Case Study 6: Concentrate Line at Florida Citrus Company

1. Case Study 6: Concentrate Line at Florida Citrus Company Mike Seide Chris Chesla Tony Niemczyk Sean McCauley

2. Introduction • Flow Line Process consisting of 5 operations • Cans are depalletized 360 at a time and sent down a conveyor to the Pfaudler Bowl • The Pfaudler Bowl fills 36 cans at a time with concentrate • The cans are then sealed, washed and grouped into batches of 4 at Stage 1 • The batch is sent to Stage 2 which accumulates 6 batches to organize for shipping • The Packmaster places the 24 cans in a box, seals the box, and sends the boxes to the palletizer • The Palletizer loads boxes 3 at a time onto the pallet. The pallet formation is 9 boxes per single layer, 10 layers high.

3. Conceptual Analysis • Increase throughput by identifying and reducing bottlenecks • Determine frequency of arrivals, batch sizes, and down time percentages • NEEDS ONE OR TWO MORE THINGS

4. Building the Model • Modeled as a basic flow line production • Used StatFit to determine the best probability distribution to model percent downtime, label change time, and flavor change over time • Black boxed similar operations into stages • Used the entity “cans” as a group of 4 cans • Used a batch size of 5 pallets

5. The Model

6. Experimentation – Initial Model • Built to determine capacity and maximize production, no downtimes incorporated • Ran 10 replications of an 8 hour shift • Production capacity of 22,900 cans per 8 hour shift Table 1: Operating Times without Downtimes

7. Experimentation – Second Model • Modeled with downtimes to determine the actual capacity of the concentrate line • Production capacity of 16416 cans per day, a 28.3% decrease in production capacity over initial model Table 2: Operating Times with Downtimes

8. Distributions • Used a pros vs. cons chart to determine the best approach to model the downtimes Table 2: Pros and Cons of Alternatives

9. Distributions Cont’d • Used StatFit to determine multiple distributions to model the label change time, the flavor change time, and the individual downtimes for the Pfaudler Bowl and Packmaster

10. Experimentation – Optimizing the Model • Created a Macro to vary the arrival frequency and arrival quantity to optimize total throughput by using SimRunner • Tested the parameters of arrival frequency from 1-10 minutes and arrival quantity of 50 – 150 cans • It was determined that an arrival frequency of 1-5 minutes produced the most throughput, anything more would be leftover WIP in the system • By performing this analysis it was determined that the arrivals do not create a sizable impact on total throughput

11. Experimentation – Optimizing the Model Cont’d • Next SimRunner tested the effect of downtime on the system • It was determined If the frequency between downtimes was high, then the line produced more full pallets, while if the frequency was small then they produced much less pallets in a given day. • These results were used to better understand the correlation between the length and frequency of downtime during production to try and optimize the model Not sure if this statement is totally correct….

12. Results – Reducing the WIP • One of the team’s tasks was to reduce the WIP, this can be achieved in two ways: • To increase the amount of time between arrivals of empty cans • Or to decrease machine down times • Increasing the amount of time between arrivals mainly affects the amount of cans on the empty can conveyor since the next location on the line is a bottleneck (the Pfaudler bowl). • By making the time between arrivals 8.1 minutes, then the output is still 8 full pallets, while the maximum amount of cans on the empty can conveyor is 1113. • Increasing the amount of time between to more than 8.1 minutes decreases the amount of full pallets that can exit the system in one day.

13. LOOKS kind of full ANY IDEAS? Results – Reducing the WIP • The final step to maximizing the throughput of the concentrate line with the current equipment is to develop a plan to eliminate the downtime from the bottleneck locations. • The historical data from this line states that Pfaudler bowl is down 22.16% of the time and the Packmaster is down 28.51% of the time. • This downtime is due to poor maintenance, lack of communication between workers, lack of attention by the workers, inefficient layout of the concentrate line, and bad machine design. • Some of these factors are difficult to handle, but there are a few that can be eliminated to improve the efficiency of the line. Table 4: Throughput with Improved Downtime

14. Recommendations • To alleviate the problems the concentrate line is facing a number of things should be done: • The Pfaudler Bowl should be running at all times to alleviate the bottleneck at this station • FCC should try to eliminate the downtime associated with the Pfaudler Bowl • To increase throughput, downtime for other machines should also be reduced. Our model shows a 10% decrease in downtime would increase productivity by roughly 38%. • The amount of time between arrivals should be no greater than 8.1 minutes to prevent production under 8 full pallets • Better employee utilization can be gained by work sharing and flexible work assignments to increase throughput

15. Questions ?