1. Case Study 1
Blanchard Importing
and Distributing
ISE 553
Faryal Qasim
Prafulla Kumar Shahi
2. 2
Executive Summary
Blanchard Importing and Distribution Inc. is suspecting that they are losing out on
profits due to holding more inventory than necessary in their system. They expect to
gain an overall 20 percent return on wine merchandising if they can reduce their
inventory levels. The reason they believe for higher inventory costs is that their
employees are following a slightly different inventory policy from the recommended
EOQ-ROP system in 1969.
This case study analyses and critiques the cost structures and corresponding EOQ
and ROP levels as implied by the 1969 policy as well that used by employees. It then
recommends a better policy that utilizes the recent demand trends, fluctuations and
seasonality better than both these policies.
Using recent demands to evaluate bottling quantities will result in a better response
to demand changes and reduce inventory holding and stock-out costs. A moving
average model is suggested for products with low demand and variability and a
multiple distribution based on high and low demand season for high and variable
demand products. The cost differences turn out to be significant with over 85 %
savings for high demand items and 42 % for low demand items when compared
with the 1969 inventory policy.
In short, Blanchard needs a better cost structure, inventory and forecasting model,
and optimal bottling quantities and reorder levels to free the capital tied up in their
production process. This case study gives recommendations for optimal levels of
these quantities.
3. 3
Table of Contents
1. Blanchard Importing and Distributing .....................................................................4
1.1 Company Background...........................................................................................................4
1.2 Current Situation....................................................................................................................5
2. Cost Coefficients ..............................................................................................................5
2.1 1969 System.............................................................................................................................5
2.1.1 Setup Costs....................................................................................................................................................6
2.1.2 Unit Cost.........................................................................................................................................................7
2.1.2 Carrying Cost................................................................................................................................................7
2.2 Policy used by Bob and Eliot ...............................................................................................8
2.3 Suggested cost coefficients ..................................................................................................8
3. Demand Characteristics ................................................................................................9
3.1 Trends .......................................................................................................................................9
3.2 Low Volume, Less Variable Demand.............................................................................. 10
3.3 High Volume, High Variable Demand ............................................................................ 12
4. Suggested (Q,R) Model.................................................................................................13
4.1 Cost Coefficients................................................................................................................... 14
4.2 Lead Time.............................................................................................................................. 14
4.3 Demand Distribution.......................................................................................................... 15
4.4 (Q,R) Values .......................................................................................................................... 16
4.5 Cost:......................................................................................................................................... 16
5. (Q,R) Model Implementation and Comparison.....................................................17
5.1 Low Volume, Low Variability Demand .......................................................................... 17
5.1.1 Comparison with 1969 and Bob & Eliot’s Policy: ..................................................................17
5.2 High Volume, High Variability Demand ........................................................................ 19
5.2.1 Comparison with 1969 and Bob & Eliot’s Policy: ..................................................................20
6. Conclusion.......................................................................................................................21
APPENDIX 1 – Residuals for Moving Average Forecast-Rum.................................22
APPENDIX 2 – Moving Average Forecasting Method-Rum.....................................22
APPENDIX 3 – Lead Time calculations (in weeks)....................................................23
APPENDIX 4 – Cost terms in (Q,R) model....................................................................23
APPENDIX 5 – Demand for Blanchard Products .......................................................25
APPENDIX 6 – Suggested Unit Costs for different products...................................25
APPENDIX 7 – Cost Coefficients for existing policies...............................................26
4. 4
1. Blanchard Importing and Distributing
1.1 Company Background
The Blanchard name was originally established as a chain of retail liquor stores, the
first of which was opened in 1938 by John D. Corey. In 1957 Corey became
interested in wholesaling alcoholic beverages and began distributing case goods to
retail outlets. To devote his full efforts to this new venture, Corey transferred
ownership of the chain of Blanchard retail outlets to other members of his family. In
1964 the present warehouse and office facility was completed, and in 1966,
equipment was installed to permit the conversion of raw bulk spirits to bottled
goods for sale under the firm's own brands and private labels. When Corey died in
1968, his son, John D. Corey, Jr., assumed responsibilities as president and treasurer
of the company. In June 1972, the firm's annual revenue was $4 million, of which $3
million represented sales to the seven Blanchard retail stores owned by other
members of the Corey family.
