Resolução capt 14 RONALD H. BALLOU.pdf

Prévia do material em texto

202
 CHAPTER 14 
THE LOGISTICS PLANNING PROCESS 
 
3 
The MILES module within the LOGWARE software is used to solve this problem. It 
computes distance based on the great circle distance formula using longitude and latitude. 
 
(a) The estimated road distance is 1,380 miles. 
 
(b) The estimated road distance is 830 miles. 
 
(c) Since both latitudes are in the same hemisphere, no adjustments need to be made. 
The estimated distance is 244 miles, or 244×1.61 = 393 km. 
 
(d) In this case, one point is east and the other west of the Greenwich line. Therefore, we 
need to set a sign convention. Let's set west longitudes as + and east longitudes as −. 
Thus, 2.20o E longitude is entered into MILES as −2.20 o. The estimated distance is 
250 miles, or 250×1.61 = 402.5 km. 
 
4 
Suppose that a certain linear grid coordinate system has been overlaid on a map of the 
United States. The grid numbers are calibrated in miles, and there is a road circuity 
factor of 1.21. Find the expected road distances between the following pairs of points: 
 Equation 14-1 in the text is used to approximate distances from linear coordinates. 
The K factor in the equation is set at 1.21. 
 
(a) Lansing, MI to Lubbock, TX 
 
 D = − + − =121 924 3 1 488 6 1 675 2 2 579 4 1 2902 2. ( . , . ) ( , . , . ) , miles 
 
(b) El Paso, TX to Atlanta, GA 
 
 D = − + − =121 1 696 3 624 9 2 769 3 2 318 7 1 4062 2. ( , . . ) ( , . , . ) , miles 
 
(c) Boston, MA to Los Angeles, CA 
 
Location X Coordinate Y Coordinate
a. From Lansing, MI 924.3 1675.2
To Lubbock, TX 1488.6 2579.4
b. From El Paso, TX 1696.3 2769.3
To Atlanta, GA 624.9 2318.7
c. From Boston, MA 374.7 1326.6
To Los Angeles, CA 2365.4 2763.9
d. From Seattle, WA 2668.8 1900.8
To Portland, OR 2674.2 2039.7
 
 203
 D = − − =121 374 7 2 365 4 1 326 6 2 7639 2 9712 2. ( . , . ) ( , . , . ) , miles 
 
(d) Seattle, WA to Portland, OR 
 
 D = − + − =121 2 668 8 2 674 2 1 900 8 2 039 7 1682 2. ( , . , . ) ( , . , . ) miles 
 
5 
The plot of the truck class rates is shown in Figure 14-1. The rates show a high degree of 
linearity. A linear regression was found with aid of the MULREG module in 
LOGWARE. The rate equation was determined to be: 
 
 R = 5.1745 + 0.0041×D 
 
 The standard error of the estimate SE is 0.9766. 
 The coefficient of determination r2 is 0.928. 
 
 The best single estimate of the rate at 500 miles is: 
 
 R = 5.1745 + 0.0041×500 
 = $7.23/cwt. 
 
Assuming the error around the regression line is normally distributed, a 95 percent 
confidence band would give a range for the actual rate. That is, 
 
 Y = R ± 1.96×SE 
 = 7.23 ± 1.914 
 
where 1.96 is the normal deviate for the normal distribution representing 95 percent of 
the area in a two-tailed distribution. The range of the estimate is: 
 
 $5.32/cwt. ≤ Y ≤ $9.14/cwt. 
 
 The r2 value of 0.928 indicates that a linear rate equation explains about 93 percent of 
the variation in the data with distance. Such a simple relationship seems to represent the 
rates quite well. 
 
 
 
 
 
 
 
 
 
 204
 
FIGURE 14-1 Plot of Truck Class Rates 
0
2
4
6
8
10
12
14
16
18
20
0 500 1000 1500 2000 2500 3000 3500
Distance, miles
C
la
ss
 ra
te
, $
/c
w
t.
Estimating line
 
 
6 
A plot of the average inventory level versus warehouse throughput is shown in Figure 14-
2. The multiple regression software in LOGWARE was used to test two equation forms. 
The first was of the form 
 
 I aTPb= 
 
and the other was of the form 
 
 I a bTP= + 
 
Both forms showed high r2 values, with the exponential form being slightly higher at 
0.9406. It was selected as the equation form to use. This equation was: 
 
 I TP= ×0 704 0 83. . 
 
where TP and I are both expressed in thousands of dollars. We can now estimate that for 
an annual warehouse throughput of $50,000,000, the average inventory would be: 
 
 
I = ×
=
0 704 50 000
5 593939
0 83. ,
, .
.
, or $5,593,939
 
 
 This type of relationship is very useful in network planning, especially warehouse 
location, to estimate how inventory levels will change when sales are reallocated to a 
varying number of warehouses. 
 
 
 
 
 205
FIGURE 14-2 Plot of Inventory Levels and Warehouse Throughput for California 
Fruit Growers’ Association 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
2
4
6
8
10
12
0 20 40 60 80 100
Annual warehouse thruput, $(Millions)
Av
er
ag
e 
in
ve
nt
or
y 
le
ve
l, 
$(
M
illi
on
s)
Estimating line
 
 206
USEMORE SOAP COMPANY 
Teaching Note 
 
The purpose of this case study is to provide students with the opportunity to evaluate and 
design a large-scale production-distribution network using real data and cost 
relationships. To assist in the substantial amount of computational effort in this problem, 
an interactive computer program (WARELOCA) is available in the LOGWARE 
collection of software modules. 
 
Major Issues 
The text of the case suggests a number of questions that are critical to production-
distribution network design. These reduce to three major issues, namely: 
 
 (1) Should plant capacity be added and, if so, when and where? 
 (2) How many warehouses are optimal and where should they be located? 
 (3) Should the current customer service level be retained? 
 
