y. samimi

Energy monitoring using statistical process control (SPC) methods makes it more straightforward to identify patterns and trends to decrease energy consumption more effectively. The literature review of energy consumption monitoring with SPC techniques generally focuses on the temporal aspect of variation. However, due to the spatial nature of energy data, enhancing these methods to incorporate temporal and spatial aspects would improve the accuracy of the diagnostic information, underscoring simultaneous detection of the time and location of changes. Thus, the main novelty of this work is the spatial modeling and spatiotemporal monitoring of electricity consumption. For this purpose, the study used actual electricity consumption data from eight western cities of Mazandaran province in the north of Iran for spatial modeling using spatial regression models and a geographically weighted regression (GWR) model. The prediction performance evaluation of spatial models showed GWR as an appropriate model, whose coefficients were monitored through a generalized likelihood ratio (GLR) chart in phase II. The GLR chart detected two changes in consumption, and its performance was confirmed based on the statements from electricity experts relying on meteorological information and floating population data. Furthermore, the performance of the GLR chart was evaluated using out-of-control average run length (ARL1) across three different scenarios. The findings indicate that the GLR chart can effectively detect any sizes of shifts (δ), ranging from 5% to 100% of the model's parameter value. Additionally, with larger values of δ, the ARL1 decreases, resulting in faster detection of changes in the model.

By rapid advancements in technologies, studying and simulating a complex system with uncertain parameters is too demanding. Based on the literature, there are three approaches to identify and simulate different systems: engineering, statistical, and engineering-statistical approaches. The purpose of this study is to apply the engineering-statistical approach to calibration and adjustment of a Laser Assisted Micro-Machining (LAMM) process with two correlated outputs which are basically known as cutting and thrust forces. This paper contributes to the existing literature by extending the most relevant approach of the calibration of single-output complicated processes to multi-output settings where discrepancy function is modeled by a multivariate Gaussian process and multivariate analysis of variance is used to identify variables whose adjustment benefits the most. For the best case reported in previous studies, Mean Squared Prediction Error (MSPE), as the comparison index, was reported around 1.48 for thrust force whereas the proposed approach resulted in a better value of 1.9425×10-4. Moreover, for cutting force output, the index was obtained as 0.21 by the Kennedy and O’Hagan, 1.41 by Roshan and Yan, and 1.6×10-8 by the presented model. These values demonstrate reasonable and comparable results for the MSPE, in comparison with the models considering the outputs individually.

Health services research (HSR) is of great importance to communities because decision-makers and public consider HSR as the primary source of information to determine how well health systems are meeting their specifications. Nowadays, in healthcare, there are many therapeutic processes whose results are obtained by some related stages. For studying these kinds of processes, commonly referred to as multi-stage processes, two concepts are important: one is risk adjustment, and the other is considering the cascade property. An example of a multi-stage therapeutic process is thyroid cancer surgery, which is usually performed on patients in two stages and part of the cancerous tumors are removed at each stage. In the two-stage thyroid cancer surgery, the quality of second surgery will be affected by the results of the first stage operation. For monitoring these processes, various control charts are used including model-based control charts. To design such charts, an appropriate model should be identified at first; then, control charts could be proposed based on the identified model. In this research, a risk-adjusted time-varying linear state space model is introduced for analyzing the multi-stage therapeutic processes. The state space models are statistical models that many researchers have used to analyze multi-stage processes. These models are based on engineering knowledge and the physical laws of real systems. Then, the model order and its parameters are estimated by Hankel singular value decomposition (HSVD) and prediction error minimization (PEM) methods, respectively. This is called input-output identification. The model performance is evaluated using numerical simulation and a real world two-stage thyroid surgery dataset. Based on the satisfactory results, one can use the model while simultaneously considering risk adjustment, cascade property, transmission error, and test error to forecast and monitor multi-stage therapeutic processes.

