Dr. Hana (Hanaé) Xishu Lee (cited as X. Li)

For nearly two decades, ocean carriers have been locked in an arms race for capacity, which has led to huge losses for many and even bankruptcy for some. We investigate the nature of this investment race by studying a long-term capacity investment problem in a duopoly under demand uncertainty. In our model, two firms make sequential capacity decisions, responding to each other's current and future capacity. We consider two types of strategies which differ in terms of how a firm considers the opponent's future capacity in its own strategy: a proactive strategy where the firm assumes that the opponent will respond using a certain strategy, or a reactive strategy where the firm assumes that the opponent's future capacity remains unchanged. In the proactive case, we allow the firm to have different assumptions on the opponent's strategy, representing different amounts of information the firm has on the opponent. For each type of strategies, we derive the firm's optimal decisions on both the timing and size of capacity adjustments, specified by an array of intervals for the optimal capacity in a given capacity space in each period. Using detailed data from the container shipping market (2000 - 2015), we illustrate how to plan competitive capacity investments, following our model. By comparing the optimal decisions specified by our model with the reality, we show that the realized capacity decisions of the leading carriers, which were often questioned as irrational, are close to optimal, assuming these carriers follow proactive strategies. By revealing the underlying structures of different strategies, that is, the stayput intervals, we show how a specific strategy brings value to firms under competition. Based on our results, we provide practical guidelines to carriers and firms which operate in a similar competitive market for implementing an effective competitive capacity strategy.

Operators of long field-life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end-of-supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end-of-supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end-of-supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross-validation results and out-of-sample risk assessments show good performance of the method to identify spare parts with high end-of-supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high-risk spare parts.

An upward line extension is a new, improved product within an existing product category for the high-end market. Many manufacturers pursue line-extension strategies, but they carry risks due to uncertain consumer taste, the internal competition between products (represented by the firm's profitability for the line extension), and the external competition between firms (represented by the firm's cost advantage over its competitor). "Bad timing" is often mentioned as one of the main reasons for new product failures. We use game theory to answer when a firm should allocate production capacity for an upward line extension under competition and consumer taste uncertainty. The firm can either act early when taste is still uncertain to benefit from the first-mover advantage or wait until consumer taste realizes. We measure consumer taste risk by the correlation between the average consumer taste and the density of consumer taste, with a strong correlation meaning a considerable risk exposure, foreshadowing a more likely hit-or-miss result for the new product. The firm's competitive advantage is based on both firms' expected marginal revenues of capacity allocation, considering the internal and external competition. We show how the competitive advantage will amplify exponentially in the face of consumer taste uncertainty and characterize the firm's decision policy by deriving a threshold for the risk index against the competitive advantage index. When the threshold is exceeded, it implies that the firm's competitive advantage is not significant enough, so it should postpone allocating capacity. Otherwise, it should act early. We apply our results to two industry examples and provide practical guidelines to manufacturers for determining the right capacity timing for an upward line extension.

Counterfeiting harms both legitimate businesses and consumers. Since counterfeits often invade a local market from foreign lands, Customs exerts a leading role in the fight deterring the entry of counterfeits and efficient Customs deterrence activities depend on the collaboration with the intellectual property right holders. We study how Customs and private enterprises can build a public-private partnership (PPP) to combat a strategic cross-border counterfeiter. We model the problem as a three-stage game with three players: Customs, a legitimate OEM, and a counterfeiter. Customs and the OEM devise their own efforts in a PPP, while the OEM also sets the price of the genuine product. The counterfeiter decides whether to enter the market as a deceptive or non-deceptive player upon entry. Our results first show that when the penalty exceeds a certain threshold determined by the choices of Customs and the OEM, a PPP can deter counterfeits or, at a minimum, suppress deceptive counterfeiting. We also find that when the penalty on the counterfeiter is too low, a PPP actually amplifies the market share of a non-deceptive counterfeiter. Second, for deceptive counterfeits, a sufficiently high Customs inspection rate serves as the key for both Customs and the OEM to pursue a PPP. Combating non-deceptive counterfeits, however, requires a sufficiently high penalty. Third, in the scenario with no PPP, the OEM may use pricing to suppress deceptive counterfeiting where: (1) the functional quality of the counterfeit is poor or (2) deceptive counterfeiting becomes too rampant. Fourth, we detect a self-correcting mechanism in the absence of a PPP in terms of the quality of the counterfeit and the perceived proliferation of counterfeiting in the marketplace. Finally, we demonstrate conditions where a PPP can increase the expected consumer surplus. By scrutinizing conditions underlying an effective PPP, our results help explain why counterfeits have become inevitable in many industries despite multiple anti-counterfeiting PPP efforts, as well as why the participation rate of legitimate firms in current PPPs is low.