Although no change can be made in the network without potentially affecting other 
variables, the attempt here will be to treat these questions sequentially to converge on a 
good network design. 
 Numerous computer runs were made to provide the basic information needed in the 
analysis. The more meaningful runs are summarized in Appendix A to this note. Tables 
1 and 2 compare selected runs for both the current-year and the future-year time periods. 
This information is used throughout the analysis of the major issues. 
 
The Plant Expansion Issue 
An attempt to meet 5-year growth goals using current plant capacity will cause the 
system having a total capacity of 1,630,000 cwt. to be out of capacity in 1.7 years. That 
is, 
 
5th-year demand 1,908,606 cwt. 
Current demand −1,477,026 
Net increase 431,580 cwt. 
 
Therefore, the average annual growth rate is 431,580/5 = 86,316 cwt. So, in (1,630,000 − 
1,477,026)/86,316 = 1.7 years all available capacity will be depleted. 
 If no expansion of plant capacity occurs, then 1,908,606 − 1,630,000 = 278,606 cwt. 
will potentially be lost by the 5th year. Sales are $100 million on 1.477 million cwt. in 
volume for a product value of $67.7/cwt. With a profit margin of 20 percent, the profit 
per cwt. would be 20%×$67.7/cwt, or $20/1.477, = $13. Thus, 278,606×13 = $3.16 
million are lost in sales. The weighted profit loss over the five-year period would be: 
 
 2/5× (0) + ( 3/5)× ((0 + 3.6))/2) = $1.08m/yr. 
 
 207
 
TABLE 1 Current-Year Comparison of Network Alternatives ($000s) 
Improved Optimum Optimum Maximum
Bench- bench- number number Relaxed Relaxed oppor-
Cost type mark mark of whses of whses service (1) service (2) tunity
Production $30,762 $30,678 $30,673 $30,675 $30,678 $30,673 $30,386
Warehouse operations 1,578 1,468 1,608 1,572 1,296 1,420 1,529
Order processing 369 354 370 358 349 354 358
Inventory carrying 457 431 508 490 390 445 500
Transportation
Inbound 2,050 1,802 1,976 1,860 1,249 1,178 1,178
Outbound 6,896 6,991 6,310 6,365 7,238 6,698 6,458
Total costs $42,112 $41,725 $41,447 $41,321 $41,201 $41,043 $40,409
Customer
service:
≤ 300 mi. 93% 93% 98% 92% 75% 88% 81%
≤ 600 mi. 98% 98%100% 100% 98% 100% 94%
No. of stocking
points 22 21 31 30 19 26 40
No. of plants 4 4 4 4 4 4 6
Savings vs.
benchmark $0 $387 $665 $791 $911 $1,069 $1,703
Savings vs. improved
benchmark $0 $0 $278 $404 $524 $ 682 $1,316
Comments: Service 600 mi con- 600 mi con- Unlimited
to match straint on straint on service,
bench- current opt no. of whses, and
mark warehouses warehouses plant cap.
 
 208
TABLE 2 Future-Year Comparison of Alternatives ($000s) 
Add plant Memphis Memphis
No plant Add plant @ Memphis and opt no. and opt no.
Cost type expansion @ Memphis & Chicago of whses of whses
Production $33,965 $39,517 $39,548 $39,524 $39,522
Warehouse operations 1,496 1,842 1,847 2,028 1,976
Order processing 393 462 454 470 460
Inventory carrying 431 505 497 591 573
Transportation
Inbound 1,647 2,350 2,000 2,614 2,426
Outbound 7,230 9,030 9,036 8,117 8,222
Total costs $45,164 $53,705 $53,382 $53,342 $53,179
Customer
service:
≤ 300 mi 98% 94% 95% 98% 92%
≤ 600 mi 99% 98% 98% 100% 100%
No. of stocking
points 20 21 20 31 30
No. of plants 4 5 6 5 5
Comments: Not all High Service
demand service held at
met level benchmark
 
 209
 
Based on a simple rate of return on investment, capturing this profit potential would yield 
1.08/4 = 27 percent annually on a $4,000,000 investment for expansion. The return 
would increase to 90 percent per year with the full loss in the 5th year. The potential 
seems great enough to justify one unit of expansion (1,000,000 cwt.). Two units of 
expansion probably cannot be justified, since adequate capacity would be available from 
the first capacity unit to meet demand requirements. The only benefit would be from the 
network design improvement. The savings would be about $323,000 per year in the fifth 
year (see Table 2) comparing one additional plant with two additional plants and keeping 
the current number of warehouses. The simple return on investment using fifth-year 
savings would only amount to about 8 percent (323,000×100/4,000,000 = 8.1%). 
 The next question is: Where should the expansion take place  at an existing plant 
or at one of the two proposed locations? From a test of expanding any of the four 
existing plants or the two proposed plant locations (runs 10 through 16 in Appendix A of 
this note), it would appear that Memphis would be the lowest cost site in the 5th year 
with Chicago next at only an additional cost of $76,000 per year (compare runs 14 and 15 
in Appendix A). Adding a plant at a new location rather than expanding an existing plant 
site saves a minimum of $281,000 annually (compare runs 11 and 14 in Appendix A of 
this note), which results from placing plant capacity closer to warehouses. 
 
