MANAGING THE BULLWHIP EFFECT. Joseph H. Wilck, IV. Ph.D. Dual Degree, Industrial Engineering and Operations Research, College of Engineering

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1 MANAGING THE BULLWHIP EFFECT Joseph H. Wilck, IV Ph.D. Dual Degree, Industrial Engineering and Operations Research, College of Engineering ABSTRACT The bullwhip effect is the inherent increase in demand fluctuation up the supply chain (i.e., away from customer). Managing the bullwhip effect is minimizing the fluctuation and variation of the demand (i.e., orders from one stage of a supply chain to the next stage of the supply chain) throughout the supply chain. This paper will offer a literature review of this topic, note the key contributions, discuss current practices for managing the bullwhip effect, and explain why it is necessary for more research to be done in the area, specifically for continuous review policies. Orders/Time Period 1. INTRODUCTION The bullwhip effect is the inherent increase in demand fluctuation up the supply chain (i.e., away from customer), as shown in Figure 1.1. Managing the bullwhip effect is minimizing the fluctuation and variation of the demand (i.e., orders from one stage of a supply chain to the next stage of the supply chain) throughout the supply chain. In order to effectively manage the bullwhip effect, the primary causes of the bullwhip effect must be understood. The main causes of the bullwhip effect were identified, and analytical proofs were constructed to show why these four causes contributed to the bullwhip effect and solutions were offered to manage the bullwhip effect by Lee, Padmanabhan, and Whang [9] and [10]. The idea of businesses sharing information was introduced by Forrester [5]. This concept, when extended to individual businesses within a supply chain, is considered the best strategy when trying to reduce the bullwhip effect. However, it is impossible to completely eliminate the bullwhip effect from a supply chain (at least, in realistic supply chains). The main purposes of this paper are to: explain the causes and implications of the bullwhip effect, summarize the techniques utilized to manage the bullwhip effect, and present research detailing why the bullwhip effect is inherent to all supply chains. Time Demand Retailer Factory Figure 1.1: The Bullwhip Effect 2. CAUSES AND IMPLICATIONS OF THE BULLWHIP EFFECT Lee, Padmanabhan, and Whang [9] logically and mathematically proved that the key causes of the bullwhip effect are: demand forecast updating, order batching, price fluctuation, and shortage gaming. When considering a periodic review policy (i.e., review the inventory at specified periods and place an order to bring inventory to a certain level), the following assumptions are made: demand is constant over time, and past demand is not used to forecast future demand, no fixed order costs, per unit cost of the product is constant over time, and an infinite amount of supply is available for a constant lead time. The optimal periodic review policy for the above assumptions is to order the previous period s demand for each upcoming period. Therefore, the demand and orders will have the same distribution and variation, and hence no bullwhip effect in the system. However, none of the aforementioned assumptions are entirely realistic for an authentic supply chain. Removing the assumptions (one at a time) leads to the four causes of the bullwhip effect. The following subsections of the report explain the analytical evidence

2 behind the causes of the bullwhip effect that were identified by Lee, Padmanabhan, and Whang [9] Demand forecast updating Demand forecast updating, also known as demand signal processing, and occurs when the first assumption is violated. Hence, demand is not constant and observed past demand is used to forecast future demand. By finding a closed form solution relating the variance of an order to the variance of the demand for a single stage within a supply chain, a theorem was proved providing two major inferences: variance of orders (i.e., which based on previously observed demand) is strictly greater than variance of true demand (even if lead time is zero), and variance of orders will strictly increase as lead time (including transit time) increases. The operations research technique used to prove this theorem involved an optimization model that minimizes the cost of an ordering system based on a forecast built on previously observed demand. The primary assumption of this model was that demand is serially correlated (i.e., demand is correlated mostly with demand from adjoining time periods). Implications of these results are that there is an inherent increase in variation of perceived