Usability is a key factor for successfully integrating new technology to aid an operator in production. It is measured using three metrics: efficiency (productivity), effectiveness (quality), and user satisfaction. One prominent technology for operator support is augmented reality (AR), which is mostly handheld or head-mounted. A human-centered approach is required to align the AR integration with the operator’s capabilities. The underlying use case in this study is an energy dashboard visualized using AR and non-AR media, namely, a monitor, tablet, and HoloLens. The resulting media applications were evaluated for usability in terms of efficiency, effectiveness, and satisfaction in the within-study experiments by 16 participants. Overall, the results showed increased efficiency and satisfaction for traditional-monitor users and increased effectiveness for tablet users. Despite the participants’ lack of experience with AR, the AR applications performed comparably to the monitor and even slightly better in some aspects. With the ongoing development of AR software and hardware, AR can become increasingly useful for machine monitoring in production. However, to use AR for more comprehensive tasks, its strengths and weaknesses must be considered.

Improving energy efficiency in industrial manufacturing remains challenging despite substantial technical potential. This has resulted in a persistent energy efficiency gap, which is increasingly understood as a socio-technical issue driven by not only technology limitations but also organizational and informational barriers. This study investigates how energy waste is targeted in practice through an in-depth single case study of an automotive company. Fifteen energy efficiency measures (EEMs) were analyzed and classified by type of energy waste addressed, digital technologies applied, and organizational knowledge required. The results show that industrial efforts primarily focus on reducing idling energy losses, while fewer measures address more complex forms of energy waste, such as over-processing losses. Digital technologies are mainly applied and rolled out at lower maturity levels, emphasizing energy monitoring and visualization. Further, different types of organizational knowledge are associated with targeting energy waste: technical knowledge dominates isolated interventions, process knowledge supports standardized technology diffusion, and leadership knowledge is required for cross-functional coordination. The findings highlight that bridging the energy efficiency gap requires the alignment of technological solutions with organizational knowledge and routines. This study contributes empirical insights into how manufacturing companies can structure and prioritize energy efficiency efforts and provides a framework to support the implementation of energy efficiency measures in practice.