Design Strategy

There are five main job types in the facility, each requiring a different amount of work. By pairing data on daily job volume and time studies on job completion times, a Python model is designed to minimize the number of workers necessary to complete the maximum number of jobs Fox Factory expects to see across different days and shifts. This model will also simulate the completion times of each job type within the warehouse given the recommended number of workers.

While this addresses the problem of the increased idle time observed among workers, there have been unnecessary wait times workers are experiencing in the warehouse. Specifically, each shift there is roughly 5.5 hours of total wait time between powered industrial vehicles (PIVs) and workers that can be eliminated with a few simple changes. By implementing a zone-based task schedule throughout the warehouse, the wait time seen can be diminished and improving the safety of all workers in the warehouse. 

The schedule is created using Excel Solver which is given a set of constraints and can then be used for linear programming. A main constraint is the time required to complete each job using the recommended number of workers output by the Python simulation. Additionally, to implement a zone-based schedule, a constraint is added such that only one activity can occur in each zone at any given time, eliminating workers having to wait on PIVs and vice versa. 

As the recommended schedule includes timing for put-aways of pallets from the receiving dock to the warehouse, it must adhere to the constraints of the consolidation of the two receiving docks into one, which has not been implemented yet. Thus, we have created a Simio simulation to model how material will now be coming into the warehouse, allowing for testing of the schedule to ensure the receiving dock never exceeds capacity.