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CHAPTER 5:VALUE OF INFORMATION
E. OldenkampSession 419 April 2016
HOW VARIABLE IS DEMAND FOR DIAPERS?
2
NEWBORN BABIES PER YEAR IN THE NL
3
Pampers demand pattern
• Steady purchase rate at the customer end• Early 1900s extreme and increasing demand variations from the
retailer to the distributor• Reasons:
– infrequent ordering– changes in prices– that is: lack of information upstream about actual customer
orders• Old SC model: replenishment took at both the distribution and
supplier side several weeks• Retailers order only when product was missing from the shelves• P&G’s procurement from suppliers based on historical sales data
4
P&G’S DIAPERS CASEconsumer sales at retailer
0
100
200300
400500
600700
800900
10001 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
retailer's orders to distributor
0
100
200300
400500
600700
800900
1000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
distributor's orders to P&G
0
100
200300
400500
600700
800900
1000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
0
100
200300
400500
600700
800900
1000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
P&G's orders with 3M
5
• P&G initiated the use of VMI systems involving 3M andWal-Mart
• Effect:– lower operating costs at Wal-Mart– increase in P&G’s market share (more room at Wal-
Mart)– P&G provided POS data to its suppliers:“This allows the suppliers to better plan their production and deliever raw and packingmaterial on a just-in-time bases”– demand driven supply chain– everybody in the SC accessed the same data
P&G’s diapers case
6
BULLWHIP EFFECT (BWE)
Order variability keeps amplifying as we move UP the SC.
7
4-STAGE SUPPLY CHAIN1,000 units
1,100 units
1,210 units
1,331 units
1,464 units
+10%
+10%
+10%
+10%
total safety stock:
1,105 units
8
CAUSES OF THE BULLWHIP EFFECT
• Four major causes:– forecasting based on order and not customer demand– ordering in large lots– price fluctuations (forward buying)– false orders
• Long lead times magnify the effect
9
CAUSES OF THE BULLWHIP EFFECT
• Four major causes:– demand forecasting– batch ordering– price fluctuations– false orders
• Long lead times magnify the effect
How does forecasting
increase the BWE?
10
DEMAND FORECASTING
Consider a simple supply chain…• single retailer• single wholesaler
Retailer WholesalerDt
Qt
L
11
DEMAND FORECASTING – MA
• p-period moving average:
�𝐷𝐷𝑡𝑡 =1𝑝𝑝�𝑖𝑖=1
𝑝𝑝𝐷𝐷𝑡𝑡−𝑖𝑖 and �𝜎𝜎𝑡𝑡2 =
1𝑝𝑝 − 1�𝑖𝑖=1
𝑝𝑝(𝐷𝐷𝑡𝑡−𝑖𝑖−�𝐷𝐷𝑡𝑡)2.
• Periodic review (𝑆𝑆 − 1, 𝑆𝑆) policy (dynamic):
𝑆𝑆𝑡𝑡 = 𝐿𝐿 + 𝑟𝑟 × �𝐷𝐷𝑡𝑡 + z × 𝐿𝐿 + 𝑟𝑟 × �𝜎𝜎𝑡𝑡• How to measure the bullwhip effect?
𝐵𝐵𝐵𝐵𝐵𝐵 = var 𝑄𝑄𝑡𝑡var 𝐷𝐷𝑡𝑡
12
DEMAND FORECASTING – MA
timeL
safetystock
tt-1
𝑺𝑺𝒕𝒕
𝑺𝑺𝒕𝒕−𝟏𝟏 𝑸𝑸𝒕𝒕 = 𝑺𝑺𝒕𝒕 − 𝑺𝑺𝒕𝒕−𝟏𝟏 +𝑫𝑫𝒕𝒕−𝟏𝟏
L13
DEMAND FORECASTING – MASimulation• Normal demand with 𝐴𝐴𝐴𝐴𝐴𝐴 = 20 and 𝑆𝑆𝑆𝑆𝐷𝐷 = 4• Forecast: 5-period MA (𝑝𝑝 = 5)• 𝐿𝐿 = 3• var 𝐷𝐷𝑡𝑡 = 16.48, var 𝑄𝑄𝑡𝑡 = 65.0 ⇒ BWE = 3.94
14
DEMAND FORECASTING – MA
• Perform the simulation 10,000 times:
– average 𝐵𝐵𝐵𝐵𝐵𝐵 = 4.74
• Lower bound on bullwhip effect
BWE = var 𝑄𝑄𝑡𝑡var 𝐷𝐷𝑡𝑡 ≥ 1+
2𝐿𝐿𝑝𝑝 + 2𝐿𝐿
2
𝑝𝑝2
𝐿𝐿 = 3 and 𝑝𝑝 = 5What is the BWE at the wholesaler?
