Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each product’s movement on a real-time basis in the inventory. It takes this supply chain to a smart supply chain management. This research proposes a Machine Learning (ML) approach for on-demand forecasting under smart supply chain management. Using Long-Short-Term Memory (LSTM), the demand is forecasted to obtain the exact demand information to reduce the overstock or understock situation. A measurement for the environmental effect is also incorporated with the model. A consignment policy is applied where the manufacturer controls the inventory, and the retailer gets a fixed fee along with a commission for selling each product. The manufacturer installs RFID technology at the retailer’s place. Two mathematical models are solved using a classical optimization technique. The results from those two models show that the ML-RFID model gives a higher profit than the existing traditional system.

Industry 4.0 is potentially innovative in the workers’ role, which is becoming increasingly involved in smart activities. In this situation, it is necessary to improve highly repetitive uncomfortable working postures to reduce physical risks. This study intends to assess physical risks during VR interaction for manufacturing work. Posture-related physical risk levels were calculated using ergonomic risk assessment tools RULA, REBA, and OWAS. Three task conditions were considered for the experiment in a VR-based car-assembly environment. An analysis of variance was applied to investigate significant differences between task conditions, and it suggested that a higher risk level was obtained while working in the overhead position for RULA and REBA, whereas the squatting position obtained a higher risk level for OWAS. Sensitivity analysis identified that the upper arm and neck were responsible for the highest risk level for RULA, the upper arm, neck, and trunk for REBA, and the back posture parameter for OWAS.

Human status detection (HSD) is important to understand the status of users when interacting with various systems under different conditions. Recently, although various machine learning algorithms have been applied to analyze and detect human status, there are no guidelines to utilize machine learning algorithms to analyze physical, cognitive, and emotional aspects of human status. Therefore, this study aimed to investigate measures, tools, and machine learning algorithms for HSD by applying a systematic literature review method. We followed the preferred reporting items for systematic reviews and meta-analysis (PRISMA) model to answer three research questions related to the research objective. A total of 76 articles were identified using two hundred keyword combinations addressing topics under HSD in the fields of human factors and human-computer interaction (HCI). The results showed that research on HSD becomes important in industrial systems, focusing on how intelligent systems based on machine learning (ML) differ from earlier generations of automated systems, and what these differences necessarily imply for HCI to design and evaluation. The tools used to collect data for HSD on different parameters are broadly discussed. Recent HSD studies seem to focus on cognitive load and emotion, whereas prior studies have focused on the detection of physical effort. This research assists domain researchers in identifying HSD approaches using different ML algorithms that are suitable for use in their research.

Presently, it is essential to increase customer service such that the Supply Chain Management (SCM) can earn more profit in a sustainable manner. In the supply chain, the manufacturer and the retailer are two players who try to maintain the joint profit of the Supply Chain (SC) without only thinking about their own respective profits. However, the retailer may not provide all information to the manufacturer. This research introduces the use of the advanced technology Radio Frequency IDentification (RFID) in a retailer’s shop to obtain exact information about customer demand. A consignment policy is used to increase the manufacturer profit, and a fixed fee is offered to the retailer to generate more profit in coordination with the manufacturer. This study is conducted with and without the effect of RFID to show the benefit of SCM even when the retailer is unreliable. Both models are solved using the classical optimization technique. Numerical findings prove that SCM can benefit from the outcome of this study even for unreliability. Coordination within SCM can be maintained for a long time by using the proposed recommendations of this study.