Industrial production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales. Previous research has shown that retail sales are correlated with the state of the economy. Since retailers source their products from other industries, the sales of their suppliers can also be correlated with the state of the economy. This correlation represents the source of systematic risk for an industry that propagates through a supply chain network. Specifically, we identify the following mechanisms that can affect the correlation between sales and the state of the economy in a supply chain network: propagation of systematic risk into production decisions, aggregation of orders from multiple customers in a supply chain network, and aggregation of orders over time. We find that the first effect does not amplify the correlation; however, the latter two intensify correlation and result in the amplification of correlation upstream in supply networks. We demonstrate three managerial implications of this phenomenon: implications for the cost of capital, for the risk-adjusted valuation of supply chain improvement projects, and for supplier selection and risk. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2187 . This paper was accepted by Serguei Netessine, operations management.

We offer a new network perspective on one of the central topics in operations management—the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with an observation: the variability of orders placed to suppliers is larger than the variability of sales to customers for most firms, yet the aggregate demand variability felt by suppliers upstream does not amplify commensurably. We hypothesize that changes to the supplier’s customer base can smooth out its aggregate demand. We test the hypothesis with a data set that tracks the evolution of supply relationships over time. We show that the effect of customer base management extends beyond the statistical benefits of aggregation. In particular, both the formation and the dissolution of customer-supplier relationships are associated with the smoothing of the aggregate demand experienced by suppliers. This provides fresh insight into how firms may leverage their customer-supplier relationships to mitigate the impact of the BWE. This paper was accepted by Jay Swaminathan, operations management.

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We analyze a revenue management problem in which a seller endowed with an initial inventory operates a selling with binding reservations scheme. Upon arrival, each consumer, trying to maximize his own utility, must decide either to buy at the full price and get the item immediately, or to place a non-withdrawable reservation at a discount price and wait until the end of the sales season where the leftover units are allocated according to first-come first-serve priority. We study structural properties of this noncooperative game, and prove the existence of a Bayesian-Nash equilibrium. The equilibrium is of the threshold type, meaning that a consumer will place a reservation if and only if his valuation is below a function of his arrival time. The computation of this consumer's strategy is intensive, and provably convergent under specific conditions. To overcome this limitation, we formulate an asymptotic version of the problem that leads to a simple closed-form solution, which is then used as an approximate equilibrium for the original problem. Our computations show that this heuristic is accurate for medium to large-size problems.Finally, through an extensive numerical study, we find that the proposed mechanism with reservations almost consistently delivers higher revenue than the standard markdown with a preannounced fixed-discount. The benefit is more emphasized when 1) The ratio between the initial number of units and the expected demand is moderate to large, or 2) the heterogeneity of the consumers' valuations is moderate to high. In our numerical experiments, the revenue gap can reach a level up to 5%, which is quite significant for retail businesses that typically operate with narrow margins.

We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers. Taking as input forecasts from other sources, such as equity analysts or time‐series models, we construct a market‐based forecast by augmenting the input forecast with one additional variable, lagged return on an aggregate financial market index. For this, we develop and estimate a martingale model of joint evolution of sales forecasts and the market index. We show that the market‐based forecast achieves an average 15% reduction in mean absolute percentage error compared with forecasts given by equity analysts at the same time instant on out‐of‐sample data. We extensively analyze the performance improvement using alternative model specifications and statistics. We also show that equity analysts do not incorporate lagged financial market returns in their forecasts. Our model yields correlation coefficients between retail sales and market returns for all firms in the data set. Besides forecasting, these results can be applied in risk management and hedging.