Seru production system is an innovative assembly system and combines the flexibility of job shop production and high efficiency of assembly lines. This production system easily adapts to product replacements, reduces product wastages by eliminating work-in-process inventories and delay time, and provides enterprises with a competitive advantage through reducing the operating costs, required workforce, and space. Besides these advantages, the disadvantage of seru type production is that, in assembly lines, specific tasks are completed in specified stations, whereas in seru production, the tasks required for the assembly of a product are completed in a yatai by a cross-trained worker. This, in turn, results in a higher risk of production errors. Accordingly in this research, a conceptual model is proposed to monitor and control multiple factors of the production process like worker, environment, assembly tools, ergonomics, storage and inventory and issue warning for prevention of process and quality errors in seru production by use of advanced analytics based on deep learning. In addition to providing support to the worker, the proposed Smart Seru Production System Model will assist system participants in obtaining and understanding data about the production processes and reacting quickly based on this information.

This study aims to design an innovative method to evaluate the effects of line-seru conversion in terms of various environmental and economic performance criteria. For this purpose, an integrated approach using simulation and data envelopment analysis (DEA) assisted with various performance criteria is proposed for the first time. The simulation studies involve diverse scenarios for the production of a circuit breaker on assembly lines balanced with RPW and COMSOAL heuristic algorithms, and seru production systems. The simulation results indicate a significant increase in labour effectiveness and station/yatai utilization ratios, complete removal of WIP inventories, which led to a growth in productivity up to 43.29%. The number of workers, carbon-dioxide emission, required workspace, training costs of workers and equipment cost data were incorporated in the results to evaluate the applicability and relative efficiency of the developed scenarios by use of the CCR and BCC models of DEA. The calculated scale efficiency shows that the scenarios for assembly lines balanced with COMSOAL and the seru-based scenarios were both fully effective. The seru production system scenario with 6 yatais was the most effective scenario due to reduced task times after conversion. Also, line-seru conversion proved to be advantageous despite the high training costs.

Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, especially during the packaging process. This study aimed to identify and classify the chicken parts for their input during the packaging process with the highest possible accuracy and speed. For this purpose, deep-learning-based object detection models were used. An image dataset was developed for the classification models by collecting the image data of different chicken parts, such as legs, breasts, shanks, wings, and drumsticks. The models were trained by the You Only Look Once version 8 (YOLOv8) algorithm variants and the Real-Time Detection Transformer (RT-DETR) algorithm variants. Then, they were evaluated and compared based on precision, recall, F1-Score, mean average precision (mAP), and Mean Inference Time per frame (MITF) metrics. Based on the obtained results, the YOLOv8s model outperformed the other models developed with other YOLOv8 versions and the RT-DETR algorithm versions by obtaining values of 0.9969, 0.9950, and 0.9807 for the F1-score, mAP@0.5, and mAP@0.5:0.95, respectively. It has been proven suitable for real-time applications with an MITF value of 10.3 ms/image.

The Seru production system is an innovative assembly system that combines the flexibility of shop floor production and the high efficiency of assembly lines. In addition to its advantages, seru-type production has the disadvantage that all the tasks required for the assembly of a product are completed in a yatai by a cross-trained worker. This results in a higher risk of production errors compared to assembly lines. In order to mitigate these risks, a real-time control system is considered necessary. Recognition of assembly tasks is needed to prevent quality defects in the Seru production system. To meet this need, in this study, a skeleton-based deep learning hybrid Convolutional Neural Network-Bi-directional Gated Recurrent Unit-Convolutional Neural Network (CNN-BiGRU-CNN) assembly task recognition model is developed. In this study, a two-stage data augmentation approach is proposed to improve the performance of the model developed by utilizing the worker skeleton data obtained with Mediapipe Holistic infrastructure from the assembly video collected from the seru production system. The effectiveness of the model was evaluated by comparing it with Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long Short Term Memory (BiLSTM), BiGRU and hybrid models created by combinations of these models. The proposed data augmentation approach improved the performance of all the models compared in the study. With the proposed hybrid CNN-BiGRU-CNN model, the best performance was achieved with a prediction accuracy of 96%.