Assembly buildings, in the context of the low-carbon transformation of the construction industry, achieve superior outcomes in terms of carbon emission reduction, enhancement of building uniformity, and optimization of resource utilization as compared to traditional buildings. However, the supply chain for assembly building is marked by a significant susceptibility to risk and a need for timely fulfillment of requirements. This paper examines the risk of disruption and capacity limitations in the assembly building supply chain resulting from supply disruptions. It establishes a three-tier supply chain for assembly buildings, including primary component suppliers, backup suppliers, assembly manufacturers, and retailers. The study compares the optimal decision-making and coordination strategies of the supply chain members under centralized, decentralized, and joint agreements. The supply chain dual-source procurement decision coordination model is constructed by incorporating capacity constraints and analyzing the effects of supply disruption probability, repurchase coefficient, revenue sharing coefficient, cost, and other parameters on the expected profits of the supply chain members using arithmetic simulation. Research has indicated that when the likelihood of a disturbance occurring rises, the anticipated financial gain for the main provider decreases, while the predicted financial gain for the secondary supplier increases. The implementation of a collaborative agreement between the assembly maker and the parts backup provider would result in much greater anticipated profits compared to the decentralized decision-making approach. The impact of the revenue sharing coefficient on the predicted earnings of retailers and assembly manufacturers is more significant compared to the repurchase coefficient. The selection bias between NA and NB techniques under capacity constraints mostly arises from the assertiveness of the wholesale asking prices of inexpensive component suppliers, leading assembly manufacturers to increasingly prefer the NA option. This paper's research successfully achieves the contractual coordination of the assembly building supply chain, enhances the resilience of the assembly building supply chain, and promotes the long-term sustainable development of the assembly building supply chain.

This paper investigates the effect of artificial intelligence (product information, recommendation, and social media exposure) and payment flexibility on the operational performance of ecommerce retailers. The study is based on Transactional Cost Analysis, Material Flow, and Technology Integration theories. It considered a sample size of 270 members out of the population of 769 employees from five ecommerce companies operating in the region (Namshi.com, Noon, Joly Chic, Extra, and Styli). The analysis involved constructing a structural equation model to examine the trickle-down effect of the variables included in the study. The study concluded that artificial intelligence and payment flexibility are the core reasons that the retailers in the region are registering operational success in the retail market.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.

The bullwhip effect happens whenever the demand order fluctuations in the supply chain (SC) escalate as they are transferred up the SC. In fact, a small change in point-of-sale demand may be interpreted by SC participants as a much bigger variability in demand. This looks like a cracking a whip, where a small flick of the wrist may yield a large motion at the end of the whip. Misstate data from one side of a SC to the other part may yield substantial sloppiness. This includes increase in inventories, shortage of cash flow, weak customer satisfaction, etc. Enterprises may efficiently reduce the bullwhip effect by completely learning its root causes. This paper presents an overview on the concept and recent development of the bullwhip effect.