Selecting Warehouses 
A simple test on the number of warehouses in the network shows that transportation costs 
are dropping more rapidly than inventory related costs are increasing (see Figure 1). This 
means that 40 active warehouses will have the lowest total cost. However, some of these 
warehouses will have low throughput. In order to maintain a minimum replenishment 
frequency and shipment size, a minimum throughput needs to be met. Approximately a 
truckload every two weeks, or 10,400 cwt. of throughput per year, is the minimum 
activity needed to open a warehouse. Therefore, any warehouse showing less than this 
throughput will be eliminated from consideration. 
 Under various assumptions about plants and their capacities, demand growth, and 
service levels, 30 to 31 warehouses seem most economical with no deterioration on 
service over the benchmark network. The following table shows selected results. 
 
 
Percent
Type of Plant of demand Total No. of
run Year capacities ≤ 300 mi. cost whses
Benchmark Current Current 93 $42,112 22
Improved
benchmark Current Current 93 41,725 21
Improved Current
benchmark 5th yr. + Memphis 94 53,705 31
Current
yr. whses Current 92 41,321 30
5th yr. 5th Current
whses year + Memphis 92 53,179 30
 
 Note that this conclusion about the number of warehouses depends on the previous 
conclusion that a Memphis plant should be added by the fifth year. The number of 
warehouses should be increased from the present 22 in both the current year and the fifth 
year. 
 
 
 
 
 
 
 
 
 
 41.4
41.6
41.8
42
To
ta
l c
os
t, 
$(
00
0,
00
0s
)
94
95
96
97
98
99
100
%
 o
f d
em
an
d 
< 
30
0 
m
i.
Cost (left scale)
Service (right scale)
 
 210
 
 
 
 
 
 
 
 
FIGURE 1 Cost and Customer Service Profiles for Alternative Network Designs 
 
 More detailed economic analysis shows that if the plants are held at current 
throughput levels, a savings realized from 30 warehouses would be $41,725,000 − 
41,321,000 = $404,000 (see previous table). If current plant capacities are used and the 
Memphis plant is on-stream in year five, the savings of the added warehouse would be: 
 
 $53,705,000 − 53,179,000 = $526,000 
 
41
41.2
22 26 30 31 36 40
Number of warehouses
90
91
92
93
Practical design
 
 211
On the average, there can be savings of approximately ($404,000 + 526,000)/2 = 
$465,000 per year by increasing the number of warehouses to 30 from the current 22. 
Since these are public warehouses, little or no investment would be required to 
implement the change. 
 Although the number of warehouses remains relatively unchanged from the current 
year to the 5th year, there is some shifting among the particular warehouses in the mix. 
The 30 warehouses in the current year should be numbers: 
 
1,2,3,4,5,7,8,11,13,14,15,16,17,18,19,20,21,25,28,31,32,33,34,35,36,37,38,40,44,45 
 
providing that the loading on the current plants is allowed up to the limits of their current 
capacity. When the Memphis plant is brought on-stream by the end of the second year, 
the warehouse mix should begin to evolve to numbers: 
 
1,2,3,4,5,7,8,11,13,14,15,17,18,19,20,21,25,28,29,31,32,34,35,36,37,38,40,44,45,47 
 
 As the Memphis plant is bought on stream, the Memphis public warehouse is closed 
and the volume is shifted to the Memphis plant as a warehouse. In addition, the 
Richmond, VA warehouse is closed and the Las Vegas, NV warehouse is opened. The 
number of warehouses remains at 30. 
 Both in the base year and in the future year, the throughputs in the plants serving as 
warehouses are within acceptable limits as the following summary shows. 
 
Plant Current- Future-
as a Thruput year year
warehouse limits solution solution
Covington 450,000 cwt. 254,471 cwt. 306,478 cwt.
New York 380,000 302,043 380,523
Arlington 140,000 66,592 66,161
Long Beach 180,000 95,943 117,288
 
Customer Service 
Currently, a high proportion of demand (93 percent) is located within 300 miles of a 
stocking point. Since the service distance may be up to 600 miles and still meet the 
company's service policy, should the service level be reduced somewhat to effect a cost 
saving? For example, using the improved benchmark as the base case (run 2), 93 percent 
of the demand is within 300 miles and 97.5 percent is within 600 miles. If a 600-mile 
constraint is applied to the current network configuration (run 23), 75 percent of the 
demand is within 300 miles and 98 percent is still within 600 miles. The total costs are 
reduced from $41,725,000 to $41,201,000, or a savings of $524,000 per year. In 
addition, if the number of warehouses in the network is optimized, the costs can be 
reduced by another $158,000 per year (run 23 vs. run 22). However, $278,000 of the 
total $524,000 + 158,000 = $628,000 can be realized without a service change. This 
leaves approximately $404,000 that can be saved by a relaxed service restriction. 
 The question now becomes one of whether the higher costs associated with the more 
restrictive service levelare justified. Since there is no sales-service relationship for this 
problem, we can only estimate the worth of the service. That is, can enough sales be 
 
 212
generated to cover the higher service level? If physical distribution costs for the com-
pany are 15 percent of sales, which is probably a conservative estimate, then 1/0.15 = 
$6.70 in sales must be generated for each dollar that is added to distribution costs. 
Therefore, to cover $404,000 in cost would require 
 
 
$404, $6.
$0.
,
000 70
71 100
38 124
×
×
=
/ lb. lb./cwt.
 cwt. 
 
increase in sales. In terms of overall demand, this would be 38,124×100/1,477,026 = 2.5 
percent. 
 But not all customers would experience a higher service level. Comparing the 
demand centers for 299,818 cwt. of demand shows a reduction in warehouse to customer 
miles. Thus, moving from a minimum cost network to one with a high service level, 
where the percent of demand less than 300 miles increases from 75 percent to 93 percent, 
requires that the 38,124 cwt. increase in demand occur in the 299,818 cwt. of demand 
affected by the change. This would be a 13 percent increase. 
 The products are not highly differentiated from others in the marketplace so that 
service plays an important role in selling these products. Whether a 93 − 75 = 18 
percentage points increase in service can result in a 2.5 percent increase in overall sales 
cannot be judged by the distribution department alone. The sales department must play 
an important part in indicating whether the additional sales are possible. If they are not 
likely to be realized, there is no incentive for a network other than the minimum cost one. 
 If this information is not available from sales, the conclusion is likely to be to 
maintain the status quo as represented by the benchmark. That is, one-day service is 
most likely to guide the design. 
 