Blanchard is a full-line alcoholic beverage house that distributes both imported and
domestic goods. It sells prebottled goods (called uncontrolled stock) to retail outlets
at wholesale prices and accounts for 45% of the firm's annual sales. The remaining
55% of Blanchard's revenue attributes to sale of controlled stock, those items that
Blanchard bottles and sells under its own brands and private labels. The controlled
stock consists of 158 products that are differentiated by bottle size, type and proof
of beverage, and brand label.
The process of converting raw spirit to bottled goods is supervised by Bob Young, a
skilled machinery operator and Eliot Wallace, a food technologist. In addition, five
part-time workers are utilized in the overall process. The process can be
summarized in three steps:
1. Withdrawal of spirit from bulk storage
2. Rectification of the spirit
5. 5
3. Bottling the finished product
1.2 Current Situation
The current production scheduling system of the company was implemented in
October 1969 and is based upon the production and demand conditions existing
then. However, many of the assumptions and scheduling procedures have changed
over the last two years. This means that the initial calculations for inventory levels
no longer apply.
At present, it is estimated that a before tax return of 20% can be earned on any
investment put into wine merchandising but due to lack of funds needed to hire
experienced wine salesmen and build up an adequate inventory of wines, the
company has been unable to exploit this trend. Additionally the company has just
about reached the limit of its borrowing capability. The only substantial source of
fund available appears to be a reduction in the inventory level.
2. Cost Coefficients
2.1 1969 System
This is an EOQ model with certain assumptions about the cost components leading
to incorrect calculation of cost coefficients and therefore inaccurate EOQ. This has
been explained in detail in the sections that follow.
The main cost components discussed are:
1. Setup cost
2. Unit cost
3. Carrying cost
6. 6
2.1.1 Setup Costs
In the 1969 system, setup cost was calculated using blending setup cost, bottle size
changeover cost, label changeover cost, and the order processing costs. Blending
setup and size changeover costs were based on the annual salaries of the employees
involved (Bob and Eliot) and the length of time required for setup. Size changeover
cost was then divided by the average number of different items being processed
between setups. Since these two costs were dependent upon the fixed annual
salaries of employees, the number of setups did not affect the overall cost of
production. This meant that these two costs were fixed irrespective of the number
of setups and they could have been ignored in the calculation of EOQ.
The label changeover cost was based on the salaries of five part-time workers in
addition to Bob and Eliot’s salaries. Again, the salaries were fixed so only the idle
time of the part-time workers affected the overall costs. Only this cost should have
been to be taken into account for EOQ calculations. This idle time was assumed to be
thirty minutes.
The order-processing cost was also fixed as it was based on yearly salaries of two
office workers. So this cost was also independent of the number of setups done in a
year and should have been ignored in EOQ calculations.
In a typical year, Blanchard operated the bottling line for only 77 days, which meant
there was plenty of setup time available without any loss of production due to
bottling changeover. Hence the downtime cost was negligible. Therefore only the
variable component of label changeover cost was affected by downtime and this
should have been the only setup cost affecting the calculation of EOQ. This cost was
$6.25 - same for all of Blanchard’s products. A simple idea for dealing with this cost
would be to utilize the idle time of these workers in some other way.
7. 7
2.1.2 Unit Cost
In 1969, unit cost was calculated using the cost per case of an item after bottling and
packaging. This price was based on a full unit cost figure that included all direct
expenses incurred in producing and selling an item plus an allocation of the
company's total fixed expenses. Since the state tax and the federal distilled spirits
tax liability was not incurred until sale of the finished product, the unit cost used in
the EOQ formula should have been determined by deducting both these taxes from
the full unit cost figure. Taxes to be included in this figure must be associated with
production process i.e. the customs duty and federal rectification tax. Same idea
applies to the fixed overhead allocation, which is associated with sale of finished
product and therefore should not have been considered in this calculation.
