By Constant Ondo
Almost all industrial companies are currently seeking to enhance their maintenance plans with predictive models. Doing so, they want to achieve two main goals : improve the performance of their production processes with fewer machine downtimes, and reduce maintenance costs with for example lower spare parts inventories.
Looking for the right predictive maintenance tools, all these companies are facing the issue of efficiency : how can they achieve visible results (better OEE, productivity increase, lower reject rate, …) as quickly as possible and without mobilizing disproportionate resources.
An issue that we adressed when developing PICC Software. And that we solved by associating the new technologies used to build predictive models (IoT and Artificial Intelligence) with knowledge management and human skills development. Through this association, we came out with a self-learning, though immediately operational, tool.
Install less sensors, but in the right place
Optimizing the number of connected industrial sensors reduces instrumentation costs and simplifies data analysis. It saves both money and time.
But to make sure the optimization will not jeopardize the gains that are expected, it is necessary to install the sensors in the right place without multiplying trial and error loops. Which is exactly what PICC Software can help you achieve by identifying the problems that have the biggest impact on your industrial performance.
For more details on that topic, we invite you to read our article « IoT applied to manufacturing : How many sensors and where ? »
Bet on the combined strength of data and return on experience
Thinking of predictive maintenance models is usually associated with Big Data and algorithms. But one tends to forget that the most quickly usable and most reliable algorithms are up to now built up through supervised learning. Which means that computers are fed with problem/solution couples based on the experience and know-how of human experts.
PICC Software offers you the possibility to speed up the creation of a cognitive bridge between data and expertise, as well as to nurture your predictive maintenance models with much more inputs by systematically collecting the feedbacks of all experts within the company. And thanks to the automatic translation in more than 26 languages, this can be done even if your experts are dispersed all over the world.
The global return on experience is then made available to everybody, at once. It can be used without delay to identify the best practices and solve malfunctions or breakdowns more efficiently.
It is also coupled with condition data collected on machines to provide new insights on unsolved problems and discover root causes that were previously unreachable.
Nuturing simultaneously artificial and collective intelligence also helps to reap the benefits of predictive maintenance earlier and at lower costs.
Promote autonomous maintenance, engage operators
Using hands-on experience feedbacks in digital tools such as PICC Software to improve maintenance is fully in line with the TPM (Total Productive Maintenance) method.
It enhances the commitment of factory floor operators by seeking their contribution in problem analysis and solving. It fosters regular incremental improvement (second pillar of TPM) by enabling to tackle any problem encountered in daily operations, and not only the most serious or recurring ones. It helps the organization as a whole to get better results by sharing experiences and know-how in order to fill knowlegde gaps (sixth pillar of TPM).
The association of hands-on experience feedbacks with the explicite knowledge of the company (machine technical documentation, maintenance procedures …) and the operating data of the production tool offers new possibilities for autonomous maintenance (first pillar of TPM), be it preventive, event-based or predictive.
This is how PICC Software is working. And by enabling operators to take charge of the first-level maintenance, it allows to achieve visible results very quickly.
Augmented human intelligence close to the machine : that’s maintenance 4.0 !
5 exclusive functionalities to improve your maintenance KPIs
PICC Software offers all the functionalities proposed by knowledge management softwares and collaborative platforms in order to solve problems and enhance collective intelligence on the factory floor.
But PICC Software also offers exclusive functionalities designed to build up more quickly relevant predictive models while improving productivity from the first day on :
Two-way IoT connectivity : PICC Software integrates data collected from sensors in real time and can as well remotely control actuators.
Automatic search of solutions in documentation : PICC Software can search and extract from any document the content that can help solve a particular problem.
Solutions ranking : PICC Software automatically analyzes the performance of each solution, using a multi-criteria scoring method, to highlight the best one.
Benefit/risk analysis : PICC Software can automatically identify if using a given solution to solve a problem can in turn generate a new problem, and which one.
Dynamic support in problem solving : PICC Software can generate sequential procedures and include logical functions in such procedures, so as to properly guide the user on the particular situation he is facing.
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