Overall Analysis and Summary 
The recommended design would involve an immediate increase in the number of 
warehouses from 22 to 30. In addition, there should be an immediate reallocation of 
demand among the existing plants. No reduction in the customer service level seems 
justified at this time. Therefore, a total cost reduction of $42,112,000 − 41,321,000 = 
$791,000 per year seems immediately achievable (run 1 vs. run 18). By the end of the 
2nd year, the Memphis plant should be brought on stream and the network should begin 
to evolve from the current design (run 24) to that for the fifth year (run 25). The addition 
of a plant is justified from the high rate of return realized from the profit potential of 
being able to continue meeting the growth in demand. 
 For the current year, a breakdown of the service and the cost changes show the 
following: 
 
 213
Current-
Bench- year Change from
Cost type mark design benchmark
Production $30,762 $30,675 $ -87 -0.3%
Whse operations 1,578 1,572 - 6 -0.4
Order processing 369 358 -11 -3.0
Inventory carrying 457 490 +33 +7.2
Transportation
Inbound 2,050 1,860 -190 -9.3
Outbound 6,896 6,365 -531 -7.7
Total costs ($000s) $42,112 $41,320 $-792 -1.9%
 
 By the fifth year, total distribution costs should be $53,179,000, or 
$53,179,000/1,908,606 = $27.86, compared with the current-year cost of 
42,112,463/1,477,026 = $28.51 per cwt. If current year costs are projected to the fifth 
year demand level, the 5th-year production/distribution costs might be 28.51×1,908,606 = 
$54,414,357, or a savings of $54,414,357 − 53,179,000 = $1,235,357 per year. Of 
course, these savings can only be realized through the addition of capacity at Memphis 
for $4,000,000. If this capacity is useful for at least 15 years, the amortization of 
$4,000,000/15 = $267,000 per year would yield a net savings of $532,000 per year. 
Overall, the design change appears to be justified. 
 
 
 214
APPENDIX A Listing of Selected Computer Runs 
Service Percent of
Run.Run No of Plant Demand con- No of Total demand within
no. description plants capacity level straint whses costs ≤ 300 ≤ 600 Comments
1 Benchmark 4 - Current - mi 22 $42,112 93% 98% Current network design
2 Improved benchmark 4 Current Current 300 21 41,725 93 98 No investment required
3 No serv constraint 4 Current Current 9000 18 40,896 71 89
4 Max opportunity 6 Current Current 9000 40 40,409 81 94 Added plants at 1m cwt
5 Future yr-imp bmk 4 Crnt+1m 5th yr. 300 21 53,777 93 98 Plant cap + 1m cwt
6 Test 27 whses 4 Current Current 300 26 41,744 95 100
7 Test 32 whses 4 Current Current 300 31 41,615 98 100
8 Test 37 whses 4 Current Current 300 36 41,501 99 100
9 Test 42 whses 4 Current Current 300 40 41,486 99 100
10 Exp Covington 4 Current 5th yr. 300 21 54,145 94 98 Covington cap + 1m cwt
11 Exp New York 4 Current 5th yr. 300 21 53,986 93 98 New York + 1m cwt
12 Exp Arlington 4 Current 5th yr. 300 21 54,709 94 98 Arlington cap + 1m cwt
13 Exp Long Beach 4 Current 5th yr. 300 21 55,251 94 98 Long Beach cap + 1m cwt
14 Add Memphis 5 Current 5th yr. 300 21 53,705 94 98 Add Memphis at 1m cwt
15 Add Chicago 5 Current 5th yr. 300 20 53,781 94 98 Add Chicago at 1m cwt
16 Add Mem & Chi 6 Current 5th yr. 300 20 53,382 95 98 Add Chi & Mem at 1m cwt
ea
17 No plant expansion 4 Current 5th yr. 300 20 45,164 98 100 Only 85.4% of demnd
served
18 Optimum whses 4 Current Current 300 31 41,447 98 100 Plants at current
capacity
19 Optimum whses 4 See cmt Current 300 30 41,563 97 100 Plants at current thruput
20 Optimum whses 5 Current 5th yr. 300 31 53,342 98 100 Memphis at 1m cwt
21 Test cust service 4 Current Current 600 31 40,996 80 100 Whses at opt no = 31
22 Test cust service 4 Current Current 600 26 41,043 88 100 Whses from opt no = 31
23 Test cust service 4 Current Current 600 19 41,201 75 98 Whses from current 22
24 Optimum whses 4 Current Current 375 30 41,321 92 100 Service level at bmk
25 Optimum whses 5 See cmt 5th yr. 375 30 53,179 92 100 Serv at bmk/Mem @ 1m cwt
 
