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I work as a forecasting specialist at Maersk Line, helping Maersk do business in a smarter, more efficient and automated fashion utilizing insights gained through data. My background is in mathematics and economics with a PhD degree in applied statistics. Having worked in research for several years I recently switched to industry to build models that get put to the test by the business.
Like most other mature, large, global organisations, Maersk Line has plenty of data sourcing and warehousing activities that feed into the global, sprawling BI reporting landscape.
Analytics, however, is a different story, with Advanced Analytics (AA) in particular considered virgin territory by most of the global organisation up until just a few years ago.
With the AA team set up and staffed just 3 years ago in 2012, our products have today helped create an organisation that increasingly demands access to Analytics-driven models, answers and applications.
The team's visible role in shaping this transformation means we are increasingly credited and positioned as go-to focal for all activities in the analytics-related knowledge space across the wider Maersk Group... and with the majority of our models deployed and in use at prototype level, focus has now shifted decisively towards close collaboration with our IT colleagues to successfully productionalise our analytics products.
As part of this effort, our forecasting framework is highest priority. Once productionalised, this will ingest our customers with the capability to build powerful, robust, reconciling forecasts with fast turnaround.
In the presented case we will share thoughts and principles behind our (we hope) well-designed and -coded forecasting framework in R, covering e.g.