Xiaohang Yue

Demand forecast sharing in a dual-channel supply chain

Abstract We assess the benefits of sharing demand forecast information in a manufacturer–retailer supply chain, consisting of a traditional retail channel and a direct channel. The demand is a linear function of price with a Gaussian primary demand (i.e., zero-price market potential). Both the manufacturer and the retailer set their price based on their forecast of the primary demand. In this setting, we investigate the value of sharing demand forecasts. We analyze the ‘make-to-order’ scenario, in which prices are set before and production takes place after the primary demand is known, and the ‘make-to-stock’ scenario, in which production takes place and prices are set before the primary demand is known. We also compare the supply chain performance with and without the direct channel under some assumptions (production cost is zero, and each demand function has the same slope of price). We find that the direct channel has a negative impact on the retailer’s performance, and, under some conditions, the manufacturer and the whole supply chain are better off. Our research extends and complements prior research that has investigated only the inventory and replenishment-related benefits of information sharing.

Recent Development in Big Data Analytics for Business Operations and Risk Management

“Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications

The papers in this special section focus on Big Data, with particular emphasis on the exploitation of data collected via industrial sensor networks (be it wireless or not) for Internet-based industrial applications. The papers explore the handling and analysis of the collected data. It is expected that analyzing the data can improve the reliability of industrial systems by predicting the occurrence potential risks and then rectification can be made accordingly. Such risks are inevitably linked to uncertain factors hidden in the systems that can be revealed by the data analysis process.

Effects of Carbon Emission Taxes on Transportation Mode Selections and Social Welfare

In this paper, we analyze how carbon emissions affect the selection of transportation modes and social welfare by using a two-stage Stackelberg gaming model. Based on this model, the governments optimal carbon-emission tax scheme and the companys optimal transportation mode and production decisions are explored. We find that: 1) whether or not the transport carbon-emission tax can increase social welfare depends on the relationships among the social cost of carbon (SCC), the transportation mode shifting threshold (TMST), and the biggest carbon-emission tax that a company can afford (BCRA); 2) a greater SCC implies a higher probability of improving social welfare via imposing transportation carbon-emission tax; and 3) a smaller TMST or BCRA yields a higher probability of improving social welfare when a carbon-emission tax is imposed. Further study shows that imposing a carbon-emission tax on the product with a higher production cost, a bigger product volume, or a bigger product density can increase the probability of improving social welfare.

The Dynamic Newsvendor Model with Correlated Demand

The classic newsvendor model was developed under the assumption that period-to-period demand is independent over time. In real-life applications, the notion of independent demand is often challenged. In this article, we examine the newsvendor model in the presence of correlated demands. Specifically under a stationary AR(1) demand, we study the performance of the traditional newsvendor implementation versus a dynamic forecast-based implementation. We demonstrate theoretically that implementing a minimum mean square error (MSE) forecast model will always have improved performance relative to the traditional implementation in terms of cost savings. In light of the widespread usage of all-purpose models like the moving-average method and exponential smoothing method, we compare the performance of these popular alternative forecasting methods against both the MSE-optimal implementation and the traditional newsvendor implementation. If only alternative forecasting methods are being considered, we find that under certain conditions it is best to ignore the correlation and opt out of forecasting and to simply implement the traditional newsvendor model.

Supply-chain redesign to reduce safety stock levels: sequencing and merging operations

We investigate the impact of the process of manufacturing and distribution on the safety stock levels in a supply chain. A pipeline hedging method is used to derive a model for estimating the safety stock levels. We propose methods and guidelines to redesign the manufacturing and distribution process to minimize the total safety stock investment for a specified service level. The product family consisting of one product and two products is studied in detail. Conditions and insights for better supply-chain management are developed. These enable us not only to decide when a process redesigning activity is appropriate, but also to suggest the scale and the format of the process redesign. Based on the results obtained, two procedures-resequencing and merging-are developed. Finally, we demonstrate how these procedures can be extended to product families consisting of multiple products in a hierarchical manner.