lower bound is 2.9215
DEMAND FORECASTING – MA
0
2
4
6
8
10
12
0 10 20 30
low
er b
ound
BW
E
p
L=1L=3L=5
5
2.92
16
DEMAND FORECASTING – MA
• Consider a multi-stage supply chain– stage k places order Qk to stage k+1– Lk is the lead time between stage k and k+1
RetailerD=Q0
L1Wholesaler Distributor
Q1
L2
Q2Factory
L3
Q3
17
DEMAND FORECASTING – MA
Decentralized: each stage bases order on previous stage’s demand• Retailer does not share demand forecast with
wholesaler• Wholesaler forecasts demand by retailer’s orders• Distributor forecasts demand by wholesaler’s orders
How to measure the bullwhip effect?
𝐵𝐵𝐵𝐵𝐵𝐵𝑘𝑘 = var 𝑄𝑄𝑡𝑡𝑘𝑘var 𝐷𝐷𝑡𝑡
= var 𝑄𝑄𝑡𝑡1var 𝐷𝐷𝑡𝑡
× var 𝑄𝑄𝑡𝑡2var 𝑄𝑄𝑡𝑡1
×⋯× var 𝑄𝑄𝑡𝑡𝑘𝑘var 𝑄𝑄𝑡𝑡𝑘𝑘−1
18
DEMAND FORECASTING – MA
Simulation• Normal demand with 𝐴𝐴𝐴𝐴𝐴𝐴 = 20 and 𝑆𝑆𝑆𝑆𝐷𝐷 = 4• Forecast: 5-period MA (𝑝𝑝 = 5)• 5 stages: customer, retailer, wholesaler, DC, MF• 𝐿𝐿1 = 3, 𝐿𝐿2= 5, 𝐿𝐿3= 2, 𝐿𝐿4= 4• BWE1= 4, BWE2=38.3, BWE3= 86.8, BWE4=187.4
19
DEMAND FORECASTING – MA
• Perform the simulation 10,000 times:– average BWE1 = 4.73– average BWE2 = 34.81– average BWE3 = 119.72– average BWE4 = 514.15
• Lower bound on bullwhip effect
𝐵𝐵𝐵𝐵𝐵𝐵𝑘𝑘 =var 𝑄𝑄𝑡𝑡𝑘𝑘var 𝐷𝐷𝑡𝑡
≥ 1 + 2𝐿𝐿1𝑝𝑝 + 2𝐿𝐿12
𝑝𝑝2 1 + 2𝐿𝐿2𝑝𝑝 + 2𝐿𝐿22
𝑝𝑝2 ⋯ 1 + 2𝐿𝐿𝑘𝑘𝑝𝑝 + 2𝐿𝐿𝑘𝑘2
𝑝𝑝2
𝐿𝐿1 = 3, 𝐿𝐿2 = 5, 𝐿𝐿3 = 2, 𝐿𝐿4 = 4, and 𝑝𝑝 = 5.What is the lower bound of the BWE at each stage?
20
DEMAND FORECASTING – MA
• Retailer
– 𝐵𝐵𝐵𝐵𝐵𝐵1 ≥ 1 + 2×35 + 2×32
52 = 2.92• Wholesaler
– 𝐵𝐵𝐵𝐵𝐵𝐵2 ≥ 1 + 2×35 + 2×32
52 1 + 2×55 + 2×52
52 = 14.6• Distributor
– 𝐵𝐵𝐵𝐵𝐵𝐵3 ≥ 2.92 × 5.0 × 2.12 = 30.952• Manufacturer
– 𝐵𝐵𝐵𝐵𝐵𝐵4 ≥ 2.92 × 5.0 × 2.12 × 3.88 = 120.094
𝐿𝐿1 = 3, 𝐿𝐿2 = 5, 𝐿𝐿3 = 2, 𝐿𝐿4 = 4, and 𝑝𝑝 = 521
DEMAND FORECASTING – MA
• Perform the simulation 10,000 times:– average BWE1 = 4.73– average BWE2 = 34.81– average BWE3 = 119.72 – average BWE4 = 514.15
≥ 2.92≥ 14.60≥ 30.95≥ 120.09
22
DEMAND FORECASTING – ES
• Demand forecast with Exponential Smoothing
�𝐷𝐷𝑡𝑡 = 𝛼𝛼𝐷𝐷𝑡𝑡−1 + (1− 𝛼𝛼)�𝐷𝐷𝑡𝑡−1• Periodic review (𝑆𝑆 − 1, 𝑆𝑆) policy:
𝑆𝑆𝑡𝑡 = 𝐿𝐿 + 𝑟𝑟 × �𝐷𝐷𝑡𝑡 + z × 𝐿𝐿 + 𝑟𝑟 × �𝜎𝜎𝑡𝑡• How to measure the bullwhip effect?
