Asia-Pacific Journal of Operational Research

Sharing forecast information helps supply chain parties to better match demand and supply. The extant literature has shown that sharing forecast information improves supply chain performance. In the big data era, supply chain managers have the ability to deal with a massive amount of data by big data technologies and analytics. Big data technologies and analytics provide more accurate forecast information and give an opportunity to transform business models. In this paper, a comprehensive review on forecast information sharing for managing supply chain in the big data era is conducted. The value and obstacles of sharing forecast information are discussed. Given the sufficient data, the appropriate approaches of analyzing and sharing forecast information are highlighted. Insights on the current state of knowledge in each respective area are discussed and some associated pertinent challenges are explored. Inspired by various timely and important issues, future research directions are suggested.

In this paper, we study a pricing and after-sales service contract design problem, where a retailer purchases products from a manufacturer and then sells to the consumers. The sales cost is the retailer’s private information and might be mined by the manufacturer via advanced learning algorithms and related big data techniques. We first develop a crisp equivalent model, based on which the optimal contracts and the supply chain parties’ profits under asymmetric information are derived. We show that, compared with the optimal wholesale price and after-sales service level with symmetric information, asymmetric cost information makes the wholesale price distorted upward when the retailer’s sales cost is low. When the retailer’s cost is high, the after-sales service level is distorted downward. We characterize the manufacturer’s loss and the retailer’s gains due to asymmetric sales cost information. This helps the manufacturer make the investment decision of big data techniques. Interestingly, we find that the retailer might voluntary disclose the sales cost information, which results in a win-win situation for the manufacturer and the retailer. This makes the manufacturer less favor big data techniques. Finally, we conduct extensive sensitivity analysis with respect to the retailer’s sales cost, the consumer’s sensitivity to the retailer’s after-sale service level, and the fraction of high-type retailers in the market.

The last decade has witnessed an increase in the number of big-data-based businesses, in particular for those industries with complicated supply chain structure. This paper investigates the pricing and production strategies in a decentralized supply chain composed of a manufacturer, a key supplier, and a general supplier. We establish two different leader–follower structure models. One is the key supplier–leader game, in which the key supplier decides the prices for the other two components, and the manufacturer and general supplier determine the order and production quantities, respectively. The other model is the manufacturer–leader game, in which the manufacturer offers the prices for the other two components, and the suppliers determine the product quantities. We show that equilibrium price and production quantity under the key supplier–leader game are higher than those under the manufacturer–leader game. The key supplier–leader game is suitable for the channel. Moreover, we show that channel payoff has a non-monotonic relation with the production cost of suppliers; it initially increases and then decreases with production costs.

In the “Post-Kyoto” Era, climate change has become a serious worldwide concern, though the international community has not yet identified a cooperative solution that satisfies all parties. The carbon tariffs, which proposed by some developed countries to address competitiveness concerns and carbon leakage from unilateral reduction measures, may impose significant hardships on developing countries. This paper tries to design a global cooperation scheme against the carbon tariffs. A differentiated carbon taxation scheme is introduced based on the principle of ability to pay (CTAP). An advanced forecasting system named the energy version of the global trade analysis model (GTAP-E) was used to compare the different impacts of carbon tariffs and the CTAP scheme. The results show that CTAP is better than carbon tariffs in terms of global GDP, welfare, and emissions reduction. The CTAP scheme could yield less welfare deterioration for developing regions than the carbon tariffs, and also lessens the competitive concerns of developed countries. The proposed CTAP scheme provides new ideas for international cooperative strategies to address climate change.

This study incorporates consumer’s low carbon awareness (CLA) and demand forecasting into supply chains that adopt the cap-and-trade system. Three demand forecasting scenarios are discussed, namely, information sharing, full information sharing, and retailer-only forecasting. Strategies for pricing and reduction of equilibrium of carbon emission are derived. We also compare the decisions and profits in the three cases and present numerical analysis.

In the background of big data era, the ability to accurately forecast the number of the Internet users has considerable implications for evaluating the growing trend of a newly-developed business. In this paper, we use four models, the Gompertz model, the Logistic model, the Bass model, and the Lotka–Volterra model, to forecast the Internet population in China with the historical data during 2007 to 2014. We compare the prediction accuracy of the four models using the criterions such as the mean absolute percentage error (MAPE), the mean absolute error (MAE) and the root mean square error (RMSE). We find that the Lotka–Volterra model has the highest prediction accuracy. Moreover, we use the Lotka–Volterra model to investigate the relationship between the rural Internet users and the urban Internet users in China. The estimation results show that the relationship is commensalism.

