Development of a warehouse model using machine learning technologies with application in receiving management

Warehouse management is a significant element of a supply chain. The warehouse's data accuracy is the foundation of the whole supply chain material flow, information flow, and capital flow. It requires the warehouse receiving management to have an accurate system that operates in real-time to collect receiving data. However, conventional solutions for data management at the receiving stage are managed manually. Human involvement in the current receiving stage means that data accuracy and real-time entry cannot be assured. This research aims to improve data accuracy and reduce human error in the warehouse receiving management. The purpose of this research is to develop a suitable Artificial Neural Network (ANN) model by using machine learning technologies, which can be applied to warehouse receiving management to improve data accuracy and reduce human errors. Based on comprehensive literature review and analysis of current algorithms, a conceptual ANN model, identified as the Artificial Neural Network for Components Identification and Counting (ANN-CIC) model, is proposed to perform components classification and counting. This study has evaluated three classic image identification algorithms by using sixteen groups of industrial components to compare classification performance. A modified white histogram correlation coefficient approach is chosen as the design model's classification algorithm after experiments. Besides, the counting model is tested. The model is verified with an enlarged dataset obtained from a local Australian Company. The simulation results demonstrated that the proposed model achieved a 91.37% accuracy rate in object classification and a 94.29% in object counting, which has outperformed the existing classical model accuracy rate, such as for VGG-16. The main contributions of this research can be highlighted as below: Firstly, a conceptual ANN-CIC model is proposed to perform the identification and counting of industrial components. Four basic geometric shapes as the attributes of images for shape analysis and pre-defined features are introduced. These introduced shapes assisted in verifying the feasibility of the preliminary experiments. Secondly, the white histogram correlation coefficient algorithm is improved by adjusting the colour ratio to achieve outstanding performance in the classification of various industrial components. Lastly, the model is simulated with industrial data that demonstrates its applicability and stability. Moreover, higher classification and counting accuracy rate are achieved, and the design goal is also achieved.