 215
ESSEN USA 
Teaching Note 
 
Strategy 
Essen USA is concerned with entire supply channel performance. The supply channel 
consists of four echelons ranging from factory to customers. The purpose of this case 
study is for the student to manipulate the supply channel variables using a channel 
simulator in order to improve individual member and system-wide performance. The 
channel variables include forecasting methods, inventory policies, transportation services, 
production lot sizes, order processing costs, and stock availability levels. Students should 
seek to optimize channel performance, although it is not expected that the optimum 
actually can be found or verified. However, improving performance over existing levels 
is achievable. 
 The SCSIM module of LOGWARE is used to simulate the demand and product flows 
throughout the multi-echelon supply chain. SCSIM is an ordinary Monte Carlo day-to-
day type of simulator. Using a simulator for performance improvement requires thinking 
of it in terms of as an experimental methodology. That is, a single run of the simulator is 
a particular event sequence generated from random numbers. Changing the seed number 
in the simulator causes a different set of random numbers to be generated and possibly 
another outcome from the same input data. A simulation run with a specified seed 
number should be viewed as a single statistical observation and multiple outcomes from 
various seed numbers should be treated as a statistical sample and analyzed accordingly, 
i.e., comparing means and standard deviations. 
 Each simulation is run for a period of 11 years with results taken from years 2 
through 11. The first year is not used since it can show unstable results due to startup 
conditions. The results appear to reach steady state by the second year, and the results for 
the 10 years thereafter are averaged to give a reasonable representation of channelperformance for a given run. The database used to represent the current performance of 
the channel, as derived from the case study, is summarized in the Appendix A of this note 
and a typical run report is shown in Appendix B. 
 This case provides students with the opportunity to observe the operation of a multi-
echelon supply channel and to assess the impact of changing key operating variables on 
individual members as well as on channel-wide performance. The effect on cost and 
customer service as well as sales, inventory, and back order levels of demand patterns, 
demand forecasting methods, inventory control methods, transportation performance, 
production lot sizing, order processing procedures, and item fill rates can be observed in 
both graphical and report forms. Most importantly, students can see the effects of supply 
chain decisions rather than project the results statistically. 
 
Questions 
 
1. What can you say about the logistics performance throughout the supply channel for 
Essen and its customers? 
 
 
 
 General observations 
 It is recognized that Essen must deal with demand that has significant seasonal peaks 
at gift-giving times of the year as shown in Figure 1. Compared with a smooth demand 
pattern, this can cause increasing demand variability upstream from the customers, as 
illustrated in Figure 2. This “bull whip” effect is partly a result of the demand for an 
upstream member being derived from the order size and pattern of its immediate 
downstream channel member. Forecast accuracy, lead-time uncertainty, and inventory 
control method also affect demand variability and the resulting cost of that variability. 
 
 
 
 
 
 
 
Figure 1 Typical Demand Pattern for Essen Over the Period of One Year 
Retailer
Distri
-butor
Ware-
house Factory
Retailer
FactoryRetailer
warehouse
Essen
warehouse
Retailer
Distri
-butor
Ware-
house Factory
Retailer
FactoryRetailer
warehouse
Essen
warehouse
216
 
Figure 2 Increasing Demand Variability of Upstream Channel Members for a 
Four-Year Period 
 
 
 
 Benchmark 
 Running the simulator (SCSIM) with a seed number of 123456 and simulated period 
of 11 years with results taken from the last 10 years, the channel generates average 
annual sales of $109.5 million for a net average annual system profit contribution of 
$24.4 million, as shown in Table 1. The question arises as to whether channel 
performance can be improved and profits increased. At least two observations can be 
made that suggest there is room for improvement. First, the inventory levels for both the 
retailer’s warehouse and Essen’s warehouse are quite high compared with the Retailer 
(see Figure 3). It is possible that Retailer inventories are too low. However, the 
inventory turnover ratio is about seven for the Essen’s warehouse (see Table 1). This is 
not particularly high for a food product that might have a turnover at least in the range of 
10 to 12. The turnover for the retailer’s warehouse appears more in line with industry 
norms of about 13 (see Table 1). 
 
 
 
s
T
s
 
w
in
s
r
 
e
d
RetailerRetailer
Retailer
warehouse Essen
warehouse
RetailerRetailer
Retailer
warehouse Essen
warehouse
 
 
217
Second, the backorders at the Retailer level do not seem to recover well from the 
easonal spike in demand. Correspondingly, the Retailer inventory turnover is 81 (see 
able 1), which is quite high. The low percentage of demand filled on request (<50%) 
uggests that inadequate inventory is being maintained to meet reasonable fill rates. 
Third, customer service levels are also low for the retailer’s warehouse and Essen’s 
arehouse. Backorder occurrences are high for both channel members. Although 
ventory levels are adequate most of the time, seasonal demand rippling through the 
upply chain causes a significant number of back orders before inventory can be 
eplenished. 
The observation is that there is an opportunity to improve channel performance, 
specially in terms of customer service. A major concern is how to mange the seasonal 
emand pattern that is causing the cyclical behavior throughout the echelons of the 
Figure 3 Inventory Levels for Four Years Using Benchmark Data
 
 218
channel. Current performance of the channel members is summarized in Table 1 for four 
simulation runs using different seed numbers. 
 
Channel member Run 1 Run 2 Run 3 Run 4 Average 
Essen’s factory 
 Total cost $73,105,904 $72,967,088 $72,616,192 $72,477,376 $72,791,640 
 Units produced 37,918 37,846 37,664 37,592 37,755 
 Cost per unit $1,928 $1,928 $1,928 $1,928 $1,928 
Essen’s 
warehouse 
 
 TO ratio 6.52 6.59 6.61 6.58 6.58 
 Fill rate <50% <50% <50% <50% <50% 
 Cost $5,578,291 $5,549,202 $5,521,236 $5,540,447 $5,547,294 
 Cost per unit $147.02 $146.50 $146.32 $146.65 $146.62 
Retailer’s 
warehouse 
 
 TO ratio 12.95 13.01 12.96 12.89 12.95 
 Fill rate <50% <50% <50% <50% <50% 
 Cost $3,873,236 $3,895,406 $3,853,890 $3,912,699 $3,883,808 
 Cost per unit $101.94 $102.15 $102.14 $103.07 $102.33 
Retailer 
 TO ratio 81.02 80.38 80.45 80.60 80.61 
 Fill rate <50% 53.02% 54.09% <50% <50% 
 Units sold 38,017 37,983 37,774 37,804 37,895 
 Cost $2,884,527 $2,768,697 $2,939,464 $2,981,607 $2,893,574 
 Cost per unit $76.19 $72.89 $77.82 $78.87 $76.44 
System 
 Profit $24,426,593 $24,591,053 $24,235,498 $24,340,563 $24,398,426 
 Profit as % of sales 22.23% 22.40% 22.20% 22.28% 22.28% 
Seed Number 123456 444444 555555 666666 
 
 
2. What steps would you suggest taking to improve logistics performance throughout the 
channel? Do any of the changes involve Essen? If so, does the company directly 
realize any cost and/or operating performance improvements? 
 
