Managing inventory efficiently is a critical factor in maintaining competitiveness within modern supply chains. Conventional item identification systems, which rely on handheld devices or fixed laser scanners, have several limitations, including the need for continuous operator involvement, unobstructed line-of-sight conditions, and relatively high maintenance costs. These limitations particularly affect small and medium-sized enterprises (SMEs), which often lack the financial resources required to implement industrial-scale automation solutions. This study presents an experimental warehouse item-scanning system based on the Arduino microcontroller platform and low-cost Radio-Frequency Identification (RFID) technology. The proposed prototype demonstrates the feasibility of integrating simple, scalable, and decentralized RFID scanning nodes into conventional warehouse environments while evaluating their performance relative to existing approaches. A key contribution of this work is the development of an exponential signal attenuation model that estimates the probability of successful tag detection as a function of the distance between the RFID tag and the antenna, as well as the level of environmental congestion. Based on this model, a probabilistic error-correction mechanism was developed to improve inventory consistency and compensate for missed tag detections. To validate the proposed approach, a central server was employed to coordinate data processing and minimize hardware-related inconsistencies. Experimental results demonstrated that the probabilistic correction algorithm improved inventory accuracy from 85%, which is typical of low-cost, non-optimized RFID systems, to 93%. The estimated deployment cost of each scanning node ranged from USD 50 to USD 75, making the proposed solution an attractive option for logistics companies and SMEs pursuing digital transformation under budget constraints. The study concludes that combining decentralized Arduino-based RFID networks with probabilistic mathematical modeling provides a practical, scalable, and cost-effective approach for improving warehouse inventory management and system reliability.