predictive analytics
Predictive Analytics

Material Handling Optimization - The Eaton Cutler-Hammer facility


The Eaton Cutler-Hammer facilityhas two separate areas of assembly. Eleven production lines compose the Load Center Assembly Area while 17 production lines compose the Metering/Meter Breaker/ACD Assembly Area. Currently, 11 fork trucks are used in the facility to bring parts from receiving to storage where they can be picked up when needed by the assembly lines. Fork trucks also take packaged goods from finished assembly to shipping. Fork truck drivers are often dedicated to one or two assembly lines in an attempt to meet their every need and to ensure that the assembly process does not incur downtime.

This project's goal was to evaluate the possibility of reducing fork truck usage in the plant by cutting trucks from the fleet, thereby reducing cost. The major areas for improvement found within the plant include a reduction in empty fork trucks, a decrease in time spent searching for parts, and a reduction in unnecessarily long travel times for drivers. Due to a larger production volume and more total trucks, the focus of the project centered on Load Center Assembly's six fork trucks. The analysis of the current routing system identified changes that were needed in the facility to improve fork truck driver efficiency.

In order to do this, new truck routes, storage options, and communication technology were thoroughly investigated. After detailed research and analysis, it was determined that a reduction in fork trucks was possible if the tasks were redistributed correctly. Also, implementing racks in storage areas helps make the drivers more efficient.

The recommendation of redistributing tasks and implementing racks met the 2-year payback requested by Eaton Cutler-Hammer. In order to reduce the usage of fork trucks, the least utilized driver was removed. His tasks were redistributed among the remaining five drivers.



After a thorough analysis of the current fork truck usage and storage systems at Eaton Cutler- Hammer, it was apparent that there was room for improvement. With the number of drivers differing between shifts and excess time traveling with empty fork trucks, a reorganization of drivers was necessary. The fork truck usage was modeled and analyzed using Simcad Pro simulation software.

The information this software offered on truck utilization and assembly line downtime was helpful in identifying fork truck inefficiencies and provided proof that it would be feasible to eliminate a truck and redistribute its tasks. Additionally, the storage inefficiencies were noted during various plant trips as many drivers were seen reorganizing large wire baskets in order to reach the ones below. This reorganization process took, on average, two minutes to complete. In order to save time in storage, drivers must be able to access any basket without any further reorganization taking place.

The way to do this is to implement racks so that any basket can be reached without wasted time. By using the simulation, it proved that the scenarios were successful when using the racks. Although, racks vary greatly in cost, Handling Innovations offers racks that meet the size and weight specifications of the Eaton plant while still meeting a two year payback when accompanied by the reduction of one driver. A request for a reduction in fork truck usage could be approached from many directions.

First, in search of eliminating all fork truck usage at Eaton, an Automated Storage and Retrieval System would be helpful. This would mean the entire fleet of trucks would be replaced by the automated system. Also, if Eaton were solely looking to increase the efficiency of the drivers, Radio Frequency Identification and communication devices provide viable options. These allow for the most current information concerning part locations and tasks to be available to the drivers. However, the sponsor requested a reduction in trucks that would meet a two year payback. These options could not directly meet the sponsor&#;s request so a redistribution of tasks was considered.

Again using the simulation software, the utilization of trucks and assembly line downtime could be tracked in various scenarios. The most reasonable scenario allowed for a reduction of one driver from the Load Center Assembly Area. This resulted in a gain of $39,954.69 in cash flows per year with an initial, one time investment of $41,724 for racks, giving a 13 month payback on the investment. The utilization values and assembly downtime measurements confirmed that this task assignment was plausible.

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