A number of actions can be taken to lower costs and improve customer service. 
Improving the forecast, shortening the lead times, changing the inventory control policy, 
and changing production lot sizes are all variables that can be altered for possible 
performance improvement. The interactions among these variables and the large number 
of variable combinations preclude finding the optimal set. However, they can be 
explored in a systematic way to find improvement. The primary focus of this analysis 
will be to increase the fill rates at the risk of increasing costs. Ultimately, revenues, 
through improved customer service, may be preserved or increased to more than 
compensate for reduced profits. 
 
Table 1 Average Annual Performance of Channel Members and the System at 
Benchmark 
 
 219
 Retailer Level 
 Start with the retailer because of the proximity to the customer. Fill rates need to be 
improved, probably in the 95-99% range as specified in the database. Inventory turns can 
be guided by the industry average of 12 turns per year. Where the two cannot be jointly 
met, service will prevail. 
 Clearly, putting additional inventory at the retail point will improve customer service. 
Using the company’s current inventory policy of stocking to demand, the target level can 
be raised without changing the review time. Exploring different target levels shows 14 
days to offer about 35 turns and a 99+% fill rate. Because of the high cost of a back 
order, total costs at the retail level drop significantly. 
 Altering the forecasting method and the settings associated with the method yield 
little opportunity for improvement. Using an exponential smoothing model with a high 
smoothing constant to better follow the seasonal changes in demand results in increased 
costs. Lowering the smoothing constant to 0.1 did not offer improvement either. 
Altering the number of periods in the moving average model did not improve costs and 
only degraded performance. Shortening the review timein the stock-to-demand reorder 
policy did have a positive effect on fill rate, but resulted in high costs and lower 
inventory turns. The tradeoff did not seem beneficial, given the fill rate and turnover 
targets. 
 
 Retail Warehouse Level 
 Determining an improved policy at this level is difficult because a 95% fill rate and 
10 to 12 inventory turns is an illusive goal. Using service as the primary target, a stock-
to-demand control policy is used with a review period of 7 days and a target of 25 days of 
inventory. The forecasting method is moving average with a period of 7 days. The 
performance achieved at this channel level is about nine inventory turns per year and a 
97% fill rate. 
 
 Essen’s Warehouse Level 
 The performance at Essen’s warehouse level seems to mimic that at the retail 
warehouse level except that there is more demand variability. Again, an inventory 
turnover ratio in the target range cannot be achieved while maintaining a high fill rate 
level. Trying to achieve high service levels with high levels of inventory is difficult, 
probably due to the extensive demand variability that filters back to this member of the 
channel. Multiple simulation runs show that a high fill rate cannot consistently be 
achieved even when on the average inventory levels are high. However, average 
performance shows an 82% fill rate and 1.5 inventory turns per year based on a 7-day 
moving average forecasting model and a stock-to-demand inventory control policy with a 
review time of 7 days and an inventory target of 25 days. 
 
 Essen’s Factory 
 The concern with the factory level in the channel is whether product should be 
manufactured in a larger lot size, but with slightly higher production time variability. 
The reduced costs seem to out weigh the negative effects of increased variability. 
Producing in the larger lot size is favored. 
 
 
 220
 Overall 
 Using the objective of improving customer service, it is not surprising that supply 
channel costs increase as shown from the reduced profit in Table 2 compared with Table 
2. The average fill rate has increased for all members of the channel, but the cost effects 
are spread disproportionately among the members. Even with a higher fill rate, the 
retailer benefits from a substantial reduction in the cost per unit sold. On the other hand, 
the cost for handling a unit of the product at Essen’s warehouse is substantially increased. 
Essen should take advantage of the cost reduction from producing in the larger batch size, 
but this does not offset the higher cost at the company’s warehouse. As an upstream 
channel member, Essen undoubtedly suffers from variability in demand, which it cannot 
entirely control. 
 The retailer benefits from the action to increase fill rates across the channel. 
However, Essen is put at a disadvantage and may take a counter action to improve its cost 
position. Essen may simply lower its inventory level by reducing the reorder target 
quantity from 25 to 10 days. This reduces Essen’s per-unit warehouse cost, but it also 
increases the costs for the retailer. The reduced inventory level at the Essen warehouse 
causes lower fill rates for the downstream retailer. Unless the retailer can find an 
incentive to reward Essen for its good service, it will be difficult for Essen to provide the 
level of service that the retailer would like and that is economically beneficial to Essen. 
 
