Discussion 2
12
Managing Uncertainty in a Supply Chain: Safety Inventory
PowerPoint presentation to accompany
Chopra and Meindl Supply Chain Management, 5e
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Learning Objectives
Understand the role of safety inventory in a supply chain
Identify factors that influence the required level of safety inventory
Describe different measures of product availability
Utilize managerial levers available to lower safety inventory and improve product availability
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The Role of Safety Inventory
Safety inventory is carried to satisfy demand that exceeds the amount forecasted
Raising the level of safety inventory increases product availability and thus the margin captured from customer purchases
Raising the level of safety inventory increases inventory holding costs
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The Role of Safety Inventory
Three key questions
What is the appropriate level of product availability?
How much safety inventory is needed for the desired level of product availability?
What actions can be taken to improve product availability while reducing safety inventory?
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The Role of Safety Inventory
Figure 12-1
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Determining the Appropriate Level
Determined by two factors
The uncertainty of both demand and supply
The desired level of product availability
Measuring Demand Uncertainty
D = Average demand per period
sD = Standard deviation of demand (forecast error) per period
Lead time (L) is the gap between when an order is placed and when it is received
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Evaluating Demand Distribution Over L Periods
The coefficient of variation
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Measuring Product Availability
Product fill rate (fr)
Fraction of product demand satisfied from product in inventory
Order fill rate
Fraction of orders filled from available inventory
Cycle service level (CSL)
Fraction of replenishment cycles that end with all customer demand being met
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Replenishment Policies
Continuous review
Inventory is continuously tracked
Order for a lot size Q is placed when the inventory declines to the reorder point (ROP)
Periodic review
Inventory status is checked at regular periodic intervals
Order is placed to raise the inventory level to a specified threshold
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Evaluating Cycle Service Level and Fill Rate
Evaluating Safety Inventory Given a Replenishment Policy
Expected demand during lead time = DL
Safety inventory, ss = ROP – DL
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Evaluating Cycle Service Level and Fill Rate
Average demand per week, D = 2,500
Standard deviation of weekly demand, sD = 500
Average lead time for replenishment, L = 2 weeks
Reorder point, ROP = 6,000
Average lot size, Q = 10,000
Safety inventory, ss = ROP – DL = 6,000 – 5,000 = 1,000
Cycle inventory = Q/2 = 10,0002 = 5,000
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Evaluating Cycle Service Level and Fill Rate
Average inventory = cycle inventory + safety inventory
= 5,000 + 1,000 = 6,000
Average flow time = average inventory/throughput
= 6,000/2,500 = 2.4 weeks
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Evaluating Cycle Service Level and Fill Rate
Evaluating Cycle Service Level Given a Replenishment Policy
CSL = Prob(ddlt of L weeks ≤ ROP)
CSL = F(ROP, DL, sL) = NORMDIST(ROP, DL, sL, 1)
(ddlt = demand during lead time)
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Evaluating Cycle Service Level and Fill Rate
Q = 10,000, ROP = 6,000, L = 2 weeks
D = 2,500/week, sD = 500
CSL = F(ROP,DL,sL) = NORMDIST(ROP,DL,sL,1)
= NORMDIST(6,000,5,000,707,1) = 0.92
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Evaluating Fill Rate Given a Replenishment Policy
Expected shortage per replenishment cycle (ESC) is the average units of demand that are not satisfied from inventory in stock per replenishment cycle
Product fill rate
fr = 1 – ESC/Q = (Q – ESC)/Q
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Evaluating Fill Rate Given a Replenishment Policy
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Evaluating Fill Rate Given a Replenishment Policy
Lot size, Q = 10,000
Average demand during lead time, DL = 5,000
Standard deviation of demand during lead time, sL = 707
Safety inventory, ss = ROP – DL = 6,000 – 5,000 = 1,000
fr = (Q – ESC)/Q = 110,000 – 252/10,000 = 0.9975
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Evaluating Fill Rate Given a Replenishment Policy
Figure 12-2
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Evaluating Safety Inventory Given Desired Cycle Service Level
Desired cycle service level = CSL
Mean demand during lead time = DL
Standard deviation of demand during lead time = σL
Probability(demand during lead time ≤ DL + ss) = CSL
Identify safety inventory so that
F(DL + ss, DL, sL) = CSL
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Evaluating Safety Inventory Given Desired Cycle Service Level
or
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Evaluating Safety Inventory Given Desired Cycle Service Level
Q = 10,000, CSL = 0.9, L = 2 weeks
D = 2,500/week, sD = 500
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Evaluating Safety Inventory Given Desired Fill Rate
Expected shortage per replenishment cycle is
ESC = (1 – fr)Q
No equation for ss
Try values or use GOALSEEK in Excel