demand up the supply chain (i.e., away from customer) because the true demand is distorted by the forecast. As more stages are added to the supply chain, the demand distortion increases - producing the bullwhip effect. Another implication is that safety stock is utilized at each stage of the supply chain because each stage has its own forecast. These implications give rise to the need for information sharing and coordination within a supply chain. Information sharing within the supply chain will inevitably lead to a better perception of true demand (i.e., there will be less distortion). In the Beer Distribution Game only the retailer knew the true customer demand [15]. Information regarding true demand was distorted at each successive stage moving up the supply chain because each stage made its own forecast. Based on Table 2.1, if lead time is 1 time unit between each stage (except between the initial stages of customer and retailer), then it takes 5 time units for the customer demand information to reach the Tier-2 Supplier. However, if information is shared throughout the supply chain, then this lead time can be mitigated. Yet, the individual forecasts for each stage will continue to cause variation between true demand and orders. These individual forecasts can be replaced by a collective forecast through coordination within the supply chain by the use of centralized control. Centralized control is the concept of having one entity (i.e., one company) control the ordering policies for the entire supply chain. A centralized control supply chain utilizes a single forecast; therefore, customer demand is not distorted by multiple forecasts. Combining information sharing and centralized control reduces the bullwhip effect; however, it is not eliminated because there is still lead time variation, price fluctuation, fixed order costs, and availability of resources (i.e., possible shortages). Yet, the risk of these factors is minimized in the supply chain since the variation is pooled through the use of centralized control. Table 2.1: Lead Time of a Supply Chain Stage Lead Time Cumulative Lead Time Customer 0 0 Retailer 1 1 Warehouse 1 2 Factory 1 3 Tier-1 Supplier 1 4 Tier-2 Supplier 1 5 * Lead time in units of time 2.2. Order batching Order batching occurs based on the frequency and size of orders from multiple customers. This cause occurs when the second constraint is violated. Three specific cases were explored: random ordering: demands from multiple customers are independent, positively correlated ordering: demands from multiple customers occur in the same period, and balanced ordering: demands from multiple customers are evenly distributed across many time periods. Based on these three cases, it was proven that the expected orders would equal the expected demand; however, the variance follows (from highest to smallest): correlated ordering, random ordering, balanced ordering, and demand. The only time the variance of balanced ordering was equal to the variance of demand was when the orders were perfectly balanced synchronized, which leads to no bullwhip effect (provided the other three assumptions hold). The operations research techniques used to prove this theorem were statistically based. The basic assumptions of the theorem were that the demands for each customer were independent and identically distributed across the time periods for each customer. Positively correlated ordering is the most realistic form, of the three ordering schemes studied, because of habits that have formed in industry. In manufacturing the use of MRP systems for planning and production has led to correlated demand. It is estimated that more than 70% of monthly manufacturing orders occur in the last week. For example, a supplier has two primary customers who are independently owned and operated; however, both of

3 those customers generally order at the end of the month. In business, many deals are struck in the 11th hour, in order to meet quarterly or annual projections. These orders are processed at the same time, forcing demand to be at the end of the time period. This phenomenon of unsynchronized orders increases the bullwhip effect. Techniques to reduce bullwhip effect that is caused by order batching include: synchronized orders and reducing lot sizes. Synchronized ordering is coordinating with customers so that orders from multiple customers are not received simultaneously. Reducing lot sizes goes against the advantages of basic economic order quantity (EOQ) principles that balance inventory and order costs; however, coordination within the supply chain can lead to lower fixed ordering costs, allowing smaller lot sizes can be more economical for both the supplier and the buyer