Therefore, the unit cost for each item should have been calculated taking the
following components into account:
1. Materials cost
2. Bottling labor
3. Variable overhead
4. Customs duty
5. Federal rectification tax
2.1.2 Carrying Cost
The only substantial component of the inventory carrying cost was assumed to be
the cost of capital. Equity was not considered as a source of funds. As a result, the
cost of capital was assumed to be 9%, the prevailing interest rate for debt available
to Blanchard. Components of the carrying cost percentage other than cost of capital
were small and amounted to only 2.5%.
At present, it is estimated that a before tax return of 20% can be earned on any
investment put into wine merchandising. So this number should have been taken as
the cost of capital as opposed to the earlier value of 9%. This is the return on
8. 8
investment Blanchard will get if it reduces the current inventory levels, freeing
some of the tied-up money for investment in wine merchandising.
2.2 Policy used by Bob and Eliot
The policy used by Bob and Eliot is the same as 1969 except for the values of setup
cost. It takes them one full day to make all the adjustments to the bottling
equipment required for the size change. So they process several combinations of
beverages and labels in that size during the run. They feel that the setup time is
significant and the number of setups affect the overall cost and therefore try to
minimize this number in their policy. Due to this reason, they add even those items
to the list that have not reached the reorder point yet.
Bob and Eliot are running only five items in June so the size changeover cost comes
out to be $17.69 as opposed to $8.85 in 1969. It is because this cost is inversely
related to the number of different items of a given size, which is half the number
used 1969 calculation.
For the label changeover cost, the time taken for the changeover is either twenty
minutes or three minutes depending on the shape of previous label. Assuming it
takes twenty minutes 90% of the time, the expected value of time is calculated and
added to the ten minutes it takes for restoration of the labeling machine. Since both
rinsing of tank and label changeover occur at the same time, the maximum of the
two times involved is taken into account for cost calculation. This gives the estimate
of $11.11 as the label changeover cost.
The costs implied by both these inventory policies have been shown in Appendix 7.
2.3 Suggested cost coefficients
As discussed in the previous sections, Blanchard should consider only those costs
that are actually affected by the bottling quantity, rather than fixed annual costs.
Based on that premise, the setup costs turn out to be $ 6.25 for all items, which
9. 9
includes only the variable component of the label changeover cost. The unit cost of
production should consist only of the components as discussed at the end of section
2.1.2 and these costs have been provided in Appendix 6. The inventory carrying cost
rate should be 22.5 % as opposed to 11.5 % as recommended by the 1969 policy.
These costs will be used in the cost calculations in Section 5.
3. Demand Characteristics
3.1 Trends
When we look at the demand data, we see an increase in sales between February ‘71
and May ‘72, and a seasonal fluctuation in demand for gin, vodka and whiskey
(Appendix 5). In comparison, demand is fairly constant for scotch and rum, with
minor fluctuations. The demand for gin has high variability, while vodka and
whiskey show both high demand and high variability. We can also see a spike in the
demand for whiskey in January 1972, and a concurrent decrease in demand for all
other items except rum. This suggests a correlation between the demand for
whiskey and these other items. However, there isn’t enough data to perform
calculations for covariance and hence this will be out of scope of the study.
There is, however, a correlation between different periods, and this too can be seen
in the demand pattern. At Blanchard, Bob and Eliot follow different forecasting
methods based on the demand volume and variability. To predict demand for July,
they add a safety factor to the demand from May. This safety factor would offset the
difference in sales between May and July, which implies use of a seasonality factor. It
is not mentioned how this safety factor is actually estimated.
