Introduction and challenges
Artificial intelligence (AI) is no longer just a buzzword but a reality in operations. Use cases are growing rapidly in many areas. Sales, supply chain, finance, purchasing and maintenance generate huge volumes of data which have untapped potential. Indeed, without data science, teams have difficulty taking advantage of these large datasets and do not have the tools to study correlations with external data, such as weather forecasts and macroeconomic metrics.
However, the use cases that have been developed demonstrate the relevance of injecting AI into processes to improve operational performance.
To achieve this, a combination of business and data expertise is needed to develop and implement pragmatic and usable AI solutions.
How we can help
Creating value through data requires assembling complementary expertise: data scientists, data engineers, analysts, and visualisation experts. The combination of these data skills combined with our business expertise allows us to provide end to end support to our customers in their AI and data science projects and build customised predictive models that adapt to their operational processes.
These models are open source algorithms, which can be maintained by our customers’ datalab teams (or Argon & Co’s teams where appropriate).
We work the following use cases, amongst others:
- Sales forecasting
- Forecasting logistics activity
- Merchandising and optimising product range
- Standard cost modelling and margin management
- Predicting quality defects in production environments and optimising process parameters
- Automated stock replenishment