Price fluctuation Price fluctuation occurs when price of a product is allowed to fluctuate. This cause occurs when the third constraint is violated. By minimizing the expected discounted cost over an infinite amount of time, a closed form solution for the cost can be obtained. The result implies that the optimal inventory policy of a consumer is to store as much as possible when the cost is low, and to expend that inventory during higher price levels. The bullwhip effect occurs since a regenerative buying cycle incurs (i.e., the interval of time periods between two consecutive periods of low price). Basically, the customer orders fluctuate based on the product cost, but demand is constant over time. The operations research techniques used to prove this theorem were to solve a maximization problem. The basic assumption of the theorem was that inventory cost was the same regardless of purchase price; however, the inherent problem did not loss any significant features. Price fluctuation includes promotions and lot-size quantity discounts. Industries that thrive on promotions often see their customers order during low price periods, and hold inventory during high price periods. Lot-size quantity discounts are rewarding customers for large batch-sizes by offering a discount on the specified quantity. Techniques to reduce bullwhip effect that is caused by price fluctuation include: everyday low prices (EDLP), specified contracts, and volume-based quantity discounts. EDLP is, as the name suggests, offering a continuously low price. EDLP contrasts the idea of promotion. This works best with functional products, and has been used frequently in the grocery industry by Kraft, Proctor and Gamble, and Pillsbury [11]. Specified contracts are coordinating with a primary customer on a set price over a set amount of time periods (i.e., EDLP for that customer). The basic premise of offering a constant price is the following: constant prices lead to steadier sales, studier sales reduce demand fluctuation, and smaller demand fluctuation enables a reduction of inventory and bullwhip effect. Volume-based quantity discounts should be used inlieu of lot-size quantity discounts. Volume-based quantity discounts are rewarding customers who order a certain amount of product over a given time period. For example, a volume-based discount for ordering 5000 units a year could be offered. Therefore, a customer will most likely order in smaller batch sizes (i.e., 100 units a week), then if there were a lot-size quantity discount. This is beneficial for both the buyer and the seller since the buyer has steadier demand (i.e., smaller bullwhip effect), and the buyer will be able to maintain smaller inventories Shortage gaming Shortage gaming occurs when shortages are known to occur in a specific market, forcing customers to exaggerate orders in hopes that through the distribution of product amongst many customers they will get their true order fulfilled. This also occurs because there is a free return policy (i.e., the customer can return unsold products for credit). Therefore, all of the risk is on the supplier. This cause occurs when the fourth constraint is violated. By developing a model based on the newsvendor problem with multiple customers. A Nash equilibrium, is established to define the customers strategies [6]. The Nash equilibrium defined by the model is mathematically manipulated to prove that it is pseudo-convex. Therefore, an optimal order quantity is derived for a customer, and compared to the general newsvendor problem solution. The general newsvendor problem (i.e., when orders are placed for true demand) solution has less bullwhip effect than the solution obtained by the Nash equilibrium model (i.e., when orders are exaggerated). During low demand periods, the Nash equilibrium model will exhibit orders that are near to the general newsvendor problem, but during high demand periods the model will have orders that are much larger than the newsvendor problem. Therefore, the bullwhip effect is further increased when shortage gaming is combined with demand forecast updating. Furthermore, in a supply chain the impact of shortage gaming is amplified down the supply chain (i.e., towards the customer). For example, if a manufacturer is in short supply, multiple distributors will play the shortage game, and then the retailers will play the shortage game. Techniques to reduce bullwhip effect that is caused by shortage gaming include: changing the allocation policy (when in short supply) to reflect past sales history and capacity reservation. By changing the allocation policy to reflect past sales history, the exaggerated orders are ignored, and thus the shortage game is not played.