In addition, Bob and Eliot use different inventory models for different products. For
gin and vodka, the sales substantially increased from 1971 and hence, Bob and Eliot
found it difficult to predict demand accurately. Consequently, the bottling quantity
for these two items was enough to last two months of demand. If the demand
exceeds their predictions, they add these items to the next bottling run to avoid a
10. 10
stock-out. For items with more stable demands, their bottling quantity is worth four
weeks of demand. This seems to be an inefficient policy, however, because as
discussed earlier, Blanchard can carry out more bottling runs without any loss and
thus reduce their EOQ.
Comparing Bob and Eliot’s policy with the 1969 inventory policy, we observe that
the latter does not take into account demand fluctuations and seasonality, which
makes the system prone to stock-outs and overages. As we will explain later, Bob
and Eliot overcompensate this drawback with by using a high EOQ leading to high
inventory levels.
We now propose an inventory model different from Bob and Eliot’s and the 1969
inventory policy. For simplicity, we select two products out of the five that are
scheduled for bottling. One will have a stable and low demand (Rum) and the other
will have high demand with high variability (Vodka). Since the demand for both
products is sufficiently high, we can assume a normally distributed lead-time
demand for calculation purposes. We use a base stock policy with a service level of
95 % for calculating bottling quantities.
If we use the same inventory policy for both products like the 1969 model, then
depending on the policy, some products like rum will end up with higher inventory
and consequently higher inventory costs, while other products with higher demand
variability like vodka will face stock-outs and face penalty costs. We choose to group
the different types of products into low volume and high volume demand categories
with different inventory policies that we will explain below.
3.2 Low Volume, Less Variable Demand
Items with less demand volume and variability can be better predicted using a
moving average forecast model. In this study, we use a five period moving average
to estimate the mean demand. To estimate the standard deviation of the demand,
we use a moving standard deviation of five periods. Since this standard deviation is
an estimate of the correct value, there is an additional term included due to forecast
11. 11
error. The expression used to calculate the base stock from the mean and variance
of the forecast is as follows:
𝜇 𝐷 =
1
𝑛
∑ 𝐷𝑖
𝑛
𝑖=1
= 𝑡ℎ𝑒 𝑚𝑜𝑣𝑖𝑛𝑔 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 𝑤𝑖𝑡ℎ 𝑝𝑒𝑟𝑖𝑜𝑑 𝑛 = 5
𝑤ℎ𝑒𝑟𝑒 𝐷𝑖 = 𝐷𝑒𝑚𝑎𝑛𝑑 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑖 𝑝𝑒𝑟𝑖𝑜𝑑𝑠 𝑏𝑒𝑓𝑜𝑟𝑒 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑝𝑒𝑟𝑖𝑜𝑑.
𝜎 𝐷 = √ 𝜎̂2 +
𝜎̂2
𝑛
= 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑜𝑓 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝑒𝑟𝑟𝑜𝑟
𝑤ℎ𝑒𝑟𝑒 𝜎̂ =
1
𝑛 − 1
∑(𝜇 𝐷 − 𝐷𝑖)
𝑛
𝑖=1
= 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑚𝑜𝑣𝑖𝑛𝑔 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛.
From the calculations (Appendix 2), we obtain the forecasted demand and standard
deviation of forecast error:
𝜇 𝐷 = 35.4, 𝜎 𝐷 = 23.68
On examining, the errors were found to be distributed normally (Appendix 1),
suggesting a good model. Using these values, we establish a base stock level using
this expression:
𝐵. 𝑆. 𝐿. = 𝜇 𝐷 + 𝑧 𝛼 𝜎 𝐷
The value of zα is obtained from standard normal distribution table. The cumulative
distribution of the standard normal zα value is equal to the service level. Assuming a
desired service level (α) of 95 %, we obtain a value of zα = 1.645. The base stock
level was found to be 75, which after accounting for the on hand inventory level,
gives us a bottling quantity of 30 cases. This is a significant change from the bottling
quantity (EOQ) of 50 followed by Bob and Eliot, a 40 % reduction in inventory.
