Xuying Zhao

Production and Transportation Integration for a Make-to-Order Manufacturing Company with a Commit-to-Delivery Business Mode

When a make-to-order manufacturing company adopts a commit-to-delivery business mode, it commits a delivery due date for an order and is responsible for the shipping cost. Without loss of generality, we consider that transportation is done by a third-party logistics company, such as FedEx or UPS, which provides multiple shipping modes such as overnight, one-day, two-day delivery, and more. When the transportation time has to be short, clearly, shipping cost is more expensive than it could have been. How should a company schedule production for accepted orders so that the company can leave enough transportation time for orders to take slow shipping modes to reduce the shipping cost? We study this problem of integrating the production and transportation functions for a manufacturing company producing a variety of customized products in a make-to-order environment with a commit-to-delivery mode of business. Various realistic scenarios are investigated in increasing order of complexity. When partial delivery is allowed by customers, we provide both a mixed-integer programming (MIP) model and a minimum cost flow model. We show that nonpreemptive earliest due date (NEDD) production schedules are optimal when partial delivery is allowed and shipping cost is a decreasing convex function with transportation time. When partial delivery is not allowed, we develop an MIP model and prove that the problem is NP-hard. An efficient heuristic algorithm with polynomial computation time is provided for the NP-hard problem. It gives near-optimal production schedules, as shown via thousands of numerical experiments. We also provide models and analysis for other scenarios where shipping cost accounts for customer locations and quantity discounts.

Coordinating a Supply Chain With a Manufacturer-Owned Online Channel: A Dual Channel Model Under Price Competition

We consider a dual channel supply chain in which a manufacturer sells a single product to end-users through both a traditional retail channel and a manufacturer-owned direct online channel. We adopt a commonly used linear demand substitution model in which the mean demand in each channel is a function of the prices in each channel. We model each channel as a newsvendor problem, with price and order quantity as decision variables. In addition, the manufacturer must choose the wholesale price to charge to the independent retailer. We analyze the optimal decisions for each channel and prove the existence of a unique equilibrium for the system. We compare this equilibrium solution to the solution for an integrated system, in which the manufacturer owns both the online store and the retailer. To enable supply chain coordination, we propose two contract schemes: a modified revenue-sharing contract and gain/loss sharing contract. We show that, in cases where the retail channel has a larger market than the online channel, such contracts enable the manufacturer to maintain price discrimination, selling the products in different channels at different prices. Finally, we perform a comprehensive numerical study to consider the impact of the model parameters on the equilibrium and to demonstrate the performance of the proposed coordination contracts. We conclude that coordination is most critical for products which are highly price sensitive and for systems in which the online and traditional retail channels are not viewed as close substitutes.

Newsvendor problems with demand shocks and unknown demand distributions

In todays competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range; no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithms performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta-algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta-algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent

Optimal Pricing and Capacity Investment for Delay-Sensitive Demand

We study a firms joint decisions on product prices, delivery lead times, and capacity investments of the production facility. We assume customers are strategic and heterogeneous in their sensitivity to waiting. The firm can offer a single service to all customers or two services with different delivery lead times and prices. We investigate a firms optimal decisions when the firm is a monopolist or under a duopoly competition. We find that a monopoly firms optimal capacity level decreases in service level. For a monopoly firm providing differentiated services, we find that the optimal facility utilization level does not depend on unit capacity cost. Furthermore, we demonstrate that a monopoly firm always gets more profits by providing differentiated services than a single service. For duopoly competition, we show the existence of a Nash equilibrium. Finally, we illustrate that a firm offering shorter lead time quotation may earn less profit than one offering longer lead time quotation when two firms compete in an industry with discrete lead times.

