## 1 Introduction  
Climate change and rising energy costs have driven global initiatives to improve energy efficiency and sustainability [1]. The industrial sector plays a significant role in these efforts, as it is one of the largest consumers of energy worldwide. In 2015, the United Nations adopted the Paris Agreement, aiming to limit global temperature rise to well below 2 \({}^{\circ}\)C above pre-industrial levels, with efforts to cap warming at 1.5 \({}^{\circ}\)C [2, 3]. The Glasgow Climate Pact (COP26) further reinforced these commitments, emphasizing the urgency of reducing greenhouse gas emissions to mitigate climate change [4]. In this context, industrial energy consumption is a key area for targeted efficiency improvements, as manufacturing and production processes account for a major share of electricity use across different regions. For example, in the UK, the industry sector accounted for 16% of total electricity consumption in 2023, while the remaining electricity was used by domestic, transport, and service sectors, including agriculture and commercial and public administration activities [5]. Similar trends are observed in other industrialized nations, where manufacturing and industrial processes represent a major share of energy consumption. Understanding these sectoral energy demands is essential for developing targeted strategies to enhance efficiency and reduce overall consumption. Within the industry sector, electric machines and drives account for the largest share of electricity consumption. However, a significant portion of their energy-saving potential remains unrealized, primarily due to insufficient transparency and limited visibility into energy consumption patterns within production processes [6]. Industry 4.0's digitalization and interconnectedness offer a solution by enhancing process transparency. Real-time monitoring and data-driven decisions help identify inefficiencies, optimize energy use, and support a climate-friendly circular economy. However, one of the main challenges in implementing different aspects of Industry 4.0 is the growing engineering skills gap. As noted by [PERSON] et al. [7], a shortage of employees with the necessary qualifications is a major obstacle to implementing I4.0 technologies. This rising demand for skilled, cross-disciplinary engineers capable of adopting emerging technologies highlights the need for stronger cooperation between universities and businesses. Such collaboration can help bridge the skills gap and accelerate the adoption of new technologies. Mitsubishi Electric Air Conditioning Systems Europe Ltd. (M-ACE), a manufacturing facility in Scotland, is in the process of adopting Industry 4.0 (I4.0) technologies to improve machine performance and production efficiency. A key focus has been on energy monitoring and cost reduction, aligning with broader sustainability goals. However, M-ACE also faces challenges related to the engineering skills gap, which has hindered the implementation of Smart Factory solutions. This shortage of skilled professionals has necessitated additional collaboration to develop and integrate these systems effectively. To address this challenge, M-ACE established Knowledge Transfer Partnerships (KTP) with Glasgow Caledonian University (GCU) to develop Smart Factory solutions tailored to its needs and rapidly implement innovative projects. These collaborations were particularly relevant as M-ACE provided a real-world manufacturing environment, allowing solutions to be tested and deployed directly on live production lines [8]. Through this partnership, it was possible to access real data, assets, and infrastructure, ensuring the practical application of Smart Factory techniques to drive tangible business improvements. As part of these collaborations, a structured, step-by-step approach has been developed to establish an effective and efficient energy monitoring system. The process begins with identifying relevant energy consumers and determining their nominal power using technical data sheets.