Todays, Information and Communications Technology (ICT), as one of the most effective factors of the advancement in all the aspects such as governance, security and risk, economics and technology, which is among the industries rapidly growing and developing. This paper examines the specific criteria and standards of ICT area for improving the quality of the services of the Telecommunications Backbone Network of Iran (TBNI). The research was done through the two methods of data collection, library and the field study. European Foundation for Quality Management Excellence Model (EFQM) was selected as the base model and with full attention to the specific requirements of ICT industry and due to adapt this model from the viewpoints of the security and risk, governance and economic, a novel integrated excellence model of TBNI was developed and customized. To achieve the highest level of reliability and  internal coherence of each criterion, based on expert opinion polls, sub-criteria was categorized and analyzed in several stages using the factor analysis approach. The causal relationships and the influence of the criteria of this model on each other were investigated and the weights of each criterion and sub-criteria, using the structural analysis and shannon entropy methods, were determined. The self-evaluation of TBNI was systematically carried out and major opportunities for the improvement had been identified. Results showed that the establishment of this model had led to an improvement in a quality of services provided by this network and the products of all organizations that use any services of TBNI.

To fulfill the macro-policies of the country in the sector of information and communications technology, the presence of an effective governing company as the telecommunications and communications backbone network is necessary to provide the requirements for upgrading the quality management of the products and services and to fulfill the strategic policies of that industry. The purpose of this research is to examine and identify the specific requirements, criteria and standards of the communications and information technology area for improving the service quality of the telecommunications backbone network of Iran. By adding the security & risk, governance and economic criteria to the latest edition of the European Foundation for Quality Management Excellence Model, the newly developed model was designed and localized as a model for the quality management and excellence of the telecommunications backbone network of Iran after validating and verifying the model. In this research, to achieve the highest level of the reliability and the internal consistency of each criterion, the related sub-criteria, using the factor analysis approach, were sorted and analyzed according to the survey of the experts in several stages. The criteria of this model consist of two categories including the Enablers and the Results. The criteria for the Enablers include the leadership, economic, people, strategy, processes & products and services, partnership & resources, security and risk criteria in addition, to the Results' criteria, including the customer results criteria, business results criteria, society results criteria and the people results criteria. The studies show that the establishment of this native model in the governing telecommunications backbone network of Iran has led to an improvement in the quality of services in this industry.

Detection of change time of the process parameters is a crucial problem in statistical process control (SPC), because more detailed information on the time and the pattern of a change can provide process managers with more eective clues for root-cause analysis and corresponding corrective actions. Parameter changes may take dierent forms including monotonic, trend, step shift, and so on. The issue frequently considered in the relevant studies involves only a single shift, whereas an out-of-control condition may be caused by multiple changes occurring in dierent points. On the other hand, recently, the issue of prole monitoring in which the quality of a process or product is represented by a functional relationship between a dependent and a number of explanatory variables has attracted a great deal of attention as witnessed by the growing number of publications in this area. Our investigation showed that the studies dealing with change point estimation in prole monitoring had neglected the case of multiple change points. This gap is noticed as the primary subject of this research and a clustering-based algorithm is proposed for estimating the number, as well as the location of the change points, while monitoring a simple linear prole. This clustering-based method, which is implemented in an iterative manner, is an extension of a similar method in monitoring univariate individual quality measures using Shewhart control charts. A decision rule determined via simulation using a pre-specied signicance level enables the algorithm to detect multiple change points of the parameters in addition to identifying out-of-control conditions. The proposed method is applied in the phase I of process monitoring, where a historical dataset is available and the ultimate goal is to nd reliable estimates of the process parameters, including the intercept and the slope of a linear prole model. Extensive simulation scenarios were devised to declare the performance of the aforementioned method.