Channel member Run 1 Run 2 Run 3 Run 4 Average 
Essen’s factory 
 Total cost $68,638,738 $74,761,258 $78,418,188 $75,400,666 $74,304,713 
 Units produced 35,950 39,286 41,268 39,622 39,032 
 Cost per unit $1,909 $1,903 $1,900 $1,903 $1,904 
Essen’s 
warehouse 
 
 TO ratio 1.47 1.42 1.51 1.46 1.47 
 Fill rate 90.65% 100% 63.16% 72.92% 81.68% 
 Cost $12,060,444 $12,449,170 $11,895,743 $12,178,581 $12,145,984 
 Cost per unit $317.51 $326.28 $311.87 $319.61 $318.82 
Retailer’s 
warehouse 
 
 TO ratio 9.24 9.02 9.14 9.28 9.17 
 Fill rate 96.64% 96.29% 97.60% 98.09% 97.16% 
 Cost $4,018,143 $4,080,160 $4,043,973 $3,992,791 $4,033,767 
 Cost per unit $105.70 $107.09 $106.55 $105.51 $106.21 
Retailer 
 TO ratio 35.44 35.36 34.99 35.14 35.23 
 Fill rate 99.52% 99.53% 99.70% 99.40% 99.54% 
 Units sold 38,017 37,936 37,979 37,730 37,916 
 Cost $727,378 $717,363 $709,772 $748,198 $725,677 
 Cost per unit $19.13 $18.91 $18.69 $19.75 $19.12 
System 
 Profit $24,423,849 $17,627,666 $14,690,765 $17,184,464 $18,481,686 
 Profit as % of sales 22.23% 16.08% 13.38% 15.69% 16.85% 
Seed Number 123456 111111 222222 333333 
Table 2 Average Annual Performance of Channel Members and the System as 
Revised 
 
 221
 
 
3. Would shipping by airfreight from Germany be a benefit to channel performance? To 
Essen? 
 
No. Selling candies to end customers at $2,890 per thousand lb. using airfreight shipping 
results in obvious loss to Essen. The cost of shipping by air is $1,833 per thousand lb., 
plus $1,000 material cost and $850 production cost results in a total cost much higher 
than selling price. There is no point to using airfreight. Running the simulation with the 
higher freight rate but lower variability confirms that channel profits would be negative. 
 
4. Is there a benefit to producing in the larger 20,000-pound batch size? 
 
Yes. This was tested in question 2. From Tables 1 and 2, it can be seen that production 
costs drop from $1,928 per unit to $1,904 per unit. The overall channel cost reduction 
overshadows the negative effects of greater length and variability in production time. 
 
 
Appendix A Simulation Database for Essen USA Under Current Conditions 
 
Title: ESSEN USA 
 
Initialization 
123456 Seed value 
11 Length of simulation, years 
2890 Annual price, $/unit 
 
Customer demand pattern 
Generate daily demand 
100 Average daily demand, units 
15 Standard deviation of daily demand, units 
1 Annual demand growth increment, % 
Monthly seasonal indices 
Month Index Month Index Month Index Month Index 
1 0.25 4 0.75 7 0.75 10 0.75 
2 1.25 5 0.75 8 0.75 11 1.50 
3 1.25 6 0.75 9 0.75 12 2.50 
 
Retailer/Level 1 
Product item data 
2220 Item value in inventory, $/unit 
1 Customer order filling cost, $/unit 
35 Purchase order processing cost, $/order 
25 Inventory carrying cost, %/year 
1 Average customer order fill time, days 
0 Customer order fill time standard deviation, days 
 
 222
98 In-stock probability, % 
670 Back order cost, $/unit 
Forecasting method 
 Moving average 
 7 Number of periods 
Reorder policy 
 Stock-to-demand control method 
 10 Target days of inventory 
 7 Review time in days 
 
Distributor/Level 2 
Product item data 
2220 Item value in inventory, $/unit 
20 Retailer order filling cost, $/unit 
75 Purchase order processing cost, $/order 
25 Inventory carrying cost, %/year 
2 Average retailer order fill time, days 
0.2 Retailer order fill time standard deviation, days 
95 In-stock probability, % 
100 Back order cost, $/unit 
Forecasting method 
 Moving average 
 30 Number of periods 
Reorder policy 
 Stock-to-demand control method 
 45 Target days of inventory 
 30 Review time in days 
 
Warehouse/Level 3 
Product item data 
1710 Item value in inventory, $/unit 
15 Distributor order filling cost, $/unit 
75 Purchase order processing cost, $/order 
20 Inventory carrying cost, %/year 
3 Average distributor order filling time, days 
0.3 Distributor order fill time, days 
95 In-stock probability, % 
25 Back order cost, $/unit 
Forecasting method 
 Moving average 
 360 Number of periods 
Reorder policy 
 Stock-to-demand control method 
90 Target days of inventory 
30 Review time in days 
 
 
 223
Factory/Source 
Product item data
850 Productioncost, $/unit 
10 Minimum production lot size, units 
10 Warehouse order filling cost, $/unit 
8 Average production time, days 
2 Production time standard deviation, days 
1000 Purchase cost, $/unit 
 
Transportation 
Transport between Distributor and Retailer
25 Transport cost, $/unit 
1 Average time in-transit, days 
0 Transit time standard deviation, days 
Transport between Warehouse and Distributor
70 Transport cost, $/unit 
5 Average time in-transit, days 
1 Transit time standard deviation, days 
Transport between Factory and Warehouse
78 Transport cost, $/unit 
9 Average time in-transit, days 
3 Transit time standard deviation, days 
 
 
Appendix B Benchmark Simulation Results with Seed Number 123456 and 
Simulation Length of 11 Years 
 
SUPPLY CHANNEL REPORT FOR SIMULATED YEARS 2 TO 11 
Yearly Simulated
average period FINANCIAL PERFORMANCE
$109,868,552 $1,098,685,520 Revenue
37,918,000 379,180,000 Cost of purchased goods
71,950,552 719,505,520 Gross margin
32,230,300 322,303,000 Production cost
Transportation costs:
949,025 9,490,250 Distributor to retailer
2,656,010 26,560,100 Warehouse to distributor
2,957,604 29,576,040 Factory to warehouse
Sales order handling cost for:
38,017 380,168 Customer orders
759,890 7,598,900 Retailer orders
569,145 5,691,450 Distributor orders
Order processing cost for:
1,638 16,380 Orders to distributor
683 6,825 Orders to warehouses
675 6,750 Orders to factory
Inventory costs
 
