Use Cases Heavy Vehicle Factory Operations Visibility & Intelligence

Factory Operations Visibility & Intelligence

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.
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Springpath Gains Real-Time Intelligence and Improves Operational Efficiencies
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.
Lean Manufacturing Process Flows
Lean Manufacturing Process Flows
A multi-national manufacturing company was unable to coordinate the work of all departments involved in the production processes. The company lacked both real-time views into the systems and methods of communication. This situation meant small problems could cause big delays in filling orders, and customers would get frustrated. Additionally, the business’s manufacturing process flow was labor-intensive and error-prone with manual checks on information coming from various departments using Excel spreadsheets. Naturally, the process was slow and not conducive to process improvements, nor was unstructured data (emails, phone calls) shared or saved for future reference. The planning manager wanted a transparent way to find out whether the manufacturing process could fulfill the production plan sent from SAP. As part of his evaluation, he needed to be able to: - Identify potential problems before they happened - Collaborate with coworkers around critical data points - Analyze the historical data, so he could continue to improve the lean production process
Real-Time IoT Tracking and Visualization Improve Manufacturing
Real-Time IoT Tracking and Visualization Improve Manufacturing
Shimane Fujitsu, a wholly-owned subsidiary of Fujitsu and a leading manufacturer of business notebooks and tablets, set out to improve processes where factory inspections found product errors. Prioritizing product rework based on shipping date was challenging, and it caused Shimane Fujitsu to incur additional shipping fees. The company needed a way to collect data to better track the location of products in the rework cycle as well as monitor progress in real time. The collected data would also help process analysis for future improvements. 

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

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