Alireza Mousavi

Importance‐performance analysis of service attributes and its impact on decision making in the mobile telecommunication industry

Purpose – Customer relationship management (CRM) strategies rely heavily on the importance and performance of the attributes that define a service. The aim of this paper is first to investigate the asymmetric relationship between performance of service attributes and customer satisfaction, and second, through a case study in the mobile telecommunication industry to prove that the importance of a service attribute is a function of the performance of that attribute.Design/methodology/approach – An empirical study using questionnaires with a focus on service enquiring about the performance of service key attributes and overall customer satisfaction was conducted. The data were fed into the Kano customer satisfaction model and the importance‐performance analysis (IPA) method for analysis and comparison.Findings – The results indicate that there is a dynamic relationship between service attributes and overall customer satisfaction. Service attributes have a different impact on customer satisfaction regardless ...

A generic framework for real-time discrete event simulation (DES) modelling

This paper suggests a generic simulation platform that can be used for real-time discrete event simulation modeling. The architecture of the proposed system is based on a tested flexible input data architecture developed in Labview, a real-time inter-process communication module between the Labview application and a discrete event simulation software (in this case Arena). Two example applications in the healthcare and manufacturing sectors are provided to demonstrate the ease of adaptability to such physical system.

Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here, we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high-quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modeling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10 percent in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5 percent of that required when using the comparable Entropy-based method.

Sim ulation-based real-time performance mon itoring (SIMMON): a platform for manufacturing and healthcare systems

This paper introduces a new technology platform that improves the efficiency and effectiveness of simulation modelling projects. A recently developed platform that integrates data acquisition management platform (primary models) and post simulation performance analysis models (synthesis) is described. The use of real-time discrete event simulation modelers as a vehicle is proposed. In recent years we have suggested a number of solutions to integrate shopfloor data with higher level information systems. All these solutions lacked two key capabilities. Firstly, the solutions were not capable of interacting with data acquisition systems without expert interference in determining the quality and quantity of input signals. Therefore, connecting input signals to key performance indicators (i.e. simulation parameters) was extremely challenging and error prone. Secondly, from health workers? and plant managers? perspective, simulation results (e.g. resource utilization, waiting times, work-in-process, etc.) did not correspond to industry performance metrics. SIMMON is proposed here to address these two problems.

Input variable selection in time-critical knowledge integration applications

Input Variable Selection (IVS) helps when processing system inputs is computationally heavy.A framework to accommodate high level perspective of different approaches to IVS is provided.Sensitivity analysis (SA) helps IVS in heterogeneous input variables with time constraint.Event-based SA proves fit by its application in an industrial drilling disaster prevention problem. The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling.Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system.The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.

Dealing With Uncertain Entities in Ontology Alignment Using Rough Sets

Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision.

Prediction and Control of Response Rate to Surveys

Synoptic Abstract This paper proposes a technique for survey designers to infer response rates based on the length and the salience (importance of subject to the respondent) of questionnaires. In order to understand the relationship between response rate, salience and the time respondents are willing to spend on a questionnaire, 430 people were surveyed. A statistical analysis was conducted on the data and a set of rules were generated for a fuzzy Inference System (FIS). Implementation of the proposed technique has resulted into a three dimensional reference table which shows the correlation between Time, Importance and Response Rate. The inferred table may be used as a guideline to estimate response rates in surveys. The provision of incentives is also considered in this work.

A generic platform to address event-based problems for universal applications: supervisory control and simulation

This paper proposes the framework and implementation architecture of a combined real-time shopfloor data collection (monitor) and Discrete Event Simulation (DES) system. It highlights the practical implementation and potential benefits of using predictive (multipass) simulation in combination with real-time Data Acquisition (DAQ) in dealing with the complexity of manufacturing environments. Normally, by nature, these environments face unpredictable events that challenge their predetermined plans and schedules. The proposed framework makes the development of a generic hybrid control capability for flexible manufacturing environments possible. This paper describes a solution that combines the advantages of real-time shopfloor control and DAQ systems and mathematical modelling tools such as DES that rely on historical data for improved system performance. It bridges the gap that currently exists between shopfloor level control/monitoring systems and higher level enterprise information systems such as Material Requirement Planning (MRP/MRP II) and Enterprise Resource Planning (ERP).

Quick-response decision-making in the food processing industry

This paper proposes a quick-response decision modeller to help managers deal with the demands of a volatile food production environment. It argues the need for a focused, real-time, high-quality information apparatus to support production managers on the shopfloor. This is achieved by providing specified definitions for the Key Performance Factors (KPF) within the industry. A cost/benefit analysis tool will be presented that reports on system performance. The article presented focuses on improving the quality of decisions made during food production process with the aim of realising the real operational costs, thus improving profit margins. By creating the proper information environment and equipping the system with a suitable prediction platform, stressed managers can be advised on various action plans and the consequences for each decision. This is to be achieved, firstly, by creating a comprehensive information environment for precise measurement of the state of the system in real-time and, secondly, by providing an accurate prediction platform for improved decision-making.

Measuring the importance of product attributes and its implication in resource allocation

This paper reports on a critical review of the Importance-Performance Analysis (IPA) method using three different measures: customer self-stated importance, regression analysis and regression analysis with dummy variables. The study confirms that product attribute importance is an antecedent of attribute performance. The importance of product attributes from the customers’ point of view may change with the fluctuation in product performance level. The results of this research help in measuring the impact of service attribute performance on customer satisfaction, which in turn can help companies to identify the product attributes that have higher returns for the business.