 Peter Venn, regional director at SAS, says predictive maintenance can reduce down time at production plants.
In a nutshell, what does SAS offer the downstream industry?
We offer software and solutions to assist our clients in efficiency, effectiveness and optimisation around their operations, particularly focusing on the optimisation of asset operations.
We also help clients with predictive asset maintenance, predicting when equipment will fail, and assisting the maintenance organisation. Also, we provide companies with demand forecasting models and solutions. In one sentence: We leverage our customers with data and knowledge, solutions and intellectual property to help them improving their operations.
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Who are your major clients in the Middle East?
We have a number of clients in the region and we are particularly strong in Saudi Arabia. We have huge success stories with them around the asset operations optimisation. The tough environmental and climate conditions in the region plus the vast desert stretches affect a large number of equipment and critical assets.
Quite often the equipment and asset doesn’t operate within its defined operational envelope. So, we assist companies to understand the true operational envelope and how to operate them efficiently and correctly.
Our solutions can help clients predict equipment failure before it occurs. We help them by saving down times. For example, last year a piece of equipment went down eight times.
If we are able to reduce the number by just one of those eight times, what would be the additional benefits for the organisation? The down time could cost a facility up to US$10 million, and being down may impact other phases of the production chain. So helping companies in saving one of these down times would save huge money. We have a lot of options to help companies in this regard.
Is awareness building around this issue?
I think that there is big awareness among our clients regarding the move to predictive maintenance. All companies are currently undertaking preventative maintenance, but they would like to move to predictive maintenance, as they want to be more efficient in their operations and reducing their operational cost.
What are the challenges facing companies to move to predictive maintenance?
The first thing is to get a handle on the data that they have. They need to collect it into one place and understand what that data means with regards to the equipment and make sense of it. We have been doing this for different industries that have a lot of data but they don’t know what to do with it.
The biggest challenge is that it’s hard for somebody to trust software to tell you that your equipment will fail tomorrow. You have to trust first and then you have to put it in the process to be able to say: Okay, I trust that this equipment is going to fail tomorrow, now I am going to take some proactive and preventative maintenance based on this information. At the end of the day, it is a belief in the data model and it takes time to overcome this challenge.
What’s your pedigree?
I’ve been working for SAS for almost eight years. My experience is on the business and information technology side. I have been working with several major oil, gas and utilities companies for ten years now. I worked in South Africa with utilities companies, and have great passion and determination for what our solutions can offer our clients.
