Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

Abstract

The iron and steel industry is one of the leading sectors contributing to the economic power of countries. It is incorporated into numerous fields, including automotive, construction, manufacturing, agriculture, defense, and healthcare. Today, the increasing supply and demand balance in iron and steel products makes the stock management and control of the products crucial. The large quantity and variety of products in the iron and steel industry make stock management and control difficult. Creating stock for the business is a very costly process and is one of the important elements of the business. Successful stock management can make the business financially advantageous. This study aims to estimate the stock costs of different products and quantities according to different dates found in the business by using data mining. For this purpose, data mining classifier models are used, and estimated costs are found. By establishing a stock tracking system throughout the supply chain, the business should register all inventory movements of the products. It should work in an integrated way with stock management costing. Thus, unforeseen decreases and financial losses in products can be detected.The iron and steel sector, a key player in enhancing competition among nations and shaping the economic landscape, caters to the needs of various industries through its commitment to sustainable steel production. The industry has encountered challenges due to the swift advancements in recent years, impacting the costs associated with iron and steel products and posing issues for the sector. Tackling these challenges is essential for fostering national development and enhancing the competitiveness of businesses. This research delves into the elements influencing the expenses within the iron and steel industry, emphasizing the importance of minimizing factors leading to elevated costs. An automation system was developed using image processing techniques, and iron and steel products were analyzed. In the automation system, k-means and twostep, one of the clustering analysis methods, was applied.A decision support system defined as SDSS (Steel Decision Support System) and BIPS (Burak's Image Processing System)has been developed to determine the inventory costs of iron and steel products, adopting a data mining approach and including clustering methods to reveal similar inventory costs. Measurements with the SDSS-BIPS automation system were 94.4% successful. Withcost levels were determined by matching the products in the iron and steel business database with their costs.