Travel and Hospitality
KLM Royal Dutch Airlines, the flag carrier airline of The Netherlands, employs over 36,000 people, operates a fleet of 229 planes, and carries passengers and cargo to 145 destinations. In 2004, it merged with AirFrance, with both airlines retaining their brands and hubs in Paris and Amsterdam. The year 2019 marked KLM’s 100th anniversary.
Mastering Data to Lead the Industry
KLM wanted to be the most customer-centric, innovative, and efficient network carrier within Europe. Stefan van Heukelum, manager of finance decision support at KLM wanted his team to contribute to this success. “Harnessing and evaluating data is key to finding priorities and efficiently executing them in our business,” explained Van Heukelum, “By focusing on data analytics and data-driven solutions and becoming a master of information, our department could provide the smartest, most valuable information and be a partner to the business.”
Uncovering Was Wasn’t Working
Process mining for digital expense accounting revealed that 90% of KLM’s processes went well. “Discovering that 10% of our processes were not carried out as instructed, that’s where Xebia really helped us and made a difference,” said Van Heukelum. One example use case revealed a revenue leakage within the cargo department. KLM ships quite a few horses and the existing process wasn’t designed for it. A workaround was being used to organize horse shipments in KLM's financial system, which created distortions in the revenue accounting process.
Analyse, Ideate, Implement
Using data science to forecast costs related to complaints and claims for delays and cancellations helped KLM solve operational disruptions and improve customer satisfaction. KLM was able to develop new solutions and productionize them: improving online payment methods, initiating a performance-alerting system, and optimizing the use of maintenance capital. From customer service to cargo shipments, KLM now has processes in place to collect and analyze information and develop new ideas, with a greater understanding of internal data analytics.