Product Designer
Product Designer, Product Manager, Head of Product, Engineers (3), QA Engineer
12 weeks
Challenge
Shipping schedules are inherently dynamic, disruptions caused by port congestion, weather, or operational delays lead to increased costs, missed deadlines, and cascading downstream effects. Vessel operators must balance fuel costs, emissions, berthing windows, and commercial trade-offs under tight time constraints. The challenge was to design an MVP that empowered vessel operators to:
Quickly generate viable recovery scenarios
Compare cost, emissions, and scheduling trade-offs
Collaborate across teams without switching tools
Results
Launched a 0→1 MVP tool for schedule recovery used by vessel operators to reduce delay-related costs.
Enabled scenario-based recovery planning, driving better decisions around speed, port calls, and fuel usage.
Established a foundation for a scalable, AI-powered recovery system within Bearing’s suite of operational tools.
90%
-20%
+10%
Context: The Liner Shipping Opportunity
Unlike tramp shipping, where vessels operate on-demand, liner shipping operates on fixed schedules, serving consistent routes for global trade. Due to its logistical complexity and asset intensity, there are fewer liner companies worldwide, which makes each one a high-value customer.
At Bearing AI, some of our largest clients were liner operators, but we often found ourselves reactively building one-off features in response to customer requests, rather than solving universal pain points. This reactive approach made it harder to scale and retain contracts with major players.
When a new Head of Product joined the team, we revisited our strategy. Through renewed customer engagement and strategic research, we uncovered a recurring challenge shared across the industry: Schedule Recovery. This case study details how we translated that insight into a focused, user-driven MVP.
Uncovering the Problem
The Product Team planned an offsite to kickoff an in-depth discovery sprint to understand our users, their workflows, and key pain points within the liner space. Prior to meeting, we set off to talk with as many possible stakeholders from various liner companies.
Persona Interviews
We interviewed over 25 stakeholders with an emphasis on vessel operators, operations teams, and fleet managers across global shipping companies. Some of the questions we asked:
“ What are your role responsibilities and KPIs?"
“What does a typical disruption look like?”
“What are your most urgent and frequent tasks?”
Jobs Matrix
From our interviews, we mapped user job responsibilities by frequency, urgency, and value, identifying the most impactful job clusters across liner shipping organizations. As we regrouped at the offsite, we had discussions around the prioritization of these jobs and where we could the most value in which problems to solve. From a comprehensive job analysis across maritime roles, we identified the highest-frequency and highest-impact responsibilities tied to schedule reliability and recovery.
Schedule Recovery & Schedule Reliability
Vessel operators, in particular handle schedule recovery and are responsible for:
Handling service schedule disruptions
Optimizing routes between ports under time and cost pressure
Communicating delays and adjusting schedules
Coordinating bunker scheduling, port windows, and compliance
Schedule reliability is the consistency at which vessels arrive at their scheduled ports in time, usually calculated as a percentage. It is one of the top KPIs for liner operators (usually with a 90% target rate) and dependent on the execution of schedule recovery.
Understanding the Problem
How Users Are Solving This Today
After our offsite, we took a deeper dive into our previous Liner interviews and employed another round of interviews with 3 more operators. Based off our interviews, we wanted to understand how users were solving this problem today. Operators relied on:
Noon Reports
Spreadsheets to manually calculate ETA impacts and fuel costs
Gut Decisions
Using experience to handle disruptions vs. data-driven decisions
Manual Port Coordination
Calling terminals to renegotiate berthing windows
Single-Scenario Planning
No ability to compare cost and schedule tradeoffs between multiple recovery options
Key Findings
Below are the top themes that came up when talking to operators and quotes to support the daily and weekly insights into their roles.
Pain Points
Against the race of time to handle any schedule disruptions, below are the main difficulties that aggravate delays further for operators.
User Flow
To ensure our designs aligned with real-world constraints and expectations, we created a comprehensive user flow diagram of the schedule recovery experience. This helped us:
Identify where users would input data (vessel, ports, schedule details)
Determine what backend data (AIS, port events, maps) we needed to pull and validate
Map out paths for error handling, corrections, and data cleanup when schedules were incomplete or malformed
Early Wireframe Concepts
Before reaching final designs, we explored several early concept wireframes that aimed to fit our ideal vision of the full user flow. These initial layouts included multiple interaction patterns, filtering options, and varying levels of user input to validate what would work best for operators:
Full customization flows: Users could define every detail upfront for precise control.
Streamlined 2–3 step flows: A quicker approach that guided users through only essential inputs.
These explorations were intentionally broad because we knew the final MVP would require scoping down features to ensure speed and simplicity under real-world constraints. Testing these divergent approaches helped us identify which features were critical and which could be deferred for later releases.
MVP Wireframes
After some discussions with our engineering team and some quick feedback from some closeby stakeholders, we began scope down our concepts into a more feasible vision for the MVP. We aimed to focus on the following steps as the most important phases for MVP:
Setup Phase: Vessel selection and schedule import from operators
Optimization Phase: Automated calculation of scenario options
Exploration and Comparison Phase: Plan comparison view with detail cost breakdowns and designing for scannability to make quick decisions