Managing the technology of integrating the production and transportation functions in assembly or flow operations for make-to-order industries

When a make-to-order manufacturing company commits a delivery due date for an order, we call it a commit-to-delivery business mode. In this mode, the manufacturing company is responsible for the shipping cost and selecting a shipping mode which is usually provided by a third party logistics company. Generally, shipping cost is higher when the selected shipping mode requires shorter shipping time. How should a company schedule production in production lines for all accepted orders so that the company can leave enough shipping time for orders to take slow shipping modes to reduce the shipping cost? We study the production and transportation integration technology for a make-to-order manufacturing company with a commit-to-delivery business mode. In the distribution scenario where partial delivery is allowed, we provide an optimal production schedule which minimizes the total shipping costs for all finished orders. When partial delivery is not allowed, we provide a near-optimal heuristic algorithm which is proved to be efficient and effective by numerical tests.

Coping with Demand Shocks: A Distribution-Free Algorithm for Solving Newsvendor Problems with Limited Demand Information

We present a new, robust, and e ective algorithm for the multiple-period newsvendor problem when there is little demand information available. In todays competitive market, demand volume and even distribution can change quickly. The algorithm needs only a rough estimate of the lower and upper bounds of demand range; no other knowledge such as the demand mean, variance, or distribution type is necessary. Through simulations we show that our algorithm performs well compared to four other standard newsvendor problem solutions in a variety of situations, except when salvage values are high.

Differentiated Lead Time and Price Quotation Management for Service Providers

Lead time, in addition to price, has become a dominant factor in competition in the service industry. A service provider often provides lead time and price quotations to customers before customers place orders. A short lead time may enable a service provider to charge a high price, but it also requires a certain capacity level to maintain a short lead time. We analyze the interrelationship among lead time, price, and capacity to decide an optimal value for each of them simultaneously. When a firm offers a menu of lead times and prices for customers to choose from, it is called differentiated quotation mode. There exists a cannibalization issue among the options in the differentiated quotation mode. Our model takes care of the cannibalization issue and provides insights to help managers design an optimal differentiated lead time and price quotation menu

Economic Analysis of Reward Advertising

This paper investigates an emerging monetization mechanism for app developers – reward advertising. With reward ads, consumers have an option to view ads in exchange for a reward such as premium content. We investigate when and how an app developer should adopt reward ads as a mechanism for monetizing content. We identify two determinants – the revenue rate of the ads and the heterogeneity of consumers’ nuisance costs of viewing ads. When the ad revenue rate is low relative to consumers’ nuisance cost, the app developer should rely on content selling and not offer reward ads (i.e., the pure content-selling strategy). Otherwise, it is profitable for the app developer to offer reward ads alone (the pure reward-advertising strategy) or in combination with content selling (the hybrid strategy). When reward ads are offered, the hybrid strategy is more profitable if consumers are highly heterogeneous in nuisance costs; otherwise, the pure reward-advertising strategy is more profitable. Interestingly, we find that a high reward rate could decrease the number of reward ads viewed due to accelerated satiation. The optimal reward rate for the pure reward-advertising strategy may increase or decrease in consumers’ nuisance cost heterogeneity. Furthermore, when the proportion of low-nuisance-cost consumers is high enough, the total consumer surplus under the pure reward-advertising strategy is lower than those under the other two strategies. Our results provide practical guidance for app developers on when to use reward advertising, how to choose optimal rewards rates, and what to expect about its impact on consumers.

Quoting Lead Times and Prices to Customers

Firms in service and make-to-order manufacturing industries often quote lead times and prices to customers. We define uniform quotation mode (UQM) as the strategy where a firm offers a single lead time and price quotation, and differentiated quotation mode (DQM) is where a firm offers a menu of lead times and prices for customers to choose from. Both modes are followed in practice. Firms should determine which is more profitable. We classify customers into two groups: lead time sensitive (LS) and price sensitive (PS). LS customers value lead time reduction more than PS customers. We develop mathematical models of both quotation modes and analyze them to determine the most profitable mode under specified situations as well as the best lead time and price quotations within each mode. We find that DQM is dominated by UQM whenever PS customers have positive utilities from UQM or LS customers have positive utilities from DQM. Otherwise, which quotation mode is better depends on multiple factors, such as customer characteristics (including lead time reduction valuation and product valuation of a customer, and the proportion of LS customers) and production characteristics (including the desired service level and service or production cost).