I see a cake on the horizon! Meteolytix use predictive analytics to drive success

Meteolytix

Predictive analytics are an extremely hot and interesting area for most organizations. No wonder, there are some extremely cool and amazing use cases. Let’s take a look at a sweet example: cake. Bakeries around the world struggle with either reducing their waste or maximizing their potential. There are days when cakes seem to be flying off the shelves. And then there are bad days when baskets full of fresh goods have to be thrown away. Finding the right balance required luck and a great amount of intuition in the past. But the German company Meteolytix has found a better way. They have developed powerful sales forecast models that utilize weather data, historical sales and information about other contributing factors. The result is a self-learning automatic closed loop statistical model which increases revenue and lowers costs by minimizing over- and under-production. IBM SPSS is at the heart of this amazing solution.

Predictive Analytics at work

CakeHow does this work? Meteolytix offer their services to a number of large bakery chains across Germany. The Meteolytix teams feed predictive models with data that is collected from a range of sources: weather data from worldwide sensors and systems, daily sales figures from store POS systems and historical sales figures from ERP systems. Data flows into a customer-specific predictive model. The system determines daily sales forecasts for each branch and each product and dispatches them to the customer’s systems. The insights enable more exact material requirements planning, production and logistics optimization as well as considerable reduction in returns. Better control of stock levels creates increased sales and greater customer retention, and less waste of valuable food.

Better performance

Meteolytix‘s approach works. Their customers are able to reduce returned goods by approximately 33%. In addition, their customers are able to streamline the order process which saves many hours of work. The output from the models can also be used for workforce planning. I am pretty sure that this is just the beginning. Take a look at the new TV spot that features this use case. It’s fun!

The Meteolytix story

The Meteolytix story is very intriguing. They have a small team, yet they are able provide measurable and very impressive ROI to their clients. Think about your business – how could you apply predictive analytics? Do you have any interesting stories to share?

You can find a detailed case study about Meteolytix on this URL.

P.S.: Many thanks to my good friend Jen Rolfe for providing me with the delicious photo. She is a very talented baker.

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