Chixiao Lu

Two-stage network Data Envelopment Analysis (2SN DEA) is widely used to evaluate decision-making units (DMUs) with internal production structures. However, conventional 2SN DEA models may produce biased efficiency estimates when the generated virtual projection point lies outside the feasible input–output production frontier. From a managerial perspective, existing models also have important limitations. They usually explain inefficiency as a stage-level production problem, while paying insufficient attention to system-level managerial inefficiency and ripple effects between connected stages. More importantly, many conventional 2SN DEA models implicitly assume that supply-chain stages can be freely rearranged across DMUs. To address these issues, we propose a unified non-radial analysis framework that imposes additional restrictions on the reconfiguration of conventional network production technology. These restrictions ensure that the generated projection point remains feasible within the input–output production frontier. The proposed model is evaluated against existing approaches using an empirical case study from the supply chain of motor manufacturing industry, along with a sensitivity analysis based on alternative reference sets. The results, further supported by the proposed theorems, demonstrate that the new approach effectively relocates previously identified exterior projection points onto the boundary of the input–output production frontier. Through the corresponding dual programming model, we further introduce a novel mechanism to account for stage-level and system-level failures, as well as the ripple effect. The proposed model enables a decomposition of the overall efficiency of each DMU into stage efficiency and system efficiency, offering deeper managerial insights.

Firms in global supply chains can strategically exploit information disclosure to amplify positive ESG commitments while concealing poor environmental and social performance through low-visibility channels. Such strategic distribution of information creates information asymmetries in which stakeholders cannot always distinguish genuinely responsible organisations from those making unsubstantiated claims. No automated, scalable mechanism exists to verify the consistency of corporate disclosure. We address this gap by proposing a retrieval-augmented large language model (LLM) verification framework. This approach adapts computational fact-checking techniques, originally used for social media misinformation detection, to the domain of corporate ESG disclosure. The framework classifies claims against independently cross-validated disclosure data, so that what a firm asserts publicly can be automatically compared with what its disclosures substantiate. We validate our approach using ESG disclosure microdata covering global clothing brands and their supply chains. A disclosure-recovery benchmark of 34,572 brand–criterion judgments shows that brand websites surface audited promotional disclosure at two to three times the rate of operational disclosure. A verification study of 606 promotional claims finds that only 5–7% can be substantiated against audited evidence, with results stable across four open-weight language models. The framework offers regulators, consumers, and supply chain partners alternative audit mechanisms to detect claim-disclosure gaps, reducing information asymmetries that facilitate deceptive communication strategies.

This paper aims to support decision-making in lean management and increase the interpretability of Artificial Intelligence (AI)-generated recommendations in business operations. Existing explainable AI approaches provide useful guidance on which features should change, but they pay limited attention to the operational cost of implementing those changes and therefore do not adequately address the lean management trade-off between customer value enhancement and resource use. To address this problem, the study integrates sentiment analysis, topic modelling, and a Free Disposal Hull-based counterfactual model within a nonparametric production framework, in which customer satisfaction is treated as the service outcome and operational expenditures are treated as inputs. The framework shows that lean management in the service industry can be considered through the counterfactual analysis of AI-generated outcomes while considering three types of managerially relevant recommendations: the identification of critical service attributes for improvement, the specification of feasible adjustment targets, and the estimation of the minimum cost required to attain the desired performance level. Using the hotel service setting as an illustrative case, the study contributes a lean-oriented and managerially interpretable approach for linking explainable AI with operational decision making under cost-conscious service improvement.

