The document discusses a case study on demand forecasting for a chemicals company in India that faced high forecasting errors due to traditional methods. A solution was implemented using data from past sales, deep learning techniques, and the creation of a real-time dashboard, which reduced forecasting error by 10%. The methodology included understanding business goals, designing solutions, and deploying them within the company's core system.

1. Demand Forecasting
Case Studies

2. Demand Forecasting
Customer Detail
Problem Statement
Challenges
Industry - Chemicals (Fragrances and Flavours)
Location - India
The supply chain team was using traditional
methods of forecasting. They were generating a
huge error in their forecasting and production
planning and wanted a robust forecasting solution.
1) High number of SKUs.
2) Some external factors weren’t captured by the
team.

3. Technology
MySQL Qlik
Python
Django App
PyTorch

4. Solution
Business Impact
● Existing data from SAP and Tally of past 10 years of sales were
taken
● Data engineering, data cleaning, & missing data removal
● Identification of internal and external factors affecting sales
● Designing of Deep Learning model for forecasting using
combination of LSTM and other algorithms
● Creation and connection of dashboard with SAP to get
forecasting in real-time
The forecasting solution was able to forecast the peak and
downfalls for a duration of next 8 months accurately. The error rate
reduced by 10% from the traditional methods.

5. Our Methodology
Understanding
Requirements
Designing Solutions
Deployment
We understand your
business goals, and get
acquainted with all the
necessary details.
Our team of experts,
starts building solutions
to deliver your business goals.
Once complete
solutions is ready
then we deploy at
your core system.
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2
3

6. Thank You
Chirag Tank
Co-Founder & CEO
Jitendra Purohit
Co-Founder & COO
+91 9820080751
+91 7021980537
chirag.tank@datenwissen.com
jitendra.purohit@datenwissen.com