4 Capacity reservation is coordinating with the primary customers that they will receive a set percentage of capacity. This concept is used by Wal-Mart and many of its suppliers, in conjunction with other supply chain coordination techniques. The problem with exaggerating orders in a supply chain is often it leads to multiple stages playing the shortage game (i.e., multiple stages exaggerate orders). This leads to an increase in safety stock, and if the demand for the product does not meet expectations, then there is an unnecessary build-up of inventory. In 2001, Cisco experienced this problem, and estimated that losses due to outdated inventory were $2.1 billion [11]. 3. TECHNIQUES TO MANAGE THE BULLWHIP EFFECT The key techniques highlighted by Lee, Padmanabhan, and Whang [9] to manage the bullwhip effect are summarized in Table 3.1. The key to managing the bullwhip effect is to share information with other members of the supply chain. The Internet, for its information sharing capabilities and Radio frequency identification (RFID) are considered the two major technological advances that can be used to manage the bullwhip effect [11]. Currently, the benefits of RFID technology are being researched, and the information sharing capacity of the technology is unknown; however, the potential is there provided the cost of RFID technology reduces. Electronic Data Interchange (EDI) is used as an information sharing tool by companies within the same supply chain [14]. Data-sharing between companies is tedious since computer systems are specific for each business. EDI utilizes standard systems (i.e., ANSI and EDIFACT) to transfer the data from one company s system to another company s system so that the humanintervention of data collection can be minimized. Information sharing is considered the key technique for reducing the bullwhip effect. However, Kwikkers [8] suggests that information sharing is an initial step to reducing the bullwhip effect within the supply chain, but that continuously using information from other stages in the supply chain can lead to additional problems. Furthermore, information is oftentimes incorrect or irrelevant. For example, point of sales (POS) data for a grocery store is not useful for a supplier of sugar. Obviously, items in the grocery store contain sugar; however, the economies of scale are not directly comparable for the two stages. It has also been shown that information sharing is redundant if the supply chain is not capable of capitalizing on that information due to long lead time [7]. Coordination in the supply chain is the next primary technique used in managing the bullwhip effect. Generally, coordination in the supply chain inherently forces information sharing within the supply chain. Coordinating forecasts and ordering policies has been shown to reduce the bullwhip effect, and Chopra [3] suggests coordination within the supply chain can also reduce cost (i.e., inventory, transportation) and increase customer responsiveness. Commonly used coordination structures and schemes include: VMI and collaborative planning, forecasting, and replenishment (CPFR). 4. THE INHERENT NATURE OF THE BULLWHIP EFFECT Research has shown that the bullwhip effect is inherent in all realistic supply chains. The models presented in Section 2 of this report proved that the bullwhip effect was inherent, unless all of the assumptions (none of which are completely realistic) are met. Further extensions to this inherent nature are examined and presented in this section. Table 3.1: Summary of Causes and Management Techniques of Bullwhip Effect Bullwhip Causes Contributing Factors Techniques to Manage Demand Forcast Updating No concept of true demand Share sales information Multiple forecasts Centralized control Long lead time Reducing lead time Order Batching High fixed order costs Synchronized ordering Random ordering Reducing lot sizes Correlated ordering Price Fluctuation Fluctuation in prices EDLP Promotions Volume-based quantity discounts Lot-size quantity discounts Shortage Gaming Inflated orders Allocate based on pased sales Free returns policies Capacity reservation *Note this table is based on a similar table from Lee, Padmanabhan, and Whang (1997a)

5 4.1. The effects of lead time Gilbert [7] presents an Autoregressive Integrated Moving Average (ARIMA) time-series model for customer demand and lead times for a multistage supply chain. The models shows that the orders and inventories for each stage are also ARIMA. Based on these models, Gilbert suggests techniques to reduce the bullwhip effect. He presumed that forecasts could either be true reflections of demand shifts or incorrect reflections of demand shift. The conclusions drawn were: forecasts that reflect true demand shifts can still cause the bullwhip effect, information sharing may be redundant, and not helpful in reducing the bullwhip effect, reduction in lead time will lead to a reduction in bullwhip effect, information sharing techniques to reduce the bullwhip effect depend on lead time (i.e., the information is not useful unless the supply chain is coordinated and fast enough to act when it is received), and