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Work-related generative artificial intelligence adoption and employee-perceived organisational performance in Thailand's manufacturing industry
(2026-10-01)
Yudhistyra, Wecka Imam
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Srinuan, Chalita
Generative Artificial Intelligence (GenAI) is increasingly integrated into organisational work processes, yet its implications for manufacturing firms cannot be inferred from adoption intention or usage frequency alone. In contrast to deterministic workplace technologies, GenAI produces probabilistic and context-responsive outputs whose organisational value depends on employees' ability to evaluate, interpret, and apply generated content effectively. This research examines how work-related GenAI use is associated with employee perceptions of GenAI-enabled organisational performance in Thailand's manufacturing sector. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the Human-Centred Design paradigm, and Knowledge Management theory, the research develops a framework in which perceived usability, knowledge acquisition, knowledge application, and performance expectancy influence behavioural intention, which subsequently affects use behaviour and employee-perceived GenAI-enabled organisational performance. Survey data from 381 manufacturing employees with prior GenAI experience were analysed using Partial Least Squares Structural Equation Modelling. Bayesian Network (BN) algorithms were subsequently employed to refine the theoretical structure by identifying conditional dependencies beyond the hypothesised model, while Artificial Neural Networks and Importance-Performance Map Analysis provided complementary predictive and managerial insights. The results support all hypothesised relationships. The BN-refined model further identifies direct links between knowledge application, performance expectancy, and organisational performance, suggesting that, in addition to adoption and use, GenAI-derived value also depends on employees' capacity to translate AI-generated knowledge into workplace outcomes. The findings advance technology adoption research by developing and validating a GenAI-specific extension of the UTAUT that integrates human-centred design and knowledge management perspectives. The research further demonstrates that organisational value is realised through a sequential pathway linking adoption antecedents, behavioural intention, and use behaviour, while providing policy-relevant evidence on workforce-driven AI adoption in an emerging economy undergoing digital transformation.
14
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Automated Assembly Machine Stopping System Using Machine Learning for Electrical Failure Prediction in Hard Disk Drive Manufacturing
(2025-01-01)
Panjasamanwong, Tanakrit
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Thanasopon, Bundit
The assembly of diode lasers and sliders is a critical process in hard disk drive (HDD) manufacturing, directly affecting product electrical performance. Delays in detecting problematic assembly machines increase scrap rates, reduce yield, and raise production costs. This research presents the development of supervised machine learning models to predict electrical testing results using key parameters from assembly machines, for example, diode laser alignment position after assembly and laser intensity measurement during assembly. Dataset was preprocessed through outlier removal and feature selection using correlation analysis and permutation importance before applying SMOTE technique to balance the minority class in the training set, followed by normalization before modeling and evaluating performance. Multiple models were developed and evaluated to select the best performing model. Evaluation results showed that the AdaBoost model combined with Random Forest achieved the highest performance, with an AUC of 92.36 %. The selected model was integrated into monitoring, alerting and machine auto stopping system when the predicted failure rate exceeded a predefined threshold. The implemented system reduces the detection time for problematic assembly machines, minimizes defective parts, and improves overall manufacturing efficiency.
1
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An Improved Neural Manufacturing Corporate Credit Rating Model Based on LSTM
(2024-01-01)
Zhang, Rui
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Chen, Binbin
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Sakdanuphab, Rachsak
This paper proposes an improved neural manufacturing corporate credit rating model based on Multi-head Self-attention (MSA) mechanism and Long Short-Term Memory (LSTM) network. The proposed model leverages MSA to simulate the marketdynamics and generate dynamic weights for each indicator based on the financial dataof all manufacturing companies. Meanwhile, LSTM is utilized to extract sequential features from long-term financial and operational data to capture the long-term financial status and reduce the risk of deviation. The experimental results show that the proposedmodel provides more objective and reliable credit ratings for manufacturing companies.In the comparison experiment with the baseline model, it was proven that the model proposed in this paper outperforms other baseline models. In the comparison experiment with SMAGRU, it was proven that the proposed model has better predictionability than SMAGRU on both datasets, and it also demonstrates that the GRU simplifies the internal computation of LSTM. The ablation experiment verified the feasibility of the two modules of the proposed model separately, which further proved the effectiveness of the proposed model.
2
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Optimal path planning on multi-robot task allocation for manufacturing with artificial bee colony algorithm
(2021-01-01)
Praserttaweelap, Rawinun
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Kiatwanidvilai, Somyot
This research presents the optimal path planning for manufacturing. The new contribution is the safety path searching by using artificial bee colony algorithm in multi-robot systems. The new RC function was introduced in this paper to represent the risk of collision path. The best safety path searching is the low risk of collision path from automatic searching on RC function. This research designs the artificial bee colony algorithm for the safety path searching based on the manufacturing environment. The proposed is to find the optimal safety path with fast convergence. With the built-in error, the simulation results illustrate the effectiveness of the optimal path planning in multi-robot systems for manufacturing.
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Competitive strategies, innovation, and firm performance in small and medium-sized enterprises of manufacturing in China
(2021-01-01)
Feng, Qi
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Chaiyasoonthorn, Wornchanok
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Chaveesuk, Singha
Development in technology and globalization have forced manufacturing industries to upgrade on methods and material capital of production. The traditional production in China's SME manufacturing industries is faces great competition and cost pressure when facing the global market competition. Innovation allows manufacturing industries to establish good competitive strategies, which make the enterprise performance long-term development. Using competitive strategies and innovation can improve the quality of life and bring professional and economic benefits to individuals and enterprises. The research will generate a conceptual framework to focus on the influence of competitive strategies and innovation on the performance, to enhance and inspire the better performance of SMEs. The framework will provide a template for the development, planning, and operation management of Chinese SME managers and offer a practical tool for diagnosing SME performance.

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