12. 12
3.3 High Volume, High Variable Demand
Based on the demand trends and seasonality, a recommended approach towards
accurate forecasting would be to use a regression approach to predict trends in
highly variable demands like gin and vodka. To forecast demand for the next period,
Winters’ trend and seasonality model could be used. Since we don’t have sufficient
data to conduct a full regression, we use a simpler approach towards forecasting the
demand.
We use two demand distributions corresponding to the two seasons of high and low
demand. The low demand period is September through February while the high
demand period from March to August. We assume that the demand for the low
season will have more or less the same characteristics throughout the season, which
implies that the mean and standard deviation of the demand observed in the low
season is the forecast for any month in the low season. Same idea goes for demands
in the high season. For this study, we have used all the values from the low and high
seasons available to calculate the required parameters. For future practical
purposes, a moving average and moving standard deviation of the high and low
season demands can be used for better forecasting. From the given demand data, we
obtain the following monthly mean and standard deviation of demand for both high
and low demand seasons:
𝜇 𝐷,𝐻𝑖𝑔ℎ = 292, 𝜇 𝐷,𝐿𝑜𝑤 = 196
𝜎 𝐷,𝐻𝑖𝑔ℎ = 94.36, 𝜎 𝐷,𝐿𝑜𝑤 = 50.78
The high and low demand seasons can be observed in the following graph
representing the annual demands observed in 1971 and 1972 so far.
13. 13
For a desired service level of 95 %, we can calculate the base stock level for June to
be 815 units, which is an 18.5 % reduction from the bottling quantity actually in use
by Bob and Eliot. By the same analogy, the service rate to be achieved for the same
demand distribution by Bob and Eliot when they use their bottling quantity turns
out to be 99.91 %. As we already know that the service level and order quantity
follow a direct curvilinear relationship and at higher service levels, increasing order
quantities are required to achieve a particular amount of increase in service level.
Hence Bob and Eliot are holding much more inventory than necessary as per their
policy.
This method of forecasting achieves lower inventory levels but is still not optimal, as
it does not take reorder levels into account while calculating the bottling quantity.
Using a (Q,R) policy as discussed in the next section, we can calculate optimal
bottling quantity and the reorder point for minimizing cost.
4. Suggested (Q,R) Model
The objective of this study is to compare our inventory policy with Bob & Eliot’s and
the 1969 model. For calculating cost optimizing bottling quantities and reorder
points, we need several variables that contribute to the model, which are: Cost
14. 14
coefficients, Lead time, Demand distribution, (Q,R) values according to policy. We
shall discuss each one of them in the following sub-sections:
4.1 Cost Coefficients
The cost coefficients, which include the setup costs, holding costs and penalty costs,
should be kept the same for all to allow a better comparison. Since we have already
established that the cost coefficients suggested by us in section 2.3 are more
accurate than either of the two existing policies, we will use our cost coefficients for
comparing costs.
Penalty costs are calculated by subtracting the total unit cost of production from the
wholesale price of the item. This is equal to the lost profit before income tax due to
not selling an item when demand was present. We do not take into account any loss
of goodwill because it is difficult to estimate a particular value. As 75 % of the
annual revenue represented sales to seven Blanchard retail stores owned by other
members of the Corey family, we can safely assume this loss to be zero. The holding
costs and setup costs have been discussed earlier.
4.2 Lead Time
The lead-time distribution is different for different policies. To elaborate this point,
let’s focus on the three components that add up to form the overall lead-time. These
components are: Review period, Scheduling time and Production time. We know
that every week the computerized inventory system issues a card for each item that
has dropped below the 3 ½ week ROP stock level. This implies a review period of
one week for all policies. Scheduling time is the time between scheduling and
starting of the bottling process. Since Bob and Eliot bottle quarts every 4 weeks,
depending on the week when they observe an item below the ROP, it can be any
value between and including 1 week and 4 weeks until they start bottling the item.
