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Factory Operations Visibility & Intelligence
Overview
Visualizing factory operations data is a challenge for many manufacturers today. One of the IIoT initiatives some manufacturers are pursuing today is providing real-time visibility in factory operations and the health of machines. The goal is to improve manufacturing efficiency. The challenge is in combining and correlating diverse data sources that greatly vary in nature, origin, and life cycle. Factory Operations Visibility and Intelligence (FOVI) is designed to collect sensor data generated on the factory floor, production-equipment logs, production plans and statistics, operator information, and to integrate all this and other related information in the cloud. In this way, it can be used to bring visibility to production facilities, analyze and predict outcomes, and support better decisions for improvements.
Applicable Industries
- Heavy Vehicle
- Automotive
Applicable Functions
- Discrete Manufacturing
Market Size
The industrial control and factory automation market are expected to reach USD 269.5 billion by 2024 from USD 160.0 billion in 2018, at a CAGR of 9.08%.
Source: markets and markets
Case Studies.
Case Study
Springpath Gains Real-Time Intelligence and Improves Operational Efficiencies
As the HCI market has grown increasingly crowded, Springpath sought to differentiate itself by providing enhanced serviceability to its customers. But it was lacking in its ability to provide fast customer issue resolution for both hardware and software components and in its ability to get meaningful, real-time intelligence from data. Springpath needed to create better operational efficiencies for its support organization. To better service its customers, Springpath would need a solution to resolve customer issues faster while collecting real-time customer data from systems to extract better insights for both internal support teams and end customers alike. Building a solution would require quick ingest and parsing of data – which was at the time often sent in the form of complex log files – and the ability to present it as actionable information. Further, a solution would need to enable support engineers to provide quick issue resolution to minimize downtime. Ideally, Springpath wanted customers to get full visibility into their own systems – and thereby be able to perform timely maintenance tasks – with the goal of maximizing uptime and ensuring optimal system performance. In addition, a solution should integrate with their issue management system, Salesforce.com Service Portal, for easy access for both the in-house support team and end-customers. “Offering world-class Customer Service in the IoT era requires a comprehensive and scalable Analytics platform that is well-integrated with an existing Support workflow,” says Brett Flinchum, VP of Global Customer Success at Springpath.
Case Study
Connecting Cows to Save the Lives of Calves with MooCall
The idea for the Moocall calving sensor came about when one of the Moocall founding team, Niall Austin lost a heifer and her calf due to a difficult calving back in 2010. He had a theory that a device to measure tail movement might be able to predict the onset of calving and brought the idea to the other Moocall founders Michael Stanley and Emmet Savage. After many months of research and development the MooCall device was born, however they needed robust and reliable, global connectivity with roaming capability to ensure that this critical data could be transferred the crucial moment. Vodafone IoT worked closely with MooCall to implement a solution that works seamlessly every time, wherever the cow might be situated.
Case Study
The Use of AR Technology in the Spatial Design of a Corporate Production Line
The cosmetics market requires manufacturers to make quick changes in the matters of both production and distribution. In this market segment, one of the principal marketing tools is grouping products in packages (for example, shampoo + conditioner, cream + makeup remover). So far such process has been carried out in the Polish division of L’Oréal through outsourcing, which has limited the possibilities of a dynamic reaction to market impulses.