An-Najah Blogs :: Amjed Mahmoud Muhammad Al-Ghanim http://blogs.najah.edu/author/amjed-al-ghanim An-Najah Blogs :: Amjed Mahmoud Muhammad Al-Ghanim en-us Mon, 09 Dec 2019 12:54:27 IST Mon, 09 Dec 2019 12:54:27 IST webmaster@najah.edu webmaster@najah.edu A Scheme for Performance Measurement System at Local Government Units in West Bank and Gazahttp://blogs.najah.edu/staff/amjed-al-ghanim/article/A-Scheme-for-Performance-Measurement-System-at-Local-Government-Units-in-West-Bank-and-GazaPublished ArticlesModern management tools that evolved through quality management initiatives have been providing effective solutions for organizational and operational problems encountered in private sector firms The success of such initiatives in private organizations has stirred up a continuing wave among public sector departments and particularly among local governments to improve service quality and performance effectiveness This paper presents a proposed scheme for a municipal performance measurement system for use at local government units in West Bank and Gaza The system is based on quantitative measures that address key major municipal performance areas; these are financial internal administrative service quality and regulatory measures A portion of the scheme has been under trial-implementation in a local municipality and initial results demonstrated the potential of using it as a basis for a national municipal performance measurement processA statistical approach linking energy management to maintenance and production factorshttp://blogs.najah.edu/staff/amjed-al-ghanim/article/A-statistical-approach-linking-energy-management-to-maintenance-and-production-factorsPublished ArticlesThis research has addressed a quantitative approach for improving energy management through applying statistical techniques aimed at identifying and controlling factors linked to energy consumption rates at manufacturing plants The paper presents analysis and results of multiple linear regression models used to establish the significance of a number of energy related management factors in controlling energy usage Regression models constructed for this purpose proved the existence of statistically valid relationships between electrical energy consumption and maintenance and production management factors namely failure rate and production rate where R2 values of the magnitude of 65 per cent were obtained Furthermore an economical treatment based on the derived regression models was formulated and demonstrated that effective management practices associated with proper maintenance cost accounting and reporting systems can result in highly significant savings in energy usage Journal of Quality in Maintenance Engineering 2003 Volume: 9 Issue: 1 Page: 25 - 37 http:wwwemeraldinsightcom10110813552510310466828The Impact of Implementing Quality Management Principles of IS09000 on Business Effectiveness: An Applied Study at Palestinian Businesseshttp://blogs.najah.edu/staff/amjed-al-ghanim/article/The-Impact-of-Implementing-Quality-Management-Principles-of-IS09000-on-Business-Effectiveness-An-Applied-Study-at-Palestinian-BusinessesPublished ArticlesThe management of quality has received considerable attention in recent years and various studies have documented analysis and results of the impact of quality management models on organizational effectiveness This research study provides details of an investigation of the impact of the principles of ISO9000 quality management system on improving organizational effectiveness in Palestine Using a recent survey of ISO9000 implementing companies data were collected and analysed about critical quality management principles: quality strategy continuous improvement leadership development and customer satisfaction and the impacts were assessed using key organizational effectiveness indicators: employee satisfaction quality and productivity Basic hypotheses were formulated and tested and the results showed that companies have indeed made significant efforts towards establishing genuine quality systems and consequently attained benefits in terms of effectiveness indicators Furthermore correlation analysis confirmed suggestions in the literature that a companys performance is positively impacted by the establishment and implementation of quality principles and quality models An-Najah University Journal for Research - Natural Sciences A ISSN: 1727-2114 Volume 17 Issue 1 2003 Pages: 053-074A Binary ART Neural Network Methodolgy for Computer-Aided Process Palnning of Milling Parametershttp://blogs.najah.edu/staff/amjed-al-ghanim/article/A-Binary-ART-Neural-Network-Methodolgy-for-Computer-Aided-Process-Palnning-of-Milling-ParametersPublished ArticlesArtificial neural network have been successfully employed for providing efficient solutions for decision making problems and gained increased significance for their use in computer integrated manufacturing environment as effective tools for improving productivity