In 2016 Kishore Magnhnani, Mark Stubbs, and Vinayak Bhide, deep industry and technology practitioners, founded an advisory business to provide services to industrial companies. Working with clients the founders were continuously faced with the limitations of current inspection technologies and they started to develop a solution that manufacturing customers found dramatically more impactful.
In 2020 they founded Shoreline IoT to productize this solution. Shoreline provides a Connected Asset Performance Management (APM) solution that enables asset-intensive industries to connect all their assets to manage performance, improve efficiency, reduce maintenance costs, extend equipment useful life by decades and unlock rich operational data.
Small defects in parts or end products can cause massive headaches to organizations large and small. According to the American Society for Quality, most manufacturing companies maintain a cost of about 15-20% of revenue related to the Cost of Poor Quality (CoPQ). This is a considerable number given the average manufacturer's gross profit is between 25 and 35%. Poor quality can lead to reputation damage to the manufacturer and major problems in the chain of production that can cost the manufacturer up to 1,000x the cost of just scrapping the defective fault.
Most manufacturers are currently using manual visual inspection and 3rd party quality audits to avoid these issues, and defects still cause the 15-20% cost mentioned above.
Further, according to OECD, there are 10m factory workers focused only on visually inspecting goods produced, which both do not produce results (30% failure rate) and are more expensive. These jobs are also not desirable and lead to the workers' poor physical and mental health. Automating these specific jobs, which does not include the automation of the rest of the production line, accounts for a $73B opportunity annually.
Shoreline’s off-the-shelf software as a service (SaaS) solution is built using multiple services from Amazon Web Services (AWS) including FreeRTOS, AWS IoT Core, and Amazon SageMaker. The deep insights and highly accurate predictions generated by Shoreline’s physics models asset library and self-supervised machine learning do not require historical records and expensive data scientists. Shoreline’s end-to-end IoT – ML offering unlocks deeper insights on equipment health.