Productivity and Sustainability Management in the Responsive Factory

Analysing Spatio-Temporal Worker Movement Patterns: Implications for Safety and Productivity in Smart Factories (2025)
Presentation / Conference Contribution
Vasantha, G., Patel, H., Hanson, J., Corney, J., El-Raoui, H., Sales, R., Quigley, J., Kasarapu, S. S., & Sherlock, A. (2025, June). Analysing Spatio-Temporal Worker Movement Patterns: Implications for Safety and Productivity in Smart Factories. Presented at 11th IFAC Conference on Manufacturing Modelling, Management and Control – IFAC MIM2025, Norway

Understanding the spatio-temporal dynamics of worker movements within complex factory environments, such as shipbuilding facilities, is crucial for proactively assessing safety and enhancing operations through potential process adjustments and factor... Read More about Analysing Spatio-Temporal Worker Movement Patterns: Implications for Safety and Productivity in Smart Factories.

Safer and Efficient Factory by Predicting Worker Trajectories using Spatio-Temporal Graph Attention Networks (2024)
Presentation / Conference Contribution
Kasarapu, S. S. K., Vasantha, G., Corney, J., Hanson, J., Quigley, J., El-Raoui, H., Thompson, N., & Sherlock, A. (2024, August). Safer and Efficient Factory by Predicting Worker Trajectories using Spatio-Temporal Graph Attention Networks. Presented at IDETC-CIE International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington, DC

Occupational accidents in manufacturing industries pose a significant risk, necessitating advanced strategies to ensure worker safety and enhance operational productivity. The unpredictable nature of worker movements, influenced by varied tasks such... Read More about Safer and Efficient Factory by Predicting Worker Trajectories using Spatio-Temporal Graph Attention Networks.

Safer and Efficient Assemblies: Harnessing Real Time Worker Movements with Digital Twins (2024)
Presentation / Conference Contribution
Kasarapu, S. S. K., Vasantha, G., Marzano, A., Corney, J., Hanson, J., Quigley, J., El-Raoui, H., Thompson, N., & Sherlock, A. (2024, August). Safer and Efficient Assemblies: Harnessing Real Time Worker Movements with Digital Twins. Presented at 21st International Conference on Manufacturing Research (ICMR2024), Glasgow

This paper addresses a critical gap in digital twin simulation within manufacturing environments by focusing on the dynamic representation of worker movements during assembly processes. We introduce an innovative approach that utilizes Ultra-Wideband... Read More about Safer and Efficient Assemblies: Harnessing Real Time Worker Movements with Digital Twins.

Smarter Facility Layout Design: Leveraging Worker Localisation Data to Minimise Travel Time and Alleviate Congestion (2024)
Journal Article
Aslan, A., Vasantha, G., El-Raoui, H., Quigley, J., Hanson, J., Corney, J., & Sherlock, A. (2025). Smarter Facility Layout Design: Leveraging Worker Localisation Data to Minimise Travel Time and Alleviate Congestion. International Journal of Production Research, 63(4), 1326-1353. https://doi.org/10.1080/00207543.2024.2374847

This paper introduces a novel methodology leveraging worker localisation data from ultrawide-band sensors to formulate alternative facility layouts aimed at minimising travel time and congestion in labour-intensive manufacturing systems. The system p... Read More about Smarter Facility Layout Design: Leveraging Worker Localisation Data to Minimise Travel Time and Alleviate Congestion.

Design Of A Serious Game For Safety In Manufacturing Industry Using Hybrid Simulation Modelling: Towards Eliciting Risk Preferences (2024)
Presentation / Conference Contribution
El Raoui, H., Quigley, J., Aslan, A., Vasantha, G., Hanson, J., Corney, J., & Sherlock, A. (2023, December). Design Of A Serious Game For Safety In Manufacturing Industry Using Hybrid Simulation Modelling: Towards Eliciting Risk Preferences. Presented at The Winter Simulation Conference 2023, San Antonio, Texas

Conventional methods used to elicit risk-taking preferences have demonstrated significant disparities with real-world behaviours, compromising the validity of the data collected. Serious gaming (SG) provides a high potential to bridge this gap. This... Read More about Design Of A Serious Game For Safety In Manufacturing Industry Using Hybrid Simulation Modelling: Towards Eliciting Risk Preferences.

Hierarchical ensemble deep learning for data-driven lead time prediction (2023)
Journal Article
Aslan, A., Vasantha, G., El-Raoui, H., Quigley, J., Hanson, J., Corney, J., & Sherlock, A. (2023). Hierarchical ensemble deep learning for data-driven lead time prediction. International Journal of Advanced Manufacturing Technology, 128(9-10), 4169-4188. https://doi.org/10.1007/s00170-023-12123-4

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manu... Read More about Hierarchical ensemble deep learning for data-driven lead time prediction.

A Knowledge Graph Approach for State-of-the-Art Implementation of Industrial Factory Movement Tracking System (2023)
Presentation / Conference Contribution
Vasantha, G., Aslan, A., Hanson, J., El-Raoui, H., Corney, J., & Quigley, J. (2023, June). A Knowledge Graph Approach for State-of-the-Art Implementation of Industrial Factory Movement Tracking System. Presented at International Conference on Flexible Automation and Intelligent Manufacturing, Porto, Portugal

Digital sensing technologies are essential for realizing Industry 4.0, as they enhance productivity, assist with real-time decision-making, and provide flexibility and agility in manufacturing factories. However, implementing these technologies can b... Read More about A Knowledge Graph Approach for State-of-the-Art Implementation of Industrial Factory Movement Tracking System.

Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation (2023)
Presentation / Conference Contribution
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., & Corney, J. (2023, June). Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation. Presented at 32nd International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2023), Porto, Portugal

In complex manufacturing industries that are not fully automated and involve human workers it is important to identify deviations from the planned production schedule and locate bottlenecks for improved efficiency. This is not an easy task as it requ... Read More about Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation.

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing (2023)
Journal Article
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing. Sensors, 23(10), Article 4928. https://doi.org/10.3390/s23104928

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at impro... Read More about Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing.

Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing (2023)
Data
Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing. [Data]. https://doi.org/10.17869/enu.2023.3100035

This data contains the worker position datasets (including the event logs) and the source codes of the discrete event simulation that are used in the research article titled "Using Worker Position Data for Human-Driven Decision Support in Labour-inte... Read More about Using Worker Position Data for Human-Driven Decision Support in Labour-intensive Manufacturing.