Pawel Lecinski is the predictive maintenance subject-matter expert for Europe and Asia for Bunge Ltd. Corp., an agribusiness and food production company based in White Plains, NY. Lecinski, based in Poland, is the Society for Maintenance and Reliability Professionals’ 2017 CMRP of the Year in the Rising Leader category. He spoke with Plant Services recently about his experience as project manager for ARROP, Bunge’s maintenance, reliability and physical asset management program, and his work to create a more-reliable plant from the ground up at a new Bunge facility in Italy.
PS: Implementing an asset management program like ARROP would be a huge task for anyone, even someone with 20 or 25 years of experience in the field. You were a relative newcomer – what were the keys to success for ARROP, and what were some things you learned in the implementation process?
PL: I implemented this program right after completing my studies. I didn’t know it was thought of as impossible, so I went ahead with the implementation – I believed ARROP would work.
I was chosen to lead the project in part due to my fluency in English, but also because of my interest in maintenance and reliability. We started this program in 2011, and at that time, we focused on making our plant successful. Now we help to make action plans for other plants.
Overall, the key to our success was the involvement and enthusiasm of everyone at the plant. While some of the employees that worked at the plant for many years were more hesitant to change, we worked hard to engage and incorporate all levels, from the floor workers to management, in the program implementation.
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Another key element in our success was communication between everyone involved in plant operations. I pushed for constant communication from the beginning. As people raised issues or questions, it was very important to answer those with communication, brainstorming, and meetings. And it was especially important to listen. Instead of telling people what to do or how to solve a problem, I would open up a dialogue and ask them what they thought the best course of action would be. While I often had my own idea of what would work best, opening this conversation was crucial for making experienced craftsmen feel valued and a part of the process. Engagement is key.
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Another issue that we overcame was the gap between the older and experienced craftsmen and younger, less-experienced practitioners with more technology fluency. We worked to bridge this gap by showing each group that the other had something to offer – the experienced craftsmen brought years of experience and knowledge and the younger employees brought a deeper understanding of technology.
I assured everyone that no one would be fired due to this new program, and in seven years, no one has. We didn’t have many of the roles in place needed, so we created new positions and filled them with current employees. The craftsmen became planners and we launched a diagnostics team.
PS: When you were working to design reliability into the new plant in Italy, doing everything from analyzing equipment design to drafting purchase specifications and creating a reliability template for other Bunge plants, what were top priorities for you?
PL: We tried to build a total reliability model and focus not only on what we did normally like predictive planning but also how to get to the root of problems and avoid issues later. We tried to make a standardization of many pieces of equipment and other items. For example, the number of types of oil – originally it was 19, as I remember, and seven brands; at the end it was one brand and only seven types. And from the first day when we started, we were able to measure all of the equipment, we were able to plan all of the jobs, so it was a totally different approach.
PS: Are there attitudes about maintenance and reliability that you see changing?
PL: I see the future as going more and more toward predictive maintenance. I believe all of the equipment will be online, and artificial intelligence will allow machines to learn about possible failures. But first, the machines will need to learn from us; we have the history data. Just now, we are testing the IoT on one asset, to see how machines will react and how best to utilize the date we’ve collected over the past seven years.