We assume the scheduling lead-time for Bob and Eliot’s policy to be a discrete
uniform distribution of 1, 2, 3 and 4 weeks. We use a discrete distribution assuming
that the review is obtained at the same point every week and bottling is started at
15. 15
the same point. For the policy that we recommend and the 1969 policy, the bottling
of quarts is not limited to once every four weeks and hence we assume a scheduling
lead-time of 1 week. This is assuming that since the plant capacity is 10000 cases
per bottling run, and the demand doesn’t exceed 3000 cases for all the five items
combined, and thus we can schedule any particular size in a week. We are also
assuming here that we can perform multiple size-changeovers in a week, since it
takes only about a day to perform a size changeover and less than a week to
produce.
The production lead-time, which is the time between the start and finish of the
bottling process, is assumed to be half a week or 1 week with equal probability. We
know that this time never exceeds one week, and as there will be either one or two
sizes bottled per week at the most, the production time will be either half a week or
1 week.
Adding up all the terms, we find that for the 1969 policy and our recommended
policy, we have a lead-time of 2.5 or 3 weeks with equal probability. For Bob and
Eliot’s policy, we get a uniform discrete distribution between 2.5 to 6 weeks, with a
spacing of 0.5 weeks.
The mean and variance of this lead time (in weeks) is found out to be (Appendix 3):
𝐹𝑜𝑟 𝑡ℎ𝑒 𝑟𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑒𝑑 𝑎𝑛𝑑 1969 𝑝𝑜𝑙𝑖𝑐𝑖𝑒𝑠: 𝜇 𝐿 = 2.75, 𝜎𝐿 = 0.25
𝐹𝑜𝑟 𝐵𝑜𝑏 𝑎𝑛𝑑 𝐸𝑙𝑖𝑜𝑡𝑠′
𝑠 𝑝𝑜𝑙𝑖𝑐𝑦: 𝜇 𝐿 = 4.25, 𝜎𝐿 = 1.1456
4.3 Demand Distribution
We use our demand distribution to calculate costs for all three policies. This is done
to allow a fair comparison. For Bob and Eliot’s policy, since they bottle vodka to last
for a two month cycle, we use a demand mean and variance equivalent to two
months. The inventory carrying cost rate is also translated to two months and this is
then divided by two to get an average monthly cost. We use different demand
16. 16
distributions for rum and vodka as already discussed in the previous section. Two
different costs for vodka are obtained for the high and low demand seasons and
since the two seasons are 6 months each, we can average the costs to obtain a more
reasonable result.
From the lead times calculated in section 4.2, we can determine the mean and
standard deviation of lead-time demand as follows:
𝜇 𝐿𝑇𝐷 = 𝜇 𝐷 𝜇 𝐿
𝜎𝐿𝑇𝐷 = 𝜎 𝐷
2
𝜇 𝐿 + 𝜎𝐿
2
𝜇 𝐷
2
These values will be different depending on the type of policy used.
4.4 (Q,R) Values
The EOQ and ROP levels from the 1969 policy and the one in use by Bob and Eliot
will be used to calculate costs for the respective policies. Excel solver will be used to
calculate Q and R for the recommended policy.
4.5 Cost:
The costs obtained from these calculations will then be compared to determine the
most effective policy. The expression for cost is (Refer Appendix 4):
𝐺( 𝑄, 𝑅) = ℎ (
𝑄
2
+ 𝑅 − 𝜆𝐿) + 𝐾
𝜆
𝑄
+ 𝑝 𝑛( 𝑧)
𝜆
𝑄
This cost can be minimized using the Excel solver. For a given Q and R, it gives the
expected cost per unit time. For the current scenario, monthly costs are used as a
benchmark for comparison. All cost coefficients and demand distributions are
converted to a monthly equivalent, except for Bob and Eliot’s Vodka bottling policy,
which will have equivalent two-month values.
