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 DistributorDistributor Orders to Factory Order 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 timeSelling 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 2 Received

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