Case Write-Up Group#15 Section 3
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National Cranberry Case Report
TO 552
Team Name: Group 15
Authors: Adam Wydra, Brendan Biache, David Amorim, Srishti Sehgal
Section: 03
To:
Hugo Schaeffer
From:
Adam Wydra, Brendan Biache, David Amorim, Srishti Sehgal
RE:
RP#1 processing
After conducting a thorough review of RP1, our team has identified key challenges and
opportunities in the plant's operational capabilities. Solving these challenges will help reduce
overtime expenses and improve relations with coop members by reducing truck waiting time.
The reason for overtime is excess delays in the plant that lead to significant waiting time for
trucks delivering and unloading cranberries. According to our analysis, the root cause of these
delays is a critical bottleneck in plant operations: wet cranberry drying capacity. While the
delivery rate of wet cranberries is at 1050 bbls per hour in a 12h period, drying capacity is only
at 600 bbls per hour. Given a total wet storage capacity of 3200, approximately 6 hours and 45
minutes after the start of operations, trucks begin to wait for the plant to dry enough wet
cranberries to allow for additional cranberries to be unloaded. After trucks finish arriving, trucks
will have to wait an additional estimated 4 hours and 15 minutes past 7pm to finish unloading all
cranberries. This results in over 16h of total unloading time, including the waiting time, and a
total “waiting period” of 9.6 hours. The result is over 163 truck hours of waiting time and
overtime pay as operations will conclude between 3am and 4am.
The answer is in tackling the root cause - which is the process bottleneck found in the drying
stage. By installing more dryers and increasing drying capacity, total throughput per hour will
increase and ultimately will reduce total waiting time for truck deliveries. In addition, converting
more dry bins to wet bins will further alleviate unloading strains as more trucks will be able to
unload cranberries before having to wait for plant processing delays.
Finally, as a secondary measure to improve plant profitability, we suggest installing a new light
meter system to improve cranberry color grading. This new system will help the plant gain great
precision in correctly grading cranberries and thus increasing the proportion of premium priced
cranberries. While this will not directly affect the cost challenges mentioned above, it will
improve the plant’s bottom line by increasing cranberry revenues.
Please see below for a detailed analysis of the points mentioned above.
Supporting Analysis
Causes of Delay
As seen below, the drying stage is the first and main bottleneck (then followed by the separators). The
calculations below are based on an hourly unloading of 1440 barrels of wet and dry cranberries (later
broken down in the 75% and 25% distribution as per the case, respectively). 1440 barrels per hour was
reached by dividing the total number of barrels by the 12h unloading period (7am to 7pm).
RP1 Process Flow and Capacity/Utilization Analysis
Wet Cranberry Inventory Build Up Diagram
Wet bin capacity at RP1 will be reached in 6.67 hours (3200 bbl capacity divided by 480 bbl build up
rate). By 7pm, trucks have finished arriving and, at this point, there is a total wet inventory build-up of
5760 bbls: 3200 in bins and 2560 in trucks that are waiting to unload. With a draw down (processing) rate
of 600 bbls per hour (dryer bottleneck), it will take 4.27 hours (2560 bbls divided by 600 bbls/hr) for the
waiting trucks to unload. Thus, total unloading (including waiting time) is 12h + 4.27h = 16.27h. This
means that total waiting time per truck is 16.27h - 6.67h = 9.60h. By calculating the area of the smaller
triangle below, we find total truck hours of waiting time: (9.6h x 2560 bbls)/2/75 bbls = 163.84.
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Analysis of Investments
Provide an economic analysis of at least three of the following four candidate investments:
Additional Dryers:
In the current scenario, the drying process is the biggest bottleneck that needs to be
resolved. Presently, each of the 3 dryers has an individual capacity of 200 bbls/hr and a cumulative drying
capacity of 600 bbls/hr.
Running with our assumption of 75% wet and 25% dry inventory, 12960 bbls/day is the total wet
inventory per day ( 75% of 17280).The operations begin at 7 AM and deliveries continue for 12 hours
resulting in 1080 bbls/hr of wet inventory and 360 bbls/hr of dry inventory arriving per hour.
With 3 dryers, current cumulative run time is 12960/600= 21.6 hours, leading to a build-up of 480 bbls/hr
(1080 bbls/hr - 600 bbls.hr). If one additional dryer is added, the rate of drying is 800 bbls/hr and the
cumulative run time is 12960/800= 16.2 hours, with a build up of 280 bbls/hr. The current capacity of 3
separators is 400 bbls/hr each and 1200 bbls/hr total. The utilization of separators improves to 96.6%
((360+800)/1200)) as against its previous underutilized capacity of 200 bbls/hr.
Currently, operations run in two shifts of 7 AM to 3 PM and 3 PM to 11 PM. The cost associated with
adding a dryer is $60,000.
In the present scenario, deliveries stop at 7 PM creating an inventory build-up of 5760 bbls/hr (480 * 12)
and the plant would only be through after 9.6 hours ( 5760/600). This leads to overtime work of 5.6 hours
(9.6 hrs- 4 hrs). Overtime cost per employee is $13/hr and spread across 27 employees would lead to an
expense of $1965.6 per day and $717,444 annually.
