Information in supply chains
 Information is usually stored in a transaction system in a company.
The transaction system is the backbone of a company’s
information technology
 Enterprise Resource Planning (ERP) software
 SAP, Oracle, Baan, JD Edwards
 Legacy software
 ERP systems’ decision support capabilities are usually limited.
Planning is usually performed using another layer of software for
each area of business.
 Supply Chain Management (SCM) software
 i2 Technologies, Manugistics, SAP-APO
 Customer Relationship Management (CRM) software
 Siebel, etc
 …
Supply chain
Customers
Data elements
 Static data
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Items
Locations
Item at location
Bill of materials
Bill of distributions
Suppliers
Approved supplier items
Lead times
 Dynamic data
 Inventory at location
 Sales orders
 Purchase orders
 In transit
 Scheduled
 Transport orders
 In transit
 Scheduled
Transactions with the customer
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SO
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Sales to the customer transacted through a Sales
Order (SO)
A sales order may be asking for different items, at
different dates described in a sales order line.
A sales order line has a requested item, requested
quantity, requested date
Each line goes through an order fulfillment process
and gets a response based on the supply available in
the system (retailer)
These responses are in the form of scheduled line
items with a promised quantity, promised date
Once the order is shipped to the customer (at a
delivered date), the delivered quantity is deducted from
the inventory at the retailer
Transactions inside the company
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TO
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Shipment within a company is transacted through a
Transport Order (TO)
A transport order may be asking for different items, at
different dates described in a transport order line.
A transport order line has a requested item, requested
quantity, requested date
Each line may or may not go through an order fulfillment
process
Once the order is shipped to the retailer (at a shipped
date), the shipped quantity is deducted from the
inventory at the distributor
Once the order is delivered at the retailer (at a delivered
date), the delivered quantity is added to the inventory at
the retailer
The inventory is in in-transit between the shipped date
and delivered date
Transactions with the supplier
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PO
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Acquisitions from a supplier are transacted through a
Purchase Order (PO)
A purchase order may be asking for different items, at
different dates described in a purchase order line.
A purchase order line has a requested item, requested
quantity, requested date
Once the order is received at the manufacturer (at a
receipt date), the received quantity is added to the
inventory at the manufacturer
Note that the same transaction is mirrored at the
supplier as a Sales Order (SO)
Bullwhip effect
Factory
Distributor
Wholesaler
Retailer
Order size
Order size
Bullwhip effect
Retailer Orders to Wholesaler
Order size
Order size
Customer Orders
Wholesaler Orders to Distributor
Distributor Orders to Factory
Bullwhip effect
 Bullwhip effect refers to the phenomenon where orders to the
supplier tend to have larger variance than sales to the buyer (i.e.,
information distortion) and the distortion propagates upstream in
an amplified form (i.e., variance amplification).
 Examples
 At P&G, diaper orders issued by distributors have a degree of
variability that cannot be explained by consumer fluctuations alone
 At Hewlett-Packard, the orders placed to the printer division by
resellers have much bigger swings and variations that customer
demands
 The role of inventory was to act as a buffer to smooth production
in response to demand fluctuations. The variance in the
production time series are should be smaller than the variance in
demand time series. So why do we have the bullwhip effect?
Causes of bullwhip effect
 Demand signal processing
 If the supply chain player updates the order-up-to-level based
on its new estimate of demand, the variance in orders it places
exceeds the variance in demand it observes.
 This gets amplified if the supply chain player does not observe
the final demand (at the retailer), but forecasts demand based
on the orders it received from downstream
 Larger the lead time, larger the bullwhip effects caused by
demand signal processing
 A special case where the demand is forecasted using moving
average method (Chen et al)
Causes of bullwhip effect
 Constrained supply
 When the demand downstream (e.g. retailers) exceeds the
capacity upstream (e.g. manufacturer)
 The typical practice for the upstream (manufacturer) is to
allocate the supply to different downstream entities (retailers)
in proportion to their orders.
