ENBIS-17 in Naples9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017
The following abstracts have been accepted for this event:
Maintenance Policies Countering Degradation of Water Supply Networks: Statistical Analysis with a Semi-Markov Model and Panel Data
Authors: Vincent Couallier (Institute of Mathematics of Bordeaux), Cyril Leclerc (SUEZ Eau France, le LyRE), Yves Legat (IRSTEA), Karim Claudio (Cetaqua (SUEZ))
Primary area of focus / application: Other: Statistical analysis of industrial reliability and maintenance data
Secondary area of focus / application: Modelling
Keywords: Reliability analysis, Multi-state models, Degradation processes, Maintenance modeling, Statistical estimation, Semi-Markov models
Submitted at 3-Mar-2017 16:46 by Vincent Couallier
Accepted (view paper)
- State 0 : the pipe is in a “leakage-free” state.
- State 1: Background leakage - initialization of the leak, invisible at the surface with a low and undetectable flow,
- State 2: Detectable leakage - still invisible at the surface, the leak can be detected by acoustic inspection,
- State 3: Visible burst -the leak appears at the surface and must be repaired.
The maintenance lead by water operators includes campaigns of acoustic inspections. These operations yield partial observations of a continuous-time degradation process. In fact only the last state (visible bursts) is exhaustively registered. In such a case, multi-state models using panel data are adequate to model the leakage degradation process. Indeed, the Semi-Markov model with panel data offers an alternative to standard survival analysis of interval censored lifetime data. We show in this work how to fit such a model to data collected since 2010 by the Bordeaux water utility. The network contains more than 30000 pipes, each of them with leak detections and bursts data, as well as characteristics like material, length, diameter, pressure, soil corrosivity, which are used as model covariates for the transition intensities.
New Applications of Statistics and Data Analysis for Marketing Research, Applications to the Cosmetic Industry
Authors: Gianvito Dongiovanni (IPSOS Italy)
Primary area of focus / application: Other: Statistics for cosmetics
Keywords: Linear regression, Bayesian networks, New analytical approaches, Markey segments
Submitted at 3-Mar-2017 23:57 by Gianvito Dongiovanni
Accepted (view paper)
Linear regression is the classical way to determine a ranking of predictors or a quantification of their respective importance for the desired outcome. New analytical approaches are gaining ground in research: combining the bootstrapping technique to Bayesian networks, it is possible to bring out the infer causal relationships of a key outcome from survey data taking into account multicollinearity between variables. Relative impacts and structural mapping are provided to help identifying opportunities in order to develop action plans that best fit business strategy and provide the greatest opportunity.
Marketing decisions are also supported by a vast amount of information generated by customers on social networks and by new data collecting methods such as open text or pictures and icons present in the survey process itself. Using a mix of monitoring tools, it is possible to identify which messages and initiatives are driving the right actions and desired outcomes.
These methods enable to provide clients with elements supporting marketing decisions such as identify market segments, decide on a level of pricing, identify the relative strength of their brand versus competitors and also understand the positioning of the brand in the market such as high end brand or value for money brand. It is also used to rank advertising concepts and decide on marketing mix optimisation.
Acceptance Sampling Plans for High Quality Processes
Authors: Stijn Luca (KU Leuven)
Primary area of focus / application: Process
Secondary area of focus / application: Quality
Keywords: Acceptance sampling, Chain sampling, High quality processes, Operating Characteristic curves
Submitted at 4-Mar-2017 18:11 by Stijn Luca
We will treat the case where sampling takes place from lots that are coming from a supplier's process which is of high quality, i.e. a proportion defects near zero is associated to the process. Traditional sampling plans won't work in this case since any sample of reasonable size will probably contain zero defects.
We will propose a generalization of the modified chain sampling plans proposed in  that is applicable for as well attributive as variable inspection. For this purpose, it is assumed that lots are drawn from a continuing stream of lots of a process with an unknown but constant fraction defects. Chain sampling plans are able to accumulate information from samples drawn from historical lots to estimate the suppliers quality. The proposed plans allow to go further into history than the standard chain sampling plans of Dodge . In contrast to zero acceptance number single sampling plans, this enables the design of steep operating characteristic (OC) -curves that possess an inflection point near zero.
