Industrial manufacturing organizations and production logistics organizations have generated significant amounts of data from sensors, time series, inline cameras, images/videos, and enterprise systems and process logs. (Han et al., 2018) This has made it possible to improve the operations from post-hoc to predictive. (Achouch et al., 2022) The objective of this organized review is to collect the existing knowledge and information on the architecture, algorithms, and methods for multimodal analytics, and this organized review has been designed and developed for three major purposes: maximizing equipment effectiveness, minimizing scrap and rework, and maximizing energy efficiency. The guidelines for systematic review PRISMA 2020 are followed, including literature from 2020 to 2025 on predictive maintenance, anomaly detection, computer vision inspection, process mining, digital twinning, forecasting, and optimization. (Systematic review of predictive maintenance practices in the manufacturing sector, 2025) The study has found that data architecture is the starting point for scalability, multimodal analytics can reduce false positives and increase the effectiveness of the early warning system when each modality measures different failure processes, improvement of KPI can be optimized when the result of decision is included in the workflow and decision-making, and forecasting and decision models are perceived as solution space for optimizing energy. (Dutta et al., 2025) The study is associated with generalization, improvement of KPI, and decision models, which are associated with sustainability and optimization of throughput, quality, and energy. (Kaushal & Chakrabarti, 2025) The next part of this chapter will include the discussion on the implementation checklist and research agenda. The next part of this chapter will include the discussion on the relevance of the study and the systematic review carried out for the journals of the first quartile of logistics and operations research journals.