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Demand forecasting

We built a sales forecasting system and optimized the volume of goods in the warehouse and the range of goods in different locations, considering each outlet's specifics. We set up a system that has processed more than 8 TB of sales data. These have helped the retail business increase revenue, improve logistics planning, and achieve other business goals.

Data Insights & Forecasting

Data Science

Retail

Business process automation

Data Insights & Forecasting

Data Science

Retail

Business process automation

88%

forecasting accuracy

0.9%

out-of-stock reduced

About the client

Retail trading company focused on production of luxury goods, available in more than 3,000 stores worldwide.

Tech stack

Python

ReactJS

Django

Pandas

Pyspark

Redis

Challenges & solutions

Challenge

Optimize the volume of goods in the warehouse by location.

Optimize the assortment of goods in various locations taking into account specifics of each outlet.

Build a sales forecasting system.

Solution

Built clusters of stores taking into account customer behavior.

Built a basic model based on time series forecasting.

Built a refined model based on the neural network taking into account additional factors such as holidays, economical indicators of countries where stores are located, weather conditions, etc.

8TB/24month sales data processed.

19%/$142mln reduced the volume of residues in warehouses.

88% accuracy of forecasting.

0.9%/from 4% decreased the out of stock level.

I think what is really special about the DATAFOREST service is its flexibility, openness, and level of quality and expertise.

Andrew M.

CEO Luxury Goods Retail

The way we deal with your task and help achieve results

Step 1 of 5

Free consultation

It's a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.

Step 2 of 5

Discovering and feasibility analysis

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs.

In Data Science, there are numerous models and approaches, so at this stage we perform a set of interviews in order to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create solution architecture, a project plan, and a list of insights or features that we have to achieve.

Step 3 of 5

Solution development

The work starts with data gathering, data cleaning and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for the further development. Ultimately, we interpret the results. Nevertheless, data modeling is about a process that requires lots of back and forth iterations. We are result focused, as it’s one of our core values as well.

Step 4 of 5

Solution delivery

Data Science solutions can be a list of insights or a variety of different models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value.

Step 5 of 5

Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.

Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

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If we experience any problems, they come back to us with good recommendations on how the project can be improved.

DevOps Experience

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70%

cost reduction