Data processing in machine learning and Artificial Intelligence (AI) can be viewed as a conventional supply chain in which data are the primary flowing artefact: they are sourced, generated, collected, stored, and shared across upstream and downstream actors to produce valuable information products and business outcomes. Within AI-data supply chains, we map common training optimisation techniques to explicit extensions of conventional supply chain production technology, focusing on two classes of structural decisions: (i) merger (e.g., ensemble learning, distributed training, and knowledge distillation) and (ii) reconfiguration (e.g., algorithm hardware matching). Although such horizontal mergers and reconfigurations provide a mechanism for productivity growth and improved resilience under disruptions, their implications for overall supply chain productivity remain difficult to forecast. To address this gap, we develop a general framework for evaluating merger and reconfiguration decisions in complex supply chains, with an application to AI-data supply chains. Leveraging key features of the Free Disposal Hull (FDH) model and the free-arrangement assumption in network nonparametric production technologies, we propose a non-radial directional distance function model to support decision-making in merger and reconfiguration-oriented supply chain design. We demonstrate the applicability of the proposed approach using a dataset that represents an AI-data supply chain.

The competitive industrial performance (CIP) index is introduced to assess countries’ capacity to produce and export manufactured goods, technological upgrading and deepening, and impact on world production and trade. In this research, these dimensions are reflected by eight sub-indicators which are integrated into a comprehensive composite indicator (CI). However, the value of the weight corresponding to each sub-indicator may significantly affect the value of the CI. In the meantime, the ignorance of the benchmark construction and consideration of decision makers’ preference and attitude may restrict the application of CIP. Therefore, a comprehensive CI evaluation and benchmark-generating system with an objective weighting incorporating process is needed. This research aims to propose a geometric CI evaluation based on a multiplicative data envelopment analysis (DEA) model without explicit input (Multiplicative-WEI) where the benchmark can be represented by a projection point. We propose the generalized Multiplicative-WEI model whose linear representation is under the unified non-radial measure framework. The proposed model is further extended to the closest target model to reflect an optimistic evaluation strategy and super-efficiency model to increase the discriminant power. Within the introduction of parametric regarding decision makers’ preferences and attitudes, we apply our comprehensive CI framework to 153 countries to evaluate their CIP in year 2016 and year 2021. The results provide countries with more valuable insights regarding their performance change based on the weight of the sub-indicator, benchmark, and CI score.

Additive Manufacturing (AM), an innovative technology, plays a pivotal role in localising supply chains, with the integration of digital technologies further enhancing production flexibility and system resilience. However, AM increases digitalisation in the supply chain, which brings a range of cybersecurity vulnerabilities. Consequently, a comprehensive risk assessment framework is necessary to assist decision-makers considering AM systems. Prior research has employed Composite Indicators (CIs) for risk evaluation, aggregating sub-indicators to assess risks. However, the approach to determining suitable weights for these sub-indicators presents notable limitations such as limited discriminant power, non-unique outcome, subjective assumption on weight preference. To solve the limitation in determining the weight, this paper introduces a multi-level (three-stage) risk assessment model based on the Multiplicative Non-Parametric Approach. This model provides a thorough evaluation of threats within AM systems, considers both individual and industrial perspectives, and determines the global weights of each sub-indicator. The empirical analysis demonstrates that the proposed multi-level model effectively identifies appropriate weights for the sub-indicators, resulting in outcomes that closely align with the original assessment results. The model's performance improves as the number of analysis stages increases, reaching its peak in the final stage of analysis. ARTICLE HISTORY

This paper provides a comprehensive analysis of Professor Rajiv Banker's significant impact on the field of Data Envelopment Analysis (DEA). Through an extensive review of his scholarly contributions, we explore three major clusters within DEA research: (1) Returns-to-Scale (RTS) and Most Productive Scale Size (MPSS), (2) Statistical Inference in DEA, and (3) Contextual Analysis. Banker's pioneering research has significantly advanced DEA methodologies, addressing fundamental challenges related to scale efficiency, statistical robustness, and the influence of contextual variables on performance. His work has bridged theoretical developments and practical applications, influencing diverse fields such as economics, finance, and management science. By examining citation trends and bibliometric data, we trace the evolution and enduring relevance of his contributions, highlighting key papers that have shaped the trajectory of DEA research. This paper also discusses the evolution of DEA models and approaches, including the integration of stochastic elements and second-stage analyses. In recognising Banker's lifetime dedication to DEA, we celebrate his lasting legacy and his transformative influence on both the academic community and practical implementations of DEA worldwide.