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Evaluating Safety Inventory Given Desired Fill Rate
Desired fill rate, fr = 0.975
Lot size, Q = 10,000 boxes
Standard deviation of ddlt, sL = 707
ESC = (1 – fr)Q = (1 – 0.975)10,000 = 250
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Evaluating Safety Inventory Given Desired Fill Rate
Use GOALSEEK to find safety inventory ss = 67 boxes
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Evaluating Safety Inventory Given Desired Fill Rate
Figure 12-3
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Impact of Desired Product Availability and Uncertainty
As desired product availability goes up the required safety inventory increases
| Fill Rate | Safety Inventory |
| 97.5% | 67 |
| 98.0% | 183 |
| 98.5% | 321 |
| 99.0% | 499 |
| 99.5% | 767 |
Table 12-1
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Impact of Desired Product Availability and Uncertainty
Goal is to reduce the level of safety inventory required in a way that does not adversely affect product availability
Reduce the supplier lead time L
Reduce the underlying uncertainty of demand (represented by sD)
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Benefits of Reducing Lead Time
D = 2,500/week, sD = 800, CSL = 0.95
If lead time is reduced to one week
If standard deviation is reduced to 400
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Impact of Supply Uncertainty on Safety Inventory
We incorporate supply uncertainty by assuming that lead time is uncertain
D: Average demand per period
sD: Standard deviation of demand per period
L: Average lead time for replenishment
sL: Standard deviation of lead time
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Impact of Lead Time Uncertainty on Safety Inventory
Average demand per period, D = 2,500
Standard deviation of demand per period, sD = 500
Average lead time for replenishment, L = 7 days
Standard deviation of lead time, sL = 7 days
Mean ddlt, DL = DL = 2,500 x 7 = 17,500
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Impact of Lead Time Uncertainty on Safety Inventory
Required safety inventory
| sL | sL | ss (units) | ss (days) |
| 6 | 15,058 | 19,298 | 7.72 |
| 5 | 12,570 | 16,109 | 6.44 |
| 4 | 10,087 | 12,927 | 5.17 |
| 3 | 7,616 | 9,760 | 3.90 |
| 2 | 5,172 | 6,628 | 2.65 |
| 1 | 2,828 | 3,625 | 1.45 |
| 0 | 1,323 | 1,695 | 0.68 |
Table 12-2
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Impact of Aggregation on Safety Inventory
How does aggregation affect forecast accuracy and safety inventories
Di: Mean weekly demand in region i, i = 1,…, k
si: Standard deviation of weekly demand in region i, i = 1,…, k
rij: Correlation of weekly demand for regions i, j, 1 ≤ i ≠ j ≤ k
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Impact of Aggregation on Safety Inventory
Total safety inventory
in decentralized option
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Impact of Aggregation on Safety Inventory
Require safety inventory on aggregation
Holding-cost savings on aggregation per unit sold
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Impact of Aggregation on Safety Inventory
The safety inventory savings on aggregation increase with the desired cycle service level CSL
The safety inventory savings on aggregation increase with the replenishment lead time L
The safety inventory savings on aggregation increase with the holding cost H
The safety inventory savings on aggregation increase with the coefficient of variation of demand
The safety inventory savings on aggregation decrease as the correlation coefficients increase
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Impact of Aggregation on Safety Inventory
The Square-Root Law
Figure 12-4
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Impact of Correlation on Value of Aggregation
Standard deviation of weekly demand, sD = 5;
Replenishment, L = 2 weeks; Decentralized CSL = 0.9
Total required safety inventory,
Aggregate r = 0
Standard deviation of weekly demand at central outlet,
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Impact of Correlation on Value of Aggregation
| r | Disaggregate Safety Inventory | Aggregate Safety Inventory |
| 0 | 36.24 | 18.12 |
| 0.2 | 36.24 | 22.92 |
| 0.4 | 36.24 | 26.88 |
| 0.6 | 36.24 | 30.32 |
| 0.8 | 36.24 | 33.41 |
| 1.0 | 36.24 | 36.24 |
Table 12-3
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Impact of Correlation on Value of Aggregation
Two possible disadvantages to aggregation
Increase in response time to customer order
Increase in transportation cost to customer
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Trade-offs of Physical Centralization
Use four regional or one national distribution center
D = 1,000/week, sD = 300, L = 4 weeks, CSL = 0.95
Total required safety inventory,
Four regional centers
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Trade-offs of Physical Centralization
One national distribution center, r = 0
Standard deviation of weekly demand,
Decrease in holding costs = (3,948 – 1,974) $1,000 x 0.2
= $394,765
Decrease in facility costs = $150,000
Increase in transportation = 52 x 1,000 x (13 – 10)
= $624,000
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Information Centralization
Online systems that allow customers or stores to locate stock
Improves product availability without adding to inventories
Reduces the amount of safety inventory
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Specialization
Inventory is carried at multiple locations
Should all products should be stocked at every location?
