A major part of SAS ’ s go-to-market strategy is to establish purposebuilt ecosystems , which are designed to fulfil a customer ’ s need or solve a complex business problem in a very specific vertical
HOW SAS AND AI CAN HELP
SAS recognises that AI has the ability to find the needle in the haystack and help leaders learn from past processes in a more scientific way . When compared to more traditional approaches like upgrading equipment to save energy , AI enables control of a number of variables to ensure the optimal settings for the machines at scale , very quickly , and with lower risk .
Furthermore , proof of concepts ( POCs ) are reliable for testing the effectiveness of energy cost optimisation products , but there are barriers to success .
SAS has proven that the approach works if customers are ready to go along on the journey . SAS has the tools that enable customers to quickly connect the real data sources in a few weeks via cloud technology .
The best approach to minimise time to value and maximise the benefit is to look at how much energy can be saved . This should be the main driver .
The role of data availability and data quality in a successful energy cost optimisation programme is to provide a backbone – this is the most important part . Unfortunately , for some customers who have a low degree of connectivity in their operations yet , it ’ s also the most expensive part . But with use cases like energy cost optimisation , investment in connectivity really pays off .
It is important that insights are relevant and actionable for a process or industrial engineer to ingest . There is a strong need for process knowledge to ensure engineers are able to take data-driven actions on the factory floor . This will enable them to understand the process variables and the potential areas available for improvement .
64 October 2023