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Pariveda helps Black & Veatch reduce cost by automating a key feature in the engineering design process leveraging SageMaker Machine Learning on AWS (Amazon Web Services).
Black & Veatch (B&V) is a leading engineering, consulting and construction company, that is employee owned and operated in more than 100 offices worldwide. With revenues of $3 billion, the company is ranked as one of the largest privately-owned companies in the United States. With their 100 year history, B&V has built a very robust process for the design and construction of each of their specialty areas (water, power, telecommunications, etc.). However, they had not found a way to leverage the prior experience of their design engineers, treating each new project as a blank slate and repeating much of the work.
Pariveda helped B & V prove that Machine Learning (ML) could be used to automate key engineering design parameter decisions within the design process by implementing an MVP application for some of the most important parts of the design process. Pariveda designed and implemented an ML system on the AWS SageMaker platform, to automatically optimize these design parameters by predicting the rate at which the components would require maintenance based on the process inputs to that step. The system is trained on historical performance at real-world plants, and uses advanced feature modeling to divide runs into logical segments, detect and ignore segments that have been affected by manual human intervention, and then train a multi-part linear regression model to predict which segments are likely given a set of plant context parameters.
Pariveda Introduces Atlas Van Lines to Machine Learning on AWS (Amazon Web Services) Services Mapping a Path to Smoother Scheduling
Atlas Van Lines (Atlas) is the second largest van line in North America, formed in 1948 by a group of entrepreneurs in the moving and storage industry. The organization was developed out of the single goal of being able to move from coast to coast with the golden rule of business being number one. Atlas leverages a large agents network that can provide dedicated and personalized support in each market. In addition to a robust footprint, Atlas also boasts stringent agent quality requirements that surpass that of the industry. Atlas is controlled and operated by Atlas World Group, based in Evansville, Indiana.
During peak moving seasons, the Atlas agent network will work together across markets to meet customer demand. However, their ability to forecast capacity was manual and labor intensive, relying on the wisdom of resources with many years of experience and gut instinct. Atlas had the historical data from 2011 forward and desire to find a way to dynamically adjust capacity and price based on future market demands.
Pariveda and AWS joined together to help Atlas unlock the possibility of proactive capacity and price management in the long-haul moving industry. Pariveda prepared the data, developed and evaluated the Machine Learning model and tuned the performance. We used SageMaker to train and optimized the model, then exported it using SageMaker’s modular nature to run using EC2.
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