the value of information designing & managing the supply chain chapter 4 byung-hyun ha...
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The Value of Information
Designing & Managing the Supply Chain
Chapter 4
Byung-Hyun Ha
Outline
Barilla SpA
Introduction
The Bullwhip Effect
Effective Forecast
Information for the Coordination of Systems
Barilla SpA
Barilla SpA is the world’s largest pasta manufacturer
The company sells to a wide range of Italian retailers, primarily through third party distributors
During the late 1980s, Barilla suffered increasing operational inefficiencies and cost penalties that resulted from large week-to-week variations in its distributors’ order patterns
DistributionChannels
Weekly Demand for Barilla Dry Products
Demand Fluctuations
The extreme fluctuation is truly remarkable when one considers the underlying aggregate demand for pasta in Italy
Causes of Demand Fluctuations Transportation discounts Volume discount Promotional activity No minimum or maximum order quantities Product proliferation Long order lead times Poor customer service rates Poor communication
Demand Fluctuations
Impact of demand fluctuation Inefficient production or excess finished goods inventory Utilization of central distribution is low
• Workers
• Equipment
Transportation costs are higher than necessary
Just-in-Time Distribution (JITD) Program
JITD proposal Decision-making authority for determining shipments from Barilla
to a distributor would transfer from the distributor to Barilla Rather than simply filling orders specified by the distributor,
Barilla would monitor the flow of its product through the distributor’s warehouse, and then decide what to ship to the distributor and when to ship it
Evaluation of the proposal JITD proposal as a mechanism for reducing these costs? Why should this work? How does it work? What makes Barilla think that it can do a better job of
determining a good product/delivery sequence than its distributors?
JITD Program
Resistance from the Distributors “Managing stock is my job; I don’t need you to see my warehouse or my
figures.” “I could improve my inventory and service level myself if you would
deliver my orders more quickly; I would place my order and you would deliver within 36 hours.”
“We would be giving Barilla the power to push products into our warehouse just so that Barilla can reduce its costs.”
Resistance from Sales and Marketing “Our sales levels would flatten if we put this program in place.” “How can we get the trade to push Barilla product to retailers if we don’t
offer some sort of incentive?” “If space is freed up in our distributors’ warehouses…the distributors
would then push our competitors’ product more than ours.” “…the distribution organization is not yet ready to handle such a
sophisticated relationship.”
Introduction
Value of Information “In modern supply chains, information replaces inventory”
• Why is this true?
• Why is this false?
Information is always better than no information. Why?
Information Helps reduce variability Helps improve forecasts Enables coordination of systems and strategies Improves customer service Facilitates lead time reductions Enables firms to react more quickly to changing market
conditions
Increasing Variability of Orders
Lee, Padmanabhan, Wang (1997)
Bullwhip Effect
Order variability is amplified up the supply chain; upstream echelons face higher variability
Main factors contributing to increase in variability Demand forecasting Lead time Promotional sales
• Forward buying
Volume and transportation discounts• Batching
Inflated orders• IBM Aptiva orders increased by 2-3 times when retailers thought
that IBM would be out of stock over Christmas
• Motorola cell phones
Impact of Promotional Sales
Order pattern of a single color television model sold by a large electronics manufacturer to one of its accounts, a national retailer
order stream
Impact of Promotional Sales
Point-of-sales Data-Original
POS Data After Removing Promotions
Demand Forecasting & Lead Time
Single retailer, single manufacturer Retailer observes customer demand, Dt
Retailer orders qt from manufacturer
Suppose a P period moving average forecasting is used
Retailer ManufacturerDt qt
L
2
2221
)(
)(
P
L
P
L
DVar
qVar
Chen et al. 2000
Demand Forecasting & Lead Time
Var(q)/Var(D) for various lead times
L=5
L=3
L=1
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30
L=5
L=3
L=1
P
Var(q)Var(D)
Demand Forecasting & Lead Time
Multi-stage supply chains Stage i places order qi to stage i+1 Li is lead time between stage i and i+1
Centralized: each stage bases orders on retailer’s forecast demand
Decentralized: each stage bases orders on previous stage’s demand
RetailerStage 1
ManufacturerStage 2
SupplierStage 3
qo=D q1 q2
L1 L2
2
2
11
221
)(
)(
P
L
P
L
DVar
qVar
k
ii
k
iik
k
i
iik
P
L
P
L
DVar
qVar
12
2221
)(
)(
Demand Forecasting & Lead Time
Var(qk)/Var(D) with regard to stages
0
5
10
15
20
25
30
0 5 10 15 20 25
Dec, k=5
Cen, k=5
Dec, k=3
Cen, k=3k=1
Var(qk)Var(D)
P
The Bullwhip Effect
Managerial insights Exists, in part, due to the retailer’s need to estimate the mean
and variance of demand The increase in variability is an increasing function of the lead
time The more complicated the demand models and the forecasting
techniques, the greater the increase Centralized demand information can significantly reduce the
bullwhip effect, but will not eliminate it
Coping with the Bullwhip Effect
Reduce uncertainty POS Sharing information Sharing forecasts and policies
Reduce variability Eliminate promotions Year-round low pricing
Reduce lead times EDI Cross docking
Strategic partnerships Vendor managed inventory Data sharing
Information for Effective Forecasts
Pricing, promotion, new products Different parties have this information Retailers may set pricing or promotion without telling distributor Distributor/Manufacturer might have new product or availability
information
Collaborative Forecasting addresses these issues
Information for Coordination of Systems
Information is required to move from local to global optimization
Questions Who will optimize? How will savings be split?
Information is needed Production status and costs Transportation availability and costs Inventory information Capacity information Demand information
Locating Desired Products
How can demand be met if products are not in inventory? Locating products at other stores What about at other dealers?
What level of customer service will be perceived?
Lead-Time Reduction
Why? Customer orders are filled quickly Bullwhip effect is reduced Forecasts are more accurate Inventory levels are reduced
How? EDI POS data leading to anticipating incoming orders.
Information to Address Conflicts
Lot Size – Inventory: Advanced manufacturing systems POS data for advance warnings
Inventory – Transportation: Lead time reduction for batching Information systems for combining shipments Cross docking Advanced DSS
Lead Time – Transportation: Lower transportation costs Improved forecasting Lower order lead times
Product Variety – Inventory: Delayed differentiation
Cost – Customer Service: Transshipment