Alexandra Melike Brintrup

University of Cambridge | Cam · Department of Engineering

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data a...

Supply chains are increasingly global, complex and multi-tiered. Consequently, companies often struggle to maintain complete visibility of their supply network. This poses a problem as visibility of the network structure is required for tasks like effectively managing supply chain risk. In this paper, we discuss automated supply chain mapping as a...

Supply chain business interruption has been identified as a key risk factor in recent years, with high-impact disruptions due to disease outbreaks, logistic issues such as the recent Suez Canal blockage showing examples of how disruptions could propagate across complex emergent networks. Researchers have highlighted the importance of gaining visibi...

The development and use of Artificial Intelligence technology for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than as a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have...

Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since the early 2000s; industrial uptake of them...

Graph distillation (GD) is an effective approach to extract useful information from large-scale network structures. However, existing methods, which operate in Euclidean space to generate condensed graphs, struggle to capture the inherent tree-like geometry of real-world networks, resulting in distilled graphs with limited task-specific information...

The ultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machine...

Supplier selection and order allocation, a longstanding challenge in supply chain management, has recently begun incorporating risk minimization alongside cost, reflecting growing interest in supply chain resilience and risk mitigation. In response, hybrid frameworks leveraging artificial intelligence and machine learning have emerged. However, cur...

A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of supply chains using data-driven...

Supply chains are dynamic systems with constantly changing data, necessitating adaptive machine learning models. While prior research emphasizes integrating new data to enhance decision-making, the need to remove obsolete or harmful data from models remains underexplored. This paper addresses the challenge of efficiently removing undesired data, su...

Organizations often struggle to identify the causes of change in metrics, such as product quality and delivery duration. This task becomes increasingly challenging when the cause lies outside of company borders in multiechelon supply chains that are only partially observable. Although traditional supply chain management has advocated for data shari...

This paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), where frequent decisions on problems such as inventory levels and delivery times require coordination among companies. Traditional SCM relies on human consensus in decision-making to avoid emergent problems like the bullwhip effect....

The objective of this special issue is to publish state-of-the-art and visionary research works on generative AI for robotics and manufacturing, including theoretical methods, technologies, case studies, and industrial applications. Topics to be covered include, but are not limited to, the following: • Generative AI for autonomous robotic systems •...

Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great technical hurdles, the removal of a participant and their related information contained in the shared model remains a challenge. To address this problem, federated unlearn...

Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Among various solution offerings, Autonomous supply chains (ASCs) have emerged as key enablers of increased integration and visib...

Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Among various solution offerings, Autonomous supply chains (ASCs) have emerged as key enablers of increased integration and visib...

Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accuracy. These systems typically require expensive camera, computing equipment and significant machine learning ex- pertise, which can substantially burden small and medium-sized enterprises. This study explores l...

Information sharing in supply chains can be challenged by privacy concerns. Equating data and information, the existing literature primarily focuses on the incentivisation behind information sharing between firms. The field of AI may bring a new way of looking at this problem by asking the following question: what if we do not share raw data but sh...

The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation, making it difficult to systematically plan for better outcomes. In this article, we propose and evaluate the use of causal machi...

A trolley is a container for loading printed circuit board (PCB) components, and a trolley optimisation problem (TOP) is an assignment of PCB components to trolleys for use in the production of a set of PCBs in an assembly line. In this paper, we introduce the TOP, a novel operation research application. To formulate the TOP, we derive a novel exte...

In today's globalized economy, comprehensive supply chain visibility is crucial for effective risk management. Achieving visibility remains a significant challenge due to limited information sharing among supply chain partners. This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply...

Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving such an attack arises in practice when only a subset of the poisoned data can be identified. This necessitates the development of methods to remove, i.e. unlearn, poison...

Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the d...

Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, manipulated, or outdated information. The key challenge lies in forgetting specific information while protecting model performance on the remaining data. While current stat...

We present a machine unlearning approach that is both retraining-and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance. This is computationally expensive and necessitates the storage of the whole dataset for the lifetime of the model. Retraining-free approache...

Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors. When a model has been trained on a dataset containing such errors its performance is reduced. This leads to the challenge of efficiently unlearning the influence of the erroneous data to improve the mod...

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply chain network, without needing the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated d...

Understanding precisely what is in food products is not always straightforward due to food fraud, differing labelling regulations, naming inconsistencies and the hierarchical nature of ingredients. Despite this, the need to detect and substitute ingredients in consumer food products is far-reaching. The cultivation and production of many ingredient...

