Sell-out forecasting is one of today’s main challenges for most manufacturing companies.
Manufacturers hardly have full control over their sales. Existing prediction engines have significant limitations due to three main reasons:
1.The complexity of extracting data from most data sources (Excel files such as media plans, PDF reports…)
2.The inability to predict several effects that impact final sales (Social Media, competition…)
3.The incapacity to account for specific industry effects (Global Shoppers effect – Luxury, environmental government initiatives – Car industry…).
Artefact delivers data-driven solutions to help companies in their quest for a reliable sell-out prediction.
Based on these above observations and thanks to our strong technical knowledge of machine learning and advanced AI techniques, we build highly comprehensive and reliable sell-out prediction models able to adapt themselves to market unpredictable effects and industry specifications.
Predicting impact of promotions on sell-out.
Manufacturers and retailers share the goal of stimulating more shopping trips, so promotional campaigns are often geared towards this mutually beneficial goal. Promotions given by retailers and manufacturers have a complex structure, which includes both monetary and non-monetary components, as well as immediate and long-term effects.
In order to optimize the strategy of promotions (quantity, price, time, product,…) and impact on sell-out, it is necessary to be able to appreciate the value and impact of them.
However promotions have a cost: either the loss of sales for similar products that would have been bought otherwise or the loss of revenue due to the promotion itself. Having a clear and self-learning evaluation of promotions is mandatory to track and optimize the use of it and Artefact is able to build such predictive models to improve promotional decisions.
Pattern and regularity detection.
Pattern detection is a fundamental branch of data analysis. It mainly consists of the recognition of patterns and regularities in data to understand specific behaviours.
Identifying an issue inside your supply chain process, detecting frauds operations or exposing suspicious behaviour within a crowd are concrete, high-value use cases. Our Artefact’s methodology is designed to detect this outlier behaviour while avoiding the trap of this scarcity phenomenon.
We make the best use of available raw data (structured data such as operation logs or even images & video recording) before the processing and modelisation steps to expose the desired anomalies.