Shell Accuport
Tasks
UX, UI, Research, Design system, Beta Product launch
Time
Aug 2018 – March 2020
Accuport is a digital platform helps shipping companies efficiently manage lubricants demand and optimise fleet performance for lubricants purchasing and consumption.
I joined Accuport during the Alpha phase as an email prompting system powered by AI, where shipping companies received an email reminder that included products to place and order in time when a vessel was approaching a good port. This proved to be well received by customers, but they wanted the ability to edit order prompts, as well as a holistic overview of the vessels. This would give them the ability to be proactive, rather than reacting to vessels that are in need of replenishing.
Vessels need lubricants to keep engines running. Lubricants are just as important as fuel, however they take a lower priority as they are replenished infrequently in a year. This can often result in orders being placed last minute, as well as emergency orders, resulting in the procurement process to be expensive and time consuming. Some vessel types do not have a fixed voyage schedule, it is unpredictable due to trading markets. This makes it difficult to plan lubricant lifts, and making a stop outside of the commercial schedule costs the shipping companies a lot of money.
Challenge
Business opportunity
Provide reminders for customers to ensure that orders are made on time, and to avoid customers going to comoetitors. Thos allows Shell to be able to fulfil orders efficiently and to use data to better forecast stock at ports, which ensures enough stock is available at busy ports and reduces waste at less busy ports.
Our users
Research
I produced a Journey map to understand current procurement workflows, how the AI order prompt solved pains, and areas where we could improve our product.
The main issue is a lack of visibility over required data causes a lot of delays within the process. Procurement operative relies on staff on ships to update them on stock, and ask for orders, this means they are a working reactively. There is no live data of stock at ports, orders have to be checked manually before being accepted.
Workshops
Product vision
Provide visibility
To be able to provide real-time data for a user’s household, both on a total and an appliance-level. This is to provide trust transparency & control.
Planning ahead
This is a simulation engine using real data that would give users a chance to try out products such as solar panels, helping to provide decision guidance before a big purchase.
Historic data - Improve on inefficiencies
A engagement feature that would motivate continued usage of the app, with potential rewards to recognise positive behaviour changes.
Order suggestions
A vessel receives an OS when the vessel has one or more low volume Main engine oils, and is approaching a good port to lift. An OS is created by the data science engines. The goal of the data science engines is to answer:
When to generate a prompt
For what port – preferred only
What products to include
What quantities
The user then able to review and edit the products and quantities, then submit and RFQ. The user then uses their normal channels to place and confirm the order.
Lift insights
Using the data we have on previous orders, Lift Insights allows customers to understand their current lifting behaviour, and what affects it.
Insights on fleets historic behaviour helps customers to make informed lifting decisions in the future, and track them against performance benchmarks.
Using our research we highlighted data that users find most important when conducting analysis:
Type of products are ordered by the vessels, volumes and order frequency
Ports the vessels place orders – Preferred ports provide better value for money, and easier delivery services
When the vessels place rushed orders and how frequently – Rushed orders can have expensive delivery, and can highlight vessels that were in an emergency
Products frequently in rushed orders – This highlights products that are often forgotten in orders.
User testing
We worked with 5 shipping management companies who used the product within their procurement workflow during this co-creation process. We were able to gather data on how they used the product, and see the impact on their procurement process.
We conducted regular qualitative user tests with the customers, and often visited their offices. This allowed us to observe them using Accuport and get a deeper understanding of their working environment.
Outcome
By having access to this data, companies can work towards more efficient lifting behaviour:
More deliveries at a selection of agreed preferred ports
Minimise small and rushed orders
Minimise the overall number of deliveries
This was not just a result of surfacing data, but also transforming the company working processed for Procurement operatives to take the lead in ensuring vessels are well stocked, instead of reacting to order requirements from the vessels. This also resulted in elevating work load for workers on the vessels.
