Contract and bill tracking platform
Transforming the contract billing process with Swissport
Our client, a leading freight and handling services company, aims to combat revenue leakage by transforming how they track and manage their current contract and billing process through the development and deployment of an AI solution.
Timeline
From explorations to final designs, the MVP solution was completed in 6 weeks while working with multiple projects at the same time.
Background
Swissport International AG is a leading provider of airport ground services and air cargo handling, operating at 286 airports across six continents. The company offers a wide range of services, including passenger services, ramp handling, cargo handling, and lounge hospitality. Swissport is known for its extensive global network and commitment to high-quality service delivery, supporting numerous airlines worldwide.
After a technical scaeablity analysis and POC, the next step was to design and develop the MVP, which will be implemented into the Swissport teams workflow. Below details the step-by-step approach taken during the project, including research, planning, design, development, testing, and optimization phases.
Planning
As we only had six weeks to design an MVP of platform, we needed to have a well defined UX plan consisting of:
User interviews and requirement gathering
User Journey Mapping, wireframing
High fidelity wireframes
Mulitple feedback/review sessions
Research & Ideation
We conducted user research with different teams (Finance, Commercial & Ops within a workshop-style structure. The goal was to understand the current billing and contracting process and the existing challenges and pain points. This help us formulate an e2e AS IS and TO BE process map and define key user journeys.
Wireframing
Developed mid-fidelity wireframes, often collaborating with the client through regular feedback sessions.
Design & Prototyping
Create intuitive user interfaces and interactive prototypes. Iteratively refined designs based on user feedback with several key stakeholders to enhance usability and visual appeal.
Implementation
Leveraged agile development methodologies to build the scheduling app from the ground up. Prioritized feature development based on user feedback and technical feasibility. Implemented AI algorithms to analyze user behavior and optimize scheduling recommendations.
Testing and Optimisation
Conducted rigorous testing across various devices and platforms to ensure compatibility and performance. Gathered user feedback through beta testing and iteratively optimized the app based on usability metrics and user satisfaction.
The resulting AI-powered billing and contract analysis web app solution, now named ARC, offers a seamless user experience, allowing teams within the Swissport business to efficiently and accurately spot price discrepancies and the main drivers of revenue leakage. This solution was split into two seemingly separate tools that serve different functions within the user journey. serving different user journey functions
Discrepancy dashboard
This dashboard pulls through the generated invoices from services carried out by swissport for several airports and airlines. Here the transformation team can view discrepancies over time, by department and understand the discrepancy drivers. This feature feature also allows them to reach a resolution.
DER (Document extraction)
This feature leverages generative to enable contractual documents to be extracted from Salesforce. The document details are structured in a way that the user can easily review flight capture details, service charges and potential discrepancies.
Visual Quality
This feature ensure that all documents extracted assessed for visual quality, this is to improve user processes and document quality.
Here, the outcomes and achievements of the project are highlighted, including user feedback, adoption rates, and industry recognition.
71%
Of the discrepancies were accurately identified with the implementation of the AI model
$3.6M
Of underbilling discrepancy identified
$2.6M
Missing charges was accurately identified making it one of the main drivers of discrepancies
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