Required level of safety inventory
Affected by coefficient of variation of demand
Low demand, slow-moving items, typically have a high coefficient of variation
High demand, fast-moving items, typically have a low coefficient of variation
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Impact of Coefficient of Variation on Value of Aggregation
| Motors | Cleaner | |
| Inventory is stocked in each store | ||
| Mean weekly demand per store | 20 | 1,000 |
| Standard deviation | 40 | 100 |
| Coefficient of variation | 2.0 | 0.1 |
| Safety inventory per store | 132 | 329 |
| Total safety inventory | 211,200 | 526,400 |
| Value of safety inventory | $105,600,000 | $15,792,000 |
| Inventory is aggregated at the DC | ||
| Mean weekly aggregate demand | 32,000 | 1,600,000 |
| Standard deviation of aggregate demand | 1,600 | 4,000 |
| Coefficient of variation | 0.05 | 0.0025 |
| Aggregate safety inventory | 5,264 | 13,159 |
| Value of safety inventory | $2,632,000 | $394,770 |
| Savings | ||
| Total inventory saving on aggregation | $102,968,000 | $15,397,230 |
| Total holding cost saving on aggregation | $25,742,000 | $3,849,308 |
| Holding cost saving per unit sold | $15.47 | $0.046 |
| Savings as a percentage of product cost | 3.09% | 0.15% |
Table 12-4
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Product Substitution
The use of one product to satisfy demand for a different product
Manufacturer-driven substitution
Allows aggregation of demand
Reduce safety inventories
Influenced by the cost differential, correlation of demand
Customer-driven substitution
Allows aggregation of safety inventory
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Component Commonality
Without common components
Uncertainty of demand for a component is the same as for the finished product
Results in high levels of safety inventor
With common components
Demand for a component is an aggregation of the demand for the finished products
Component demand is more predictable
Component inventories are reduced
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Value of Component Commonality
27 PCs, 3 components, 3 x 27 = 81 distinct components
Monthly demand = 5,000
Standard deviation = 3,000
Replenishment lead time = 1 month
CSL = 0.95
Total safety inventory required
Safety inventory per common component
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Value of Component Commonality
With component commonality
Nine distinct components
Total safety inventory required
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Value of Component Commonality
| Number of Finished Products per Component | Safety Inventory | Marginal Reduction in Safety Inventory | Total Reduction in Safety Inventory |
| 1 | 399,699 | ||
| 2 | 282,630 | 117,069 | 117,069 |
| 3 | 230,766 | 51,864 | 168,933 |
| 4 | 199,849 | 30,917 | 199,850 |
| 5 | 178,751 | 21,098 | 220,948 |
| 6 | 163,176 | 15,575 | 236,523 |
| 7 | 151,072 | 12,104 | 248,627 |
| 8 | 141,315 | 9,757 | 258,384 |
| 9 | 133,233 | 8,082 | 266,466 |
Table 12-5
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Postponement