 This leads to retailers ordering more than they need in order to
get more share from the supply
 In theory, the order quantity (equilibrium order quantity) where
retailers are competing in such a setting exceeds the order
quantity (standard newsboy order quantity) where the retailers
assume infinite capacity at the manufacturing level
 Note also these inefficiencies may occur even though there is
no real shortage, but the retailers perceive that there is
shortage at the manufacturing level
Causes of bullwhip effect
 Order batching
 Retailers do not order every time they face a demand as a
result of
 Periodic review process
 Setup costs associated with ordering
 As a result, the retailers batch their orders which leads to
distortion in demand information
 This distortion is magnified when there are multiple retailers
and their ordering is not synchronized
 Distortion is highest when ordering is correlated
 Distortion is smallest when ordering is balanced
 Larger the review period, higher the distortion
Causes of bullwhip effect
 Price variations
 If the manufacturers are offering promotions, retailers may act
by procuring more than they currently need in anticipation of
future demand (i.e., forward buy)
 In theory, the order-up-to-level in one period changes with the
procurement cost in that period
 This leads to further distortion in the demand information
communicated to the manufacturer
 The result is higher inventory costs at both ends
 Since the retailers need to keep inventory ahead of the need
 Since the manufacturers need to prepare in advance for the surge
in demand created by the promotion
The impact of forecasting
 Smoother the forecast (e.g., larger the number of
periods used in moving average), smaller the increase
in order variance
 If the demands are positively correlated, larger the
correlation, smaller the increase in order variance
 Larger the lead time, larger the increase in order
variance
 Sharing demand information will significantly reduce
the bullwhip effect but will not eliminate it.
Ways to reduce bullwhip effect
 Demand signal processing
 Allow access to end customer demand to all
members in the supply chain (share POS)
 Sell-thru data in contracts at HP, Apple, IBM
 Single control of replenishment
 Make the manufacturer responsible for replenishing the
supply chain, i.e., Vendor Managed Inventory (VMI) for
companies like P&G and Wal-Mart
 Reduce the lead times
 Quick response systems in apparel industry, flexible
manufacturing
Ways to reduce bullwhip effect
 Constrained supply
 Allocate supply based on the final demand not
based on orders received
 GM, HP and TI allocating based on sales history
 Remove the perceptions that the supply will be short
 Share the production and inventory information with
downstream
 Reduce the buyer’s flexibility
 Construct contracts that will restrict the order quantities
 Eliminate constraints on the supply by collaborating
with retailers
Ways to reduce bullwhip effect
 Order batching
 Reduce order costs
 Reduce paperwork, implement EDI for ordering
 Reduce transportation costs
 Reduce the desire for full truck loads
 Allow mixed truckloads (P&G)
 Use third party logistics (3PL) companies for efficient
transportation
 Synchronize ordering
 Move away from correlated ordering to balanced ordering
Ways to reduce bullwhip effect
 Price variations
 Stop manufacturer’s trade promotions
 Everyday Low Pricing (EDLP) by P&G, etc
 Savings through forward buying may be illusive
 Justify forward buying by also considering inventory
carrying costs
 Implement purchase contracts (synchronize
purchase and delivery schedules)
 Still offer promotions and/or quantity discounts but allow
multiple shipments over time at the same price
Consequences of bullwhip effect
 Bullwhip effect leads to higher variance in
demands as observed by the upstream
members of the supply chain
 This requires
 Higher safety stock
 A more flexible production system and/or higher
smoothing costs in production
 A more flexible transportation system and/or higher
smoothing costs in transportation
Bullwhip effect and different players
 What if the members of the supply chain are part of
different organizations or companies?
 Reducing bullwhip effect certainly reduces costs at the
manufacturer end, does it have any impact on retailer
end?
 Are all actions taken to reduce the bullwhip effect
“Pareto improving”?
 Who will pay for the efforts to reduce the bullwhip
effect?