Artificial Intelligence (AI) has emerged as a complementary technology in supply chain research. However, the majority of AI approaches explored in this context afford little to no explainability, which is a significant barrier to a broader adoption of AI in supply chains. In recent years, the need for explainability has been a strong impetus for r...

While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploi...

New firm-level data can inform policy-making

Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This achieves economies of scale, thus reducing cost, greenhouse gas emissions and road congestion. But which carrier should partner with whom, and how much should each carrier...

Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO 2. Whilst well-studied in operations research-industrial adoption remains limited due...

Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO 2-eq. Whilst well-studied in operations research-industrial adoption remains limited...

The pursuit of long-term autonomy mandates that robotic agents must continuously adapt to their changing environments and learn to solve new tasks. Continual learning seeks to overcome the challenge of catastrophic forgetting, where learning to solve new tasks causes a model to forget previously learnt information. Prior-based continual learning me...

Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, the...

Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, manipulated, or outdated information. The key challenge lies in forgetting specific information while protecting model performance on the remaining data. While current stat...

Heavy goods vehicles are vital backbones of the supply chain delivery system but also contribute significantly to carbon emissions with only 60% loading efficiency in the United Kingdom. Collaborative vehicle routing has been proposed as a solution to increase efficiency, but challenges remain to make this a possibility. One key challenge is the ef...

Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause lies outside of company borders in multi-echelon supply chains that are only partially observable. Although traditional supply chain management has advocated for data shari...

While the increased use of AI in the manufacturing sector has been widely noted, there is little understanding on the risks that it may raise in a manufacturing organisation. Although various high level frameworks and definitions have been proposed to consolidate potential risks, practitioners struggle with understanding and implementing them. This...

Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines reputations, waste passengers time and money, and directly impact the environment. This study adopts a network science approac...

Although Machine Learning (ML) in supply chain management (SCM) has become a popular topic, predictive uses of ML in SCM remain an understudied area. A specific area that needs further attention is the prediction of late deliveries by suppliers. Recent approaches showed promising results but remained limited in their use of classification algorithm...

Tightening lending standards are motivating companies to adopt supply chain financing, with invoice backed lending to remedy financial stress. These financial objects depend on company-to-company relationships. The accumulation of these dyadic relationships creates complex supply network topologies. Companies within these networks are selfish and h...

The modern civil aircraft engine is a type of highly complex engineering system in design, manufacturing, and life-cycle management. They are constantly operated under extreme and critical conditions, and yet, high reliability and safety are top priorities in the civil aviation industry. To ensure top performance and efficiency in operations, engin...

Tightening lending standards are motivating companies to adopt supply chain financing , with invoice backed lending to remedy financial stress. These financial objects depend on company-to-company relationships. The accumulation of these dyadic relationships creates complex supply network topologies. Companies within these networks are selfish and...

With manufacturing companies outsourcing to each other, multi-echelon supply chain networks emerge in which risks can propagate over multiple entities. Considerable structural and organizational barriers hamper obtaining the supply chain visibility that would be required for a company to monitor and mitigate these risks. Our work proposes to combin...

Supplier selection and order allocation (SSOA) are key strategic decisions in supply chain management which greatly impact the performance of the supply chain. Although, the SSOA problem has been studied extensively but less attention paid to scalability presents a significant gap preventing adoption of SSOA algorithms by industrial practitioners....

Smart manufacturing uses data-driven solutions to improve performance and operations resilience, requiring large amounts of data delivered quickly, enabled by telecom networks. Disruptions can shut down a network; avoiding them needs responsiveness to network usage, achievable by embedding autonomy into the network with fast and scalable algorithms...

Although safety stock optimisation has been studied for more than 60 years, most companies still use simplistic means to calculate necessary safety stock levels, partly due to the mismatch between existing analytical methods’ emphases on deriving provably optimal solutions and companies’ preferences to sacrifice optimal results in favour of more re...

In this paper, we define and conceptualize the emerging practice of “Digital Supply Chain Surveillance (DSCS)” as the proactive monitoring of digital data that allows firms to track, manage, and analyze information related to a supply chain network without needing the explicit consent of firms involved in the supply chain. After reviewing approache...

Supplier selection and order allocation (SSOA) are key strategic decisions in supply chain management which greatly impact the performance of the supply chain. The SSOA problem has been studied extensively but the lack of attention paid to scalability presents a significant gap preventing adoption of SSOA algorithms by industrial practitioners. Thi...

While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploi...

Nowadays, flight delays are quite notorious and propagate from an originating flight to connecting flights, which lead to big disruptions in the overall schedule. These disruptions cause huge economic losses, affect the reputation of airlines, lead to a wastage of time and money of passengers, and have a direct environmental impact. This paper pres...