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Stock relocation

The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.

Pharma

Data Science

Supply-chain automation

Data Insights & Forecasting

Pharma

Data Science

Supply-chain automation

Data Insights & Forecasting

10%

productivity boost

7%

increase in sales

About the client

One of the largest pharmacy chains. A product list consisting of 13k namings in more than 2k stores in 30 regions.

Tech stack

Python

Pyspark

SciPy

TensorFlow

PostgreSQL

Hadoop

Challenges & solutions

Challenge

Build an optimal assortment list at each salespoint, taking into account individual specifics.

Solution

The mathematical model was built according to:
- location of drug-stores;
- housing density;
- the routes of metro stations/transport junctions;
- locations of hospitals, fitness facilities, shopping/business centers, healthcare, educational institutions/educational facilities, etc.

Based on the company's 48-month sales data analysis, the clusters were built to determine the optimal assortment list for each drug-stores.

Integration with POS terminals to control the scouting of pharmacists.

The team reliably achieves what they promise and does so at a competitive price. Another impressive trait is their ability to prioritize features more critical to the core solution.

Mark S.

Partner Pharmacy network

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.

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