In the context of 'environmental sustainability', the industrial sector confronts environmental difficulties due to the growing energy demand. Energy efficiency improvements are required to solve these challenges in the industrial business. It is frequently important to predict energy consumption to measure energy efficiency. In 2016, about three-quarters of the world's CO2 emissions were driven by the use of energy. About 30% of emissions are attributed to the industrial sector, with 24.2% related to energy use, making it the most significant source of emissions. As a result of the high use of energy, it is possible to cut CO2 equivalent emissions while simultaneously providing a financial incentive for businesses to improve their energy efficiency. Industrial enterprises need precise estimates of their energy usage to implement preventative and mitigation steps to reduce the environmental impact, avoid regulatory fines, and increase competitiveness. The FAO (Food and Agriculture Organization of the United Nations) predicts that food production would need to rise by 70% by 2050 but energy output will only increase by 33%. As a result, there will almost certainly be a growing gap between energy and food production over the same period. The general view is that energy and resource usage reductions in the food industry are critical to the profitability of the business, food security, and reaching the Government's emissions reduction objectives. As a result, increasing energy efficiency has emerged as a critical concern for the food industry.
Artificial intelligence (AI) and Machine Learning (ML) have great potential to address the global concern of sustainability. For instance, businesses can use machine learning to find relationships between environmental performance and process characteristics as well as monitoring and incident investigation . The contemporary industrial environment encourages the collection of massive volumes of data, the bulk of which seems to be unexamined by the majority of businesses. The increased use of data-driven models in manufacturing is being fuelled by the growing availability of data as a result of the widespread use of relatively affordable Industrial Internet of Things Technologies (IIoT) and the rising processing capacity of cloud computing. Due to its excellent problem-solving and dimensionality capabilities, data-driven methods such as machine learning are often hailed as a superior analytical approach. Despite this, there is a lack of study on its use in demand-side energy forecasts. In the context of energy efficiency, the majority of existing machine learning research targets issues in the petrochemical industry. As of now, there are just a few published studies on the use of machine learning methods in other sectors to achieve energy-related goals. To support and drive industry 4.0 technologies adoption and enhance the implementation of sustainable industrial development practices, this research will investigate the use of ML techniques to improve energy efficiency in food manufacturing environments.
3D printing (3DP) technology is a manufacturing method that allows the user, whether organisations or individuals, to produce solid three-dimensional objects that are "printed" from raw materials based on access to 3D computer data. This manufacturing process could lead to a whole new era in production methods and revolutionise manufacturing by improving product affordability, speed, customisation and precision. Furthermore, many experts believe that 3DP technology will influence different aspects of today's supply chains, especially the inventories, warehouses, production processes and lead times. However, adopting such technology can be challenging for companies as it is strongly believed that such implementation is a strategic decision for companies that requires significant planning, analysis and integration between different parties. Implementation frameworks are therefore needed to provide general guidance and to classify the key factors and considerations that would support companies to deliver a successful implementation. This research combines quantitative and qualitative research approaches with background theory to analyse the different implementation frameworks of advanced manufacturing systems and develops an implementation framework for 3DP technology. The proposed framework divides the adoption considerations and factors into four key dimensions: strategic considerations, technical considerations, organisational and supply chain considerations, and external considerations. The significant contribution of the study is the proposed 3DP implementation framework which companies in different fields can utilise to develop their 3DP implementation strategies and plans.