ENBIS-26 Conference

Session

Data Analytics and Data Science: Case Studies

Sep 8, 2026, 10:55 AM

Conveners

Data Analytics and Data Science: Case Studies

• Morten Bormann Nielsen (Danish Technological Institute)

Data Analytics and Data Science: Case Studies

• Andrea Ahlemeyer-Stubbe (Ahlemeyer-Stubbe)

Data Analytics and Data Science: Case Studies

• Elena Barzizza (University of Padova)

143. Health Insurance Fraud Detection using Claim-Based Profiling

Prof. Sotiris Bersimis (University of Piraeus, Greece)

9/8/26, 10:55 AM

Statistics in Pharma / Healthcare

Medical claim expenses are inherently compositional, as fraud-relevant patterns often emerge from the relative allocation of costs across categories rather than from total expenditure alone. We propose a claim-level fraud screening framework based on compositional profiling, using the Aitchison distance to compare new claims with a historical reference distribution. Statistical significance is...

38. Simulating a key performance index for a family of future products

Mr carlo leardi (tetra pak packaging solutions)

9/8/26, 11:15 AM

Data Analytics and Data Science: Case Studies

A complex system is currently under validation as implemented in its initial instantiation. The technical bet regards more than doubling a key performance at parity of the other ones. The preliminary estimation has been performed by simulation in the concept’s exploration phase by risk reduction by Fault Tree Analysis. The current studies are devoted to allowing the estimation of the...

97. Dirty laundry – A data quality journey from ENBIS Active session to nationwide system redesign for industrial textile management

Dr Morten Bormann Nielsen (Danish Technological Institute), Robert Heck (Danish Technological Institute)

9/8/26, 11:35 AM

Data Analytics and Data Science: Case Studies

At ENBIS-24 in Leuven, we brought an emerging challenge to the ENBIS Active Session: a large industrial laundry operator managing millions of textile items across multiple sites wanted to understand and extend textile lifespans as part of a circular economy strategy using existing operational data on textile discarding events. The key advice we received — to not trust the data from the outset...

72. An IoT-inspired concept for data-driven service provision and resource planning in the tourism sector, using a campsite as an example

Dr Eva Scheideler (Technische Hochschule Ostwestfalen-Lippe)

9/8/26, 12:00 PM

Data Analytics and Data Science: Case Studies

The digitalisation of tourism facilities means that these facilities have access to a wide range of IoT-like data sources. This article presents a conceptual approach that describes how such heterogeneous data streams can be used to systematically improve service offerings, resource planning and operational decisions through targeted short-term, medium-term and long-term forecasts. The...

112. Multivariate Six Sigma for the Optimization of the Meat Roasting Process in the Ready-to-Eat Food Industry

Mr Alberto Ferrer-Hermenegildo (Kensight Solutions S.L.)

9/8/26, 12:20 PM

Data Analytics and Data Science: Case Studies

This talk presents a Six Sigma project developed in a ready-to-eat food company aimed at optimizing a meat roasting process while balancing food safety, product appearance, juiciness, and production yield.
Following the DMAIC methodology, historical data analysis, Measurement System Analysis (Gage R&R), and Root Cause Analysis tools were initially applied to understand process variability and...

120. Risk analysis of forest fires in Sicily, based on 2010-2023 observation period

Stefano Barone (University of Palermo)

9/8/26, 12:40 PM

Data Analytics and Data Science: Case Studies

Introduction.
Forest fires are complex phenomena causing significant damage to the environment and human health, habitat destruction, soil erosion, greenhouse gas emissions, and biodiversity loss. They are increasing globally, with extreme events becoming more frequent and destructive. Understanding their root causes and influencing factors is crucial.
Methods.
This work focuses on...

32. Nonparametric tests for multivariate stability data

Riccardo Ceccato (University of Padova)

9/9/26, 2:30 PM

Data Analytics and Data Science: Case Studies

Stability studies are commonly conducted to evaluate how product characteristics evolve during storage. In many industrial applications, several quality attributes are measured repeatedly over time for multiple products, generating multivariate longitudinal datasets. A key objective in these studies is to compare products in terms of their stability and identify those exhibiting more stable...

42. Clustering for Detecting Careless Respondents: A Case Study

Silvia Villanova (University of Padova)

9/9/26, 2:50 PM

Data Analytics and Data Science: Case Studies

The presence of careless respondents represents a well-known threat to the quality of survey data. Respondents who provide inattentive or random answers can distort statistical analyses, reduce measurement reliability, and bias substantive conclusions. A variety of indicators have been proposed in the literature to detect such respondents, including response pattern measures such as longstring...

45. Preference mapping for product optimization: a case study

Mr Alessandro Fanesi (Department of Management and Engineering, University of Padova, Stradella San Nicola, 3, Vicenza, 36100, Italy)

9/9/26, 3:10 PM

Data Analytics and Data Science: Case Studies

Understanding how technical product characteristics translate into consumer perception remains a key challenge in product development. This study presents a case study in which preference mapping techniques are used to explore the relationship between laboratory-based technical measurements and consumer evaluations.
A set of products was characterized through a series of objective...