If an additional dryer is added, total inventory build-up at 7 PM would be 3360 (12*280) and the plant
would need to operate for 4.2 hours (3360/800) to be through. This requires overtime work of 0.2 hrs (4.2
hrs- 4 hrs), leading to overtime expense of $70.4 per day and $25, 696 per year. The potential for savings
by adding one dryer is $692,748 per year.
Bin Conversions:
It is stated that holding bins 25-27 were strictly utilized for wet berries, holding bins
1-16 were only used for dry berries, and bins 17-24 could be utilized for either wet or dry berries.
Considering the NCC forecasts 75% of berries will be wet and that farmers are moving more toward the
wet harvesting technique, we made the required assumption that bins 17-24 need to be used for the wet
berry storage. However, as shown and explained in the inventory buildup diagram above, the wet berry
storage capacity (3200 bbl) will be eclipsed 6.67 hours into each 12 hour delivery period, causing a total
waiting period for delivery trucks of 9.60 hours and a total unloading time of 16.27 hours.
Investments should strictly be made to alleviate the actual bottleneck in the flow process. The bottleneck
in this situation is the drying of berries. Therefore, we would recommend at first to invest in more dryers
or more efficient dryers to speed the process up so that the berries can be moved from storage through the
process quicker. Although not the bottleneck, storage could be enhanced to reduce costs of truck drivers
and overtime. Considering dry berries will compromise only 25% of total berry usage, and that dry berry
storage bin utilization is only 9%, it is seen that converting a number of bins 1-16 to wet storage is
beneficial. How many to convert is based on the economics and financials of the overtime rates for NCC
laborers, trucker costs and wait times, and the overall cost of converting bins at $10,000/bin.
Seeing that inventory buildup would be alleviated at ~4am, a maximum of 9 workers would need to work
past their shift end time of 11pm. Therefore, at an overtime rate of 1.5x base rate of $8.00/hr, the total pay
for 9 additional seasonal workers for the 5 hours of additional work to reach the 4am 0 quantity inventory
would be (9workers)x(1.5*$8)x(5hours)= $540/day for overtime NCC labor costs. Leasing the truck
drivers is $100/hr, and it was calculated that there were 163.84 truck hours of waiting. Therefore, each
day of such high supply would cost $16,384. By adding bins 1–16 gradually to wet storage, capacity
would increase by 250 barrels/bin, therefore alleviating some of the $16,384 costs due to wait time of
truck. However, costs for NCC laborers would still be needed to process the berries through the rest of the
process until 4am due to the drying bottleneck.
Light Meter System for Color Grading:
Unlike other improvements at RP#1 that reduce bottlenecks,
increase throughput and cycle time to improve overall efficiency, the Light Meter System will only have a
direct impact on profits by reducing the amount of defects in color grade sampling for the cranberries.
The current process has the Chief Berry Receiver estimate the grade of the berries using color pictures to
grade the berries Nos. 1, 2A, 2B or 3. Truckloads that are rated at a No. 3 receive an additional $1.50
premium per bbl. RP#1 paid the $1.50 premium on 450,000 bbls while only half were found to actually
be No. 3 quality. This year alone saw RP#1 overpay for berry quality by $337,500. By investing in a
Light Meter System to be operated by a full-time operator with the same yearly salary as the Chief Berry
Receiver, the RP#1 would save nearly $129,000 dollars with a 50% improvement over their current
system. RP#1 would save $264,000 dollars every year if the computer system can decrease their defect
rate by 90%. RP#1 would yield an extra $44,375 with only a 25% improvement. As long as the system
can provide a yield improvement of 12%, the system will reduce the costs at the RP#1 facility. Other
considerations could include analysis on the throughput of RP#1 with and without the Light Meter system
to see if other operations will be improved or hindered by the inclusion of this system.
Conclusions
By following the recommendations laid out in this analysis, we believe facility RP#1 can improve their
throughput and cycle time by relieving the bottleneck strain specifically located in the drying process.
This is the most important step to alleviating the backlog of truck deliveries as the build-up rate and
draw-down rate will be more in line. The addition of a dryer will significantly reduce overtime
production costs. This should be combined with the bin conversion to change unused dry capacity to
wet-harvest berry capacity, thereby increasing the amount of time trucks can unload before reaching full
capacity. Lastly, facility RP#1 can invest in the Light Meter grading system. While this investment alone
does not affect the truck backlog or overtime, it is a good investment for RP#1 to decrease their defect
rate in berry grading, thereby providing them with savings that can be reinvested into other plant
operations. With these improvements, the National Cranberry Cooperative should improve relations with
their members using facility RP#1.
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Grade: 50 points, allocated as follows
Element
Points
Possible
Points
Received
Comments
Memo contents
5
Capacity and
utilization analysis
10
Inventory buildup
diagram
10
Investment
Analysis
20
Conclusions
5
Total
50
Grader’s Initials: __________________