𝐵𝐵𝐵𝐵𝐵𝐵 = var 𝑄𝑄𝑡𝑡var 𝐷𝐷𝑡𝑡
≥ 1 + 2𝛼𝛼𝐿𝐿 + 2𝛼𝛼2𝐿𝐿22−𝛼𝛼
𝛼𝛼 = 0.3 and L=3 ⟹ lower bound = 3.75
23
DEMAND FORECASTING – ES
0
10
20
30
40
50
0 0.2 0.4 0.6 0.8 1
low
er b
ound
BW
E
L=1L=3L=5
𝜶𝜶0.3
3.75
24
DEMAND FORECASTING – ES• Consider again a multi-stage supply chain
– stage k places order Qk to stage k+1– Lk is the lead time between stage k and k+1
RetailerD=Q0
L1Wholesaler Distributor
Q1
L2
Q2Factory
L3
Q3
𝐵𝐵𝐵𝐵𝐵𝐵𝑘𝑘 =var 𝑄𝑄𝑡𝑡𝑘𝑘var 𝑄𝑄𝑡𝑡0
≥ 1 + 2𝛼𝛼𝐿𝐿1 +2𝛼𝛼2𝐿𝐿122− 𝛼𝛼 1 + 2𝛼𝛼𝐿𝐿2 +
2𝛼𝛼2𝐿𝐿222− 𝛼𝛼 ⋯ 1 + 2𝛼𝛼𝐿𝐿𝑘𝑘 +
2𝛼𝛼2𝐿𝐿𝑘𝑘22− 𝛼𝛼
25
• Both forecasts have the same variance of the forecast errors when
𝛼𝛼 = 2𝑝𝑝 + 1
• Filling in the numbersVar(𝑄𝑄𝑀𝑀𝑀𝑀)Var(D) ≥ 1 + 2L
p + 2L2p2
Var(𝑄𝑄𝐸𝐸𝐸𝐸)Var(D) ≥ 1 + 4L
p + 1 +4L2
p(p + 1)
COMPARISON OF THE TWO FORECASTING TECHNIQUES
MA: 𝑝𝑝 = 5 and 𝐿𝐿 = 3 ⇒ lower bound = 2.92ES: α = 0.333 and 𝐿𝐿 = 3 ⇒ lower bound = 4.20
26
COMPARISON OF THE TWO FORECASTING TECHNIQUES
0
2
4
6
8
10
12
14
0 10 20 30
low
er b
ound
BW
E
p
MA - L=1MA - L=3MA - L=5EX - L=1EX - L=3EX - L=5
27
CAUSES OF THE BULLWHIP EFFECT
• Four major causes:– demand forecasting– ordering in large lotsizes– price fluctuations– false orders
• Long lead times magnify the effect
How does batch ordering increase the
BWE?
28
CAUSES OF THE BULLWHIP EFFECT
• Four major causes:– demand forecasting– batch ordering– price fluctuations– false orders
• Long lead times magnify the effect
How do price fluctuations increase the
BWE?
29
PRICE FLUCTUATIONS (1)
Point-of-Sales Data
30
PRICE FLUCTUATIONS (2)
Point-of-Sales Data – after removing promotions
31
Point-of-Sales Data – after removing promotions and trend
PRICE FLUCTUATIONS (3)
32
CAUSES OF THE BULLWHIP EFFECT
• Four major causes:– demand forecasting– batch ordering– price fluctuations– false orders
• Long lead times magnify the effect
How do false orders
increase the BWE?
33
CAUSES OF THE BULLWHIP EFFECT
• Four major causes:– demand forecasting– batch ordering– price fluctuations– false orders
• Long lead times magnify the effect
How do longer lead times
magnify the BWE?
