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

…

Customers

Supply chain

Data elements

Static data 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

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

SO

Transactions inside the company

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

TO

Transactions with the supplier

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)

PO

Bullwhip effect

Factory

Distributor

Wholesaler

Retailer

Bullwhip effect

Customer Orders Retailer Orders to Wholesaler

Wholesaler Orders to Distributor Distributor Orders to Factory

Ord

er

size

Ord

er

size

Ord

er

size

Ord

er

size

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 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 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

Season Start

Season End

Order Placed

Lead time Selling season

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

Season Start

Season End

Order 1 Placed

Lead time

Selling season

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

Order 2 Placed

Lead time

Order 2Received

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

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

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