Delay product differentiation or customization until closer to the time the product is sold
Have common components in the supply chain for most of the push phase
Move product differentiation as close to the pull phase of the supply chain as possible
Inventories in the supply chain are mostly aggregate
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Postponement
Figure 12-5
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Value of Postponement
100 different paint colors, D = 30/week, sD = 10,
L = 2 weeks, CSL = 0.95
Total required safety inventory,
Standard deviation of
base paint weekly demand,
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Impact of Replenishment Policies on Safety Inventory
Continuous Review Policies
D: Average demand per period
sD: Standard deviation of demand per period
L: Average lead time for replenishment
Mean demand during lead time,
Standard deviation of demand during lead time,
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Impact of Replenishment Policies on Safety Inventory
Periodic Review Policies
Lot size determined by prespecified order-up-to level (OUL)
D: Average demand per period
sD: Standard deviation of demand per period
L: Average lead time for replenishment
T: Review interval
CSL: Desired cycle service level
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Impact of Replenishment Policies on Safety Inventory
Probability(demand during L + T ≤ OUL) = CSL
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Impact of Replenishment Policies on Safety Inventory
Figure 12-6
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Evaluation Safety Inventory for a Periodic Review Policy
D = 2,500, sD = 500, L = 2 weeks, T = 4 weeks
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Managing Safety Inventory in a Multiechelon Supply Chain
In multiechelon supply chains stages often do not know demand and supply distributions
Inventory between a stage and the final customer is called the echelon inventory
Reorder points and order-up-to levels at any stage should be based on echelon inventory
Decisions must be made about the level of safety inventory carried at different stages
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The Role of IT in Inventory Management
IT systems can help
Improve inventory visibility
Coordination in the supply chain
Track inventory (RFID)
Value tightly linked to the accuracy of the inventory information
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Estimating and Managing Safety Inventory in Practice
Account for the fact that supply chain demand is lumpy
Adjust inventory policies if demand is seasonal
Use simulation to test inventory policies
Start with a pilot
Monitor service levels
Focus on reducing safety inventories
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Summary of Learning Objectives
Understand the role of safety inventory in a supply chain
Identify factors that influence the required level of safety inventory
Describe different measures of product availability
Utilize managerial levers available to lower safety inventory and improve product availability
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DL = Di σL = σ i 2 + 2 ρijσ iσ j
