Xavier Brusset

Assessing and hedging the cost of unseasonal weather: Case of the apparel sector

Retail activities are increasingly exposed to unseasonal weather causing lost sales and profits, as climate change is aggravating climate variability. Although research has provided insights into the role of weather on consumption, little is known about the precise relationship between weather and sales for strategic and financial decision-making. Using apparel as an illustration, for all seasons, we estimate the impact on sales caused by unexpected deviations of daily temperature from seasonal patterns. We apply Seasonal Trend decomposition using Loess to isolate changes in sales volumes. We use a linear regression to find the relationship between temperature and sales anomalies and construct the historical distribution to determine sales-at-risk due to unseasonal weather. We show how to use weather derivatives to offset the potential loss. Our contribution is twofold. We provide a new general method for managers to understand how their performance is weather-related. We lay out a blueprint for tailor-made weather derivatives to mitigate this risk.

Dynamic supply chain coordination games with repeated bargaining

A supplier will behave opportunistically to extract a rent from his customer.The customer mis-represents the costs of dealing with the supplier to protect his rent.The supply chains efficiency is affected even though opportunistic behavior is absent.Dual-sourcing finds further justification here. Coordination in a supply chains may require investment in relationship-specific assets (RSA) including information systems and human resources from all or a subset of the partners. These investments are typically partially non-verifiable, possibly based on internal resources or opportunity costs. A supplier offers a single-price single-period contract to a downstream manufacturer who accepts or turns to a non-strategic outside option. Both parties invest in relationship-specific assets (RSA) accordingly. Using a game theoretic framework of repeated single-period bargaining under asymmetric information and outside options, we show how a supplier may behave opportunistically. We show how this rent extraction threat is mitigated when the manufacturer mis-informs the supplier or hides information from her. As a result of both behaviors, our model explains how supply chain coordination and efficiency are impaired. On a normative basis, we provide the manufacturer with new justifications for both dual sourcing and distorting information. Numerical examples illustrate the results.

Optimizing an objective function under a bivariate probability model

The motivation of this paper is to obtain an analytical closed form of a quadratic objective function arising from a stochastic decision process with bivariate exponential probability distribution functions that may be dependent. This method is applicable when results need to be offered in an analytical closed form without double integrals. However, the study only applies to cases where the correlation coefficient between the two variables is positive or null. A stochastic, stationary objective function, involving a single decision variable in a quadratic form is studied. We use a primitive of a bivariate exponential distribution as first expressed by Downton [Downton, F., 1970. Bivariate exponential distributions in reliability theory. Journal of Royal Statistical Society B 32, 408–417] and revisited in Iliopoulos [Iliopoulos, George., 2003. Estimation of parametric functions in Downton’s bivariate exponential distribution. Journal of statistical planning and inference 117, 169–184]. With this primitive, optimization of objective functions in Operations Research, supply chain management or any other setting involving two random variables, or calculations which involve evaluating conditional expectations of two joint random variables are direct. We believe the results can be extended to other cases where exponential bivariates are encountered in economic objective function evaluations. Computation algorithms are offered which substantially reduce computation time when solving numerical examples.

Weather Effect on Apparel Sales in France

In 2012, French apparel industry suffered weak sales for the fifth consecutive year. Even if economic conditions were not favorable, trade professionals feel that the weather played a significant role. Its impact on retail sales in general has not been formally quantified. This has become an urgent issue as climate change is aggravating naturally occurring climate variability and is becoming a source of uncertainty for climate-sensitive economic sectors. In this paper we provide managers with tools to evaluate the impact of temperature anomalies on sales volumes. We present a statistical method to separate out the weather effect from the underlying real performance of apparel sales. The model has been developed for the retail economic sector but can be extended to all fast moving consumer goods both in France and abroad. These models are applicable to supply chain managers and business analysts.