Background: Indoor Positioning Systems (IPS) have gained increasing relevance in logistics, offering solutions for safety enhancement, intralogistics management, and material flow control across various environments such as industrial facilities, offices, hospitals, and supermarkets. This study aims to evaluate IPS technologies’ performance and applicability to guide practitioners in selecting systems suited to specific contexts. Methods: The study systematically reviews key IPS technologies, positioning methods, data types, filtering methods, and hybrid technologies, alongside real-world examples of IPS applications in various testing environments. Results: Our findings reveal that radio-based technologies, such as Radio Frequency Identification (RFID), Ultra-wideband (UWB), Wi-Fi, and Bluetooth (BLE), are the most commonly used, with UWB offering the highest accuracy in industrial settings. Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. Overall, hybrid approaches that integrate multiple technologies demonstrated enhanced accuracy and reliability, effectively mitigating environmental interferences and signal attenuation. Conclusions: The study provides valuable insights for logistics practitioners, emphasizing the importance of selecting IPS technologies suited to specific operational contexts, where precision and reliability are critical to operational success.

Background: Inventory record inaccuracy (IRI) causes discrepancies between physical and digital inventories, leading to production delays and customer dissatisfaction. Cycle counting, in this context, is a common corrective action. Pareto-based ABC analysis is widely used to decide which items to inspect, but it often oversimplifies inventory decisions, and recent studies suggest that multi-criteria decision-making (MCDM) and machine learning (ML) may offer more effective solutions. Methods: This study applies the analytic hierarchy process (AHP) method, combined with K-means (AHP-K), to classify stock-keeping units (SKUs) into three groups with distinct counting policies. A selection procedure is then applied to identify an optimal ML algorithm and compare its classification with the original AHP-K results; each model in this phase is trained on a subsets of 100 SKUs. A Veto method is also introduced to improve output consistency for both AHP-K and the best ML method, and a comparative cost evaluation is presented. Results: The ML-AHP-K-Veto classification achieves over 90% accuracy. Analysis of a dataset of 12,863 SKUs from a mechanical manufacturing company shows minimal cost differences between ML-based and MCDM classifications, but significant differences compared to Pareto-based costs. Conclusions: ML can effectively address IRI, supporting the development of pure ML applications, including decision-maker (DM) preferences, to manage cycle counting strategies.