 224
260,414 2,604,145 Retailer
1,627,909 16,279,092 Distributor
1,988,984 19,889,837 Warehouse
Back order costs
2,584,458 25,844,580 Retailer
535,730 5,357,300 Distributor
363,478 3,634,775 Warehouse
$24,426,593 $244,265,928 Net profit contribution
Appendix C Simulation Database for Essen USA as Revised for Service 
Improvement 
 
Title: ESSEN USA 
 
Initialization 
123456 Seed value 
11 Length of simulation, years 
2890 Annual price, $/unit 
 
Customer demand pattern 
Generate daily demand 
100 Average daily demand, units 
15 Standard deviation of daily demand, units 
1 Annual demand growth increment, % 
Monthly seasonal indices 
Month Index Month Index Month Index Month Index 
1 0.25 4 0.75 7 0.75 10 0.75 
2 1.25 5 0.75 8 0.75 11 1.50 
3 1.25 6 0.75 9 0.75 12 2.50 
 
Retailer/Level 1 
Product item data 
2220 Item value in inventory, $/unit 
1 Customer order filling cost, $/unit 
35 Purchase order processing cost, $/order 
25 Inventory carrying cost, %/year 
1 Average customer order fill time, days 
0 Customer order fill time standard deviation, days 
98 In-stock probability, % 
670 Back order cost, $/unit 
Forecasting method 
 Moving average 
 7 Number of periods 
Reorder policy 
 Stock-to-demand control method 
 14 Target days of inventory 
 7 Review time in days 
 
 225
 
Distributor/Level 2 
Product item data 
2220 Item value in inventory, $/unit 
20 Retailer order filling cost, $/unit 
75 Purchase order processing cost, $/order 
25 Inventory carrying cost, %/year 
2 Average retailer order fill time, days 
0.2 Retailer order fill time standard deviation, days 
95 In-stock probability, % 
100 Back order cost, $/unit 
Forecasting method 
 Moving average 
 7 Number of periods 
Reorder policy 
 Stock-to-demand control method 
 35 Target days of inventory 
 7 Review time in days 
 
Warehouse/Level 3 
Product item data 
1710 Item value in inventory, $/unit 
15 Distributor order filling cost, $/unit 
75 Purchase order processing cost, $/order 
20 Inventory carrying cost, %/year 
3 Average distributor order filling time, days 
0.3 Distributor order fill time standard deviation, days 
95 In-stock probability, % 
25 Back order cost, $/unit 
Forecasting method 
 Moving average 
 7 Number of periods 
Reorder policy 
 Stock-to-demand control method 
25 Target days of inventory 
7 Review time in days 
 
Factory/Source 
Product item data
825 Production cost, $/unit 
20 Minimum production lot size, units 
10 Warehouse order filling cost, $/unit 
10 Average production time, days 
2.1 Production time standard deviation, days 
1000 Purchase cost, $/unit 
 
 
 226
Transportation 
Transport between Distributor and Retailer
25 Transport cost, $/unit 
1 Average time in-transit, days 
0 Transit time standard deviation, days 
Transport between Warehouse and Distributor
70 Transport cost, $/unit 
5 Average time in-transit, days 
1 Transit time standard deviation, days 
Transport between Factory and Warehouse
78 Transport cost, $/unit 
9 Average time in-transit, days 
3 Transit time standard deviation, days 
 
 
	FIGURE 14-1null Plot of Truck Class Rates
	
	Major Issues
	The Plant Expansion Issue
	
	
	
	Customer
	Cost type expansion @ Memphis & Chicago of whses of whses
	Selecting Warehouses
	
	
	Percent
	
	FIGURE 1nullCost and Customer Service Profiles for Alternative Network Designs
	Plant Current- Future-
	Customer Service
	Overall Analysis and Summary
	APPENDIX A Listing of Selected Computer Runs
	
	
	
	Questions
	General observations
	Benchmark
	Essen’s factory
	Units produced
	Cost per unit
	Essen’s warehouse
	Cost per unit
	Retailer’s warehouse
	Cost per unit
	Retailer
	Cost per unit
	System
	Retailer Level
	Retail Warehouse Level
	Essen’s Warehouse Level
	Essen’s Factory
	Overall
	Essen’s factory
	Units produced
	Cost per unit
	Essen’s warehouse
	Cost per unit
	Retailer’s warehouse
	Cost per unit
	Retailer
	Cost per unit
	System
	Initialization
	Customer demand pattern
	Generate daily demand
	Monthly seasonal indices
	Retailer/Level 1
	Product item data
	Forecasting method
	Reorder policy
	Distributor/Level 2
	Product item data
	Forecasting method
	Reorder policy
	Warehouse/Level 3
	Product item data
	Forecasting method
	Reorder policy
	Factory/Source
	Product item data
	Transportation
	Transport between Distributor and Retailer
	Transport between Warehouse and Distributor
	Transport between Factory and Warehouse
	Appendix BnullBenchmark Simulation Results with Seed Number 123456 and Simulation Length of 11 Years
	Initialization
	Customer demand pattern
	Generate daily demand
	Monthly seasonal indices
	Retailer/Level 1
	Product item data
	Forecasting method
	Reorder policy
	Distributor/Level 2
	Product item data
	Forecasting method
	Reorder policy
	Warehouse/Level 3
	Product item data
	Forecasting method
	Reorder policy
	Factory/Source
	Product item data
	Transportation
	Transport between Distributor and Retailer
	Transport between Warehouse and Distributor
	Transport between Factory and Warehouse