Demand Forecasting extension

Efficiently handles tasks such as data extraction from various sources, processing historical sales data, and applying forecasting algorithms to predict future demand patterns, enhancing the accuracy and speed of demand forecasting processes.

INFORMATION
Use Case
RPA
Industry
Supply Chain
DETAILS
Challenge

In the fast-paced world of supply chain management, accurately predicting demand is critical to optimise inventory levels, reducing costs, and ensuring product availability. However, traditional demand forecasting methods often struggle to cope with the complexity of modern supply chains. Challenges include dealing with data extraction from diverse sources, processing historical sales data efficiently, and applying forecasting algorithms that can adapt to dynamic market conditions. The need for a novel approach is evident to address these hurdles effectively.

Solution

To overcome the challenges in supply chain demand forecasting, we partnered with a leading client in the industry to develop a solution. Our solution is designed to enhance the accuracy and speed of demand forecasting processes by incorporating advanced technologies and tailored features.

We worked closely with our client to build a solution that seamlessly extracts data from various sources, such as sales records, customer feedback, and market trends. The core features of our solution include:

  1. Data Integration: Our solution aggregates data from multiple sources, including internal databases, external market data, and real-time customer feedback, ensuring a comprehensive dataset for forecasting.
  2. Machine Learning Algorithms: We implemented advanced machine learning algorithms that adapt to changing market conditions and provide more accurate demand predictions over time.
  3. Customised User Interface: The user interface is tailored to the specific needs of supply chain professionals, making it intuitive and user-friendly. It allows users to easily input parameters, visualise forecasts, and make data-driven decisions.
  4. Scalability: Our solution is designed to scale with the client's operations, accommodating a growing dataset and increasing forecasting demands without compromising performance.
Results

The implementation of our demand forecasting extension has led to significant improvements in the supply chain industry:

  1. Improvement in Key Performance Indicator: Our solution has consistently achieved improved demand forecasting accuracy, reducing overstock and understock situations.
  2. Enhancement of User Experience Feature: Users can now access real-time dashboards and reports, providing them with a holistic view of demand patterns and enabling more informed decision-making.
  3. Streamlining of Operational Process: With automated data extraction and forecasting, our solution has reduced manual workload and processing time, allowing supply chain professionals to focus on strategic tasks.

Techstacks Used

Technologies and Tools
• ML: TensorFlow, scikit-learn, AutoML • Database: PostgreSQL • Visualization: Tableau • Cloud Computing: AWS, Azure • ETL: Extract, Transform, Load (ETL) tools

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