predictive analytics
Predictive Analytics

Plant Efficiency Improvement - Collins & Aikmans Rantoul Plant


Collins & Aikman's Rantoul Plant One currently maintains two of their three assembly lines in operation for the production of instrument panels for various automotive manufacturers. These lines are supplied with plastic components produced by high-tonnage injection molding machines within the plant and parts from external suppliers. In order to facilitate material flow within the plant, numerous high-low fork trucks are utilized in the material handling process.

To remove inefficiencies in this process, an analysis of the current material handling methods is undertaken in order to improve organization and reduce operating costs. Problems that hinder efficiency and reduce safety include traffic bottlenecks, inconvenient storage locations, a surplus of fork trucks, and excessive empty fork truck travel.

To meet the two-year payback the following was recommended:

(1) replace the inoperative southeast door,
(2) reorganize the GMX-001 assembly line to open the aisle now being blocked by an overhead conveyor, and
(3) remove three trucks. If dunnage handling can be streamlined there is an opportunity to remove three additional trucks.



Through the collection and compilation of the data sets listed in the appendices and through observation of process flow and daily fork truck activity at the plant, a thorough understanding of the material handling procedures was obtained. From this information a baseline simulation was created in an attempt to represent the current situation. Analysis revealed several problem areas, and led to solutions aimed at increasing fork truck fleet utilization rates, reducing the size of the fork truck fleet, and increasing safety in the plant.

The baseline simulation was then modified, implementing these solutions individually and collectively. The simulation confirmed that there is under utilization of the fleet and that there are inefficiencies in the material handling processes. In the Scenario 1, the removal of one molding truck and two shipping trucks and the subsequent reallocation of the tasks of these three trucks increased the average utilization from 35.07% to 46.75%. This higher level of utilization is still well within the bounds of what can be demanded of the fleet. A reduction of three trucks will save Collins & Aikman nearly $160,000 every year. Replacement of the inoperative door and reorganization of the GMX-001 assembly line successfully opened up new routes to the outdoor storage facility, relieving congestion in the northeast corner of the plant and reducing traffic through the two most heavily trafficked intersections within the simulation.

In addition, these changes decreased the average fork truck utilization rate by 4.63%. Replacing the door will cost Collins & kman approximately $15,000, as will reorganizing the assembly line, for a total cost of $30,000. Three additional trucks were removed for Scenario 6, in which the dunnage recycling plan was introduced in conjunction with the changes made in Scenario 4. This showed a drop in average utilization of 4.95% from the modified baseline. It should be reiterated that the feasibility of implementing this recycling strategy has not been determined, but the possibility of reducing the fork truck fleet from sixteen to ten is very encouraging. The storage changes discussed should also be considered, but the effects of such changes were not tested in this analysis.

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