The Emergence of Smart Factories: Artificial Intelligence and the Connected Workforce Pave the Way.
A diverse range of use cases contribute to the success stories in various manufacturing sectors, with the Connected Workforce as a significant theme.
The manufacturing sector is experiencing a considerable transformation as cutting-edge technologies like edge computing, artificial intelligence/machine learning, and streaming analytics, combined with real-time data, pave the way for innovative solutions and smarter factories. As well as this, workforce related technologies such as Connected Workforce Solutions are changing the way training, observations, assessments and much more are monitored with intelligent single source of truth platforms.
A report by Fortune Business Insights states that the global big data market in the manufacturing industry was valued at $3.22 billion in 2018 and is projected to reach $9.11 billion by 2026, with a CAGR of 14.0% during the forecast period.
The Evolution of Intelligent Manufacturing and the Connected Workforce
In manufacturing, “the edge” refers to the production environment where cameras, sensors, machines, and assembly lines generate data. Enterprises utilize edge computing technology to collect and process data from these sources or from connected automation control systems. Technologies like streaming data analytics and AI are employed to analyse the data, enabling real-time insights for quick decision-making and immediate action. Similarly on a Connected Workforce Solution technology is improving the way employees progress with smart features such as capability tracking. Capability tracking refers to the process of monitoring the skills, competencies and qualifications of an organisations employees through a digital platform.
At the same time, the vast influx of data at the edge can paradoxically hinder transformation. Expanding data sets, including new data types across new edge locations, can overwhelm edge technology with their sheer volume, even as user expectations for real-time insights increase.
Despite these challenges, manufacturers and other industrial firms continue to innovate at the edge, differentiating themselves based on their ability to extract value from edge data. Today, this involves utilizing AI and ML to process massive data sets and deliver insights in near real-time at the point of data creation and consumption. A significant theme in this process is the importance and relevance of the Connected Workforce.
AI-Driven Advantages at the Edge
- Reduced defects: AI can monitor parts entering and moving through the factory. Computer vision accelerates and automates the work in progress throughout the production cycle. Defects can be identified, flagged, and traced back to individual processes or components in real-time for immediate remediation, rather than after a defective product is discovered.
- Minimized breakdowns: AI-powered predictive maintenance systems utilise data from sensors and IoT devices to pinpoint the exact location of maintenance needs, saving technicians significant time in diagnosing and enabling the organization to proactively predict and prevent future equipment failures. This proactive approach to maintaining equipment and processes at optimal performance levels safeguards workers, minimizes disruptions, and reduces maintenance costs.
- Bridging Translations gaps: A feature included on Connected Workforce Solutions is auto translations. This feature means that any training documents you have on the platform will be instantly translated to the languages required by the employees at each site. Auto translation technology in manufacturing training makes it more accessible to employees who speak different languages. This is also not just documents, this includes competency checks, observations and assessment questions giving your employees the highest capabilities from their training progressions.
Leveraging Edge AI for Enhanced Value
Implementing AI at the manufacturing edge offers numerous enticing benefits but also presents unique challenges that must be addressed for successful manufacturing edge AI deployments. Organisations need to establish a robust foundation of backend infrastructure and consulting services to fully comprehend the entire journey from ingesting edge data to achieving the desired business outcome.
To simplify deployment, integration, security, and management, configured systems built by manufacturing AI experts can hasten time-to-value with solutions specifically designed for smart manufacturing use cases. Choosing an engineering-validated AI solution can help businesses overcome adoption barriers, such as a lack of onsite AI expertise. Complimentary to this is choosing the best Connected Workforce solution to suit your manufacturing company. Your Learning Development Platform should be at the epicentre of your company’s training programmes. It gives you the invaluable ability to build, publish and gather data surrounding your training efforts.
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