The escalating environmental challenges faced by the industrial sector, particularly concerning environmental sustainability, are principally due to increasing energy demands. Energy efficiency, a vital factor in mitigating these challenges, hinges on the ability to predict energy consumption effectively. As of 2016, energy use was responsible for approximately 75% of global CO2 emissions, with the industrial sector contributing around 30% to this total. Specifically, energy usage in the industrial sector constituted 24.2% of the emissions, signifying the most significant source. In this context, businesses can accrue financial benefits from reducing CO2 equivalent emissions, given the high energy consumption. Such reductions necessitate improvements in energy efficiency to circumvent regulatory penalties, lessen environmental impact, and bolster competitiveness. Therefore, industrial enterprises must accurately estimate their energy usage.
Projections by the Food and Agriculture Organization (FAO) indicate that while food production needs to surge by 60% by 2050, energy output will increase by a meagre 33%. This forecast highlights the imminent and widening discrepancy between energy and food production, reinforcing the importance of energy and resource conservation in the food industry. Ensuring food security, meeting governmental emissions reduction targets, and maintaining the sector's profitability all underscore the necessity of boosting energy efficiency in the food industry. To this end, advanced technologies, particularly Machine Learning (ML) - a subfield of Artificial Intelligence (AI) - could be instrumental. ML can elucidate relationships between environmental performance and process characteristics, thereby aiding in monitoring.
Despite the influx of large data volumes from the contemporary industrial environment, most businesses have not harnessed the full potential of this data. The growing availability of data through Industrial Internet of Things Technologies (IIoT) and the evolving processing capabilities of cloud computing promote the use of data-driven models in manufacturing. However, the application of ML, despite its notable problem-solving and dimensionality capabilities, remains underrepresented in research on demand-side energy forecasting.
This research, thus, aims to investigate the use of ML techniques to enhance energy efficiency in food manufacturing systems. Such an approach will not only stimulate the adoption of Industry 4.0 technologies but will also encourage sustainable industrial development practices.
MSc Dissertation (2019)
3D Printing Technology: A Proposed Framework for Implementation
The advent of 3D Printing (3DP) technology, a transformative method enabling the creation of solid, three-dimensional objects from raw materials based on digitised data, is poised to revolutionise manufacturing processes. Its potential benefits encompass a broad spectrum of product affordability, speed, customisation, and precision. Simultaneously, it has the capacity to significantly impact the modus operandi of contemporary supply chains, potentially restructuring practices related to inventories, warehouses, production processes, and lead times.
Nonetheless, integrating this technology into an existing organisational structure presents substantial challenges. It is paramount to recognise that the successful adoption of 3DP technology requires strategic foresight, meticulous planning, robust analysis, and harmonious integration between varying factions of an organisation. Therefore, comprehensive implementation frameworks are not just beneficial, but rather, they are essential in outlining the critical factors, elements, and considerations that could pave the way to a successful technology adoption.
This research bridges the gap between theoretical and empirical analysis through a blend of quantitative and qualitative methodologies. It undertakes a detailed exploration of the different implementation frameworks of advanced manufacturing systems, striving towards the development of a comprehensive and practical implementation framework for 3DP technology. This proposed framework categorises adoption considerations and factors into four primary dimensions: strategic considerations, technical considerations, organisational and supply chain considerations, and external considerations.
The defining contribution of this research is the provision of a practical 3DP implementation framework. This tool stands as an invaluable resource for organisations across diverse fields, aiding them in the formulation and execution of effective 3DP implementation strategies and plans.