Data Integration in IT Systems in Supply Chains

Integrating IT systems in the automotive industry remains a technically complex and costly process, particularly in environments based on legacy ERP and WMS platforms. Existing research discusses QR codes and process automation separately. However, little attention is paid to non-intrusive integration architectures that combine standardized identification and intelligent automation. This study develops and implements a universal logistics integration model based on GS1-compliant QR codes and JSON data structures combined with intelligent process automation (IPA). A case study from an automotive company is presented. Analysis indicates that the main loss factors include misidentification of assets, manual data entry, and lack of feedback on delivery status. The architecture proposed in this manuscript consists of three modules: INTELOGBOT (2.07), IPABOT (1.79), and APIBOT (1.79). A structured QR-JSON identifier schema was also designed to ensure platform-independent data exchange. This eliminated manual data re-entry and enabled real-time inventory and delivery synchronization. Furthermore, it also automated logistics documentation and reduced identification errors in inbound and outbound operations. The research contribution consists of developing a solution that enables the connection of QR codes with intelligent IPA-based bots, providing a repeatable framework for advanced automation of logistics processes in complex supply chains.