Optimizing Inventory Management and Fulfillment

Stock relocation solution

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

Data Insights & Forecasting

Pharma

Data Science

Data Insights & Forecasting

productivity boost

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

Hadoop

The client's needs

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.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

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

How we provide data integration solutions

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.

At this stage, we need to ensure that we understand the full scope of the project. We receive from you or perform a set of interviews and prepare the following documents: integration pipeline (which data we should get and where to upload), process logic (how system should work); use cases and acceptance criteria; solution architecture. Ultimately we make a project plan which we strictly follow.

Step 3 of 5

Solution development

At this stage, we build ETL pipelines and necessary APIs to automate the process. We attract our DevOps team to build the most efficient and scalable solution. Ending up with unit tests and quality assurance tests to ensure that the solution is working properly. Focus on Results is one of our core values as well.

Step 4 of 5

Solution delivery

After quality assurance tests are completed, we deliver solutions to the client. Though we have over 15 years of expertise in data engineering, we are expecting client’s participation in the project. While developing the integration system, 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.

All Success Stories

Performance Measurement

The Retail company struggled with controlling sales and monitoring employees' performance. We implemented a software solution that tracks sales, customer service, and employee performance in real-time. The system also provides recommendations for improvements, helping the company increase profits and improve customer service.

17%

increase in sales

25%

Improvement in Employee KPI Achievement Rate

Amir R.

CEO Fashion Retailer

View case study

They easily understand industry-specific data and KPIs, and their efficiency as a team allows them to deliver results quickly.

Data-driven marketing

We created a solution that helped optimize the customer base to get the most out of the customer data. This solution notifies the client about the services/goods, which they would likely buy, according to the gathered information.

20%

sales growth

200%

traffic boost

Jeremy Groves

CEO ThinkDigital, Digital and Marketing Agency

View case study

They developed solutions that brought value to our business.

AI Web Platform for Data-Driven E-commerce Decisions

Dropship.io is a powerful data intelligence platform that helps e-commerce businesses identify profitable products, analyze market trends, and optimize sales strategies. Using large-scale data scraping, AI-driven insights, data enrichment solutions, integrations with Shopify, Meta, and Stripe, it enables smarter product decisions and drives revenue growth.

3M+

total unique users

600M+

products monitored

Josef Ganim

Founder & CTO Dropship.io

View case study

AI-Powered E-commerce Platform: Data-Driven Case

DevOps Experience

The ML startup faced high costs during its growth for a data-driven platform infrastructure that processes around 30 TB per month and stores raw data for 12 months on AWS. We reduced the monthly cost from $75,000 to $22,000 and achieved 30% performance over SLA.

2k+

QPS performance

70%