and decision quality The function of process planning in machining operations is a prominent one for neural network applications since it has direct impact on overall manufacturing productivity This paper presents analysis and results of applying self-organizing neural networks to the selection of machining parameters of milling processes The importance of this approach stems from the ability of neural nets to handle vague or ill-structured problems and the inherent capability of generalizing solutions to unseen problems Furthermore self-organizing neural networks do not require full knowledge of `output` data needed during the training phase; only a small portion of the data is needed for model calibiration Simulations using ART1 neural model were applied to the selection of the tool material type and tool entry strategy and the results demonstrated a high potential for the development of neural network modules for practical applicationsA heuristic technique for generating minimal path and cutsets of a general networkhttp://blogs.najah.edu/staff/amjed-al-ghanim/article/A-heuristic-technique-for-generating-minimal-path-and-cutsets-of-a-general-networkPublished ArticlesReliability evaluation techniques employ a variety of tools for system modeling and calculation of reliability indices Amongst the most popular tools are network-based algorithms founded on the concept of minimal paths and minimal cutsets This paper presents a heuristic programming technique for deducing minimal paths of a network In this technique only minimal paths are immediately generated without explicitly determining whether or not a path is minimal This technique has been implemented on a digital computer to generate a minimal path matrix which is in turn utilized to generate minimal cutsets of the network In terms of computational speed the results obtained compare well with existing algorithms The technique requires minimum memory storage and minimum user-defined data to represent the topology of a network and follows a modular design strategy Use of the algorithm is illustrated by examples Computers Industrial Engineering Volume 36 Issue 1 January 1999 Pages 45- An Unsupervised Learning Neural Algorithm for Identifying Process Behavior on Control Charts and a Comparison with Supervised Learning Approacheshttp://blogs.najah.edu/staff/amjed-al-ghanim/article/An-Unsupervised-Learning-Neural-Algorithm-for-Identifying-Process-Behavior-on-Control-Charts-and-a-Comparison-with-Supervised-Learning-ApproachesPublished ArticlesThe applications of supervised pattern recognition techniques on control charts have shown a substantial improvement in the ability to utilize the information of the chart more effectively than conventional run rules One major assumption underlying this methodology is that the user has a set of well-defined patterns to detect and a sufficient number of training examples In practice however sufficient training examples may not be readily available owing either to the inability to simulate these patterns or to the lack of real process data This paper presents a new approach to detect and identify unnatural patterns on control charts based on the unsupervised self-organizing neural paradigm The unsupervised methodology is based on ART1 networks The paper discusses training and testing algorithms to train and test the network using a set of unlabelled natural patterns obtained from the process during normal operation A comparison is also presented between this unsupervised approach and a major supervised methodology namely the statistical learning technique For the unsupervised methodology the false alarm rate has substantially improved over that of the supervised methodology while the rate of identification is higher for the supervised system The higher rate of identification has been achieved at the cost of providing additional unnatural pattern information to implement the supervised system strategy Computers Industrial Engineering Volume 32 Issue 3 July 1997 Pages 627-639 Automated Unnatural Pattern Recognition on Control Charts Using Correlation Analysis Techniqueshttp://blogs.najah.edu/staff/amjed-al-ghanim/article/Automated-Unnatural-Pattern-Recognition-on-Control-Charts-Using-Correlation-Analysis-TechniquesPublished ArticlesPattern recognition techniques are currently pursued to identify unnatural patterns on quality control charts This approach has been shown to enhance the ability to utilize the information of the chart more effectively than conventional run rules This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts The system is based on correlation analysis where a set of optimal matched filters are generated To illustrate the design methodology and operation of the system a set of commonly encountered patterns is utilized such as the trend the systematic and the cyclic patterns A training algorithm that minimizes the probabilities of Type I and Type II errors i presented To evaluate the system