Extending social responsibility to small and medium-sized suppliers in supply chains: A fuzzy-set qualitative comparative analysis

Buying firms’ sales and reputation will be greatly damaged by any non-responsible behaviors on the part of suppliers, especially when those suppliers, like small and medium-sized enterprises (SMEs), have restricted resources and capabilities. To eradicate these risks, a growing number of buying firms have introduced socially responsible supplier development (SRSD). SRSD, including monitoring and evaluating suppliers, can provide them with incentives and assistance. Based on configuration theory and contingency theory, fuzzy-set qualitative comparative analysis (fsQCA) is adopted in this study to examine how SRSD practices adopted by buying firms, supply chain partnership, and market turbulence affect the corporate social responsibility (CSR) performance of their SME suppliers. We find that, as core factors, supplier monitoring, supplier assistance, and supply chain partnership can work together with peripheral conditions to achieve superior CSR performance. In addition, even at different levels of market turbulence, superior CSR performance can be realized through different causal configurations.     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

Biofuel supply chain optimal design considering economic,environmental, and societal aspects towards sustainability

Biofuel supply chain design plays a critical role in facilitating the large‐scale substitution of biofuel for traditional fossil fuels with a cost‐effective and environmentally friendly manner towards sustainability. This paper proposes a multiobjective optimization model for a 4‐layer biofuel supply chain network using mixed integer nonlinear programming while considering the benefits from economic, environmental, and societal aspects. The model can be used either to optimize an existing biofuel supply chain network or to guide the construction of a new biofuel supply chain network. The profit, the greenhouse gas emissions in transportation, and the market share of biofuel were set as targets for optimization. The selection of the participators at each layer, and the amount of the material flow between each pair of selected supplier and customer located at two adjacent layers were modeled as decision variables. The conventional weighted aggregation method was used to unify 3 objectives after normalization. Particle swarm optimization was used to solve this high‐dimension multiobjective problem to obtain a near optimal solution. A numerical case study based on the state of Missouri in the United States was implemented to verify the effectiveness of the proposed model. The results of the case study illustrate that the benefits in terms of transportation emission, profit, and market share can be achieved simultaneously. Using the equal weights configuration in conventional weighted aggregation as an example, a 21% reduction of the transportation emission, a 33% increase of the profit, and a 2% augmentation of the market share were achieved compared to the benchmark scenario.     

Distributed ledger technology in supply chains: a transaction cost perspective

With the emergence of distributed ledger technology (DLT), numerous practitioners and researchers have proclaimed its beneficial impact on supply chain transactions in the future. However, the vast majority of DLT initiatives are discontinued after a short period. With the full potential of DLT laying far down the road, especially managers in supply chain management (SCM) seek for short-term cost-saving effects of DLT in order to achieve long-term benefits of DLT in the future. However, the extant research has bypassed grounding long-term as well as short-term effects of DLT on supply chain transaction with empirical data. We address this shortcoming, following an abductive research approach and combining empirical data from a multiple case study design with the corresponding literature. Our study reveals that the effects of DLT on supply chain transactions are two-sided. We found six effects of DLT solutions that have a cost-reducing or cost avoidance impact on supply chain transactions. In addition, we found two effects that change the power distribution between buyers and suppliers in transactions and a single effect that reduces the dependency of supply chain transactions on third parties. While cost-reducing and avoidance as well as dependency-reducing effects are positive effects, the change in power distribution might come with disadvantages. With these findings, the paper provides the first empirical evidence of the impact of DLT on supply chain transactions, which will enable managers to improve their assessment of DLT usage in supply chains.     

