Channel Integration, Sales Dispersion, and Inventory Management

Santiago  Gallino; Antonio  Moreno; Ioannis  Stamatopoulos

Article | Management Science

Channel Integration, Sales Dispersion, and Inventory Management

by Santiago Gallino, Antonio Moreno and Ioannis Stamatopoulos

Abstract

We study the effects of the introduction of cross-channel functionalities on the overall sales dispersion of retailers and the implications of these effects for inventory management. To do that, we analyze data from a leading U.S. retailer who introduced a “ship-to-store” (STS) functionality that allows customers to ship products to their local store free of charge when those products are not available in their local store. Based on the fact that stores prioritize carrying products for which local demand is high, we test the hypothesis that introducing the STS functionality increased the retailer’s overall sales dispersion. We find that, on average, the contribution of the 90% lowest-selling products to total sales increased by 0.75 percentage points, increasing sales dispersion. Calibrating conventional inventory-ordering models, we show that to respond optimally to the observed increase in dispersion, the retailer would need to increase its cycle and safety inventories by approximately 2.7%. Our paper points out the effect of an increasingly important retail phenomenon (channel integration) on a key factor for inventory management (sales dispersion).

Keywords: Retail operations; online retail; channel integration; sales dispersion; long tail; empirical operations; Inventory management; omnichannel retail; Marketing Channels; Integration; Sales; Logistics; Operations; Management; Retail Industry;

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Citation:

Gallino, Santiago, Antonio Moreno, and Ioannis Stamatopoulos. "Channel Integration, Sales Dispersion, and Inventory Management." Management Science 63, no. 9 (September 2017): 2813–2831.

The Indian banknote demonetization in 2016 was one of the most significant international events of that year. Overnight, 86% of Indian currency in circulation was declared invalid unless exchanged for new bills. The sudden and unexpected demonetization constituted a large shock to the entire Indian economy. One effect of the ensuing cash shortage was a large and sustained increase in the adoption and usage of digital payments. We use detailed sales data consisting of more than two and a half million transactions from a leading Indian online retailer to empirically investigate the effects of payment digitization on the online retail industry. We take advantage of the demonetization as a source of exogenous variation that induced a subset of consumers to switch to digital payments from more commonly used cash-on-delivery payments. We show that consumers who switch to digital payments maintain their purchase frequency but spend more and are less likely to return their purchases. Our findings show that firms in emerging markets may enjoy gains from consumer demand in addition to operational gains resulting from payment digitization.

Article | Production and Operations Management | October 2018

The Operational Value of Social Media Information

Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts. We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. We find that using social media information results in statistically significant improvements in the out-of-sample accuracy of the forecasts, with relative improvements ranging from 12.85% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best-performing method (random forest) yields an out-of-sample mean absolute percentage error (MAPE) of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.

Keywords: social media; Machine learning; Information; Sales; Forecasting and Prediction;

Citation:

Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774. View Details

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Channel Integration, Sales Dispersion, and Inventory Management

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