Innowise Group has expanded the client’s existing supply chain capabilities with DSaaS to forecast materials shipment terms and reduce customer churn rate.
Our customer is a manufacturer of electronic devices and components for them, including mobile phones, TV remotes, DVD and CD players, digital cameras and others.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
Ensuring a well-functioning network of suppliers is crucial to guarantee timely order deliveries. Our customer has already optimized supply chain performance to maximize profitability by mitigating risks of fluctuating demand, inefficient operations, and volatile materials prices. Furthermore, they implemented rigorous planning and scheduling, comprehensive inventory control systems, and continuous monitoring for quality assurance.
Nonetheless, our client still faced missed delivery deadlines and errors in strategic resource planning. For improved accuracy and predictability in operational performance, they wanted an advanced DS & ML-based solution to collect and analyze large volumes of data and make realistic predictions on delivery terms.
As our client manufactures complex digital devices consisting of many parts (resistors, inductors, capacitors, transistors, diodes, etc.), they require stable, manageable supply chains with certain risks calculated. They wanted a broad overview of all previous interactions with partners, empowered by ML capabilities to digest and predict future shipments and prevent delays or interruptions in deliveries.
Based on that, Innowise Group suggested building a smart contract analysis platform that includes DS and MLOps to turn raw data into actionable insights. Our project team took full advantage of these technologies and implemented AI/ML in the supply chain to shield procurement processes and alleviate adverse effects.
At the first stage, our specialists clarified and redefined the customer’s objectives since the original proposal had many issues in terms of feasibility and end-use. Throughout the development process, our specialists applied additional AutoML approaches to boost model delivery rates. As our model received more samples similar to recent ones, we implemented a custom resampling technique that reduced the data drift effect.
Our project team worked on the Scrum methodology with bi-weekly sprints and everyday meetups. The project manager stayed in touch with the client, accommodating changes in the scope. All the tasks were tracked in Jira, with the PM assigning jobs and supervising overall performance.
Currently, the project is active, with our team working on improving output prediction and integrating ML supply chain modules.
Innowise Group enriched the customer’s supply chain ML capabilities with a DSaaS extension to predict delivery terms. Thanks to ML and DS algorithms that consider the multitude of variables within a complex supply chain system, the client can now continuously monitor for potential procurement issues and plan shipments more thoroughly, preventing information silos. Thanks to the novel solution, the customer confidently manages supply chain processes without worrying about unforeseen complications or operational delays. Additionally, thanks to machine learning in the supply chain, our client can now make informed decisions that contribute to operational excellence and increased revenues across digital sales points.
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After examining requirements, our analysts and developers devise a project proposal with the scope of works, team size, time, and cost estimates.
We arrange a meeting with you to discuss the offer and come to an agreement.
We sign a contract and start working on your project as quickly as possible.