performance a testing algorithm as well as a set of newly-defined performance measures are introduced The obtained results based on extensive simulation runs have proved the effectiveness of correlation analysis for control chart pattern recognitionA programming technique for generating minimal paths a general networkhttp://blogs.najah.edu/staff/amjed-al-ghanim/article/A-programming-technique-for-generating-minimal-paths-a-general-networkPublished ArticlesThis paper presents a heuristic technique for deducing minimal paths of a network It generates only minimal paths without explicitly determining whether or not a path is minimal This technique has been implemented on a digital computer to generate a minimal path matrix It terms of computational speed the results obtained compare well with existing algorithms The technique requires minimum storage in memory and minimum user-defined data to represent the topology of a network and follows a modular design strategy Use of the algorithm is illustrated by examples Automated process monitoring using statistical pattern recognition techniques on X-bar control chartshttp://blogs.najah.edu/staff/amjed-al-ghanim/article/Automated-process-monitoring-using-statistical-pattern-recognition-techniques-on-X-bar-control-chartsPublished ArticlesQuality control charts are statistical process control tools aimed at monitoring a manufacturing process to detect any deviations from normal operation and to aid in process diagnosis and correction The information presented on the chart is a key to the successful implementation of a quality process correction system Pattern recognition methodology has been pursued to identify unnatural behaviour on quality control charts This approach provides the ability to utilize patterning information of the chart and to track back the root causes of process deviation thus facilitating process diagnosis and maintenance Presents analysis and development of a statistical pattern recognition system for the explicit identification of unnatural patterns on control charts Develops a set of statistical pattern recognizers based on the likelihood ratio approach and on correlation analysis Designs and implements a training algorithm to maximize the probability of identifying unnatural patterns and presents a classification procedure for real-time operation Demonstrates the system performance using a set of newly defined measures and obtained results based on extensive experiments illustrate the power and usefulness of the statistical approach for automating unnatural pattern detection on control chartsThe application of a neural network methodology to the analysis of a dyeing operationhttp://blogs.najah.edu/staff/amjed-al-ghanim/article/The-application-of-a-neural-network-methodology-to-the-analysis-of-a-dyeing-operationPublished ArticlesThe purpose of a dyeing process is to impart into a fiber a color that has desirable qualities through the use of a bath solution The success of an operation is dependent on a variety of factors including fiber content dye composition dyebath pH time and temperature In the event of a failed run the addition of a correct amount of each dye that will move the dye run from a fail condition to a pass condition is a subjective judgement of an experienced operator This paper presents a neural network approach for analyzing the dyeing process Predictions of dye additions were obtained with promising resultsUnnatural pattern recognition on control charts using correlation analysis techniques http://blogs.najah.edu/staff/amjed-al-ghanim/article/1995-9-1Published ArticlesThis paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts The system is based on correlation analysis where a set of optimal matched filters are generated To illustrate the design methodology and operation of the system a set of commonly encountered patterns is utilized such as the trend the systematic and the cyclic patterns A training algorithm that minimizes the probabilities of Type I and Type II errors is presented To evaluate the system performance a testing algorithm as well as a set of newly-defined performance measures are introduced The obtained results have shown the effectiveness of correlation analysis for control chart pattern recognitionA neural network-based methodology for machining knowledge acquisitionhttp://blogs.najah.edu/staff/amjed-al-ghanim/article/A-neural-network-based-methodology-for-machining-knowledge-acquisitionPublished ArticlesThe paper presents results of a study on collecting machining strategies for machining assistants and process planning These efforts are being conducted at the NMSU-Integrated Manufacturing Systems Laboratory IMSL Goals of the project aim at improving and advancing the solicitation documentation and automation of machining knowledgedata acquisition and integration with CADCAMCAE systems This paper emphasizes the knowledge acquisition phase of the study utilizing artificial neural networks