<p>Industrial refrigeration is one of the world's most ubiquitous and energy-intensive services, underpinning critical sectors such as food processing, logistics, and manufacturing. Improving energy efficiency in these systems is not only a matter of economic necessity but also a key driver for sustainability and climate action. Recent advances in science, engineering, and digital transformation have opened new pathways for optimising refrigeration systems. The integration of machine learning, digital twins, and smart analytics enables continuous optimisation, maloperation detection, and asset integrity evaluation, helping move&#xa0;beyond traditional time-based maintenance and advisory platforms to practical, data-driven control solutions. This paper explores the principles and practical implementation of digital transformation in industrial refrigeration, focusing on Beca’s proprietary Maestro model predictive controller and its alignment with recommendations from the Australian Meat Processing Corporation (AMPC) guidebooks. Through case studies and data insights, we demonstrate how this digital tool can deliver energy savings of up to 36%, improve asset management, and support sustainability goals. The paper concludes with a discussion of the broader economic and environmental impacts, and the potential for scaling these solutions across the global refrigeration asset base.</p> Graphical abstract <p></p>