Digital manufacturing is clearly taking hold. In its Global Manufacturing Outlook 2020 report, KPMG observed that 48 percent of the industrial manufacturers it surveyed have accelerated their digital transformation strategies by years. However, Industry 4.0 initiatives have almost exclusively focused on efficiency and improved decision-making around operations, such as production, sales, supply chain, and accounting.
Meanwhile, most safety management is still largely manual, using Excel spreadsheets and filing cabinets packed with paper records—processes that often leave manufacturers reacting to incidents rather than preventing them in the first place. However, when it comes to safety, mitigation is too little, too late.
According to the National Safety Council (NSC), workplace injuries in the U.S. cost $171 billion per year, and the manufacturing sector ranks sixth highest in the number of preventable fatal work injuries. The human costs alone are high, but workplace injuries hurt manufacturers in other ways as well. Notably, a machine involved in a serious accident will remain down for a week or more, slowing production and potentially leading customers to turn to other manufacturing sources.
Job injuries also contribute to a negative view of manufacturing that can hinder recruiting and hiring. This adds to the hurdles manufacturers already face in competing for talent with companies offering higher wages and signing bonuses.
It all points to the need for manufacturers to make predictive, data-driven safety their next Industry 4.0 milestone if they want to compete effectively for employees and customers.
The good news is that many manufacturers already have at least some of the technologies in place to collect important safety data. For example, many companies have implemented real-time monitoring—employing sensors to track whether a machine part is wearing to the point where it will start producing defective products or fail altogether. This allows the manufacturer to replace the part before it starts impacting quality or production schedules.
Similarly, data from machine sensors can detect if wear or alignment is putting a part—perhaps a drill or blade—at risk of injuring a worker. Such insights enable manufacturers to schedule maintenance or repair before there is an issue.
But manufacturers also have to look at other factors. Does an employee have the right training to use a particular piece of equipment safely? Is that person’s certification up to date? Are any employees working extended shifts that may lead to exhaustion and accidents? In order to analyze this data, it needs to be structured and normalized, so that it is repeatable across all existing records.
The natural inclination is for manufacturers to take advantage of their existing data. However, if this information is stored in spreadsheets, PDFs, Google docs or file cabinets, it will need to be entered into a database that structures the data for consistency. This process can take months to years to complete and cost tens or hundreds of thousands of dollars.
By contrast, those manufacturing firms already using applications such as enterprise resource planning (ERP); environmental, health and safety (EHS); and/or human resources (HR) can begin to capture and analyze safety-related data. That is because these applications automatically normalize the data and store it in a database.
Manufacturers without a modern ERP, EHS or HR solution should consider implementing one of these applications. We have seen companies that take this approach start collecting data digitally and gain important insights from their safety programs in as little as four weeks.
Once the core applications are in place, manufacturers should aim to have all employees collaborate in the collection of safety-related data. The key to workers’ participation is letting them use mobile phones to access web applications, which automatically normalize the data. The information that employees contribute might include data about training, hours on the job before taking a break, or other factors that can contribute to safety risks.
More recently, technologies, such as Quick Response (QR) codes, are providing a way for manufacturers to identify individual employees via their mobile phones and effectively act as digital signatures that can be stored and tracked with other information. This can help track workers at risk because, for example, they lack or have outdated training on particular safety measures.
Beyond structuring their data, manufacturers also need to ensure they are collecting the right information. One company’s experience provides insight into a couple common mistakes. The team there analyzed data they had been collecting for four years, and a resulting statistic was that more injuries occurred in June—something they already knew.
The team had made two mistakes. First, they captured age groups instead of individuals’ ages. Second, they didn’t normalize the data around active workers, an important factor since there are more workers onsite in June. As a result, they didn’t have the necessary details to analyze whether certain conditions led to a higher rate of injury per worker.
The lesson is that it’s better to collect more, and more detailed data, instead of trying to anticipate how the company will want to group it when running reports or analyzing the information.
Capturing detailed data is becoming even more important as business applications increasingly incorporate artificial intelligence (AI) and machine learning capabilities that can scan thousands of data points to find associations people are likely to miss.
Imagine using AI assistants to scan 1,000 hazard assessments in the database and identify the 10 that answered “no,” making it possible to focus on this small group of anomalies and apply a remedy.
Then, imagine walking on the shop floor and seeing a worker on a Genie lift. A supervisor could text the AI assistant to see if that person has been trained to use the Genie. Maybe the response includes not only that the employee has been trained, but that their certification has expired, so a safety bot can schedule training for the worker. These are just two examples of how artificial intelligence can play a powerful role in preventing injuries.
With detailed data on hand, manufacturers can take advantage of the analytics and reporting functionality in applications to prevent incidents and facilitate government and industry compliance. And they will be well-positioned to take advantage of AI and machine learning for even deeper insights into proactively protecting workers in the future.
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