Optimizing Inventory Management with AI
Artificial Intelligence
Client company:Greenzone
Project description
"How can Greenzone optimize their inventory management and marketing by implementing an AI-controlled system that provides insight into sales trends and seasonal patterns, to buy in more efficiently and to mark them more specifically?"
This system offers Greenzone the possibility to improve stock availability, reduce costs and better align promotional campaigns with customer needs, which leads to a more efficient and effective business process.
Context
One of the biggest challenges with which Greenzone has to do is maintain a clear and consistent overview of the sales trends of flowers during the year. This insight is essential, because only with a good understanding of which flowers are popular at what time, the company can effectively plan how much they should buy, which types of flowers should be ordered, and when exactly this should be done. Without a structured overview of these trends, Greenzone relies on the intuition of experienced employees, which increases the chance of errors.
This problem is exacerbated by the fact that new employees at Greenzone often do not have the experience of predicting these trends in the same way as their more experienced colleagues. It often takes years to build up the expertise that is needed for effective stock management. In the meantime, decisions can be made that lead to shortages, surpluses or waste. Moreover, the lack of a structured approach causes inconsistency, making business operations vulnerable during staff fluctuations or extensions.
Results
To tackle these challenges, Greenzone could greatly benefit from an AI-powered system that provides accurate and reliable sales forecasts for their products. This system would utilize an advanced AI model to analyze historical sales data, seasonal patterns, and external factors such as holidays and weather conditions.
The AI model would predict the quantities of flowers required for specific periods, enabling the purchasing department to make precise and efficient ordering decisions. By accurately forecasting demand, the system would help Greenzone maintain optimal stock levels, reduce waste, and prevent shortages.
This initial implementation would focus on developing a robust AI model that delivers actionable insights to the purchasing team. The model's predictions would be presented in a clear and accessible format, allowing all employees involved in inventory management to make data-driven decisions, regardless of their experience level. This foundation sets the stage for further enhancements, such as integrating a calendar or dashboard in future projects.
Downloads
Research Document – Greenzone (PDF)