BUSI 613 Week 1 Journal Article Review 1 Example

Journal Article Review: Lee, Padmanabhan and Whang on Information Distortion and the Bullwhip Effect

The Article and Its Purpose

Lee et al. (1997) set out to explain a puzzle that managers in many industries had observed: the orders a company places with its suppliers vary much more than the sales it makes to its own customers, and the variation grows larger at each step further from the final consumer. The authors called this amplification the bullwhip effect, after the way a small movement of a whip's handle becomes a large swing at its tip. Their purpose was to show that the effect results from rational decisions by supply chain members facing particular conditions, rather than from irrational behavior, and to identify ways to reduce it.

Summary of the Article

The authors open with examples from industry. Procter & Gamble found that although consumer demand for its diapers was steady, retailers' orders fluctuated considerably and the company's own orders to its materials suppliers fluctuated even more. Hewlett-Packard observed similar distortions in orders for its printers. These examples suggested that something in the structure of supply chains, not in consumers' behavior, was creating the variation.

Using mathematical models of a simple supply chain, the authors identify four causes. The first is demand signal processing: when a retailer forecasts demand from the orders it receives, and updates its forecast and safety stock each period, a temporary increase in demand leads it to raise its order by more than the increase itself, and this amplification compounds at each stage. The second is rationing and shortage gaming: when supply is short and manufacturers allocate product in proportion to orders, buyers inflate their orders to secure more, then cancel when the shortage ends, giving the manufacturer a false picture of demand. The third is order batching: because ordering and transportation carry fixed costs, buyers accumulate demand and order periodically, often at the same time as other buyers, producing spikes in orders. The fourth is price variation: promotions and discounts encourage buyers to purchase ahead of need, so orders reflect pricing rather than consumption.

For each cause, the authors propose remedies. To address demand signal processing, they recommend sharing point-of-sale data with upstream members and arrangements such as vendor-managed inventory, in which the supplier manages the customer's inventory based on actual sales. To address rationing, they recommend allocating scarce supply on the basis of past sales rather than current orders and sharing capacity information. To address batching, they suggest electronic ordering to reduce fixed ordering costs and mixed truckloads that allow smaller, more frequent orders. To address price variation, they recommend everyday low pricing and reducing the frequency of trade promotions.

Analysis and Evaluation

The article's main contribution is to explain a widely observed phenomenon with a small number of mechanisms that managers can recognize and act on. By showing that each participant's behavior is rational from its own perspective, the authors shift attention from blaming partners to redesigning the information and incentive structures of the supply chain. That shift has shaped decades of supply chain practice, from collaborative forecasting programs to vendor-managed inventory.

The article's lasting insight is that the bullwhip effect is caused less by uncertain demand than by how supply chains process information about demand. Its limitations lie mainly in its simplicity. The models assume relatively simple supply chains and stable demand processes, and the remedies are presented as though information sharing were costless and partners willing to cooperate. Later research has tested how much each remedy actually helps. Cachon and Fisher (2000) studied the value of sharing demand information in a model calibrated to realistic conditions and found that information sharing reduced supply chain costs by a modest amount on average, while reducing lead times and batch sizes produced much larger savings. Their finding does not contradict Lee and colleagues but refines their emphasis: sharing data matters, but changing the physical flow of goods, through faster and smaller replenishments, may matter more.

Another limitation is that the article focuses on the costs of variability without fully addressing the organizational barriers to the remedies. Everyday low pricing, for example, requires manufacturers to give up promotions that sales teams and retailers value, and vendor-managed inventory requires a level of trust that many partners lack. These barriers explain why the bullwhip effect remains common more than two decades after the article appeared. Research on supply chain contracts shows that incentives can be realigned, but at a cost. Cachon and Lariviere (2005) found that revenue-sharing contracts, in which a supplier charges a lower wholesale price in exchange for a share of the retailer's revenue, can coordinate a supply chain in many settings, but that they are costly to administer and work poorly when the retailer's own selling effort drives demand. The remedies Lee and colleagues proposed therefore depend on contracts and relationships that the article takes for granted.

Connection to the Course Textbook

The article connects directly to the course text's opening chapters, which present supply chain management as the coordination of flows of materials, information and money among independent organizations to deliver value to the final customer. The bullwhip effect is a vivid example of what happens when information does not flow well: each member optimizes its own decisions, and the chain as a whole carries excess inventory, suffers stockouts and incurs higher costs. The textbook's emphasis on viewing the supply chain as an integrated system rather than a series of separate companies is precisely the perspective the article recommends. The concepts of forecasting, safety stock and lead time introduced in the early chapters are the building blocks of the article's models.

Application to Practice

Consider a composite regional grocery distributor that supplies 120 independent supermarkets. The distributor's buyers see large swings in store orders for canned goods even though consumer purchases are stable. The article's framework suggests four questions. Are stores forecasting from their own orders rather than from checkout data? Are they ordering in large, infrequent batches to fill trucks? Are they buying ahead of the distributor's monthly promotions? Do they inflate orders when the distributor rations scarce items?

If the answers are yes, the remedies follow. The distributor could collect checkout data from stores that use compatible systems and forecast from consumer demand directly, offer mixed-pallet deliveries so stores can order smaller quantities more often, replace monthly promotions with stable everyday pricing on staples and allocate scarce items according to each store's past sales. Following the finding of Cachon and Fisher (2000), the distributor should give particular priority to shortening its replenishment cycle, perhaps moving from weekly to twice-weekly deliveries, since faster replenishment may reduce variability more than information sharing alone.

Conclusion

Lee et al. (1997) provided a clear, influential explanation of why order variability grows upstream in supply chains and how companies can reduce it. Its four causes remain a practical checklist for diagnosing supply chain problems, and its remedies have become standard practice in many industries. Later research suggests that the physical changes the authors recommended, shorter lead times and smaller batches, deserve as much attention as information sharing. The article remains essential reading for any manager seeking to understand why supply chains behave as they do.

References

Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032-1048. https://doi.org/10.1287/mnsc.46.8.1032.12029

Cachon, G. P., & Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Management Science, 51(1), 30-44. https://doi.org/10.1287/mnsc.1040.0215

Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546-558. https://doi.org/10.1287/mnsc.43.4.546