ENBIS-26 Conference

Session

Statistical Process Monitoring

Sep 7, 2026, 10:55 AM

Conveners

Statistical Process Monitoring

• Antonio Lepore (Università degli Studi di Napoli Federico II - Dept. of Industrial Engineering)

Statistical Process Monitoring

• Panagiotis Tsiamyrtzis (Politecnico di Milano)

Statistical Process Monitoring

• Sven Knoth (Helmut Schmidt University Hamburg, Germany)

99. An entropy-based distribution-free approach for profile monitoring

Praise Obanya (North-West University)

9/7/26, 10:55 AM

Statistical Process Monitoring

Profile monitoring is a branch of Statistical Process Monitoring (SPM) that uses statistical methods to identify irregularities in process data. The data is characterized by a profile, or response curve, observed over a given time interval. Profile monitoring consists of two main phases: the first involves defining an in-control (IC) profile, and the second focuses on comparing subsequent...

40. Nonparametric Statistical Process Control for Monitoring Structural Changes in Regional Drought Dynamics

Michele Scagliarini (University of Bologna)

9/7/26, 11:15 AM

Statistical Process Monitoring

This study develops a statistical process control framework for monitoring drought as a stochastic process characterized by frequency, duration, and severity. The analysis focuses on the Emilia-Romagna region (Italy) and relies on a spatially weighted SPEI-12 index, ensuring a robust and representative aggregation of regional climatic conditions. The main contribution from a statistical...

68. Linear Profile Monitoring of Functional Data

Davide Forcina (Università degli Studi di Napoli Federico II)

9/7/26, 11:35 AM

Statistical Process Monitoring

Linear profile monitoring assesses the stability of a process described by a linear relationship between a scalar response variable and multiple explanatory variables. When both the response and explanatory variables are functions, this translates into tracking the stability of the underlying functional linear model (FLM). However, unlike the scalar setting, where batches of data points are...

1. When a Cusum Stops, What Confidence Is There that the Alarm Is Not False?

moshe pollak (hebrew university of jerusalem)

9/7/26, 12:00 PM

Statistical Process Monitoring

In the framework of the Cusum procedure, the evolution of a false alarm has a well-understood stochastic behavior. So, if observations preceding an alarm were to exhibit a behavior that is significantly different, there would be reason to reject the hypothesis that the alarm is false.

We develop a test of this difference. The method is applied to detecting a change in a Covid-19 context...

6. On the Design and Performance of a Control Chart for Monitoring Continuous data in (0,1) when Process Parameters are Unknown

Athanasios Rakitzis (University of Piraeus, Department of Statistics and Insurance Science)

9/7/26, 12:20 PM

Statistical Process Monitoring

In this work, we consider monitoring continuous data in the unit interval and investigate the statistical design and performance of a two-sided Shewhart chart when the process parameters are unknown. The most common distribution assumed for such data is the Beta distribution. Although control charts based on the Beta distribution have been studied by several authors, the case of estimated...

95. Some Stylized Facts of the Conditional Expected Delay (CED)

Sven Knoth (Helmut Schmidt University Hamburg, Germany)

9/7/26, 12:40 PM

Statistical Process Monitoring

The popular zero-state average run length (ARL) is just the mean of the random run length, which is the core element of a control chart. However, more appropriate measures for evaluating the detection power make use of the conditional expected delay (CED), which is the mean of the detection delay for a given change point position $\tau = 1, 2, \ldots$ under the condition that no false alarm...

83. Multivariate SPC of Smart Greenhouses for Berry Production: A Hybrid SPC–ML Framework — Moroccan Case Study

Abdellah AMZIL (Computer, Networks, Mobility and Modeling Laboratory (IR2M), Faculty of Sciences and Techniques, Hassan First University of Settat)

9/9/26, 2:30 PM

Statistical Process Monitoring

Berry greenhouses in the Souss-Massa region of Morocco sustain high-value exports of strawberries, blueberries, and raspberries, but their yield and fruit quality are highly sensitive to microclimate deviations. Dense IoT sensor networks generate high-dimensional, autocorrelated, and non-stationary data that violate classical Shewhart, CUSUM, and MEWMA assumptions.

We propose a hybrid...

124. Self-Starting Control Charts for Few-Shot In Situ Monitoring in Customized Manufacturing

Prof. Bianca Maria Colosimo (Politecnico di Milano)

9/9/26, 2:50 PM

Statistical Process Monitoring

Additive manufacturing processes are increasingly characterized by high customization, small batch sizes, and limited availability of historical data, making traditional statistical process control approaches difficult to apply. This work proposes a self-starting monitoring framework for few-shot additive manufacturing environments, enabling effective process monitoring from the earliest...

144. Practical challenges and statistical solutions for process monitoring and control in dairy production

Ewa Szymanska (FrieslandCampina)

9/9/26, 3:10 PM

Statistical Process Monitoring

Industrial manufacturing processes are complex, often including many known and unknown factors like various raw materials and their properties, multiple production lines, hundreds of process variables and different product quality parameters. Using data analysis to understand, optimize and monitor and control several parts of these processes is a common practice with big proven value. However,...