Industrial Management Studies

Abstract Accurate demand forecasting in online retail is a fundamental challenge in supply chain management and inventory planning, as demand patterns are typically influenced by complex temporal relationships, cross-product network interactions, and item-specific semantic features. Many traditional time-series approaches and even certain deep learning models struggle to simultaneously capture these dynamic, non-linear dependencies. To address this challenge, this study introduces a novel forecasting framework based on a Transformer architecture, capable of integratively modeling long-lag temporal dependencies, cross-product network effects, and item semantic information. This empirical-computational research evaluates the proposed model using two real-world online retail datasets. Following data preprocessing and the removal of incomplete records, the data were partitioned into training and testing sets. The proposed model was subsequently trained utilizing a multi-head attention mechanism within the Transformer framework, incorporating semantic representations extracted from product descriptions. The results demonstrate that the proposed Transformer model achieves a Mean Absolute Error (MAE) of 2.68 and a Root Mean Square Error (RMSE) of 3.85, outperforming both the Autoregressive Integrated Moving Average (ARIMA) model (MAE: 4.37, RMSE: 5.96) and the Gradient Boosting algorithm (MAE: 3.91, RMSE: 5.12). This improvement indicates that the proposed framework effectively identifies complex demand patterns and cross-product relationships.

Abstract The oil and gas industry faces challenges such as complexity of a multi-layered and global supply network, operational and geopolitical risks, cybersecurity threats in critical infrastructure, sustainability pressures, and need for real-time data-driven decision-making. The main objective of the study is to design and analyze an autonomous supply chain framework based on artificial intelligence, big data and Internet of Things in the oil and gas industry with an emphasis on sustainability and cybersecurity. The research is applied and developmental in terms of its purpose; qualitative-quantitative in terms of the nature of the data; and descriptive in terms of the data collection method, which conducted using the Fuzzy Cognitive Mapping method. The study period is summer and fall 2025 and winter 2026. The population of the study is Iranian oil and gas industry experts, which completed by 23 experts using a structured questionnaire using purposive sampling. Achieving autonomy in the oil and gas supply chain is contingent upon the synergy and application of advanced digital technologies. Within this context, operational intelligence serves as a crucial mediating factor, transforming technological capabilities into economic and environmental value. In other words, the establishment of data-driven digital infrastructure is the primary prerequisite for attaining sustainability, while resilience and trust within this ecosystem can only be realized through robust cybersecurity. The proposed model, functioning as a decision support system, empowers managers in this industry to optimally prioritize digital investments, evaluate the implications of security policies, and simulate various digital transformation scenarios prior to operationalization.

Abstract In the competitive automotive parts manufacturing industry, accurately identifying and prioritizing failure modes is essential for preventing financial losses, ensuring passenger safety, and maintaining quality standards, since systematic risk management directly affects production efficiency and product reliability. This applied, descriptive-analytical study aimed to identify and prioritize failure modes in the constant-velocity (CV) joint assembly process of an Iranian automotive parts manufacturer by integrating the Fuzzy Best-Worst Method (FBWM) and Fuzzy VIKOR (FVIKOR). A panel of fifteen purposively selected experts, each with at least ten years of relevant experience, took part in a three-round Delphi survey using a five-point Likert scale to screen the candidate risk factors and failure modes; the retained risk factors were then weighted using FBWM, and the screened failure modes were ranked using FVIKOR. The findings showed that severity received the highest relative weight among the retained risk factors, and that failure modes associated with the tensile strength and dimensional and spline tolerances of the CV-joint components received the highest priority for corrective action. The pairwise comparisons of the expert panel showed a high level of consistency, and combining Delphi screening with fuzzy multi-criteria weighting and ranking improved prioritization accuracy compared with the traditional risk priority number approach. The findings provide production managers with a reliable and replicable basis for preventive decision-making and process optimization in automotive parts manufacturing.

Abstract The flow shop scheduling problem with sequence-dependent setup times and operator constraints represents a complex and practically relevant production planning challenge, as it simultaneously integrates sequencing, timing, and human resource allocation decisions. Exact solution approaches become computationally intractable for medium- and large-scale instances due to the combinatorial nature of the problem. This study proposes an integrated modeling and solution framework aimed at minimizing total order tardiness while jointly incorporating key operational constraints, including technological precedence, machine non-overlapping, sequence-dependent setup times, and operator capacity limitations. A mixed-integer linear programming (MILP) model is first developed to explicitly capture sequencing, scheduling, and operator assignment decisions within a unified structure. Given the computational complexity of the model, a metaheuristic solution approach based on Particle Swarm Optimization (PSO) is designed. To accommodate the combinatorial structure of the problem, a continuous encoding mechanism combined with a constructive decoder is implemented to enforce feasibility during solution evaluation. The proposed approach is validated through an industrial case study and multiple independent runs with different random seeds. Performance is assessed using the best objective value, mean and standard deviation of results, computational time, and convergence behavior. Results demonstrate that the proposed PSO achieves high-quality and stable solutions with acceptable computational effort. Sensitivity analysis indicates that stronger exploration enhances robustness and solution quality at the cost of longer runtime, whereas exploitative settings accelerate convergence but may reduce solution quality.

Abstract With the advent of artificial intelligence (AI), human resource management has encountered fundamental opportunities and challenges, making the enhancement and sustainability of knowledge workers’ productivity a critical concern. This study aimed to analyze the factors influencing the experience of knowledge workers in the AI era and to develop a managerial roadmap within a mission-based university context. This applied research adopted an exploratory–explanatory, cross-sectional mixed-methods design based on an inductive approach. Data were collected using researcher-developed questionnaires grounded in the ISO 34000 Human Resource Management Standard and validated through an expert panel of university specialists. Interpretive Structural Modeling (ISM) and MICMAC analysis were employed to identify and classify the relationships among the factors, while the Analytic Network Process (ANP) was used to determine their relative importance and weights. The findings revealed that intelligent leadership and organizational culture are the most influential underlying drivers, exerting the greatest impact on other dimensions of employee experience. These factors serve as the foundation for shaping and improving the work environment of knowledge employees in the AI era. Based on the results, a comprehensive managerial roadmap was developed and validated, comprising five phases: foundational preparation, managerial and cultural transformation, job experience optimization, emergence of innovation and scientific leadership, and retention of knowledge capital. The proposed roadmap provides a practical framework for effective policymaking and strategic decision-making in knowledge-based organizations, particularly higher education institutions undergoing AI-driven transformation.