34
COUNTERACT THE BULLWHIP EFFECT
1. Avoid independent demand forecasting by different SC actors
2. Break order batches3. Stabilize prices4. Eliminate false orders
35
IMPACT OF CENTRALIZED INFORMATION
Moving average:• Decentralized
𝐵𝐵𝐵𝐵𝐵𝐵 = Var 𝑄𝑄𝑘𝑘Var 𝐷𝐷 ≥ �
𝑖𝑖=1
𝑘𝑘1 + 2𝐿𝐿𝑖𝑖
𝑝𝑝 + 2𝐿𝐿𝑖𝑖2𝑝𝑝2
• Centralized
𝐵𝐵𝐵𝐵𝐵𝐵 = Var 𝑄𝑄𝑘𝑘Var 𝐷𝐷 ≥ 1 + 2
𝑝𝑝�𝑖𝑖=1
𝑘𝑘𝐿𝐿𝑖𝑖 +
2𝑝𝑝2�𝑖𝑖=1
𝑘𝑘𝐿𝐿𝑖𝑖2
𝐿𝐿1 = 3, 𝐿𝐿2 = 5, 𝐿𝐿3 = 2, 𝐿𝐿4 = 4, and 𝑝𝑝 = 5What is the lower bound of the BWE at the SC partners?
36
DEMAND FORECASTING – MA
• Retailer
– 𝐵𝐵𝐵𝐵𝐵𝐵1 ≥ 1 + 25 × 3 +
225 × 9 = 2.92
• Wholesaler
– 𝐵𝐵𝐵𝐵𝐵𝐵2 ≥ 1 + 25 × 3 + 5 + 2
25 × 9 + 25 = 6.92• Distributor
– 𝐵𝐵𝐵𝐵𝐵𝐵3 ≥ 1 + 25 × 3 + 5 + 2 + 2
25 × 9 + 25 + 4 = 8.04• Manufacturer
– 𝐵𝐵𝐵𝐵𝐵𝐵4 ≥ 1 + 25 × 3 + 5 + 2 + 4 + 2
25 × 9 + 25 + 4 + 16 = 10.92
37
• Perform the simulation 10,000 times:– average BWE1 = 4.73– average BWE2 = 8.37– average BWE3 = 9.41 – average BWE4 = 11.67
≥ 2.92≥ 6.92≥ 8.04≥ 10.92
MOVING AVERAGE
38
LOWER BOUNDS OF BWE -- MA
0
5
10
15
20
25
30
0 10 20 30
low
er b
ound
BW
E
p
k=1Dec - k=3Dec - k=5Cen - k=3Cen - k=5
39
decentralized centralized
simulation lower bound simulation lower bound
Retailer 4.73 2.92 4.73 2.92
Wholesaler 34.81 14.60 8.37 6.92
Distributor 119.72 30.95 9.41 8.04
Manufacturer 514.15 120.09 11.67 10.92
MOVING AVERAGE
40
IMPACT OF CENTRALIZED INFORMATION
Exponential smoothing:• Decentralized
𝐵𝐵𝐵𝐵𝐵𝐵 = Var 𝑄𝑄𝑘𝑘Var 𝐷𝐷 ≥ �
𝑖𝑖=1
𝑘𝑘1 + 2α𝐿𝐿𝑖𝑖 +
2𝛼𝛼2𝐿𝐿𝑖𝑖22− 𝛼𝛼
• Centralized
𝐵𝐵𝐵𝐵𝐵𝐵 = Var 𝑄𝑄𝑘𝑘Var 𝐷𝐷 ≥ 1 + 2𝛼𝛼�
𝑖𝑖=1
𝑘𝑘𝐿𝐿𝑖𝑖 +
2𝛼𝛼22− 𝛼𝛼�𝑖𝑖=1
𝑘𝑘𝐿𝐿𝑖𝑖2
41
EXPONENTIAL SMOOTHING
0
10
20
30
40
50
0 0.2 0.4 0.6 0.8 1
low
er b
ound
BW
E
alpha
k=1Dec - k=3Dec - k=5Cen - k=3Cen - k=5
42
MANAGERIAL INSIGHTS
• Variance increases up the supply chain in both centralized and decentralized cases
• Variance increases:– additively with centralized case
– multiplicatively with decentralized case
• Centralizing can significantly reduce the bullwhip effect – although cannot eliminate it completely!
