Semester of Graduation
Industrial and Manufacturing Systems Engineering
First Major Professor
Dr. Gary Mirka
Master of Science (MS)
Manufacturing companies for decades have relied on forklifts as their workhorses for material handling. However, in recent years, productivity, cost and safety concerns have led manufacturing companies to reduce and eliminate the use of forklifts. While there are many alternatives to the traditional forklifts, tugger tow trains deliveries (tuggers) have been the common and the most effective choice for regular material handling activities within manufacturing facilities. Tugger carts are towing vehicles that can be in the form of manned or unmanned systems. The latter is generally classified as automated guided carts and are unsurprisingly more expensive than their counterparts and are still long way from becoming a convincing choice for manufacturing companies. The low profile of these tuggers enable them to tow large loads and have the ability to drop/pickup full and empty carts to/from the respective stations during a single circuit which provides great flexibility in designing the tugger routes. However, these tuggers pose new physical fatigue issues to the material handlers - tugger drivers who previously rarely left their fork trucks. On average a tugger driver will have to walk, lift, pushup and push heavy loads to and from stations between 10 to 60 feet per container. As a result, companies are forced to take into consideration these ergonomic factors when designing tugger routes and their work shift times. This study analyzes these constraints and proposes an automated process in calculating the metabolic energy expenditure of tugger drivers in manufacturing plants using metabolic energy expenditure prediction analysis. The proposed program was run for a simulated sample data created based on literature. The results provide insights about the manual material handlers’ energy expenditure and its variations while performing tasks and while resting, throughout their work shifts. This information can be useful for managers to better balance the material handling jobs among multiple operators and to allow relaxation times for proper recovery which will reduce the possibility of physical fatigue related injuries.
Ganesan, Arul Pandian, "Automated Ergonomics Assessment of Material Handling Activities" (2018). Creative Components. 30.