Automation of ultrasonic-based product tracking & traceability in supply chains

In the sweeping tide of digital evolution, the Internet of Things (IoT) is emerging as a significant catalyst, spearheading a colossal upsurge in deploying various sensors as the Industrial 4.0 buzzword continues dominating all platforms where industry captains converge. Modern-day Supply Chain Management (SCM), product tracking, and traceability are paramount for ensuring efficiency, quality control, and regulatory compliance. At the heart of IoT are wireless sensors and various other sensors forming the ecosystem of technologies that interact. This dissertation explores optimizing ultrasonic-based systems as wireless sensors for tracking and traceability in SCM and logistics. While widely used, traditional radio-frequency identification (RFID) tags and barcode systems encounter limitations in environments with interference or when tracking through dense materials. Ultrasonic technology, with its ability to penetrate various media and provide high- resolution data, presents a promising alternative for environments where other tracking technologies underperform. It can thus be used with near-field communication (NFC) technology and real-time GPS tracking, traditionally reserved for tracking goods in transit. The research investigates the unique attributes of ultrasonic signals for product identification, focusing on frequency modulation, signal processing techniques, and integration with existing digital frameworks. The study utilizes simulation models and field testing to examine the reliability, accuracy, and cost-effectiveness of ultrasonic-based tracking across various supply chain stages. Innovative algorithms and device design modifications address key challenges, such as signal attenuation, environmental noise, and energy consumption, enhancing signal clarity and data retrieval efficiency. This project primarily focused on the costs and benefits of using a simple ultrasonic sensor for product tracing and tracking. A cheap ultrasonic sensor was used and connected to an Arduino device and an affordable buzzer and LED for audible and visual alerts to anyone nearby. These simple, standard, low-cost devices were programmed with open-source code and libraries downloadable in Arduino IDE to achieve comparable results that previously cost an arm and a leg with current technologies such as Blockchain (BCT), global positioning system (GPS), or RFID. The sensor was programmed to sense objects passing through at a certain distance and then increment a count that displayed real- time results locally and on a centralized cloud platform. This enabled the results to be monitored and queried in any part of the world where there is internet connectivity. These methods in SCM have been quite expensive to set up and maintain, thus prompting the need for an IoT-based system with low-cost input but reliable performance to achieve the purpose. This project also aims to provide a solution for automatically tracking and tracing goods without human involvement before goods are packaged for transportation, where GPS tracking is ineffective. Results demonstrate that automated ultrasonic tracking can improve product traceability, particularly in complex industrial environments where traditional methods struggle. By incorporating ultrasonic technology, supply chains benefit from enhanced visibility, which supports real-time inventory management, reduces errors, and increases responsiveness to disruptions. This dissertation concludes with recommendations for implementing ultrasonic systems in conjunction with existing technologies such as Blockchain technology, RFID scanning and tagging systems, and other IoT based infrastructure, offering a heterogeneous approach that maximizes the strengths of each technology to create a robust, scalable solution for modern supply chains.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

2025

Author dc:creator

• Mkombwe, Anorld

Advisor dc:contributor.advisor

• Winberg, Simon

• Product tracking
• Supply chain

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/11427/42514

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/42514