43
COUNTERACT THE BULLWHIP EFFECT
1. Avoid independent demand forecasting by different SC actors
2. Break order batches3. Stabilize prices4. Eliminate false orders
44
BREAK ORDER BATCHES
• Reduce replenishment costs• Economies of scale in transportation• Use third-party logistics companies
• P&G requires all orders from retailers to be full TL: combination of products reduces lot size
Think about EOQ model: Q*= 2DKh
→ reduction of fixed order cost by factor k2, results in reduction order size of only factor k
45
COUNTERACT THE BULLWHIP EFFECT
1. Avoid multiple demand forecast updates2. Break order batches3. Stabilize prices4. Eliminate false orders
46
SHORT TERM DISCOUNTING
Qd
Q*
t
inve
ntor
y le
vel
Identify Qd that maximizes the reduction in total cost(material cost + order cost + holding cost)
47
Assumptions• Discount will only be offered once• Order quantity Qd is a multiple of Q*• Retailer takes no action of influence the demand
Notation• C = wholesale price• d = discount on wholesale price• h = holding cost % of wholesale price
SHORT TERM DISCOUNTING
EOQ model: 𝑄𝑄∗ = 2𝐷𝐷𝐷𝐷ℎ𝐶𝐶
48
SHORT TERM DISCOUNTING
• Estimate total cost of ordering 𝑄𝑄𝑑𝑑 in discount period• TC 𝑄𝑄𝑑𝑑 = material cost + order cost + inventory cost
= 𝐶𝐶 − 𝑑𝑑 𝑄𝑄𝑑𝑑 + 𝐾𝐾 + 𝑄𝑄𝑑𝑑2 𝐶𝐶 − 𝑑𝑑 ℎ 𝑄𝑄𝑑𝑑
𝐷𝐷
discount period = 𝑄𝑄𝑑𝑑𝐷𝐷
Qd
Q*
t
inve
ntor
y le
vel
49
SHORT TERM DISCOUNTING• Estimate total cost of ordering 𝑄𝑄∗ in discount period• TC 𝑄𝑄∗ = material cost + order cost + inventory cost
= 𝐶𝐶𝑄𝑄𝑑𝑑 + 𝐾𝐾 𝑄𝑄𝑑𝑑𝑄𝑄∗ +
𝑄𝑄∗2 ℎ𝐶𝐶 𝑄𝑄𝑑𝑑
𝐷𝐷
= 𝐶𝐶𝑄𝑄𝑑𝑑 + 𝑄𝑄𝑑𝑑𝐷𝐷 2𝐷𝐷𝐾𝐾ℎ𝐶𝐶 (since 𝑄𝑄∗ = 2𝐷𝐷𝐷𝐷
ℎ ).
discount period = 𝑄𝑄𝑑𝑑𝐷𝐷
Qd
Q*
t
inve
ntor
y le
vel
50
• Cost difference: 𝐹𝐹 𝑄𝑄𝑑𝑑 = TC 𝑄𝑄𝑑𝑑 − TC 𝑄𝑄∗
• Take the derivative and set to zero
𝐹𝐹′ 𝑄𝑄𝑑𝑑 = 0 ⇒ 𝑄𝑄𝑑𝑑 = 𝑑𝑑𝐷𝐷ℎ 𝐶𝐶−𝑑𝑑 + 𝐶𝐶𝑄𝑄∗
𝐶𝐶−𝑑𝑑
SHORT TERM DISCOUNTING
51
• D = 120,000/year• C = $3• h = 20% (annual)• K = $100
SHORT TERM DISCOUNTING
− Q* = 6,324 units− cycle time = 0.63 months
Assume a promotion is offered (d=$0.15)
• Qd = dD(C−d)h +
CQ∗
C−d
= 0.15 × 120,000(3−0.15) × 0.2 + 3 × 6,324
3 − 0.15
= 38,236 units
cycle time = 3.82 months
52
CAMPBELL SOUP COMPANY
• Flagship product: cans of condensed soup• Backwards integration
53
CUSTOMER DEMAND
Types:• retailer (45%)• wholesaler (47%)• other (8%)
54
SUMMARY BULLWHIP EFFECT
Cause of bullwhip
Information sharing Channel alignment Operational efficiency
demand forecast update
- understanding system dynamics
- use POS data- EDI- internet
- VMI- discount for
information sharing- direct shipping
- lead time reduction- echelon based
inventory control
order batching
- EDI- Internet
- discount for truckload assortment
- delivery appointments- consolidation- logistics outsourcing
- reduction in fixed cost of ordering by EDI or electronic commerce
pricefluctuations
- EDLC - EDLP
false orders - sharing sales, capacity, and inventory data
- allocation based on past sales
55