Project SummaryThe growing demand for e-commerce has resulted in an increase in warehouses and distribution centers, alongwith the needed workforce to run the operations. For improved efficiency, companies are shifting to parts-to-person systems for order fulfillment to reach productivity levels near 500 items/hour per worker. These systemscreate manual order picking jobs that are highly repetitive and primarily involve the arm and shoulder. Repetitivearm movements, performed for prolonged durations without adequate rest, can result in fatigue and discomfortfor the shoulder, which can lead to musculoskeletal disorders (MSDs). Both stock movers and order fillers haveabove average incidence rates of injuries involving days away from work. Reducing the number of MSDs is anobjective of the Transportation, Warehousing, and Utilities (TWU) Council and the Musculoskeletal Health (MSH)Cross-Sector NORA Agendas. Preventing MSDs depends on effective job design and work-rest schedules thatminimize fatigue. However, current practice relies on fatigue models developed for static muscle loading, whichfail to account for the dynamic demands experienced by order pickers. Thus, the primary objective of theproposed project is to enable prediction of fatigue and recovery resulting from manual order picking, focusing onparts-to-person systems with highly repetitious shoulder work. A secondary objective is to translate the researchto practice (r2P) by providing practitioners with these predictive models to enable incorporation into their jobevaluation and design practices. These objectives address the MSH cross-sector agenda call for research onthe integration of real-time data with validated predictive models that address the variability in tasks and work-rest cycles. The models will be constructed from data collected during an in-lab study. Using a central compositedesign, fatigue development will be evaluated across a range of load levels and repetition rates, and recoveryfrom fatigue will be measured across a range of rest durations. Subjects will complete four periods of orderpicking, separated by designated rest periods. Dependent measures will include subjective ratings of fatigue,kinematics data from wearable sensors, and task performance. These measures will be unified into a fatigueoutcome metric using functional regression. Then, reliability theory will be applied to predict the unified outcomeduring repeated fatigue and recovery cycles as degradation and inverse degradation processes, respectively,accounting for task conditions, worker characteristics, and time. Field validation at a partner warehouse will beperformed, where model predictions will be compared to worker subjective ratings for three order picking jobs.Once validated, the models will be packaged into a web-based application which will be disseminated topractitioners (output), enabling prediction of future worker fatigue levels, which is more informative than existingmethods that provide a snapshot of the worker?s current condition or risk. Application of the revised models canfacilitate improved workplace design and job scheduling to accommodate the capacities of order pickers, whichsupports the long-term goals of preventing musculoskeletal disorders and improving worker health (outcome).