T h i s p a p e r p r o p o s e s a c u m u l a t i v e s u m (C U S U M) c o n t r o l c h a r t f o r m o n i t o r i n gc u s t o m e r s a t i s f a c t i o n a n d l o y a l t y. T h i s c o n t r o l c h a r t i s d e v e l o p e d o n t h e b a s i s o f o r d e r e d c a t e g o r i c a l o b s e r v a t i o n s f o r c u s t o m e r s a t i s f a c t i o n a n d l o y a l t y w h i c h c a n b e a s s e s s e d r e g u l a r l y b y m e a n s o f c u s t o m e r s a t i s f a c t i o n s u r v e y s. I n c a l c u l a t i n g c o n t r o l s t a t i s t i c a s w e l l a s s e t t i n g c o n t r o l l i m i t, c u s t o m e r-s p e c i f i c c h a r a c t e r i s t i c s a r e t a k e n i n t o a c c o u n t a s a n o b s e r v a b l e c o v a r i a t e w h i l e l a s t i n f o r m a t i o n o f c u s t o m e r b e h a v i o r i s u s e d t o u p d a t e t h e d i s t r i b u t i o n o f t h e u n o b s e r v a b l e p a r t o f c u s t o m e r a t t i t u d e i n a B a y e s i a n m a n n e r. T o a c c o u n t f o r t h e t i m e v a r y i n g f u n c t i o n a l r e l a t i o n s h i p b e t w e e n c u s t o m e r s a t i s f a c t i o n a n d l o y a l t y, f i r s t a M a r k o v c h a i n m o d e l i s u s e d t o i n d i c a t e h o w c u s t o m e r's p a s t p e r c e p t i o n i n t e r m s o f h i s s a t i s f a c t i o n l e v e l m a y i n f l u e n c e h i s f u t u r e a t t i t u d e a n d t h e n w e u s e a l o g i s t i c r e g r e s s i o n m o d e l t o d e s c r i b e h o w c u s t o m e r's l o y a l t y l e v e l i s a f f e c t e d b y h i s s a t i s f a c t i o n l e v e l. E x p l a i n i n g t h a t c u s t o m e r s a t i s f a c t i o n m a y h a v e l o n g b u t d i m i n i s h i n g e f f e c t o n c u s t o m e r l o y a l t y, t h e M a r k o v c h a i n m o d e l h a s p r e c e d e d t h e l o g i s t i c r e g r e s s i o n m o d e l t h a t r e l a t e s s a t i s f a c t i o n t o l o y a l t y. T h e p a r a m e t e r s o f t h e r e g r e s s i o n m o d e l b e t w e e n s a t i s f a c t i o n a n d l o y a l t y p r o v i d e a s u i t a b l e g r o u n d f o r d e s i g n i n g C U S U M c o n t r o l s t a t i s t i c. T o c a l c u l a t e a n a p p r o x i m a t e c o n t r o l l i m i t f o r t h e C U S U M c o n t r o l c h a r t, a M a r k o v c h a i n a p p r o a c h i s a d o p t e d e n a b l i n g u s t o o b t a i n a p r e-s p e c i f i e d i n-c o n t r o l a v e r a g e r u n l e n g t h, g i v e n c u s t o m e r's p a r t i c u l a r c h a r a c t e r i s t i c s t h a t i n c l u d e b o t h o b s e r v a b l e a n d u n o b s e r v a b l e p a r t s. I t i s w o r t h m e n t i o n i n g t h a t a b e t a d i s t r i b u t i o n s e r v e s a s t h e p r i o r d i s t r i b u t i o n f o r c u s t o m e r's u n k n o w n p a r t o f i n f o r m a t i o n w h o s e d i s t r i b u t i o n c a n b e u p d a t e d u s i n g l a s t o b s e r v a t i o n s r e g a r d i n g c u s t o m e r s a t i s f a c t i o n a n d l o y a l t y v i a c u s t o m e r s a t i s f a c t i o n s u r v e y. R e s u l t s o f i n v e s t i g a t i n g t h e p e r f o r m a n c e o f C U S U M c o n t r o l c h a r t i n o u t-o f-c o n t r o l s i t u a t i o n s w h e r e t h e p a r a m e t e r s o f t h e f u n c t i o n a l r e l a t i o n s h i p v a r y a r e a l s o a n a l y z e d t o e v a l u a t e t h e d e t e c t a b i l i t y o f t h e p r o p o s e d m o n i t o r i n g a p p r o a c h w i t h r e s p e c t t o t h e c h a n g e s i n c u stomer attitude.