ECON 201 Case Study Report 1: THREE JAYS CORPORATION
Please find this annotated write-up and my Excel spreadsheet. My comments are in RED font below. I suggested them for Q1-Q2 and Q3-Q4 together for better flow
...
ECON 201 Case Study Report 1: THREE JAYS CORPORATION
Please find this annotated write-up and my Excel spreadsheet. My comments are in RED font below. I suggested them for Q1-Q2 and Q3-Q4 together for better flow of logic. Your final score (now available on Canvas) is shown at the end of this document. As this was the first case analysis of the course, I have graded your write-up based on your ideas and supporting logic, not whether your answers are right or wrong.
Make a cover page next time.
1. Using the data in case Exhibit 4 and the 2012 annual demand, calculate the EOQ and ROP quantities for the five SKUs scheduled to be produced in the last week of June. How do these amounts compare with those calculated in 2011? Compare the increases in EOQs with the increases in annual demand. (2.5 points)
The 2012 Annual Demand is given as
Exhibit 5: Monthly Sales Data
Label Type Ja n Fe b
Mar
Apr Ma
y
June
July
Aug
Sept Oc t
Nov
Dec Year Total
3Js Strawberry Ja m
2012 345 301 325 299 344 296 329 334 349 325 289 333 3,869
Marran Markets
Raspberry Jelly
2012 229 270 236 279 273 255 236 232 235 276 244 241 3,006
2013 744 737 425 379 571 2,856
Kerry's Marts Peach Jam
2012 156 176 174 144 160 178 155 159 178 166 176 148 1,970
2013 167 146 78 84 117 592
Dom's Food Blueberry Jam Stores
2012 92 109 98 99 102 111 103 99 94 104 107 93 1,211
2013 100 99 80 139 108 526
AAA Grocers Apple/Mint Jelly
2012 66 77 79 69 65 66 68 67 62 74 71 68 832
2013 73 63 110 146 88 480
The EOQ and ROP quantities for the five SKU’s based on 2012 annual demand is given as
Total Set up cost (S) Annual Deman d (D) Carryin g Cost
(i) % Unit Cost (C) EOQ
(cases) ROP
(cases)
Strawberry Jam 63.7 3869 9% 28.34 440 223
Raspberry Jam 63.7 3006 9% 30.52 373 173
Peach Jam 63.7 1970 9% 26.86 322 114
Blueberry Jam 63.7 1211 9% 29.01 243 70
Apple/Mint Jelly 63.7 832 9% 26.32 212 48
As Demand increased from 2011 to 2012, the EOQ’s also increased
Deman d (2010) Deman d (2012) Increase in Demand EOQ (2010) EOQ (2012) Increase in EOQ
2993 3869 29.27% 387 440 13.70%
2335 3006 28.74% 329 373 13.37%
1492 1970 32.04% 280 322 15.00%
886 1211 36.68% 208 243 16.83%
625 832 33.12% 183 212 15.85%
So, if Annual Demand doubles, the EOQ will increase by sqrt(2)
2. Brodie is uncertain if the costs presented in case Exhibit 2 are appropriate for determining the EOQs. What changes would you recommend, and why? Should the cost of the three idle part-time workers be included when the production line is down? Using the 2012 annual demand, and your recommendations, recalculate the EOQs for the five SKUs. (2.5 points)
In set up costs, the cost of part time workers should also be included, as they are idle at that time. Assuming the salary of each part time worker to be half that of full time worker
So, Total salary of 3 part time workers, during idle time of 1 hour = 3*0.5*23.5 = $35.25 So, new set up cost = $63.7 + $35.25 = $98.95
In carrying cost, storage cost was considered as 0%, which should be more because, there is always an opportunity cost of storing one inventory over another.