燕麦蒽酰胺生理功能研究进展

目的 探究尾静脉注射短链酰基辅酶A脱氢酶(SCAD)重组腺病毒能否改善自发性高血压大鼠(SHR)血管重构。方法 实验分为6组:Wistar+NS组、Wistar+GFP组、Wistar+Ad-SCAD组、SHR+NS组、SHR+GFP组和SHR+Ad-SCAD组。腺病毒包装的SCAD和GFP纯化后,以尾静脉方式注射给药8周。采用超声心动图检测大鼠心功能情况；采用无创血压仪检测大鼠的血压变化；主动脉HE染色、天狼星红染色、DHE染色、TUNEL染色、EVG染色,观察血管重构。采用实时荧光定量PCR检测相关基因mRNA表达变化、Western blot检测相关蛋白表达变化,观察游离脂肪酸、一氧化氮(NO)和ATP含量变化。结果 (1)尾静脉注射SCAD重组腺病毒,大鼠主动脉中SCAD蛋白出现过表达,mRNA水平增高,SCAD酶活性增加；(2)在病理状态下,增加主动脉中SCAD表达,可以降低血压,改善心功能,改善血管管腔大小,减少胶原沉积,减少血管活性氧(ROS)的生成和细胞凋亡；(3)在病理状态下,SCAD的表达增加可减少血清和主动脉中游离脂肪酸的含量,增加组织中的ATP水平,激活内皮型一氧化氮合酶(eNOS)磷酸化,增加主动脉NO的生成。结论 自发性高血压大鼠主动脉SCAD的表达增加能逆转高血压血管重构,可能与其减少血清中游离脂肪酸的含量、增加NO水平、减少活性氧生成、消除氧化应激有关。     

Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art,challenges and opportunities

Blockchain possesses the potential of transforming global supply chain management. Gartner predicts that blockchain could be able to track $2?T of goods and services in their movement across the globe by 2023, and blockchain will be a more than $3 trillion business by 2030. Nowadays, a growing number of blockchain initiatives are disrupting traditional business models in each sector. In this paper, we provide a timely and holistic overview of the state-of-the-art, challenges, gaps and opportunities in global supply chain and trade operations for both the private sector and governmental agencies, by synthesising a wide range of resources from business leaders, global international organisations, leading supply chain consulting firms, research articles, trade magazines and conferences. We then identify collaborative schema and future research directions for industry, government, and academia to jointly work together in ensuring that the full potential of blockchain is unleashed amidst the socioeconomic, geopolitical and technological disruptions that global supply chains and trade are facing.     

Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

A semiconductor distributor that plays a third-party role in the supply chain will buy diverse components from different suppliers, warehouse and resell them to a number of electronics manufacturers with vendor-managed inventories, while suffering both risks of oversupply and shortage due to demand uncertainty. However, demand fluctuation and supply chain complexity are increasing due to shortening product life cycle in the consumer electronics era and long lead time for capacity expansion for high-tech manufacturing. Focusing realistic needs of a leading distributor for semiconductor components and modules, this study aims to construct a UNISON framework based on deep reinforcement learning (RL) for dynamically selecting the optimal demand forecast model for each of the products with the corresponding demand patterns to empower smart production for Industry 3.5. Deep RL that integrates deep learning architecture and RL algorithm can learn successful policies from the dynamic and complex real world. The reward function mechanism of deep RL can reduce negative impact of demand uncertainty. An empirical study was conducted for validation showing practical viability of the proposed approach. Indeed, the developed solution has been in real settings.     

Dynamic Stabilization of DNA Assembly by Using Pyrrole-Imidazole Polyamide

We used N-methylpyrrole (Py)-N-methylimidazole-(Im) polyamide as an exogenous agent to modulate the formation of DNA assemblies at specific double-stranded sequences. The concept was demonstrated on the hybridization chain reaction that forms linear DNA. Through a series of melting curve analyses, we demonstrated that the binding of Py−Im polyamide positively influenced both the HCR initiation and elongation steps. In particular, Py−Im polyamide was found to drastically stabilize the DNA duplex such that its thermal stability approached that of an equivalent hairpin structure. Also, the polyamide served as an anchor between hairpin pairs in the HCR assembly, thus improving the originally weak interstrand stability. We hope that these proof-of-concept results can inspire future use of Py−Im polyamide as a molecular tool to modulate the formation of DNA assemblies.