17. 17
These costs have been calculated using the demand forecast for the month of June. It
should be noted that since a moving average is used to forecast low demand items,
the cost would need to be recalculated every month. For the high demand items, the
expected monthly costs for high and low demand seasons have already been
calculated.
5. (Q,R) Model Implementation and Comparison
5.1 Low Volume, Low Variability Demand
As mentioned earlier, we use “Blanchard’s 80 proof Rum” as an example of the low
demand scenario. A moving average of five periods is used to forecast mean demand
for June. This and the standard deviation of forecast error are calculated using the
method and formulas discussed in Section 3.2. The demand distribution used has a
mean and standard deviation of:
𝜇 𝐷 = 35.4, 𝜎 𝐷 = 23.68
The value of the mean and standard deviation of lead-time used is according to the
policy the cost is being calculated for and its values have been calculated in section
4.2. From these values, we can calculate the mean and standard deviation of lead-
time demand using the expressions in section 4.3.
On plugging in the values of the different terms in the model and entering the
demand distribution for rum, we get the following (Q,R) values that minimize the
cost:
𝑄∗
= 81, 𝑅 = 67
5.1.1 Comparison with 1969 and Bob & Eliot’s Policy:
Inventory levels and cost savings:
We can see from the optimal bottling quantity being lower than the recommended
1969 EOQ by 41 % that Blanchard will be setting up more often. This is due to our
18. 18
improved cost structure that reduces the setup costs significantly from the 1969
model and Bob and Eliot’s policy.
The monthly costs, mentioned in section 4.5, are tabulated below:
% 𝑐𝑜𝑠𝑡 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 =
(𝐶𝑜𝑠𝑡 𝑜𝑓 𝑝𝑜𝑙𝑖𝑐𝑦 − 𝑄∗
)
𝐶𝑜𝑠𝑡 𝑜𝑓 𝑝𝑜𝑙𝑖𝑐𝑦
𝑋 100
Rum
Holding
cost
Penalty
cost
Setup
cost
Total
Savings by using
recommended policy
1969 policy 6.41 10.65 1.61 18.68 42.65%
Bob & Eliot's
1972 policy
2.80 32.60 4.43 39.82 73.10%
Recommended
policy
7.21 0.78 2.73 10.71
We can observe that Blanchard spends about 43 % more than the recommended
policy if it uses the original 1969 policy with its own EOQ and ROP values. The cost
incurred using the current policy in use by Bob and Eliot can also be reduced by
over 73 % if our recommended policy is used. This also shows how Bob and Eliot
cause almost double the expenditure by just not following the 1969 policy.
A close analysis of the recommended bottling quantities and reorder levels suggest
that there is a high variance in the demand, and a high reorder level as a result. The
bottling quantity is kept high to keep the inventory position high to avoid stock-
outs. As we see from the model, stocking out is much more expensive than holding
inventory in Blanchard. This is in line with the observation that Blanchard currently
operates way below the operating capacity, which includes a bottling capacity of
10000 cases, and storage capacity, which includes a large margin for future growth,
as the finished goods never occupied more than 50 % of the reserved space. As a
result, each unit not sold causes Blanchard to lose out on profit which is more than
19. 19
the cost incurred for holding finished goods in inventory. This is evident from the
fact that they can afford to hold a safety stock of 43 cases, which is more than the
monthly demand of 35. Comparing this with the safety stock held in the 1969 policy
and Bob and Eliot’s policy, we can see the difference. At this point we should note
that calculation of demand is based on limited data which shows a high variance,
hence the high safety stock.
5.2 High Volume, High Variability Demand
For high demand, “Blanchard’s 80 proof Vodka” will be used to compare the costs
incurred in the different inventory policies. From earlier discussion, we know that
the demand for vodka is highly variable with both seasonality and trend. A
regression approach and Winters’ trend and seasonality model can more accurately
represent the demand fluctuations and reduce the forecast errors. Due to lack of
sufficient data and for simplicity, we use the multiple distributions method to
calculate the mean and standard deviation of demand as described in Section 3.3.