Optimal Scheduling, Coordination, and the Value of RFID Technology in Garment Manufacturing Supply Chains

Motivated by industrial practices, we explore in this paper the optimal supply chain scheduling problem in garment manufacturing with the consideration of coordination and radio frequency identification (RFID) technology. We consider the case in which a garment manufacturer receives orders from multiple retailers, and needs to determine the optimal order set to take and the corresponding optimal production schedule. We model the problem as a flowshop scheduling problem, uncover its structural properties, and prove that the problem is NP-hard in the ordinary sense only. We contribute by first developing a practical and effective pseudopolynomial dynamic programming algorithm to find the globally optimal solution in reasonable time; second, proposing an implementable method to achieve win–win supply chain coordination; and third, showing the good performance of RFID technology deployment. We further determine the critical threshold value of the order number with which the total manufacturing capacity must be increased if companies in the supply chain wish to improve their profits.

On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions

In this paper, we consider a permutation flowshop scheduling problem with the total flow time as the schedule performance measure. A proactive–reactive approach is designed to simultaneously deal with stochastic disruptions (e.g., machine breakdowns) and dynamic events (e.g., newly arriving jobs and delay in job availability). In the proactive stage, the stochastic machine breakdown is hedged against the construction of a robust and stable baseline schedule. This schedule is either optimized by incorporating uncertainty into two surrogate measures or obtained by simulation. Robustness is measured by the expected schedule performance, while stability is measured by the aggregation of dissatisfactions of manager, shopfloor operator, and customers using the prospect theory. In the reactive stage, we assume that the stochastic and dynamic disruptions concurrently occur. Unlike the simple right-shifting method, a more effective rescheduling approach is proposed to balance the realized schedule performance with stability. A common issue in these two stages is the conflict between objectives. Thus, we propose a hybridization strategy that successfully enhances the classic Non-dominated Sorting Genetic Algorithm (NSGA-II and the hybridized algorithm outperforms NSGA-II, multiobjective evolutionary algorithm based on decomposition, and multiobjective memetic algorithms designed for deterministic scheduling problems. Finally, extensive computational studies on the Taillard flowshop benchmark instances are conducted to illustrate the effectiveness of the proposed proactive–reactive approach and the algorithm hybridization strategy.

A Novel Hybrid Ant Colony Optimization Algorithm for Emergency Transportation Problems During Post-Disaster Scenarios

The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants’ tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms.

Carbon emission reduction and pricing policies of a supply chain considering reciprocal preferences in cap-and-trade system

The traditional self-interest hypothesis is far from perfect. Social preference has a significant impact on every firm’s decision making. This paper incorporates reciprocal preferences and consumers’ low-carbon awareness (CLA) into the dyadic supply chain in which a single manufacturer plays a Stackelberg-like game with a single retailer. This research intends to investigate how reciprocity and CLA may affect the decisions and performances of the supply chain members and the system’s efficiency. In this study, the following two scenarios are discussed: (1) both the manufacturer and the retailer have no reciprocal preferences and (2) both of them have reciprocal preferences. We derive equilibriums under both scenarios and present a numerical analysis. We demonstrate that reciprocal preferences and CLA significantly affect the equilibrium and firms’ profits and utilities. First, the optimal retail price increases with CLA, while it decreases with the reciprocity of the retailer and the manufacturer; the optimal wholesale price increases with CLA and the retailer’s reciprocity, while it decreases with the manufacturer’s reciprocity. The optimal emission reduction level increases with CLA and the reciprocity of both the manufacturer and the retailer. Second, the optimal profits of the participants and the supply chain increase with CLA, the participants’ optimal profits are concave in their own reciprocity and increase with their co-operators’ reciprocity. Third, the participants’ optimal utilities increase with CLA and their reciprocity. Finally, the supply chain efficiency increases with the participants’ reciprocity, while the efficiency decreases with CLA.