total supply chain lead time (not the number of stages), is the primary cause of the bullwhip effect for forecasts that reflect true demand shifts. The results of Gilbert s [7] research show that reducing lead time for stages and for the entire supply chain will further enable the management of the bullwhip effect. Furthermore, the variation in lead time is a key contributor to the need for safety stock [15]. Therefore, lead time plays a key role in the manageability of the bullwhip effect Benefits of managing the bullwhip effect Metters [13] research suggests that reducing the bullwhip effect can dramatically reduce costs. However, managing bullwhip effect is often hard to do. Information sharing and coordination within the supply chain are two wellknown management techniques, but solutions for one supply chain may not be easily transferable to other supply chains. Therefore, there is still unrealized potential in cost savings possibilities. Contrary to Metters, other researchers [2] showed that reducing the bullwhip effect may not prove to be economical. A supply chain s efficiency is based on total supply chain cost. Reducing the bullwhip effect, in certain instances, may increase transportation cost to the point where it is better to allow the bullwhip effect to occur (to an extent), rather than to minimize it as much as possible. The basic premise for the Chen s and Samroengraja s [2] research is that when incorporating a business strategy to reduce the bullwhip effect, that economic analysis should be completed because reducing the bullwhip effect do not necessarily reflect savings in total supply chain cost Centralized control The research of Chen et al [1] prove that forecasting demand will inevitably lead to the bullwhip effect even with information sharing. Different model supply chains were established to prove that as the level of information sharing and centralized control increased, the bullwhip effect decreased. However, the model supply chains did not consider multiple entities at each stage of the supply chain. Yet, the basic premise is that centralized control will not completely eliminate the bullwhip effect is due to the fact that lead time (and also the square of lead time) affects the bullwhip effect. Furthermore, in a decentralized supply chain the bullwhip effect is multiplicative; whereas, in a centralized supply chain the bullwhip effect is additive. Therefore, the bullwhip effect of equivalent supply chains has the capability to be much lower in centralized supply chains The effects of forecasting Dejonckheere, Disney, Lambrecht, and Towill [4] prove that forecasting demand will inevitably lead to the bullwhip effect. This was initially proven [9]; however, the result was further extended to show that the order-upto policy (i.e., periodic review) could be enhanced in order to minimize the bullwhip effect. However, it is the opinion of this report writer that this policy has limited relevance to real-world applications. The reason for this opinion is that the extension of the order-up-to policy is complex, and the use of periodic review policies is generally for non-critical items [15]. Therefore, why would a company go through the trouble of updating its policy for non-critical parts? Furthermore, with the advances in technology (i.e., RFID), more products will inevitably be analyzed on a continuous review policy therefore making order-up-to policies obsolete The effects of marketing Lee, Padmanabhan, and Whang [9] suggest that EDLP should be used instead of promotions. However, through the use of simulation Lummus, Duclos, and Vokurka [12] suggests that marketing and promotions can be used to without causing an increase to bullwhip effect if information is shared correctly. The research suggests that promotions are inevitable (i.e., Christmas sales), but if information is shared throughout the supply chain, then the bullwhip effect can be decreased. Proctor and Gamble (a manufacturer) utilizes a system where retailers inform them of upcoming promotions so that products can be available if the retailer should run out of stock [15].

6 5. REFLECTIONS AND OBSERVATIONS The bullwhip effect is an inherent problem of supply chains that has been noticed in industry since at least the 1950 s by Forrester. Research surrounding the term bullwhip effect began in the 1990 s, and currently the research is focused on quantifying (or measuring) the bullwhip effect and managing the bullwhip effect. One could argue that in order to manage the bullwhip effect, the bullwhip effect must first be quantified. However, the overwhelming area of research is centered on order-up-to policies (i.e., periodic review policies). The research of Lee, Padmanabhan, and Whang considered a periodic review policy. However, higher priority items should be under a continuous review policy [15]. Furthermore, with advances in technology, such as RFID, continuous review is becoming easier. Therefore, mathematical models and proofs could be used to derive the evidence of the bullwhip effect in continuous review policies. Further research in quantifying (or measuring) and managing the bullwhip effect should be done on continuous review policies. The researchers have failed to gravitate towards the real world applications and problems regarding this area. Instead, they prove (again and again) that forecasting demand from previous demand implies that the bullwhip effect exists for periodic review policies. Meanwhile, products are actually being reviewed on a continued basis, and the research is not directly applicable. This may be one hindrance as to why a lot of the theoretical research in this area has failed when being directly applied to industrial problems. 