So, considering storage cost as 2%, new carrying cost = 6% + 2% + 3% = 11% Some of the basic assumptions of EOQ are debated
• The demand is not uniform throughout the year, which may lead to stock outs
• The order of new batch takes time and is not done instantly. For this case, the ROP should be adjusted to include the lead time to place order
Total Set up cost (S) Annual Demand (D) Carrying Cost (i) % Unit Cost (C) EOQ
(cases) ROP
(cases)
Strawberry Jam 98.95 3869 11% 28.34 496 223
Raspberry Jam 98.95 3006 11% 30.52 421 173
Peach Jam 98.95 1970 11% 26.86 363 114
Blueberry Jam 98.95 1211 11% 29.01 274 70
Apple/Mint Jelly 98.95 832 11% 26.32 238 48
Brodie’s first assignment in his internship is to update the EOQ and ROP quantities for all 141 SKUs, to reflect the current levels of demand (D), because the original calculations were done in 2011 with sales figures from 2010. This task is simple for the ROP, where:
ROP = 3wks (leadtime) D(annual demand) 52(wks/year)
Making changes to the EOQ amounts is more complicated, as several logical errors exist in the data that are used as inputs for the EOQ formula. Specifically, these are the setup cost (S), the unit cost (C), and the inventory carrying cost, which is expressed here as a percentage (i). (Note: Sometimes the variables i and C are combined in the EOQ formula. When this occurs, the product of i * C is represented by the symbol H, which is the inventory holding cost in dollars per unit, per year.)
Errors in Calculating EOQs
Setup cost (S) errors
These errors result from incorrectly including an allocation of fixed annual expenses as components of the total setup cost. Setup costs should include only actual, out-of-pocket costs (as should all the costs used in the calculation of the EOQ) that are directly related to setting up the production line to make a specific item (SKU). Jake Evans and Josh Francis — as well as the buyers who purchase the raw ingredients and packaging material for 3Js — are full-time workers who earn a predetermined yearly salary. Consequently, reasonable changes in the number of setups per year will not change the total cost of these individuals. Thus, regardless of whether they are employed by Fremont Jams and Jellies, or 3Js, there are no incremental costs that are incurred with respect to setups. Therefore, the costs of changing the rails on the assembly line to accommodate different jar sizes — as well as the costs of cleaning the equipment and switching out the jar labels between batch sizes — should not be included in the setup cost (S). Similarly, the costs of the two individuals employed in the kitchen should not be included in the setup costs. These costs should be considered only as the maximum capacity f these individuals is approached and additional people and/or equipment are required. At that point, changes could be
considered that would postpone the need for expansion, which would suggest that an appropriate tradeoff analysis should be conducted. Thus, with the current production system, the only relevant setup costs are the part-time wages paid to the three temporary workers, who are idle while the line is shut down for cleaning and label changes. The setup costs are therefore equal to:
S = 3 workers * $12.50 per hour * 1 hour = $37.50
Notably, the cost of these temporary workers is not included in the original cost calculations, most likely because they were not working and hence were not considered as part of the setup costs. Nevertheless, their cost is an out-of-pocket expense that must be included.
Carrying cost (i) errors
The cost of carrying inventory typically consists of three components: (a) the cost of storing the inventory, which can include storage costs (building costs, etc.) and labor and equipment costs associated with storage, insurance, and taxes; (b) the cost of obsolescence, spoilage, and shrinkage; and (c) the cost of capital, which is the cost of the money that is tied up in inventory. Because FJ&J is not charging 3Js for storing its finished goods, the primary component of this parameter is the cost of capital, which can vary. There are three scenarios:
1) If a firm has an excess of cash, then the cost of the capital tied up in inventory is the interest lost that could have been earned by investing the money.
2) If the funds could be better used — other than in inventories — by investing in a project
that would generate additional revenues and profits, or significantly reduce costs, then the cost of capital is the opportunity cost associated with not having the funds available for that specific project.
3) If the firm needs to borrow money to establish these inventories, then the cost of capital
is equal to the interest rate on the borrowed funds.
Here, although the cost of borrowing capital is 6%, the actual cost of capital is the opportunity cost associated with the inability of the firm to launch a new marketing campaign due to its lack of funds, which is estimated to be 20%. This is the projected contribution to overhead and profit that the additional revenues would generate. The 3% that is used for storage recognizes that FJ&J is not charging for the actual storage of finished goods, but it does include the cost of obsolescence, insurance, and taxes. Thus, the cost of carrying inventory is:
i = 20% + 3% = 23%
Unit cost errors (C)
Again, we should include only costs that represent out-of-pocket costs associated with the jams and jellies that are being produced. The components of the unit cost (C) should therefore include only those costs that are directly incurred each time a run is made. This is represented in case
Exhibit 3 as the “Total Variable Cost,” which does not include the fixed overhead cost allocation; that allocation is currently included in the calculations.