Lead time distribution is calculated as per the discussion in section 4.2. The lead-
time demand is calculated using the same method as in section 5.1.
All the required values are input and the calculated optimal (Q,R) values for the high
and low seasons for each policy are found to be:
𝑄 𝐻𝑖𝑔ℎ
∗
= 309, 𝑅 𝐻𝑖𝑔ℎ = 477, 𝑄 𝐿𝑜𝑤
∗
= 245, 𝑅 𝐿𝑜𝑤 = 302
The effect of high demand variance and expensive stock-outs is clear in the high
reorder levels in the optimal policy. We will now discuss the expected costs and
savings in using this method below.
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5.2.1 Comparison with 1969 and Bob & Eliot’s Policy:
Inventory levels and cost savings:
Due to lesser setup costs in our recommended cost coefficients, the bottling quantity
is lower than both the 1969 policy by 5.2 % and 25 %for the low season and high
seasons respectively. Same values when compared with the policy used by Bob and
Eliot are 69 % and 75.5 % respectively. This implies that the number of setups in a
given amount of time will be higher compared to the other two policies.
As mentioned before, we average the costs observed in the high and low demand
seasons. For Bob and Eliot’s policy, the high and low demand season costs calculated
are for two months and are averaged for one month. The monthly costs and savings
are calculated again and tabulated below:
Vodka
Holding
cost
Penalty
cost
Setup
cost
Total
Savings by using
recommended policy
1969 policy 8.45 165.36 4.66 178.47 85.32%
Bob & Eliot's
1972 policy
6.61 442.93 1.52 451.07 94.19%
Recommended
policy
18.93 1.82 5.45 26.20
We can observe that if Blanchard follows the recommended policy, it would save
over 85 % of total costs when compared to the 1969 policy. Bob and Eliot’s
inventory policy causes Blanchard to spend over 94 % more than it should if it used
the recommended policy. Their costs are about 2.5 times higher than the costs
expected to be incurred if the 1969 policy was used. This is a staggering expense
incurred by Blanchard simply due to the use of a wrong inventory policy.
As discussed in section 5.1.2, the reorder levels are high due to high demand
variance and penalty costs. The optimal safety stock of 167 cases held for the low
demand season in the recommended policy is higher than the lead-time demand of
135 cases. The same explanation can be cited for high reorder points as in the low
demand case. The plant currently runs on a limited capacity and can store higher
inventory without much loss. On the other hand, stocking out is expensive.
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6. Conclusion
Blanchard Importing and Distribution Inc. expects to gain an overall 20 percent
return on wine merchandising if they can reduce their inventory levels. In order to
reduce inventory levels, we analyzed cost structures and corresponding EOQ and
ROP levels for the official policy (1969) as well as the policy in practice by the
employees. We found that the 1969 system was not in sync with the current
demand levels. Moreover Bob and Eliot are actually minimizing the overall setup
costs by reducing the setups as much as possible. This leads to higher bottling
quantities and therefore higher costs. So we suggested a more accurate cost
structure by eliminating some redundant quantities from the calculation of bottling
quantities and reorder levels.
We also recommend using a new inventory policy that utilizes the recent demand
trends, fluctuations and seasonality better than both these policies. We suggest a
moving average model for products with low demand and variability and a multiple
distribution based on high and low demand season for high and variable demand
products. Using a (Q,R) model, involving two products with contrasting demands,
we demonstrated the cost savings that could be achieved if this model is used. The
overall cost savings turned out to be significant, but especially large for high
demand items when compared with the 1969 inventory policy.
For scheduling bottling with other products, minimizing the size changeovers by
bottling two products with similar sizes and/or labels together will help reduce the
number of setups and thus reduce setup costs. In summary, a better cost structure,
inventory and forecasting model, and optimal bottling quantities and reorder levels
will help Blanchard free the capital tied up in their production process.
