Manufacturing

Participating core and specialty programs: Center for Maritime Safety and Health Studies, Center for Occupational Robotics Research, and National Center for Productive Aging and Work.

Employers, workers, researchers, insurance companies, and technology manufacturers use NIOSH information to utilize emerging technologies to reduce musculoskeletal disorders among manufacturing workers.

  Health Outcome Research Focus Worker Population* Research Type
A Low back, upper extremity musculoskeletal disorders (MSDs) Increased use of robotics Where robotics are used (esp. in food, wood product, foundries, and transportation equipment manufacturing), workers with non-standard work arrangements and other vulnerable workers Basic/etiologic

Intervention

B Low back, upper extremity MSDs Increased use of exoskeletons Workers who do manual material handling tasks (esp. in food, wood product, foundries, and transportation equipment manufacturing), workers with non-standard work arrangements and other vulnerable workers Intervention
C Low back, upper extremity MSDs Using sensors or sensor-less technologies to measure risk factors for MSDs Workers who do forceful physical activities using torso or upper body (esp. in food, wood product, foundries, and transportation equipment manufacturing), workers with non-standard work arrangements, and other vulnerable workers Basic/etiologic

Intervention

* See definitions of worker populations

Activity Goal 4.3.1 (Basic/Etiologic Research): Conduct basic/etiologic research to better measure risk factors for musculoskeletal disorders, as well as understand how emerging technologies might help prevent and/or increase risk of musculoskeletal disorders in the manufacturing sector.

Activity Goal 4.3.2 (Intervention Research): Conduct intervention studies to develop and assess the effectiveness of interventions to prevent musculoskeletal disorders among manufacturing workers.

Burden

Mechanization and automation has changed the nature of the work demands in the manufacturing industry and introduced new tasks to the shop floor that never existed previously. Interventions that may have addressed an issue several years ago may no longer be pertinent to how work is performed now. The incidence rate for musculoskeletal injuries resulting in days-away-from-work for the manufacturing sector was 33.4 per 10,000 equivalent full-time workers compared to an incidence rate of 29.8 for all private establishments in 2015 [BLS 2016]. This translates to approximately 41,000 severe musculoskeletal injuries in Manufacturing. Manual material handling tasks, while not entirely eliminated, have changed dramatically in the last 25 years. Work-related musculoskeletal disorders (MSDs) or overexertion surveillance data from BLS [2016], Ohio Bureau of Workers’ Compensation [Meyers et al. 2017], and Washington State Department of Labor and Industries [2017] offer evidence that the food, wood product, foundries, and transportation equipment manufacturing subsectors have the greatest burden. However, currently the available data for prioritizing industry burden by body region (e.g., low back, upper extremities) is limited. Rapid advances in robotics and other emerging manufacturing technologies are likely to present new risks or exacerbate existing risks due to lack of experience with robots in varied work settings, potential unforeseen hazards, and unanticipated consequences in the manufacturing industry.

Need

Overall, there is a need to coordinate current ergonomic guidelines, guidelines and tools to address the challenges found in current work environments and demands. Research efforts are especially needed to identify risk factors and prevent MSDs among worker populations who utilize or interact with machinery for material handling (e.g., conveyors) or processing (e.g., metal or woodworking machines), emerging industrial machines (e.g., robots, collaborative robots) and vulnerable workers or those with non-standard work arrangements. In particular there is a need to identify scenarios in which the use of robots and other emerging technologies can contribute to MSDs. Research must still be accomplished to identify the costs, benefits and effectiveness of the proposed interventions (including any productivity gains that can be documented). Research is needed to identify the range of potential interventions for a particular issue including both engineering and administrative controls and their relative advantages. The adoption and dissemination of effective interventions has the potential to dramatically reduce the frequency and severity of MSDs in the workplace.

BLS [2016]. Nonfatal cases involving days away from work: selected characteristics by detailed industry with musculoskeletal disorders, All U.S., All workers, Private industry, (2011-2015). Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics.

Meyers AR, Al-Tarawneh IS, Wurzelbacher SJ, Bushnell PT, Lampl MP, Bell J, Bertke SJ, Robins DR, Tseng C, Wei C, Raudabaugh JA, Schnorr TM [2017]. Applying machine learning to workers’ compensation data to identify industry-specific ergonomic and safety prevention priorities — Ohio, 2001–2011. Manuscript submitted for publication.

Washington State Department of Labor & Industries [2017]. Aggregate Washington State workers’ compensation claims data by industry group, injury type, including work-related musculoskeletal disorders, 2011-2014. Unpublished Tableau Packaged Workbook.

Page last reviewed: April 24, 2018