Alchemai is a US-based startup empowering supply chain risk managers to comprehend, anticipate, predict, and simulate network risk with unprecedented speed and accuracy. Using Artificial Intelligence, they reveal hidden patterns, correlations, and connections within supply chain networks, converting supply chain risk into a competitive advantage.
When Alchemai approached us, they had an ambitious goal: to change the way supply chains are managed. They noticed that existing supply chain risk management systems typically react to problems that have already occurred and as such, are unable to proactively support robust and efficient supply chains.
The idea for the product was based on the direct insights from the market and the potential clients, and the company has already had a clickable Proof of Concept. The goal was to build a product that would be able to find the patterns, correlations, and connections within supply chain networks, use these inputs to manage and predict the supply chains, and that could be delivered to the potential client.
In order to better understand the value that the product should bring to its final users (and meet their expectations in the future), we wanted to interview them, learn their pains and objectives.
The first thing to do after understanding business goals standing behind the idea and the needs of the users was to translate this idea into a product backlog that would organize the process of development. Then, we focused on managing the backlog by redefining priorities based on user feedback, development team progress, and other factors.
The client already had the designers and the frontend team. Our job was to take care of what’s inside the application – from planning its architecture to making sure all the mechanics beneath will meet stringent requirements.
Building a product that would be able to analyze enormous volumes of data to find the patterns, correlations, and connections within supply chain networks required putting Artificial Intelligence at its heart.
The supply chains can be possibly impacted by numerous factors: geopolitics, delays made on the sub-contractors level, logistics, natural disasters. And this impact can be measured with the value of lost contracts, contractual penalties, or storage costs – the pain is real. The challenge for the supply chain managers is to predict possible delays and react accordingly.
Existing supply chain risk management systems often simply react to the problems that have already occurred and as such, they can’t support supply chain risk managers efficiently.
From the very beginning, the product built by Alchemai was meant to solve a specific problem. Insights from the market and its target customers were incorporated in the process of design & development from the very beginning. The challenge was to translate these insights into requirements organized in a solid backlog that would help to manage the whole process.
Before we built the backlog, we wanted to talk to potential users of the product, the supply chain managers, and find out more about their problems and needs. We did it in cooperation with the client’s team. Thanks to that, we were able to prioritize the features and start building the MVP with the ones that were the most crucial from the users’ perspective.
In order to support the Product Owner with managing the product and its development, the PO Proxy has joined the team. The role of a PO Proxy was to help the Product Owner manage the backlog, set priorities, and define acceptance criteria.
In order to manage the supply processes efficiently, it’s important to know whether there will be any delays and if they occur – to be aware of where in the supply chain it will happen and how it impacts the customer’s revenue. Utilizing Machine Learning, our models are able to predict different risks related to supply chains and their effects.
As the client already had the designers and a frontend team, it was important to team up with them and cooperate closely. From the beginning, we established a true partnership, working as one team. We went through the research phase together, shared the conclusions, and planned the general rules of cooperation. We were working in Agile sprints that we planned together with all the team members. Despite having different areas of expertise, members from both teams did not hesitate to suggest improvements to others, both in terms of technological and organizational issues. Moreover, we were updating ourselves during daily standups, and documenting the results in cyclical periods.
Apart from that, we actively participated in discussions with other members, supporting in making product decisions and cooperating on solutions for the AI module with the client’s stakeholders.
After the AI-powered MVP was launched, it was presented to the potential client. Neoteric team has also joined the demo to answer technical questions and support Alchemai during the presentation. The demo was successful and resulted in having a chance to prove the product’s value in practice during a 2-week long pilot.