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L
,s
L
)=NORMINV(CSL,D
L
,s
L
)
ss = F–1(CSL,DL,σL)– DL = NORMINV(CSL,DL,σL)– DL
ss=F
–1
(CSL,D
L
,s
L
)–D
L
=NORMINV(CSL,D
L
,s
L
)–D
L
ss = FS –1(CSL)×σL = FS
–1(CSL)× LσD
ss=F
S
–1
(CSL)´s
L
=F
S
–1
(CSL)´Ls
D
= NORMSINV(CSL)× LσD
=NORMSINV(CSL)´Ls
D
DL = DL = 2×2,500 = 5,000
σL = LσD = 2 ×500 = 707
D
L
=DL=2´2,500=5,000
s
L
=Ls
D
=2´500=707
ss = Fs –1(CSL)×σL = NORMSINV(CSL)×σL
ss=F
s
–1
(CSL)´s
L
=NORMSINV(CSL)´s
L
= NORMSINV(0.90)×707 = 906
=NORMSINV(0.90)´707=906
ESC = 250 = –ss 1– Fs ss σL
⎛
⎝ ⎜
⎞
⎠ ⎟
⎡
⎣ ⎢ ⎢
⎤
⎦ ⎥ ⎥ +σL fs
ss σL
⎛
⎝ ⎜
⎞
⎠ ⎟
ESC=250=–ss1–F
s
ss
s
L
æ
è
ç
ö
ø
÷
é
ë
ê
ê
ù
û
ú
ú
+s
L
f
s
ss
s
L
æ
è
ç
ö
ø
÷
= –ss 1– Fs ss 707 ⎛
⎝ ⎜
⎞
⎠ ⎟
⎡
⎣ ⎢
⎤
⎦ ⎥+707 fs
ss 707 ⎛
⎝ ⎜
⎞
⎠ ⎟
=–ss1–F
s
ss
707
æ
è
ç
ö
ø
÷
é
ë
ê
ù
û
ú
+707f
s
ss
707
æ
è
ç
ö
ø
÷
250 = –ss[1– NORMDIST(ss /707,0,1,1)]
250=–ss[1–NORMDIST(ss/707,0,1,1)]
+707NORMDIST(ss /707,0,1,0)
+707NORMDIST(ss/707,0,1,0)
ss = NORMSINV(CSL)× LσD
ss=NORMSINV(CSL)´Ls
D
= NORMSINV(.95)× 9 ×800 = 3,948
=NORMSINV(.95)´9´800=3,948
ss = NORMSINV(.95)× 1×800 =1,316
ss=NORMSINV(.95)´1´800=1,316
ss = NORMSINV(.95)× 9 ×400 =1,974
ss=NORMSINV(.95)´9´400=1,974
DL = DL σL = LσD 2 + D2sL
2
D
L
=DL s
L
=Ls
D
2
+D
2
s
L
2
Standard deviation of ddlt σL = LσD 2 + D2sL
2
Standard deviation of ddlt s
L
=Ls
D
2
+D
2
s
L
2
= 7×5002 +2,5002 ×72
=17,500
=7´500
2
+2,500
2
´7
2
=17,500
ss = FS –1(CSL)×σL = NORMSINV(CSL)×σL
ss=F
S
–1
(CSL)´s
L
=NORMSINV(CSL)´s
L
= NORMSINV(0.90) ×17,500 = 22,491 hard drives
=NORMSINV(0.90)´17,500
=22,491 hard drives
= FS –1(CSL)× L ×σ i
i=1
k
∑
=F
S
–1
(CSL)´L´s
i
i=1
k
å
DC = Dii=1 k
∑ ; var DC( ) = σ i2 + 2 ρijσ iσ j i> j ∑
i=1
k
∑ ;
D
C
=D
i
i=1
k
å
; varD
C
(
)
=s
i
2
+2r
ij
s
i
s
j
i>j
å
i=1
k
å
;
σD C = var DC( )
s
D
C
=varD
C
(
)
DC = kD σD C = kσD
D
C
=kD s
D
C
=ks
D
= FS –1(CSL)× L ×σD
C
i=1
k
∑
=F
S
–1
(CSL)´L´s
D
C
i=1
k
å
= FS –1(CSL)× L × H
DC × σ i –σD
C
i=1
k
∑ ⎛
⎝ ⎜⎜
⎞
⎠ ⎟⎟
=
F
S
–1
(CSL)´L´H
D
C
´s
i
–s
D
C
i=1
k
å
æ
è
ç
ç
ö
ø
÷
÷
ss = k × Fs –1(CSL)× L ×σD
ss=k´F
s
–1
(CSL)´L´s
D
= 4 × Fs –1(0.9) × 2 × 5
= 4 × NORMSINV(0.9) × 2 × 5 = 36.24 cars
=4´F
s
–1
(0.9)´2´5
=4´NORMSINV(0.9)´2´5=36.24 cars
σD C = 4 ×5 =10
s
D
C
=4´5=10
ss = Fs –1(0.9)× L ×σD
C = NORMSINV(0.9)× 2 ×10 =18.12
ss=F
s
–1
(0.9)´L´s
D
C
=NORMSINV(0.9)´2´10=18.12
ss = 4× Fs –1(CSL)× L ×σD
ss=4´F
s
–1
(CSL)´L´s
D
= 4× NORMSINV(0.95)× 4 ×300 = 3,948
=4´NORMSINV(0.95)´4´300=3,948
σD C = 4 ×300 = 600
s
D
C
=4´300=600
ss = Fs –1(0.95)× L ×σD
C
ss=F
s
–1
(0.95)´L´s
D
C
= NORMSINV(0.95)× 4 ×600 =1,974
=NORMSINV(0.95)´4´600=1,974
= 81× NORMSINV(0.95)× 1×3,000
=81´NORMSINV(0.95)´1´3,000
= 399,699 units
=399,699 units
= NORMSINV(0.95)× 1× 9 ×3,000
=NORMSINV(0.95)´1´9´3,000
= 14,804 units
=14,804 units
= 9×14,804 =133,236
=9´14,804=133,236
ss =100× Fs –1(CSL)× L ×σD
ss=100´F
s
–1
(CSL)´L´s
D
=100× NORMSINV(0.95)× 2 ×10 = 2,326
=100´NORMSINV(0.95)´2´10=2,326
σD C = 100 ×10 =100
s
D
C
=100´10=100
ss = Fs –1(CSL)× L ×σD
C = NORMSINV(0.95)× 2 ×100 = 233
ss=F
s
–1
(CSL)´L´s
D
C
=NORMSINV(0.95)´2´100=233
DL = DL
σL = LσD
D
L
=DL
s
L
=Ls
D
ss = FS –1(CSL)×σL = NORMSINV(CSL)× LσD,ROP = DL + ss
