Pipple develops an algorithm to automate a part of their distribution planning.
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Pipple optimized the order batching process at the 88.000 m2 picker-to-part warehouse of Coolblue in Tilburg by developing advanced order batching algorithms.
Pipple is working on various projects for improved data driven humanitarian aid.Examples are machine learning models to predict which areas have highest priority for help shortly after a natural disaster, and block chain models to optimize peer-to-peer cash transfers.
The Pipple team are highly skilled and motivated data scientists and the social focus is both in their and our dna. Therefore it is great to have Pipple as data science partner to help us with our mission to make humanitarian aid more data driven.
Pipple has a team of data scientists working at various projects within Achmea.
Pipple participated in a research project with Bright Cape, SICS RISE and Philips Consumer Lifestyle to develop a tool that is able to predict the quality of the products in the factory mass production line in real-time based on sensor data from the machines.
Pipple built various forecasting models to predict customer behavior, such that CJIB is able to react more customer specific. Besides, Pipple taught the CJIB Data Science team to increase their R and data science skills.
Pipple built a text mining tool to automatically classify incoming phone/web/mail messages, increasing Arag’s conversion rate.
Pipple optimized several warehouses of Rhenus Contract Logistics in the Netherlands and Germany.
Pipple is not only good at analytics and algorithm development but the way we work together is what I like the most. They understand our needs and we communicate on the same level. That makes it quite easy to know what we want and make improvements together that really help improving our daily business.
Pipple developed and runs an algorithm that predicts the location, time and duration of the use of cars in anticipation of the development of a Guaranteed Mobility Platform by Amber Mobility.