Transforming e-commerce operations efficiency: How Beerwulf was able to scale operations using advanced demand forecasting & automation
How can you scale the challenging process of creating themed packs that contain various—perishable—types of beer, especially with regard to stock replenishment? Learn how Beerwulf leveraged AI and data-driven insights to obtain more adequate sales forecasts and significantly decrease out-of-stock and overstock situations.
Client Introduction
Hans, CEO at Beerwulf
- Industry: Craft Beer E-commerce Platform
- Role: Chief Executive Officer
- Company Size: €36M in revenue; a Heineken subsidiary
Beerwulf is an online retailer in the craft beer industry and operates as a subsidiary of Heineken. The company specializes in offering a diverse range of craft beers through its e-commerce platform, including curated selections from various regions and brewers. Growth in a rapidly emerging market was key for the management team.
The Struggle of Scaling E-Commerce Operations
Initially, Beerwulf struggled with a manual and error-prone replenishment process. One of its key processes is the creation of themed beer packages that combine various beers in one box. On top of that, beer is a perishable product that spoils and has an expiration date. The reliance on manual and error-prone methods for inventory management and purchase orders, therefore, often led to overstocking, sometimes understocking, and operational inefficiencies, which hampered customer satisfaction and financial performance.
Introducing Cost-Effective Data-Driven Tooling That Can Scale
Our team at Crunch got to work after first identifying these issues in a proactive outreach and comprehensive analysis of the data & challenges. Our expertise in operations management, data analytics and automated solutions positioned us as a knowledgeable partner to guide the team toward a quick and appropriate solution. It enabled us to help create and implement a scalable & cost-effective toolset.
Solution Outline
We first consolidated the data to make it usable for data science applications. We then implemented an advanced sales forecasting algorithm. The crucial element here is that the algorithm needed to suggest the right purchase quantities while considering certain business rules, such as supplier constraints. Some suppliers, for instance, would have a minimum purchase quantity, certain important discounts, etc.
As a final step, we provided an easy-to-use dashboard that enables the business process manager to quickly accept or adjust the suggestions, decide, and send quantities to the ERP system and suppliers with one single click.
These innovations helped streamline operations by integrating real-time data analysis with automated order processing, significantly reducing manual labor and error rates.
Results
The new systems significantly decreased out-of-stock situations, enhancing the customer experience and boosting revenue. Operational time spent on replenishment decreased from three days to just half a day each week, while overstock costs were significantly cut, improving overall financial health.
Conclusion
The case exemplifies how advanced sales or demand forecasting and excellent business understanding of the challenge at hand can resolve significant operational challenges, leading to better customer experiences, and fostering the crucial business growth the team was looking to achieve.
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