ss=F
S
–1
(CSL)´s
L
=NORMSINV(CSL)´Ls
D
,ROP=D
L
+ss
Mean demand during T + L periods, DT+L = (T + L)D
Mean demand during T+L periods, D
T+L
=(T+L)D
Std dev demand during T + L periods, σT+L = T + LσD
Std dev demand during T+L periods, s
T+L
=T+Ls
D
OUL = DT+L + ss
ss = FS –1(CSL) ×σD+L = NORMSINV(CSL) ×σT+L
Average lot size, Q = DT = DT
OUL=D
T+L
+ss
ss=F
S
–1
(CSL)´s
D+L
=NORMSINV(CSL)´s
T+L
Average lot size, Q=D
T
=DT
Chapter 12 • Managing Uncertainty in a Supply Chain: Safety Inventory 343
The next step is to evaluate the distribution of demand during the time interval T ! L. Using Equation 12.2, demand during the time interval T ! L is normally distributed, with
The safety inventory in this case is the quantity in excess of DT+L carried by Wal-Mart over the time interval T ! L. The OUL and the safety inventory ss are related as follows:
(12.17)
Given the desired CSL, the safety inventory (ss) required is given by
(12.18)
The average lot size equals the average demand during the review period T and is given as
(12.19)
In Figure 12-6, we show the inventory profile for a periodic review policy with lead time L " 4 and reorder interval T " 7. Observe that on day 7, the company places an order that determines available inventory until day 18 (as illustrated in the line from point 1 and point 2). As a result, the safety inventory must be sufficient to buffer demand variability over T ! L " 7 ! 4 " 11 days.
We illustrate the periodic review policy for Wal-Mart in Example 12-13.
Evaluation Safety Inventory for a Periodic Review Policy
Weekly demand for Legos at a Wal-Mart store is normally distributed, with a mean of 2,500 boxes and a standard deviation of 500. The replenishment lead time is two weeks, and the store manager has decided to review inventory every four weeks. Assuming a periodic-review replenishment policy, evaluate the safety inventory that the store should carry to provide a CSL of 90 percent. Evaluate the OUL for such a policy.
Analysis: In this case, we have
Average demand per period, D " 2,500
Standard deviation of demand per period, sD = 500
EXAMPLE 12-13
Average lot size, Q = DT = DT
ss = FS-11CSL2 * sT+L = NORMSINV1CSL2 * sT+L OUL = DT+L + ss
Standard deviation of demand during T + L periods, sT+L = 1T + LsD Mean demand during T + L periods, DT+L = 1T + L2D
5
OUL
DT
DL Safety Inventory
T = 7
L = 4 W
ar eh
ou se
In ve
nt or
y
10
L
T L
SS 0
Review Point 0
Review Point 1
Review Point 2
Review Point 3
15 Days
20 25
2
1
FIGURE 12-6 Inventory Profile for Periodic Review Policy with L " 4, T " 7
M12_CHOP3952_05_SE_C12.QXD 11/15/11 6:52 PM Page 343
Mean demand during T + L periods, DT+L = (T + L)D = (2 + 4)2,500 = 15,000
Mean demand during T+L periods, D
T+L
=(T+L)D
=(2+4)2,500=15,000
Std dev demand during T + L periods, σT+L = T + LσD = 4 + 2( )500 = 1,225
Std dev demand during T+L periods, s
T+L
=T+Ls
D
=4+2
()
500=1,225
ss = FS –1(CSL) ×σD+L = NORMSINV(CSL) ×σT+L
= NORMSINV(0.90) ×1,225 = 1,570 boxes
ss=F
S
–1
(CSL)´s
D+L
=NORMSINV(CSL)´s
T+L
=NORMSINV(0.90)´1,225=1,570 boxes
OUL = DT+L + ss =15,000+1,570 =16,570
OUL=D
T+L
+ss=15,000+1,570=16,570