The recent COVID-19 epidemic has caused immeasurable disruptions to our daily lives but also brought to light existing challenges in worker safety as society faces an uncertain shutdown. The term “essential worker” alone speaks volumes on the importance of these unsung heroes’ contribution to the routine operations supporting our daily lives.
The meatpacking sector is not an emerging field, but rather one that traces back to the early start of industrial manufacturing itself. While the meat processing industry has taken significant steps towards modernization, it is still dependent on a human workforce. The nature of the job exposes workers to muscular-skeleton injuries and fatigue, and companies continue to leverage new technology developments to improve working conditions and safety.
The answer to addressing some of the most salient concerns in existing industrial practices may come from the wireless connected products that grew from the industrial manufacturing sector itself. According to Robert Schmid, Deloitte Digital IoT chief technologist, “these sensors [in IIoT technology] gather data, store it wirelessly, and use analytics and machine learning to take some kind of action.”
The Industrial Internet of Things is rapidly disrupting an industry that has long experienced strenuous worker conditions and predictive analytics may be the key to creating a new standard for worker safety.
The first step, as with all technology design, is recognizing how the insights derived can best align with and address existing industrial needs. For IIoT to be truly effective in improving worker safety and contribute towards better industry standards, we must take a closer look at the set of Ergonomics Guidelines developed by the American Meat Institute in response to worker safety concerns.
This article addresses key insights from extensive user interviews with industry leaders and which responsible IIoT designs for worker safety solutions must account for. I’ll then explore data-driven design solutions that will help not only the meatpacking industry but the manufacturing sector as a whole take tangible steps towards an industry standard for worker safety post-COVID-19.
Historic Bureau of Labor Statistics (BLS) data shows that there is great potential for worker safety progress. The incidence rate of non-fatal occupational injuries and illnesses has been reduced by half over the last decade as member companies of the Meat Institute came together to brainstorm solutions prioritizing worker safety. These improvements have been attributed to two major initiatives in the meat industry.
The first of which is the American Meat Institute’s (AMI) decision to make workplace safety a non-competitive issue, thus encouraging member companies to collaborate on improving safety practices.
The second initiative is a set of Voluntary Ergonomic Guidelines for the Meat Packing Industry, developed jointly by the U.S. meat industry, OSHA and the United Food and Commercial Workers (UFCW) union. These guidelines, hailed by OSHA as a “model” for other industries has directly translated to increased efforts in reducing workplace hazards. As technologies advance, modern development of new tools and equipment have also significantly reduced levels of injuries in meatpacking industry.
However, what remains at the forefront of cumulative trauma disorders (CTDs) are work injuries due to ergonomic hazards and risk factors. What accounts for this increase in CTDs is largely associated with the changes in technology that exposes employees to increased repetitive motions and culminates in over $15 billion in costs for the employer.
A great resource in the progress to address worker safety concerns is wearable technology. Harnessing the potential of industry IOT will allow employers to not only better understand and reduce unergonomic motions but rather create a new standard for worker safety
While data analytics reveals crucial insights in addressing these key guidelines, the wearable technology and information collected are not enough to achieve tangible results in reducing worker injury without a deeper understanding of the human factors of the Internet of Things. This begins with understanding your solution’s target audience, and who you are designing for, which will also reshape the design considerations and requirements for the final product.
Effective IoT solutions are ones that provide high usability to the end-user and offer clear-cut benefits without creating additional friction for the user. This often requires conducting user research on your audiences and the ecosystem that the design will be implemented so that data can be presented in a comprehensive and actionable manner.
Iterate Labs’ partnered with ForgeHarmonic to delve into understanding the in’s and out’s of the meat processing industry to design an IIoT based solution that builds upon the innovative hardware design to best meet the user’s needs.
“Safety Monitor” to Utilize Predictive Analytics
The biggest concern among industry leaders and production managers is how to identify and correct problematic movements and risk factors before they cause injury. Predictive analytics is a set of commonly thrown-around words that are like the holy grail of design and data science, yet fine-tuning how that becomes actionable and achieves the desired end result is much harder.
One potential solution can be a “Safety Monitor”, characterized by a set of timely checkpoints and recommended “action items” for line supervisors and production managers to quickly scan for developments in risk and safety scores while incorporating alerts for time-sensitive information.
Long term analysis can reveal important insights about areas for improvement and building a better training process with more comprehensive safety precautions derived from worker data analytics.
Communicating Transparency Via Daily Motion Summary To Workers
In addition, it’s critical to design for the needs of the workers which the ergonomic data is meant to directly serve. AMI’s Ergonomics Program emphasizes the importance of leveraging worker agency to self-correct unergonomic repetitive motion, but this is not possible if they do not have adequate feedback to correct problematic movements. Thus, the efficacy of ergonomic solutions through motion analytics is directly aligned with a transparent solution for workers.
One potential solution to providing near real-time feedback to workers and enable them to play a bigger part in ensuring their own safety is to provide them with the data insights to correct potentially dangerous movements before they become a bigger problem. SMS motion summary reports at the end of each shift with 2–3 key metrics regarding the percentage of unergonomic motions occurring can be a tremendous step towards increasing worker safety and cutting down on CTD’s.
These are just a few examples of how sensors, data analytic, and IoT solutions can achieve more when designed from a human factors perspective.  Communicating to workers how their data is collected and used and  understanding the challenge areas of the production managers and building a solution compatible and complementary to users’ existing work ecosystem will go a long way with maximizing the potential of IoT solutions.