Chuan Shi, Rajesh Jugulum, Harold Ian Joyce, Jagmet Singh, Bob Granese, Raji Ramachandran, Donald Gray, Christopher H Heien and John R. Talburt
This paper aims to propose a funnel methodology that selects business data elements for data quality improvement practices at a financial company. Data quality is crucial in…
Abstract
Purpose
This paper aims to propose a funnel methodology that selects business data elements for data quality improvement practices at a financial company. Data quality is crucial in post-crisis recovery of the financial services industry. This allows the bank to monitor its critical data assets and improve its business operation by Six Sigma engagement that benefits from the good quality of data.
Design/methodology/approach
A funnel methodology is invented. It utilizes a rationalization matrix and statistical methods to identify critical data elements (CDEs) for data quality efforts from numerous candidates across business functions. The “Voice of the Customer” is achieved by including subject matter experts, whose knowledge and experience contribute to the entire process.
Findings
The methodology eliminates redundancy and reduces the number of data elements to be monitored, so that attention becomes focused on the right elements. In addition, the methodology ensures that the conduct of the data quality assessment is framed within a context of the functional area’s business objectives.
Originality/value
Measuring and improving data quality form a solid foundation of every Six Sigma engagement. When presented with large data elements, determining what to measure can be an arduous task. Having a proven systematic and valid process to reduce the CDE candidate pool becomes an operational necessity of paramount importance, and this justifies the value of the proposed methodology. Its implementation is described by a Basel II case study. The methodology is not restricted to financial services industry, and can be used readily in any other industry that requires data quality improvement.
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Keywords
Mahmoud Sabry Shided Keniwe, Ali Hassan Ali, Mostafa Ali Abdelaal, Ahmed Mohamed Yassin, Ahmed Farouk Kineber, Ibrahim Abdel-Rashid Nosier, Ola Diaa El Monayeri and Mohamed Ashraf Elsayad
This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to…
Abstract
Purpose
This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.
Design/methodology/approach
To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance, and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.
Findings
The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.
Originality/value
This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.
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Saeed Hasanpoor, Zahra Mansourpour and Navid Mostoufi
The purpose of this paper is to fundamentally develop a mathematical model for predicting the particle size distribution (PSD) in fluidized beds because their hydrodynamics depend…
Abstract
Purpose
The purpose of this paper is to fundamentally develop a mathematical model for predicting the particle size distribution (PSD) in fluidized beds because their hydrodynamics depend on the PSD and its evolution during operation. To predict the gradual PSD change in a fluidized bed by using the population balance method (PBM), the kinetic parameter for agglomerate formation should be known and this parameter, in this work, is determined by the results of computational fluid dynamic–discrete element method (CFD-DEM) simulation.
Design/methodology/approach
Momentum and energy conservation equations and soft-sphere DEM are used to simulate the agglomeration phenomenon at high temperature in a two-dimensional air-polyethylene fluidized bed in bubbling regime. The Navier–Stokes equations for motion of gas are solved by the SIMPLE algorithm. Newton’s second law of motion is applied to describe the motion of individual particles. Collision between particles is detected by the no-binary search algorithm.
Findings
A correlation is proposed for estimating the kinetic parameter for agglomerate formation based on collision frequency, collision efficiency and inlet gas temperature. Based on the corrected kinetic parameter, the PBM is able to predict the PSD evolution in the fluidized bed in a fairly good agreement with the results of the CFD-DEM.
Research limitations/implications
The results of the agglomeration process cannot be compared quantitatively with experimental results. Because three-dimensional fluidized bed mostly contains millions of particles and simulating them takes a long computing time in DEM. As far as temperature is a dominant parameter in the agglomeration process, effects of inlet gas temperature are examined on the kinetic parameter. On the other hand, wider and deeper insights in which the effect of other parameters, such as velocity and so on will be studied, is one of the goals in the authors’ next works to compensate for the shortcomings in this work.
Originality/value
This study helps to understand the effect of the inlet gas temperature during the agglomeration process on the kinetic parameter and provides fundamental information in dealing with kinetic parameter to attain PSD in fluidized bed by the PBM.