Our client is a leading supplier of construction materials, offering end to end transport services and waste removal solutions. With a team of over 800 employees spread across 50 locations, they have a vital role in the UK’s construction and waste management sectors. Transporting over 20 million tonnes of materials annually means that operations are complex, requiring effective logistical management.
The client was facing significant challenges in managing the large volume of paper tickets and delivery records generated by their supply chain. With around 40,000 tickets to process manually, the task of reading, digitising and matching these documents was time-consuming and highly prone to human error.
The existing system used to partially scan and read tickets was not performing well, leading to additional manual effort. The organisation needed a more efficient, automated way of processing delivery tickets. The ability to link delivery tickets to core systems and validating invoices was critical to improve overall operational efficiency.
Following our discovery work with the client, we focused on using the Sentinel AI-powered solution to enhance the client’s ticket processing and invoicing workflow. Our solution involves 4 key features:
Our AI system reads and digitises scanned delivery tickets, including handwritten delivery notes, and automatically extracts key data such as net weight, type of delivery and other relevant criteria.
The AI system matches the scanned tickets to the corresponding records in the Company’s invoicing and transport systems, based on predefined criteria like delivery type and weight. This helps to ensure that only correct and verified transactions are processed.
Our solution integrates with the client’s core systems, including finance and logistics, allowing for the seamless flow of data across platforms. This integration ensures that anomalies can be quickly identified, and discrepancies addressed in real-time.
Given the variety of formats in which the tickets are received, we proposed implementing a manual override feature, allowing operators to manually accept or reject matches if required. This allows flexibility to handle cases whilst automating most of the process.
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