The Effects of Product Line Breadth: Evidence from the Automotive Industry

Antonio  Moreno; Christian  Terwiesch

Article | Marketing Science

The Effects of Product Line Breadth: Evidence from the Automotive Industry

by Antonio Moreno and Christian Terwiesch

Abstract

Using a detailed data set from the U.S. automotive industry, we enrich the existing literature on product line breadth with new results that highlight previously unexplored operational aspects of its benefits and costs. We find that expanding product line breadth has a significant effect on increasing mismatch costs arising from the increased demand uncertainty associated with product proliferation. These mismatch costs are manifested through additional discounts and inventories. The effect of product line breadth on mismatch costs is comparable in magnitude to the effect on production costs, suggesting that the operational benefits of inventory pooling achievable by rationalizing product lines can be very substantial. Furthermore, we quantify the benefit of using a platform strategy to mitigate the effects of a broad product line on production costs. Finally, we propose an additional, attribute-based measure of product line breadth and find that product line breadth can work as a hedge against changes in demand conditions. For example, automakers that offer a broader range of fuel economy levels increase their market share and reduce their average discounts as gas prices become more volatile.

Keywords: variety; pricing; automotive industry; marketing/operations interface; platforms; empirical operations management; Product Marketing; Production; Management; Market Platforms; Auto Industry;

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Citation:

Moreno, Antonio, and Christian Terwiesch. "The Effects of Product Line Breadth: Evidence from the Automotive Industry." Marketing Science 36, no. 2 (March–April 2017): 254–271.

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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The Effects of Product Line Breadth: Evidence from the Automotive Industry

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