Algorithms will be proposed to design the proposed plans when the OC-curve have to pass through two predetermined points that define producer’s and consumer’s risk. Experiments will show that for small fraction defects the required sample size is smaller compared to the classical chain sampling plans of Dodge.
 K. Govindaraju and C. Lai, A modified ChSP-1 chain sampling plan, MChSP-1, with very small sample sizes, American Journal of Mathematical and Management Sciences 18 (1998), pp. 343–358.
 H. Dodge, Chain sampling inspection plan, Industrial Quality Control 11 (1955), pp. 10–13.
Discovering Communities in Customer Purchase Behavior by Means of Social Network Analytics
Authors: Jasmien Lismont (KU Leuven), Bart Baesens (KU Leuven; University of Southampton), Wilfried Lemahieu (KU Leuven), Jan Vanthienen (KU Leuven)
Primary area of focus / application: Mining
Secondary area of focus / application: Business
Keywords: Big Data, Community mining, Customer target groups, Direct marketing, Retail, Cocial network analytics
Submitted at 5-Mar-2017 10:45 by Jasmien Lismont
Accepted (view paper)
Consecutively, descriptive social network techniques are applied to the customer network. Specifically, we apply community mining in order to provide understanding for the development of customer target groups. Various algorithms exist, such as modularity-based, spectral, and dynamic algorithms; but most algorithms are developed for unipartite graphs. Some metrics for bipartite community mining exist which can offer a solution, e.g. BRIM and CoClusLSH. Furthermore, the complexity of these algorithms needs to be taken into account. Many algorithms, e.g. based on betweenness, are not feasible for a large network. Finally, profit measures such as recency, frequency and monetary values, or customer lifetime values can be attached to the customer groups or clusters found in the network. This work is currently still ongoing.
The Use of Attribute Charts to Monitor the Process Mean
Authors: Linda Ho (University of São Paulo)
Primary area of focus / application: Other: DOE and statistical process monitoring in South America
Keywords: ARL1, ARL0, Gauge device, Classification, Optimization, Genetic algorithm
Submitted at 6-Mar-2017 01:38 by Linda Ho
The aim of this research is to provide an overview of the recent attribute control charts proposed to monitor the process mean based on the results of the classification showing that it is possible to design attribute charts to have good performance economically and in terms of ARL1 like the traditional Shewhart X-bar chart.
Applicability of Software Reliability Models
Authors: Nikolaus Haselgruber (CIS Consulting in Industrial Statistics GmbH)
Primary area of focus / application: Reliability
Secondary area of focus / application: Modelling
Keywords: Reliability, Software, Testing, Modelling
Submitted at 6-Mar-2017 10:52 by Nikolaus Haselgruber
This presentation discusses the adequacy of the term “Software Reliability” in general and it will give a short overview of relevant models. Further, important aspects to be considered for practical application and an example from automotive industry will be presented.
 Z. Jelinski and P. Moranda (1972). Software reliability research, in Statistical Computer Performance Evaluation, W. Freiberger (Ed.), Academic Press, 1972, pp.465-497.
 B. Littlewood and J. Verrall (1973): A Bayesian Reliability Growth Model for Computer Software, Journal of the Royal Statistical Society, Series C, Vol. 22.
 N. F. Schneidewind (1975): Analysis of Error Processes in Computer Software, Sigplan Note, Vol. 10.
 J.D. Musa, and K. Okumoto (1983). Software Reliability Models: Concepts, Classification, Comparisons, and Practice. In J. K. Skwirzynski (Ed.), Electronic systems effectiveness and life cycle costing, NATO ASI Series (pp. 395-424). Heidelberg: Springer-Verlag.
 A. L. Goel (1985): Software Reliability Models: Assumptions, Limitations, and Applicability, IEEE
 C. Wohlin, M. Höst, P. Runeson and A. Wesslén (2001): Software Reliability, in Encyclopedia of Physical Sciences and Technology (third edition), Vol. 15, Academic Press.
 A.P. Sage and W.B. Rouse (2009): Handbook of Systems Engineering and Management, Wiley, New York.