Ershi Qi

Bio: Ershi Qi is an academic researcher from College of Management and Economics. The author has contributed to research in topics: Medicine & Supply chain. The author has an hindex of 2, co-authored 3 publications receiving 89 citations.

Quick response and supply chain structure with strategic consumers

Abstract: This work explores the impact of quick response on supply chain performance for various supply chain structures with strategic customer behavior. By investigating pricing and inventory decisions in decentralized supply chains under revenue-sharing contracts and in centralized supply chains, we study the performance of four various systems and compare the value of quick response in different supply chain structures. The results show that if the extra cost of quick response is relatively low, the value of quick response would be greater in centralized systems than in decentralized systems. On the other hand, if the extra cost is high, decentralized supply chains reap more incremental profits from adopting quick response. We also find that revenue-sharing contracts enable a decentralized supply chain to outperform a centralized supply chain, but only allow limited flexibility of allocating total profits between a manufacturer and a retailer.

Real-time order acceptance and scheduling for data-enabled permutation flow shops: Bilevel interactive optimization with nonlinear integer programming

Abstract: With the fourth-generation industrial revolution, manufacturing industries are focusing on dynamic, fully autonomous, and more customer-oriented production systems. This customer-oriented change converts classically static customer demand into that which is dynamic and real-time, as no prior information regarding customer demand is known in advance. This paper focuses on real-time order acceptance and scheduling (r-OAS) for a data-enabled permutation flow shop. To compensate for the shortage in prevailing approaches that make bottleneck-based decisions or assume that the intermediate buffers among workstations are infinite, an r-OAS scheme is generated based on a data-driven representation, which can concisely predict the dynamic production status of flow shops and the corresponding makespan of a job with finite intermediate buffer constraints. Using this representation, real-time job release planning (r-JRP) can be coupled with r-OAS to minimize various operational costs of flow shops (i.e., the costs of the work-in-process, earliness, and tardiness). In terms of the inherent interactive mechanism between r-OAS and r-JRP, in which r-OAS generates a decision space for r-JRP and r-JRP then feeds the lowest operational costs back for use in r-OAS decision-making, a bilevel interactive optimization (BIO) is formulated to simultaneously address the two subproblems based on the Stackelberg game. The r-OAS acts as the leader, while r-JRP acts as the follower. The BIO is a type of nonlinear integer programming, and a bilevel tabu-enumeration heuristic algorithm is developed to solve it. The efficiency of the BIO is verified through a practical case study. The results show that the BIO can increase the net revenue of flow shops by 2.97%, compared to the bottleneck-based approach, and by 2.45% and 0.92%, respectively, compared to step-by-step methodologies.

Happy work: Improving enterprise human resource management by predicting workers’ stress using deep learning

Abstract: Recently, workers in most enterprises suffer from excessive occupational stress in the workplace, which negatively affects workers’ productivity, safety, and health. To deal with stress in workers, it is vital for the human resource management (HRM) department to manage stress effectively, bridging the gap between management and stressed employees. To manage stress effectively, the first step is to predict workers’ stress and detect the factors causing stress among workers. Existing methods often rely on the stress assessment questionnaire, which may not be effective to predict workers’ stress, due to 1) the difficulty of collecting the questionnaire data, and 2) the bias brought by workers’ subjectivity when completing the questionnaires. In this paper, we aim to address this issue and accurately predict workers’ stress status based on Deep Learning (DL) approach. We develop two stress prediction models (i.e., stress classification model and stress regression model) and correspondingly design two neural network architectures. We train these two stress prediction models based on workers’ data (e.g., salary, working time, KPI). By conducting experiments over two real-world datasets: ESI and HAJP, we validate that our proposed deep learning-based approach can effectively predict workers’ stress status with 71.2% accuracy in the classification model and 11.1 prediction loss in the regression model. By accurately predicting workers’ stress status with our method, the HRM of enterprises can be improved.

The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era

Abstract: Supply chain operations have entered the digital era with the emergence of blockchain technology. In this paper, we discuss how the mean–variance (MV) approach can be applied to explore global supply chain operations risk with air logistics in the blockchain technology era. To be specific, we examine the related literature from four areas, namely air-logistics operations, demand management, supply management, and supply-demand coordination. We propose how the blockchain technology can be applied to facilitate the implementation of mean-variance risk analysis for global supply chain operations. We then highlight several promising areas for further studies. A future research agenda is developed.

Incorporating social media observations and bounded rationality into fashion quick response supply chains in the big data era

Abstract: In this paper, we study the fashion quick response program with social media observations, demand forecast updating, and a boundedly rational retailer. We analytically find that the likelihood of having good social media comments on the product plays a critical role in affecting the value of quick response, and its impact is mediated by the fashion retailer’s prior attitude towards the market demand. We then demonstrate how a Pareto improving situation can be achieved under quick response, and uncover that manipulating social media comments can benefit the manufacturer under the surplus sharing contract, but not under the two-part tariff contract.