Assumptions in the EOQ Calculations
There are several assumptions inherent in the EOQ formula, many of which are not applicable to the 3Js situation. These include:
1) Unit cost remains constant and does not vary (as would be the case with quantity disc unts). This is valid, given the information in the case.
2) There are no stock-out costs. While these costs are not mentioned in the case, they need
to be considered in determining how much inventory in the form of safety stock to have on hand.
3) Demand is constant and known. At 3Js, however, the firm is g owing; the actual demand
is not known, but estimated based on some type of forecasting method.
4) Production capacity is always available when it is needed. In other words, there is no lag time between when product is required and when it is produced. At 3Js, however, the production of a specific size jar is scheduled for a given week, and any requirements prior to that week will be delayed until that time. (Note: When Assumptions 3 and 4 are valid, stock-outs most likely will not occur, which is why there is no need to consider them in the EOQ formula.)
5) There are no interactions among the different items produced. There are significant
interactions at 3Js, however, because of the relatively high setup cost associated with changing the jar size. As a result, items are grouped to reduce this cost, thereby affecting when they are produced.
Comparing Old and New EOQs
The calculation of the original EOQs shown in Table 1 below (and the attached Excel spreadsheet) are presented in case Exhibit 4:
Table 1. EOQ Calculations Using Existing Method (see case Exhibit 2) and 2010 Sales Data
Product (12 oz.) 3Js Marran Kerry Dom AAA
sales/wk 58 45 29 17 12
S = Setup Cost
63.70
63.70
63.70
63.70
63.70
D = Annual Demand (cases) 2,993 2,335 1,492 886 625
I = Carry Cost 9% 9% 9% 9% 9%
C = Full Cost/Case 28.34 30.52 26.86 29.01 26.32
EOQ (old)
387
329
280
208
183
ROP (3 weeks) 172.7 134.7 86.1 51.1 36.1
If we just update this using the 2012 sales data, we have the comparison shown in Table 2 below (and the attached Excel spreadsheet):
Table 2. EOQ Calculations Using Existing Method (see case Exhibit 2) and 2012 Sales Data
Product (12 oz.) 3Js Marran Kerry Dom AAA
sales/wk 74 58 38 23 16
S = Setup Cost
63.70
63.70
63.70
63.70
63.70
D = Annual Demand (cases) 3,869 3,006 1,970 1,211 832
I = Carry Cost 9% 9% 9% 9% 9%
C = Full Cost/Case 28.34 30.52 26.86 29.01 26.32
EOQ (old)
440
373
322
243
212
% Increase - Sales
29.3%
28.7%
32.0%
36.7%
33.1%
% Increase - EOQ 13.7% 13.5% 14.9% 16.9% 15.4%
The increases in sales range from 28.7% to 36.7%, but the increase in EOQs ranges from 13.5% to 16.9%. This difference is attributable to the fact that the EOQ is directly related to the square root of the demand, and not the demand itself. When demand doubles, the EOQ increases approximately 41% (the square root of 2 being 1.41). If we introduce the corrected costs, we have the revised EOQs shown in Table 3 below (and the attached Excel spreadsheet):
Table 3. EOQ Calculations with Recommended Costs and 2012 Sales Data
Product (12 oz.) 3Js Marran Kerry Dom AAA
Sales/wk 74.40 57.80 37.90 23.30 16.00
S = Setup Cost
37.50
37.50
37.50
37.50
37.50
D = Annual Demand (cases) 3,869 3,006 1,970 1,211 832
I = Carry Cost 0.23 0.23 0.23 0.23 0.23
C = Cost/Case 25.79 27.97 24.31 26.46 23.77
EOQ (new)
221
187
163
122
107
% Increase - Sales
29.3%
28.7%
32.0%
36.7%
33.1%
% Increase - EOQ -42.8% -43.1% -42.0% -41.2% -41.7%
ROP (4 weeks)
297.6
231.2
151.5
93.2
64.0
As noted in Table 3 above, the new EOQs are significantly less than the initial EOQs, even though sales have increased significantly. This can be attributed mainly to the significant difference in the setup cost and the cost of carrying inventory, both of which drive down the EOQ.
