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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
Pitney Bowes Industrial Internet
The world's 1st-ever Industrial Internet platform, Predix, becomes a pathway for revenue generation and notable new cost savings for Pitney Bowes' production mail business. An ordinary mail business, this is not. Of the 150 million pieces of mail produced each day in the US, the majority go through Pitney Bowes machines.
Case Study
Testing Engagement for a Fortune 500 Manufacturing Company
The client wanted to reduce operating costs while increasing efficiency and consistency within the IT and quality assurance organizations. Executive management mandated continuous process improvement, but the environment lacked consistent processes or tools to manage. A cost-effective software testing was needed.
Case Study
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.