The Challenge : Transitioning to a smart factory model
Battery manufacturers struggle to achieve high energy density batteries whilst ensuring quality of production at the same time: when they want to produce high capability lithium battery, the scrap rate increases to up to 75%. Understanding the manufacturing process from the start to finish by gathering data is therefore crucial to solve this conundrum.
Solutions : Using data to offer new intelligence
Thanks to its long expertise in automotive, having worked with major players around the globe, Expleo’s knowledge of the manufacturing process is first-class. Working like a management consultant alongside the client, we can investigate existing problems in the most user–centric way, present our findings to them and lay out the objectives of a data optimisation programme.
As an engineering company with IT capabilities, we work in a 360 way, helping the client understand its entire data ecosystem, manage and analyse it to build intelligence and stronger expertise.
Outcome : Creating a data-led company
Thanks to the use of sensors in the production machines and throughout the manufacturing process, the client can accurately collect data, run agnostic analyses on datasets through Data Science, Machine Learning (ML) and Visualisation tools and obtain new insights.
Expleo’s work is making a significant difference to the client’s reputation as a smart manufacturer, able to understand the process parameters from various data sets, understand its customer’s pain points and the solutions that can be deployed to deliver customer-orientated batteries for end users.
- End-to-end investigative agnostic data analysis
- Better understanding the manufacturing process can drastically improve battery quality, and therefore the high scrap rates, saving significant costs.
- From a classical battery manufacturing company initially, the client is now progressively transitioning to a data-driven company.
- The client is able develop a mature Big Data platform comprising of a cloud architecture to perform data collection and aggregation and offer new types of services.
- In the near future, deeper analysis of the data feeds coming from end users and their datalakes could derive additional insights and therefore further commercial opportunities.
Manufacturing & Supply Chain