Big Data

Over the last years, the importance of logistics has changed significantly. While logistics used to be a core function of most companies, logistics services nowadays are often outsourced to service providers. This leads to new organizational structures and enables innovative business models. Caused by the digitalization of logistics, efforts for integration and coordination rise and can only made controllable by intelligent use of IT. This contribution examines the field of tension of logistics and IT. It shows which challenges to face and how to overcome these by using Big Data technologies.

Economy, enterprises and employees are sustainably affected by digital transformation. The new opportunities of education and training to evaluate learner behavior digitally are of particular interest. Learning analytics can be used for specific optimization of learning forms and content for the benefit of learners and of the training company.

The implementation of industry 4.0 concepts requires a new understanding of data processing and analysis. Data Science integrates approaches of mathematical modelling and performant implementation to analyse data of specific application areas. Within this first article, the basics of Data Science are presented and perspectives for a data-driven production and logistics are discussed. Within a second article in a following edition, the process steps for structured data analysis will be explained and illustrated by means of application examples.

The volatile world urges companies to react more agile to fast changes. Monitoring is a major building block for corporate agility. A lack of understanding of causal interrelations between a multitude of volatility drivers demands a new monitoring approach. Big data is presented as an adequate tool and its agility support is discussed from a data and analytics point of view. Conclusively, guidelines for developing a monitoring system are discussed.