6. CONCLUSIONS The bullwhip effect is inherent to every realistic supply chain. This concept has been proven through research and mathematical models time and time again. In practice this concept is visually seen in all supply chains, centralized or decentralized, service or manufacturing, etc. The constant theme is that the bullwhip effect must be managed. Managing the bullwhip effect is challenging. Best practices include sharing information with members of the supply chain and coordinating efforts with partners in the supply chain. Notice the constant theme involves multiple partners within a supply chain. Ravindran [15] suggested that individual companies are not competing against each other, but rather entire supply chains are competing against each other. Generally, a smaller bullwhip effect in a supply chain is better for all companies involved. The significant reasons for managing the bullwhip effect are reductions in inventory, obsolete inventory, and shortage costs. It was noted by Gilbert [7] that total supply chain lead time is an important factor when managing bullwhip effect. Reducing total lead time within a supply chain must be completed with information sharing and coordination. Business leaders know that information sharing is the key management technique for reducing the bullwhip effect. However, what business leaders fail to realize is that the shared information must be correct and relevant or adverse effects could occur [8]. Furthermore, business leaders do not realize the inherent nature of the bullwhip effect, and how it is impossible to completely eliminate. Reducing the bullwhip effect may not yield the most economical results [2]. Generally, inventory costs and stockout costs are reduced enough to warrant a system change; however, transportation costs (or some other cost) could become higher and reduce the bottom line. There is no substitute for a basic economic analysis when implementing a change within the supply chain system. Extensions in research include analyzing the bullwhip effect and offering management techniques for continuous review ordering policies. These policies are best for the priority products [15]; therefore, it makes sense that these policies should be analyzed to determine how the bullwhip effect can be further reduced. The underlying theme is that the bullwhip effect is inherent to all supply chains, eliminating the bullwhip effect is therefore impossible, but managing the bullwhip effect should be an initiative of every supply chain. Furthermore, managing the bullwhip effect is an entire supply chain effort; it cannot be done by a single company (i.e., stage) within the supply chain. 7. REFERENCES [1] Chen, F., Z. Drezner, J.K. Ryan, and D. Simchi-Levi. Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information, Management Science, 46(3), , [2] Chen, F., and R. Samroengraja. Order Volatility and Supply Chain Costs, Operations Research, 52(5), , [3] Chopra, S. and P. Meindl. Supply Chain Management. Second Edition, Prentice Hall, , [4] Dejonckheere, J., S.M. Disney, M.R. Lambrecht, and D.R. Towill. Measuring and Avoiding the Bullwhip Effect: A Control Theoretic Approach, European Journal of Operational Research, 147, , [5] Forrester, J.W. Industrial Dynamics, Harvard Business Review, July-August, 1958.

7 [6] Game Theory.net. URL: ( html), Mike Shor, [7] Gilbert, K. An ARIMA Supply Chain Model, Management Science, 51(2), , [8] Kwikkers, R. Lean Supply Chain Planning, Collaborative Systems for Production Management. IFIP TC5/WG5.7 Eighth International Conference on Advances in Production Management Systems, 59-71, September [9] Lee, H.L., V. Padmanabhan, and S. Whang. Information Distortion in a Supply Chain: The Bullwhip Effect, Management Science, 43(4), , 1997a. [10] Lee, H.L., V. Padmanabhan, and S. Whang. The Bullwhip Effect in Supply Chains, Sloan Management Review, 38(Spring), , 1997b. [11] Lee, H.L., V. Padmanabhan, and S. Whang. Comments on Information Distortion in a Supply Chain: The Bullwhip Effect, Management Science, 50(12), , [12] Lummus, R.R., L.K. Duclos, and R.J. Vokurka. The Impact of Marketing Initiatives on the Supply Chain, Supply Chain Management: An International Journal, 8(4), , [13] Metters, R. Quantifying the Bullwhip Effect in Supply Chains, Journal of Operations Management, 15(2), , [14] Nahmias, S. Production and Operations Analysis. Fourth Edition, McGraw-Hill, , [15] Ravindran, A.R. Course Lectures and Notes: Supply Chain Engineering, IE 597I, Industrial and Manufacturing Engineering Department, Pennsylvania State University, Fall 2005.