3. Compare your results with those obtained using the data in case Exhibit 2. What do you attribute the differences to? After speaking to Jake and Joshi, Brodie is now not sure if the EOQ model is the most appropriate for the current production process. Evaluate the scheduling method that Jake and Joshi are using. Why are they not following the established system? (2 points)
The EOQ has increased because the setup cost increased. Higher setup cost means higher order cost per unit.
Jake and Josh were not following the established system because
• They felt that changing the lines required more costs, so they reduced number of line changes as much as possible
• They felt that a longer run ensured that there are less production stops due to emergency situations while changing lines
• They feared stock out and so made sure they had almost 2 weeks of safety stock
The scheduling method that Jake and Josh has been using is erroneous in nature because it leads to high inventory levels, although it may lead to lower setup costs, in the longer run it affects the inventory turnover ratio.
4. Compare the established EOQ/ROP procedure (described in case Exhibit 2) with the one that Jake and Joshi are using. Which system do you prefer? What improvements do you recommend? (2 points)
I prefer the EOQ/ROP procedure is more suited to the case of 3J. The improvements suggested are
• An instant order is placed when an ROP hits red line. This is done considering the fact that the production line for the new batch can be started within 3 weeks (lead time)
• The EOQ and ROP are updated once per year based on past year’s demand, rather than updating the inventory order every month
• There should be an emergency quota of one week every month, for any urgent requirements, which may lead to stock outs.
Evaluation/Analysis of the Formal EOQ/ROP System
We can now shift the analysis to the broader issue of applying EOQ/ROP to the current system at 3Js. Why are Jake and Josh not using the EOQ figures for scheduling production? What are the
advantages and disadvantages of using EOQ/ROP methods, given the current circumstances at 3Js? Two aspects of production operations make the application of the formal EOQ/ROP system undesirable: 1) lack of production flexibility, and 2) demand is not constant.
Lack of production line flexibility
Use of the EOQ method assumes that the production equipment is always immediately available for processing a batch (run) of an item whenever the reorder point is reached. This is not the situation at 3Js, however, wherein 141 products, or SKUs, compete for one production line (while the overall capacity of the line is not an issue, scheduling to produce a specific SKU is very constrained).
The jam and jelly production line appears to have more than en ugh capacity, but the equipment used in the process is fairly inflexible. The fact that it takes two individuals an hour to adjust the equipment every time there is a change in jar size makes it virtually impossible for the line to respond quickly to individual production requirements for specific SKUs. The three-week ROP lead time permits the production of some items to be delayed, thereby allowing the batching of several items of a given size into a single “mega” batch or run. However, stock-outs are sure to occur because of these delays. For example, Jake Evans says that they will be running the 8- ounce jars in two weeks and hopes that they will not run out of any SKUs in this size before then. The inflexibility of the production line causes conflicts to arise between the pr ducti n requirements for SKUs of different size jars.
Using the EOQ method also generates a conflict among the production requirements of a given size. Producing EOQ volumes for each SKU within a single-size jar will result in future requirements being out of phase for making these items in the next production run. Thus, the production of EOQ quantities guarantees a random distribution of the ROP among all 141 SKUs, including the four jar sizes, thereby preventing the effective synchronization of groups of items into “mega” batches or runs.
Demand is not constant
As mentioned earlier, the EOQ and ROP quantities assume that demand is evenly distributed throughout the year and is perfectly predictable. At 3Js, however, demand has been increasing, and this trend is expected to continue. While seasonality does not appear to be an issue in this instance, it would cause additional problems when using the EOQ and ROP. In that case, the fixed three-week lead time would represent different amounts of inventory on hand that could vary significantly. As a result, using a fixed ROP will lead to unexpected stock-outs for some items and excess inventory for others, depending on the time of year.