A modeling methodology for blog recommendation and forecasting based on information entropy is presented. With the increasing popularity of smartphones and the rapid development of the mobile Internet, the amount of user-generated content such as blogs is increasing daily. Valuable information, such as bloggers’ opinions, feelings, and attitudes, is often part of this content. Particularly in the context of an emergency, this information should also be used to facilitate decision making. The current blog recommendation model examines primarily users’ interests or content similarity, whereas in this paper, the value of the blog is considered. The primary contribution of this paper is the proposal of an information-entropy-based blog recommendation model for finding valuable blogs to facilitate decision-making in an emergency context. A series of indicators for evaluating a blog in an emergency context are proposed. Using the method of information entropy, a blog recommendation model is developed. The model can also be used to forecast the value of emergency blogs in the future. The model has been tested and validated using crawled data from the Sina Blog, and the results have demonstrated that the proposed model can effectively determine the value of emergency-related blogs.

Online peer-to-peer (P2P) lending is an emerging financial mode that combines the Internet with private lending to provide unsecured lending among individuals. The interest rate and risk depend on online lenders and borrowers’ behavior choices and game in the context of P2P lending. In this paper, we propose an evolutionary behavior forecasting model for online participants based on the risk preference behavior of lenders and the credit choice of borrowers. We highlight four evolutionary equilibrium states of online lenders and borrowers’ behavior and their effects on the risk of online P2P lending platforms. We run a numeric experiment using the Paipaidai platform in China as a case and find that the evolutionary behavior of online lenders and borrowers is determined by the mutual effect of the interest rate, information gathering cost, borrowing cost, and yield rate. This paper uses evolutionary game methodology to analyze online P2P lending behavior in China and explores P2P fund success from the dual perspective of lenders and borrowers.

The constant elasticity of variance (CEV) model is widely studied and applied for volatility forecasting and optimal decision making in both areas of financial engineering and operational management, especially in option pricing, due to its good fitting effect for the volatility process of various assets such as stocks and commodities. However, it is extremely difficult to conduct parameter estimation for the CEV model in practice since the precise likelihood function cannot be derived. Motivated by the gap between theory and practice, this paper initiatively applies the Markov Chain-Monte Carlo (MCMC) method into parameter estimation for the CEV model. We first construct a theoretical structure on how to implement the MCMC method into the CEV model, and then execute an empirical analysis with big data of CSI 300 index collected from the Chinese stock market. The final empirical results reveal insights on two aspects: On one aspect, the simulated results of the convergence test are convergent, which demonstrates that the MCMC estimation method for the CEV model is effective; On the other aspect, by a comparison with other two most frequently used estimation methods, the maximum likelihood estimation (MLE) and the generalized moment estimation (GMM), our method is proved to be of high accuracy and has a simpler implementation and wider application.

This paper discusses optimal order-taking strategies under competing trade credit policies with varying demands. This study examines three decision-making scenarios, namely, (i) a centralized supply chain, (ii) a decentralized supply chain, and (iii) a coordinated supply chain with a buyback contract. Optimal decisions are obtained for each scenario. We find that if the average forecast value of credit sensitivity is higher than the real value, then the supply chain is at risk of having an overestimated performance; if the average forecast value of credit sensitivity is lower than the actual value, then the supply chain is at risk of having an underestimated performance. As the sensitivity of the credit competition increases, the supply chain’s performance decreases. The supply chain can be coordinated by a buyback contract.

Emergency incidents can trigger heated discussions on microblogging platforms, and a great number of tweets related to emergency incidents are retweeted by users. Consequently, social media big data related to the emergency incidents is generated from various social media platforms, which can be used to predict users’ retweeting behavior. In this paper, the characteristics of individuals’ retweeting behaviors in emergency incidents are analyzed, and then 11 important characteristics are extracted from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage. A back propagation neural network (BPNN) model called PRBBP is used to predict retweeting behavior in such emergency incidents. Based on PRBBP, an algorithm called PRABP is proposed to predict the number of retweets in emergency incidents. The experiments are performed on a large-scale dataset crawled from Sina weibo. The simulation results show that both the PRBBP model and the PRABP algorithm proposed by this paper have excellent predictive performance.