Information sharing
 Consider a two level supply chain: a manufacturer and
a retailer
 The retailer sharing its information with the
manufacturer
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Demand
Inventory
Inventory policy
Promotion plan
 The manufacturer sharing its information with the
retailer
 Inventory
 Capacity
Information sharing
 Consider a two level supply chain: a manufacturer and
a retailer (Lee et al)
 The retailer sharing its information with the
manufacturer
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Demand
Inventory
Inventory policy
Promotion plan
 The manufacturer sharing its information with the
retailer
 Inventory
 Capacity
Sharing demand information
 The retailer sharing demand information with the
manufacturer
 Benefits to the manufacturer
 Reduced variance to the manufacturer
 Reduced safety stock at the manufacturer
 Reduced flexibility need at the manufacturer
 Reduced smoothing costs at the manufacturer
 Benefits to the retailer
 No immediate benefits if the retailers assumes infinite capacity
at the manufacturer (a fixed lead time)
 If the manufacturer does not have infinite capacity, the actual
lead time in fact could be reduced if the retailer faces a more
stable demand through demand information sharing
Sharing demand information
 The retailer and the manufacturer should make
arrangements so that
 Retailer shares the information
 Manufacturer shares the cost savings
 The type of arrangements
 Use of vendor managed inventory to save retailer’s overhead
and processing costs
 Manufacturer offering discounts to retailer
 Manufacturer reducing lead time
Sharing demand information
Sharing inventory information
 The retailers share their inventory position with the
manufacturer (Cachon and Fisher)
 Inventory information sharing improves manufacturer’s
decision making in two ways
 Ordering decisions (Clark and Scarf)
 Echelon stocks are needed for “optimal” control
 Retailer out of balance situations needs to be identified
 Allocation decisions (Schwarz)
 Inventory allocation based on inventory position rather than order
size
 Inventory allocation based on the actual inventory position (at the
time the orders are shipped to the retailers)
Sharing promotion information
 The retailers share their promotion plans with the
manufacturer (Iyer and Ye)
 Information sharing is more beneficial
 In an environment with a high level of customer stockpiling
(high promotion sensitivity for customers)
 Toilet paper versus coffee
 In a more competitive and less predictable environment (high
variance)
 Retail promotions may decrease profits for the
manufacturer unless promotion information is shared
Information and forecast accuracy
Lead time
Order
Placed
Selling season
Season
Start
Season
End
 Forecasts are more accurate as you move closer to the season
 More information about tastes and preferences of customers
 More information about economical and competitive factors
 Procurement costs are more as you move closer to the season
 Faster modes of transportation
 Faster and more responsive suppliers
 Suppliers quoting cheaper prices well before the season
Information and forecast accuracy
Selling season
Lead time
Lead time
Order 1
Placed
Season
Start
Order 2
Placed
Order 2
Received
Season
End
 Forecasts are more accurate when you observe a portion of
demand
 Active learning about demand
 Forms the basis for “Accurate Response”
 Procurement costs are more
 Complicated procurement and production planning
 Faster modes of transportation
 Faster and more responsive suppliers
 More flexibility
Observing a portion of demand increases forecast
accuracy: Sport Obermeyer (Fisher and Raman, 1996)
Improving forecasts through
cooperation
 Customer demand depends on many factors
 Pricing, promotions
 Release of new products
 Competitors
 Not all of these issues are controlled by a single player
in the supply chain
 All of the participants in the supply chain should
collaborate to arrive at an agreed-upon forecast
 Cooperative forecasting or forecast collaboration
Other uses of information
 Helps to coordinate the supply chain
 Need information about the entire chain for global optimization
 Helps to locate the desired product across the supply
chain (order fulfillment)
 Helps to reduce lead times
 Reduce the portion of the lead time linked to order processing,
paperwork, stock picking, transportation delays through
systems like EDI
 Reduce the lead times at the supplier as they can better
anticipate demand through systems like POS
Information and supply chain trade-offs
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Lot size-inventory trade-off
Inventory-transportation cost trade-off
Lead time-transportation cost trade-off
Product variety-inventory trade-off
Cost-customer service trade-off
Lee et al.
 A two level supply chain
 Auto-correlated customer demand
 Retailer shares (or does not share) the demand
information with the manufacturer
 Information sharing may significantly reduce
inventories and total costs. Reductions increase as
 Demand correlation increases
 Variance increases
 Service level increases
Cachon and Fisher
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One supplier, N retailers
The supplier and retailers use (R, nQ) policy: if the inventory position
drops below R, order enough batches of Q units to bring it back to or
above R.
Demands at the retailers are independent of each other and independent
over time.
Traditional information: The supplier only sees the orders from retailers,
orders based on its on inventory position (installation stock) and allocates
the limited supply using a batch priority scheme.
Full information: The supplier observes inventory positions at the retailers
and allocates the limited supply to retailers based on this information.
Orders are also based on echelon stock.
Lower bound: A lower bound on all supply chain costs
Computational study
 Savings through full information are not significant
 More savings through lead time or batch size reduction