数据与AI业务操作
Demand forecasting and AI-driven supply chain: customized predictive engines to optimise operational processes.
Sell-out forecasting is one of today’s main challenges for most manufacturing companies.
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.
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…).
We design and deliver concrete actions through an exhaustive framework.
从数据管理战略到确保组织符合 GDPR 要求，我们的团队帮助并建议管理人员如何优化数据管理以提高绩效。>我们相信，我们会为每一位客户创造独特的解决方案，并整合他们的团队，设计出量身定制的高效组织。
Predicting impact of promotions on sell-out
制造商和零售商的共同目标是刺激更多的购物之旅，所以促销活动往往是为了这个互利的目标。零售商和制造商给予的促销活动有一个复杂的结构，其中包括货币和非货币部分，以及即时和长期的影响。
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
模式检测是数据分析的一个基本分支。它主要包括对数据中的模式和规律性的识别，以了解特定行为。
识别你的供应链过程中的问题，检测欺诈行为或暴露人群中的可疑行为是具体的、高价值的用例。我们的Artefact"的方法旨在检测这种异常行为，同时避免这种稀缺性现象的陷阱。
在处理和建模步骤之前，我们充分利用现有的原始数据（结构化数据，如操作日志，甚至图像和视频记录），以暴露出所需的异常情况。
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