The COVID-19 pandemic caused a drastic drop in passenger air transport demand due to two forces: supply restriction and demand depression. In order for airlines to recover, the key is to identify which force they are fighting against. We propose a method for separating the two forces of COVID-19 and evaluating the respective impact on demand. Our method involves dividing passengers into different segments based on passenger characteristics, simulating different scenarios, and predicting demand for each passenger segment in each scenario. Comparing the predictions with each other and with the real situation, we quantify the impact of COVID-19 associated with the two forces, respectively. We apply our method to a dataset from Air France-KLM and show that from March 1st to May 31st, 2020, the pandemic caused demand at the airline to drop 40.3% on average for passengers segmented based on age and purpose of travel. 57.4% of this decline is due to demand depression, whereas the other 42.6% is due to supply restriction. In addition, we find that the impact of COVID-19 associated with each force varies between passenger segments. The demand depression force impacted business passengers between age 41 and 60 the most, and it impacted leisure passengers between age 20 and 40 the least. The opposite result holds for the supply restriction force. We give suggestions on how airlines can plan their recovery using our results and how other industries can use our evaluation method.

Early lifecycle demand forecast is critical to consumer technology products with a fast innovation speed, as firms which compete on these products focus on timely responding to market changes through new product development and efficient product diffusion, rather than sustaining product sales. The challenge for obtaining an accurate long-range forecast is that sales volumes at the early lifecycle stages are small, which limits the forecast accuracy. We propose a two-step lifecycle forecast approach for consumer technology products with limited sales data. First, we segment products based on market and clustering. Second, we apply the Bass model to aggregated products in a group using the average periodic sales of all products in the group and then use the forecast for related new products. We validate our approach using a dataset collected from Philips Netherlands, which contains consumer healthcare products sold in US and China over an 8-year timespan. The results suggest that for forecasting the lifecycle of a new product, models based on aggregated products generally perform better than models based on an individual product. It highlights the value of data aggregation in product lifecycle forecasts. Clustering is also useful for improving the forecast accuracy: when aggregation is done using sufficient product sales data, the aggregated model based on products with which the new product has the most sales pattern similarities could provide a more accurate forecast than other aggregated models. Based on our results, we provide a practical guideline to firms for obtaining an accurate early product lifecycle forecast.

The COVID-19 pandemic has caused global economic turmoil. Although many companies have suffered huge losses, some have flourished by changing their old ways of doing business. We investigate the business transformation process under drastic market changes and time pressure, with a focus on decision speed and structure in the decision & planning phase, the implementation structure and monitoring in the implementation phase, and reinforcement after the implementation. Through case studies in a variety of industries, including manufacturing, e-commerce, and finance, we explore how companies in specific contexts have dealt with the above-mentioned critical factors when transforming their business during the pandemic, whether the experienced transformation processes differ from theory, and if so, how. The examples of business transformations cover eight categories, including work from home, the use of augmented reality, internet of things, and business model redesign. Our findings reveal how these transformations are perceived and evaluated by companies one year into the pandemic. In addition, we show how decision speed, structure of the decision-making process, structure of the implementation process, and scale of the implementation impact the completion time of the transformations. Based on our results, we provide suggestions to companies for an effective business transformation in times of crisis.

Trust and fairness in information and revenue sharing are critical to the development of the sharing economy. We study a contracting problem between a platform and its content supplier, in which the platform can offer to manage the content for the supplier and share revenue afterward (contract A), or let the supplier manage his content and report trade while charging him a commission fee (contract B). The platform decides the revenue-sharing ratio and how much revenue information to disclose. The supplier evaluates a contract based on utility maximization, considering his trust in the platform's information and his fairness concern over the revenue-sharing ratio. By developing a browser contracting game and letting 71 management-major students play as the platform, we first demonstrate the practical potential of our model. Then using a game-theoretical model, we derive the optimal contracting mechanism in different scenarios. Our results first show that the supplier's trust does not always affect the platform's optimal decisions. Second, the optimal revenue-sharing ratio of contract A increases either with the element of trust or with the element of fairness, but never both. Third, although honesty does not always pay for the supplier under contract B, dishonesty always costs. We also find that the supplier who wants to be treated fairly will cheat more and there is a virtuous circle between the commission fee and the supplier's cheating behavior. Based on our results, we provide practical guidelines to share-based platforms on how to consider suppliers' trust and fairness concern in contracting.