Demand forecasting is a critical component of inventory management in retail, as it allows retailers to optimize their inventory levels, reduce waste and minimize stockouts. Machine learning algorithms can be used to analyze historical sales data and other relevant factors, such as seasonality, holidays, promotions, and external events, to generate accurate predictions of future demand.
Here’s how UiPath AI Center and UiPath Apps could be used to implement such a solution:
->Data collection: UiPath robots could be used to collect sales data from various sources, such as POS systems, e-commerce platforms, and social media.
->Data preprocessing: The collected data could be preprocessed using UiPath Studio, which provides a wide range of data manipulation and transformation activities. This step may include data cleaning, normalization, and feature engineering.
->Model development: UiPath AI Center used to build and train a demand forecasting model using machine learning algorithms, such as regression, time series analysis, or deep learning.
->Model deployment: The trained model is deployed to UiPath AI Center, where it can be integrated with other automation processes.
->UiPath apps: An application is activated for stock managers. A list of best-selling products that are going out of stock is displayed. When the manager clicks on ‘place the order,’ a job is triggered which activates a robot to place the order for the relevant product.
Attached is a workflow of the to-be processe and a first example of a UiPath Apps dashboard.
Industry categories for this use case: Customer Service, Logistic, Marketing Sales
Skill level required: Advanced
UiPath Products that were used: UiPath Studio, UiPath AI Center, UiPath Apps, UiPath Orchestrator
Other applications that were used: python
Other resources: -
What is the top ROI driver for this use case?: Minimize risk and ensure compliance in operations