PdM and begin realizing efficiency and cost benefits sooner rather than later.



Assessing Your Current Situation


Implementing PdM often requires a subtle mindset shift in multiple areas of facility management and maintenance. Since PdM relies heavily on well-structured real-time data, tactics like paper work orders and manipulating Excel spreadsheets need to be replaced with digital processes. That’s why a gap analysis is critical in the beginning stages.


The most important piece to your PdM audit is gaining a clear picture of how you’re collecting, storing, organizing and deploying data. You’ve likely already made substantial investments in things like sensors to collect information and cloud infrastructure to store data. But for PdM to work, the entirety of your dataset needs to be organized so automation systems can make the most of the information you feed into it.


For example, data must be collected from sensors and historical failures, then analyzed to create the PdM plan. With the plan, work orders and technicians can be deployed on a dynamic, predictive basis based on identified triggers. In a recent survey by PwC, maintenance leaders cited asset condition, asset usage, and maintenance history as the main three data types used for PdM. But to capture this data, you’ll need the right technologies and organizational maturity to begin to structure and systemize the data gathering.



Operationalizing in Your Business


Once you have the technology backbone in place in the form of an Internet of Things (IoT) infrastructure, data structuring and analysis, you can then begin to build out processes for boots-on-the-ground technicians. Having a great plan isn’t necessarily going to yield results. Deploying the plan is a critical component.


Implementing PdM isn’t a strictly technological matter, either. You’ll need to focus on creating a supportive organization -- emphasizing digital-first from the boardroom to technicians -- for PdM to be successful. Your PdM plan should be clear to all stakeholders involved, and your implementation should embed continuous feedback loops to improve data and track results. That’s why pilot programs are important. By piloting PdM on select pieces of equipment types, you’ll be able to fine tune data collection, analysis and deployment methods.


Multiple roles within the company should work cross-functionally with a predictive maintenance mindset. This means data scientists and reliability engineers receiving continual feedback from technicians with data captured during the course of a repair job. For predictive maintenance to work, you’ll need to have processes and technology in place that automatically structures data so that the data scientists and engineers can make best use of that information.


Implementing predictive maintenance also requires new working procedures and policies for deploying technicians. Managers will need to understand the implications of when a “trigger goes off” relative to the rest of the maintenance tasks on their schedule.  For example, if a sensor is set to send a notification if temperature is above a certain threshold, who turns this into a work order and what is the priority for scheduling a technician?


As with most digital transformation activities, PdM impacts people, processes, and technologies. It is important to involve people from across your organization -- even in the early stages of making changes.



Solutions to Support a PdM Rollout


Now that you’ve audited your current data collection and maintenance processes, you can start turning towards specific solutions that will help you get from A to B in a streamlined fashion. Some solutions, for instance, help large organizations structure data collection and analyze data while others support the implementation by automatically scheduling work and implementing predictive maintenance on a dynamic, ongoing basis.


Mobile devices will be a cornerstone of PdM plans in action. For instance, you could provide on-demand video instructions on predictive maintenance tasks. And as the technician completes their tasks, they can use an app to capture condition and repair data in a structured way. This can then be fed into future predictive planning analysis and scheduling priorities.


Planning and scheduling solutions can automatically create work schedules based on your predictive maintenance policies. For example, a rule can be to prioritize predictive work on mission critical machinery over routine maintenance or corrective maintenance on lower priority assets.


Implementing predictive maintenance in the real-world is all about data, planning, and execution. A gap analysis can help you understand what types of new technologies and processes need to be put in place to make progress towards PdM. You’ll need to become more data-centric than ever and commit to fine tune your maintenance processes. Finally, make sure to enlist experienced vendors and partners that can support you with the right technologies and know-how that will empower your organization and processes into tomorrow.



Find the Right Solutions  


Moving from reactive to preventive to predictive maintenance is a journey. To make progress, improve the structure of your data and processes with maintenance automation solutions. Sigga has 20 years of experience with SAP PM and maintenance workflows plus a history of successful deployments across a wide range of industries. Consider the Sigga Planning & Scheduling solution, it is the most complete and flexible automation solution for SAP PM with the ability to add custom rules and data tables to automate more of your routine processes. Sigga also has an integrated Mobile EAM solution to streamline maintenance work while improving data capture with technicians.


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