Of course, the seasonality issue could be addressed with a simple adjustment. The average EOQ and ROP amounts could be multiplied by a monthly seasonality index for the current month (the monthly seasonality index equals the actual demand for a given month divided by the “average”
demand, which assumes a constant rate of sales throughout the year). However, we would still be left with the issue of an inflexible production line.
Evaluation/Analysis of the Improvised System
Realizing that the EOQ scheduling techniques are not practical for 3Js, Jake and Josh have developed an improvised system that establishes a fixed time interval between the runs f a specific jar size (four weeks). This technique is more commonly known as the Periodic Order Quantity (POQ) method. However, by retaining the ROP triggering aspect of the formal system, Jake and Josh have invoked two conflicting rules for scheduling the production runs. One rule calls for producing SKUs at a fixed future date, and the other calls for producing a SKU whenever the stock level of an item falls below its ROP trigger level. Use of this hybrid system also leads to conflicts among the production requirements of different SKUs like those described above, under the formal major EOQ/ROP system. In addition, using the ROP trigger quantities as a buffer stock provides varying lead times as described previously.
Jake and Josh assume that following the formal EOQ system—and thereby having to produce SKUs as called for by this system—would be prohibitively expensive and inconvenient. Their assumption is correct. Some consideration might be given to the formal system, but since 3Js has 141 products, it is almost undoubtedly prohibitive. The production line is currently underused, so the equipment could be adjusted for size changes more often without creating any bottleneck. At the same time, the equipment would likely not have to be changed every time a new SKU was produced, because consecutive runs would often be for products of the same jar size. However, changes associated with different jar sizes would most likely occur a lot more often than is the case currently. Because Jake and Josh are full-time employees of FJ&J, any increased costs associated with such changes would not be incurred until additional people would have to be hired (to adjust the equipment) and/or the capacity of the production line was reached. Even if these additional employees were hired by 3Js, the same would be true. If the company had fewer SKUs, the original EOQ system might make sense (alternatively, more emphasis could be placed on reducing changeover costs related to different jar sizes, which is what just in time r lean practices focus on).
The quantity of each item scheduled to be produced under the improvised fixed interval system is based on a crude forecasting model for predicting future demand, which assumes it is equal to last month’s sales adjusted for growth from last year. The relative inaccuracy of this forecasting model in light of the continuing growth of sales could lead to more stock-outs.
The major costs with which management should be concerned in this case are carrying costs and stock-out costs, which have been totally ignored.
Recommendations
The improvised system that Jake and Josh currently use represents an improvement over the formal EOQ/ROP system. It uses a fixed-time interval system, which makes sense when there is a high degree of dependency between products; in this instance, the dependency is the shared setup cost that is incurred whenever there is a size change. As noted earlier, the EOQ system assumes no interdependencies among products. However, the improvised system can be improved further by adopting the following recommendations:
1) Do not use the EOQ method to determine the quantity to be produced. Instead, produce only the amount necessary to ensure there is enough stock for eight weeks, plus a two- week safety stock. With this approach, approximately half the 12-ounce SKUs will be produced every eight weeks and the remaining SKUs will be produced on alternating eight-week intervals. Because the jar size remains the same, the setup cost to switch to a new type of jam or jelly, and/or to change labels is fairly small, so it would not be a problem to produce additional SKUs as the need arises (because of forecasting errors), even though they are not scheduled for another four weeks. As the production line is currently running at less than 40% capacity (based on a 40-hour week), there appears to be ample time available to absorb any increases in the number of setups required. As management develops experience with the new system, both the two-week safety stock and the eight-week interval between productions of individual SKUs can be reduced.
2) Improve the forecasting of future sales. This effort can be accomplished in two parts.
First, develop long-term trends that capture the growth that 3Js has experienced. Second, if it appears to be warranted, develop a monthly seasonality index to reflect seasonal changes in sales. As management improves the accuracy of forecasts with these appoaches, the amount of safety stock can be reduced. Better forecasting will not only reduce the probability of stock-outs but also reduce the amount of safety stock on hand.
3) Produce a greater number of different but same-size items in each production run to
allow better control of a larger part of the inventory.
Final score: 9.0/10
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