The ability to provide a safe manufacturing workplace and effectively support compliance has never been more vital. Today’s employers have learned the importance of not only managing day-to-day workplace safety but also being prepared to maintain health and safety during major events, such as wars, natural disasters, and pandemics. And while safety has always been critical in any manufacturing environment, it gains even more significance in the context of Manufacturing 4.0.
With the increased integration of digital technologies and automation systems and the implementation of advanced technologies, such as robotics, artificial intelligence, and the Internet of Things, employees will interact ever more closely with automated systems and use interconnected devices, sensors, and automated machinery. Maintaining proper safety standards and protocols for these technologies is crucial to safeguard employees and prevent incidents, such as equipment malfunctions, electrical hazards, and unexpected and unplanned interactions between humans and machines.
Beyond their potential to impact an employee’s quality of life, incidents are expensive. The direct costs, including workers’ compensation and medical and legal services, can be substantial, but the indirect costs of an incident shouldn’t be underestimated; these include retraining, investigations, corrective measures, lost productivity, damage to equipment, reputational risk, and costs associated with lower employee morale, absenteeism, and retention.
To manage health and safety, manufacturers track a host of key metrics, such as
However, many organizations struggle to get timely notifications of health and safety incidents in their workplaces let alone use their data to model and predictively identify potential risks in their manufacturing environments. But it’s essential that they develop these abilities—and the right data platform can help. By using data and analytics to identify potential hazards and implement preventive measures, companies can mitigate risks, prevent accidents and incidents, and reduce the likelihood of production disruptions, financial losses, and reputational damage.
This use case describes the data analytics architecture required to ingest, store, manage, and gain insight from data to improve health and safety in the manufacturing sector.
The architecture presented here incorporates the most commonly recommended Oracle components used to build an analytics architecture that covers the entire data analytics lifecycle, from discovery through to measurement and action. Manufacturers vary by type and complexity, but in general, the services outlined here will come into play when building data analytic architectures that focus on the wide variety of factors that impact workplace health and safety.
Our solution is composed of three pillars, each supporting specific data platform capabilities. The first pillar provides the capability to connect, ingest, and transform data.
There are three main ways to inject data into an architecture to enable manufacturing organizations to improve workplace health and safety.
Data persistence and processing is built on two (optionally three) components. Some customers will use all of them, others a subset. Depending on the volumes and data types, data could be loaded into object storage or loaded directly into a structured relational database for persistent storage. When we anticipate applying data science capabilities, then data retrieved from data sources in its raw form (as an unprocessed native file or extract) is more typically captured and loaded from transactional systems into cloud storage.
The ability to analyze, learn, and predict is facilitated by two services enabling three analytics approaches and access to the modeled data.
Descriptive analytics can help manufacturers identify potential risks and hazards by analyzing historical data, incident reports, and near-miss incidents. By identifying patterns and trends, analytics can highlight areas that require attention and help you prioritize risk mitigation efforts. Additionally, analytics can enable a continuous improvement mindset when it comes to health and safety. By analyzing performance metrics and safety data over time, manufacturers can identify areas for improvement, set benchmarks, and track progress toward safety goals.
When accidents or incidents occur, descriptive analytics can help investigate the root causes and underlying factors. By analyzing incident reports, witness statements, and other relevant data, patterns and trends can be identified, helping you carry out targeted interventions to prevent similar incidents in the future.
Manufacturers have a responsibility to prioritize the health and well-being of their employees. A safe work environment is not only a legal and ethical requirement, but it also impacts employee morale, job satisfaction, and overall well-being. Prioritizing health and safety helps create a positive work culture, enhances employee engagement, and reduces the risk of workplace accidents and injuries. However, ensuring a healthy and safe work environment is particularly challenging in the manufacturing industry—and the challenges are likely to increase over the coming years, driven by Manufacturing 4.0.
In this evolving landscape, manufacturers must not only ensure health and safety measures are in place but also continuously identify opportunities to improve those measures and take preventive actions to avoid accidents, injuries, and occupational illnesses. To do this, they need robust data- and analytics-driven health and safety processes supported by technology, enabling them to
Ultimately, these actions help safeguard the physical health and safety of workers, reduce the likelihood of absenteeism and medical expenses, and help maintain productivity and operational continuity.
Learn how to manage manufacturing operations more efficiently using a data platform that helps improve performance with machine learning.
Learn how to consolidate plant data more efficiently and get insights faster with Oracle Data Platform for manufacturing.
Learn how to optimize assets with a data platform that enables predictive maintenance with machine learning.
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