Enhanced Parcel Arrival Forecasting for Logistic Hubs: An Ensemble Deep Learning Approach

The rapid expansion of online shopping has increased the demand for timely parcel delivery, compelling logistics service providers to enhance the efficiency, agility, and predictability of their hub networks. In order to solve the problem, we propose a novel deep learning-based ensemble framework that leverages historical arrival patterns and real-time parcel status updates to forecast upcoming workloads at logistic hubs. This approach not only facilitates the generation of short-term forecasts, but also improves the accuracy of future hub workload predictions for more strategic planning and resource management. Empirical tests of the algorithm, conducted through a case study of a major city's parcel logistics, demonstrate the ensemble method's superiority over both traditional forecasting techniques and standalone deep learning models. Our findings highlight the significant potential of this method to improve operational efficiency in logistics hubs and advocate for its broader adoption.

Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment

The growing emphasis on energy efficiency and environmental sustainability in global supply chains introduces new challenges in the deployment of hyperconnected logistic hub networks. In current volatile, uncertain, complex, and ambiguous (VUCA) environments, dynamic risk assessment becomes essential to ensure successful hub deployment. However, traditional methods often struggle to effectively capture and analyze unstructured information. In this paper, we design an Large Language Model (LLM)-driven risk assessment pipeline integrated with multiple analytical tools to evaluate logistic hub deployment. This framework enables LLMs to systematically identify potential risks by analyzing unstructured data, such as geopolitical instability, financial trends, historical storm events, traffic conditions, and emerging risks from news sources. These data are processed through a suite of analytical tools, which are automatically called by LLMs to support a structured and data-driven decision-making process for logistic hub selection. In addition, we design prompts that instruct LLMs to leverage these tools for assessing the feasibility of hub selection by evaluating various risk types and levels. Through risk-based similarity analysis, LLMs cluster logistic hubs with comparable risk profiles, enabling a structured approach to risk assessment. In conclusion, the framework incorporates scalability with long-term memory and enhances decision-making through explanation and interpretation, enabling comprehensive risk assessments for logistic hub deployment in hyperconnected supply chain networks.

Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes

In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish this, we utilize a modified CUSUM (Cumulative Sum) procedure on the company's spatial-temporal order data as well as a GLR (Generalized Likelihood Ratio) based method. We model the order data using the Hawkes Process Network, a multi-dimensional self and mutually exciting point process, by discretizing the spatial data and treating each order as an event that has a corresponding node and time. We apply the methodologies on the company's most ordered item on a national scale and perform a deep dive into a single state. Because the item was ordered infrequently in the state compared to the nation, this approach allows us to show efficacy upon different degrees of data sparsity. Furthermore, it showcases use potential across differing levels of spatial detail.