ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

ENBIS – the European Network for Business and Industrial Statistics – is a platform where statistical practitioners and academic statisticians from Europe and beyond meet, exchange ideas and design new projects. ENBIS sponsors an annual conference and numerous additional events, many of them web-based.

The annual conference takes place every September and features presentations from a wide variety of sectors, ranging from manufacturing to service, from private to public sector. The ENBIS-17 conference will be held on  September 9-14 2017 in Naples (Italy). The conference booklet containing the programme and abstracts will be published before the conference and inserted in delegates' packs. A special number of Quality and Reliability Engineering International (QREI) featuring selected papers presented in ENBIS-17 will be published in 2018 following a special post-conference Call for Papers to the conference participants and the wider ENBIS community.

A number of distinguished keynote speakers will give plenary talks at ENBIS-17. The list includes Arnoldo Frigessi (University of Oslo, Norway), Paolo Nesi (University of Florence, Italy), Jeff Wu (Georgia Institute of Technology, USA; 2017 Box Medalist), Alberto Pasanisi (EDF R&D and EIFER, Germany; Best Manager Award) and Antonio Canale (University of Padua, Italy; Young Statistician Award). Additional Information on the keynote speakers and award recipients can be found in the files below.

Confirmed pre- and post- conference events in the framework of ENBIS-17 are:

-        Joint ECAS-ENBIS 2-days summer school (9-10 September 2017) entitled Big Data in Business and Industry and organized in Procida Island (near Naples). Registration for this course can be done here. See more at: http://www.unior.it/ateneo/15340/1/ecas-enbis-course-2017.html

 -        Workshop on Large-Scale Statistical Process Monitoring. Registration for this course can be done here.

The conference programme further includes invited and contributed sessions, workshops and panel discussions.

ENBIS-2017 includes as a preconference event a joint ECAS-ENBIS 2-days summer school (September 9-10) entitled Big Data in Business and Industry.  It is organized in Procida Island (near Naples) by L. D'Ambra and R. Kenett, around four topics:

  1. Improving the quality of official statistics (G. Barcaroli),
  2. Association rules analysis using compositional data methods (M. Vives-Mestres and R. Kenett)
  3. Big data and industrial statistics (R. Kenett)
  4. Multivariate spatio-temporal methods for large datasets (A. Fassò).

Contributions to ENBIS-17 take the form of presentations, which can consist of advances in scientific research, overviews and introductions (presenting an area of expertise to the uninitiated), and best practice examples (sharing experiences of practitioners). The deadline for abstract submissions is May 10, 2017.

Session topics include but are not limited to the following areas:

-      Design of physical  & computer experiments

-      Statistical engineering

-      Measurement uncertainty

-      Time series modeling and forecasting

-      Process modeling and control

-      Process improvement in healthcare

-      Reliability and safety

-      Operations research and management

-      Operational risk management

-      Analysis of massive data sets

-      Data mining and warehousing

-      Risk analysis and assessment

-      Statistical computing

-      Business analytics

-      Statistics in the pharmaceutical industry

-      Teaching business and industrial statistics

-      Quality improvement and Six Sigma

-      Statistical process control

-      Statistics in the shipping industry -      Statistics for ergonomics













In addition to the session topics mentioned above, in 2017 special attention will be given to the discussion about Smart Cities, Big Data in Life Sciences, Reliability, Quality Engineering applied to Advanced Manufacturing, and Shipping Data.