Analysis

Analyzing Workflow

We constructed a Spaghetti diagram to study the workers' flow through the facility in order to analyze their mobility. We concentrated on a few items to keep things simple, but we still noticed a significant loss in efficiency when comparing throughput of a full pallet with a partial pallet. Because of the amount of cases per time and distance travelled, when an employee travels to a location and returns to the dock with a full pallet, throughput is substantially higher. When an employee travels to a location and only selects a few instances, the throughput suffers as a result of just selecting a few cases while still traversing the same distance and time.

Figure 1: Current State Spaghetti Diagram 

The spaghetti diagram clearly showed that we were targeting a major inefficiency in operations and that a new workflow is required for overall operation improvement.

Analyzing Distance Traveled

The estimated distance travelled per item on each client order list was examined and evaluated using historical Frito-Lay data of 73 orders with over 2,000 data points. Assuming that each item list amounts to a single trip, we calculated a mean distance traveled of 1.67 miles per trip.

Figure 2: Normal Distrubution Graph

Assuming that each item list amounts to a single trip, we calculated a mean distance traveled of 1.67 miles per trip.

Analyzing Performance Levels

We then used the previous data to calculate the current performance levels of the present system and concluded that it is already operating at a 3 sigma level.

This was a significant discovery, and we now had a baseline to beat. The warehouse runs quite smoothly in general, thus a performance level of 3 sigma was not unexpected.

Analyzing Performance Risk

We needed to examine the risks associated in the current system and the future system we had in mind before moving on to the next step of implementation. A Risk Analysis Matrix can be used to compare the system's existing dangers to the risks we expect to encounter after mitigation. The matrix works by balancing the likelihood of an incident happening with the impact that incident could have on an individual or the operations workflow. Both elements contribute to the danger level. The risk level can be placed within the matrix to help see the impact.

Figure 3: Risk Mangement Matrix

The most significant hazards found in the current system were within operations. The largest danger is that the leads will assign an inaccurate dock to an employee. This would apply to an employee loading or unloading a truck from a poorly designated dock, which would cause the worker to potentially increase the distance travelled by up to 40%. Employees picking products during the outgoing process are determined to be the second most inefficient. The existing system does not use logic to sort the customer picking list, resulting in extra trips and motion waste. The risk management matrix assigned a level of 70 to high operational risk.

Figure 4: Risk Mangement Analysis

Something to factor in is that an increase in job volume could be harmful to emplyees if implemented wrong. This could increase the likelihood of an injury caused by repetitive motion. As a result, it is advised to spend more time stretching before beginning a shift to reduce the chance of injury.