Knowledge that Transforms

This study examines the use of wine futures (i.e., advance selling of wine before it is bottled) as a form of operational flexibility to mitigate quality rating risk. At the end of a harvest season, the winemaker obtains a certain number of barrels of wine that can be produced for a particular vintage. While the wine is aging in the barrel, expert reviewers taste the wine and create a barrel score, indicating the potential quality of the wine and offering clues as to whether, when bottled, it will be superior wine. Based on the barrel score, the wine producer determines (1) the percentage of its wine to be sold as futures and (2) the price of the wine futures. After one more year of aging, the wine is bottled, and the reviewers provide a second review of the wine and assign a bottle score that influences the market price of the wine. Our study makes three contributions. First, we develop an analytical model that incorporates uncertain consumer valuations of wine futures and bottled wine and the uncertain bottle rating that is assigned to the wine at the end of the production process. Our analysis provides insights into how the barrel score, consumer preference (through a conditional-value-at-risk perspective) and the winemaker’s preference influence the winemaker’s allocation and pricing decisions. Our second contribution relates to the impact of consumer heterogeneity on the optimal allocation and pricing decisions. Contrary to common belief that the winemaker may be better off when consumers are more homogeneous, our results demonstrate that the winemaker can achieve a higher level of profitability when the market is filled with consumers that are heterogeneous. Third, we test our findings using data collected from Bordeaux wineries engaging in wine futures. Our empirical analysis demonstrates that (1) barrel scores play a significant role in the two decisions regarding the quantity and price of wine futures, and (2) the wine futures market provides a sizable financial benefit to the winemakers. Our analysis yields recommendations for artisanal and boutique wineries that have limited or no experience selling wine futures.

The bullwhip effect and production smoothing appear antithetical because their empirical tests oppose one another: production variability exceeding sales variability for bullwhip, and vice versa for smoothing. But this is a false dichotomy. We distinguish between the phenomena with a new production smoothing measure, which estimates how much more variable production would be absent production volatility costs. We apply our metric to an automotive manufacturing sample comprising 162 car models and find 75% smooth production by at least 5%, despite the fact that 99% exhibit the bullwhip effect. Indeed, we estimate both a strong bullwhip (on average, production is 220% as variable as sales) and robust smoothing (on average, production would be 22% more variable without deliberate stabilization). We find firms smooth both production variability and production uncertainty. We measure production smoothing with a structural econometric production scheduling model, based on the generalized order-up-to policy.

A retailer cannot sell more than it has in stock; therefore, its sales observations are a censored representation of the underlying demand process. When a retailer forecasts demand based on past sales observations, it requires an estimation approach that accounts for this censoring. Several authors have analyzed inventory management with demand learning in environments with censored observations, but the authors assume that inventory levels are known and hence that stockouts are observed. However, firms often do not know how many units of inventory are available to meet demand, a phenomenon known as inventory record inaccuracy. We investigate the impact of this unknown on demand estimation in an environment with censored observations. When the firm does not account for inventory uncertainty when estimating demand, we discover and characterize a systematic downward bias in demand estimation under typical assumptions on the distribution of inventory record inaccuracies. We propose and test a heuristic prescription that relies on a single error statistic and that sharply reduces this bias.

We consider a manager who invests in knowledge development of a product and a process design team as well as knowledge transfer between teams throughout a new product development (NPD) project. Knowledge development at a particular time (e.g., prototyping and experimentation) increases a team’s level of knowledge at that time. In contrast, the recipient’s benefits from knowledge transfer may be lagged because of the difficulties in articulating and documenting knowledge as well as the challenges regarding its interpretation and application. Over time, as each team embeds knowledge in the NPD project, the levels of product and process performance increase, thereby increasing the net revenue earned at the product launch time. In a key contribution to the literature, analytic conditions are given that characterize the dynamic rates at which knowledge development and knowledge transfer occur throughout the project. We show that the investment in knowledge development for each team and knowledge transfer between teams may be constant, front-loaded, back-loaded, U-shaped, or the peak rate may be delayed over time. As such, we show how concurrent engineering is optimally pursued throughout the NPD project.

Key to the mass adoption of electric vehicles (EVs) is the establishment of successful business models based on sound understanding of consumer behavior in adopting this new technology. In this paper, we study the impact of two major barriers to mass adoption of EVs: (i) range anxiety, the concern that the driving range of EVs may be insufficient to meet the driving needs, and (ii) resale anxiety, the concern that used values of EVs may deteriorate quickly. Using a stylized model calibrated to a data set based on the San Francisco Bay Area, we show that although both types of consumer anxieties typically harm the firm’s profit, they often improve consumer surplus. In addition, we show that a business model that requires consumers to lease the EV batteries (rather than purchase them) may lead to a greater level of adoption and emission savings when the level of resale anxiety is high. Further, a business model that offers EV range improvement through enhanced charging infrastructure typically yields greater adoption and consumer surplus, but lowers the firm’s profit, compared with one that offers enlarged batteries. Overall, we find that the combinations of battery owning/leasing with enhanced charging service, referred to as the (O, E) and (L, E) models in our paper, typically yield the best balance among the objectives of EV adoption, emission savings, profitability, and consumer surplus, when the degree of resale anxiety is low and high, respectively.