Summary – Production planning case study
Paulaner, one of Germany’s largest and most recognized breweries headquartered in Munich, produces 3 brands across 20 types of beer with up to 30 packaging variations for both domestic (70%) and international (30%) markets. The brewery faced unique operational challenges due to its central Munich location, which severely limited storage capacity and forced them to operate with just one day of inventory while serving customers effectively.
To overcome these constraints, Paulaner implemented Eyelit Technologies’ Collaborative Demand Planning system, incorporating sophisticated forecasting methods that included weather data, seasonal patterns, and other consumption-affecting factors. Within four months of implementation, the results were remarkable: short-term forecast accuracy reached 90% and long-term accuracy achieved 85%, enabling Paulaner to maintain excellent customer service despite severe space limitations.
About the company – Paulaner German brewery
Headquartered in Munich, Paulaner is one of the largest breweries in Germany. Also, it’s one of the best known with 3 brands, 20 types of beer, and up to 30 packaging types. 70% of Paulaner’s distribution is domestic, and 30% is international.
The challenge – forecasting accuracy
Paulaner is one of the top brewers in Germany and is located in the town of Munich. The physical location of the plant in the center of town poses unique capacity challenges for Paulaner, as the city surrounds it. These storage limitations forced Paulaner to service its customers with as little as one day of inventory.
What Paulaner needed in order to raise customer service under these circumstances was more accurate forecasts. In fact, the very low safety inventory restrictions mandated the forecasts to be exceptionally accurate, down to the product package level, by measure of one day.
Paulaner had never used forecasting systems before so they turned to Dr. Michael Bell to spin up a focused team to find the right Demand Planning solution. There were three key strategies that Paulaner wanted to employ in order to increase the accuracy of forecasts.
They were:
- Provide the best statistical forecast by applying sophisticated time series extrapolative methods including the embedment of additional information such as weather forecasts and other factors that were thought to have an effect on beer consumption
- Provide an easy to use graphical-user-interface that can be used by sales people who spent a lot of time with customers and little time in front of computers
- Establish a closed-loop process to measure the accuracy of the forecast and continually improve the methods used in the statistical forecasts and sales forecasts
Demand planning solution
Paulaner went through a software selection process that started out with 26 software vendors and in the end chose Eyelit Technologies as the solution provider. A structured S&OP planning process using the Eyelit Technologies Collaborative Demand Planning system was implemented in order to create a consensus demand plan that the company would use to guide production.
A comprehensive process was put in place to determine what key factors would be used in the statistical forecast and to enable the sales people to input their short term forecasts.
The key factors that were included in the statistical forecast were established by interviewing the people in the company with the most experience. As these key factors were identified, mathematical models were established to measure their effect on the forecast based on past experiences. Factors such as weather, temperature, seasons, and holidays were included and tested.
Finally, a set of criteria was chosen to drive the forecast. The sales people were trained to put their forecast into the system directly, or through the Excel interface that is part of Eyelit Technologies’ Collaborative Demand Planner.
Value felivered – 85%+ improvement in forecast accuracy
The solution went into production within 4 months after the implementation start-date. The system has been measuring forecast accuracy and delivery performance trends since the go-live.
The key metrics improved are as follows:
- Short-term forecast accuracy (6 weeks lag) has improved to 90%
- Long-term forecast accuracy (18 months lag) has improved to 85%
- Inventory is limited by the fact that there is no room to put any extra products and Paulaner is doing much better servicing their customers in spite of this restriction