Shamsuddin Ahmed

Application of QFD in product development of a glass manufacturing company in Kazakhstan

Purpose – The purpose of this paper is to demonstrate how quality function deployment can be used to improve the quality of tinted glass produced by a glass manufacturing company in Kazakhstan.Design/methodology/approach – Data were collected using a combination of Delphi method, unstructured, and semi‐structured survey. Principal component and Pareto analysis were used to identify the ranking of customer wants needed to improve the acceptability of the product in the market.Findings – The paper suggests that satisfying all customer needs require the deployment of all the technology and resources available to the company. It illustrates the possible courses of action company management can take based on prevailing market conditions.Research limitations/implications – The research shows the specific requirements of customers for tinted glass used in industrial settings. From supply chain perspective, downstream customer opinions were used to identify the desired product attributes.Practical implications – ...

Classification standard in large process plants for integration with robust database

KKS is defined as Kraftwerk Kennzeichen System indicating process plant designation system. It is used to identify and classify equipment and components in process plant. Several systems of nomenclature are available. Two methods are widely used. One is the American system and the other is the European system. The European system is known as KKS and its taxonomy is comprehensive. The system provides a convenient method to identify plant equipment and its operation. It also covers the buildings and structures, thereby providing comprehensive identification within the system. The number allocated by the KKS system to equipment is broken down into a number of levels. There is a field or set of fields within each level and each field occupies a letter or a number according to a convention. It is shown how the KSS identification and classification system is used to develop database system for plant maintenance and management. The classification and identifications of plant equipment is taken as an example to show how the data structure is designed. The main thrust has been the equipment codification system in order to develop the database standards in information technology within energy industry.

Supply chain planning for water distribution in Central Asia

– The role of business logistics for a water distribution company in Central Asia has become a major concern. As the marketing environment is getting more and more competitive, the company is forced to focus on the efficiency of its supply chain management operations both by improving customer service, increasing its profitability and productivity. The purpose of this paper is to report upon the designing of a responsive supply chain for water distribution in Central Asia., – A logistic plan to satisfy customer requirement for water distribution in a Central Asian city subject to satisfactory service levels both in the number of distribution centers (DCs) and truck delivery schedule is outlined in this paper. The logistics plan includes repositioning the DCs in relation to the customer location for efficient distribution. The problem is formulated as truck delivery schedule using a new algorithm where single distribution centre is converted into a multiple warehouse location problem. The problem is solved using WINQSB software. Further, the current DCs are appraised with the software and suggested possible new locations for convenience., – The application part of this case study consists of identifying water DCs in city limits. By developing improved distribution and logistics management, the study aims at economical operations, convenient zonal distributions, and responsive SCM characteristics. To this end, a spatial distribution plan and route sequencing solution is developed for water distribution., – The paper shows how to improve logistic network that results in cost savings, convenient zonal distributions, and responsive SCM operations. To this end, a spatial distribution plan and route sequencing is developed for water distribution.

Derivative Free Optimization in Higher Dimension

Non-linear optimizations that do not require explicit or implicit derivative information of an objective function are an alternate search strategy when the derivative of the objective function is not available. In factorial design, the number of trials for experimental identification method in Em is about (m+ 1). These (m+ 1) equally spaced points are allowed to form a geometry that is known as regular simplex. The simplex method is attributed to Spendley, Hext and Himsworth. The method is improved by maintaining a set of (m+ 1) points in m dimensional space to generate a non-regular simplex. This study suggests re-scaling the simplex in higher dimensions for a restart phase. The direction of search is also changed when the simplex degenerates. The performance of this derivative free search method is measured based on the number of function evaluations, number of restart attempts and improvements in function value. An algorithm that describes the improved method is presented and compared with the Nelder and Mead simplex method. The performance of this algorithm is also tested with artificial neural network (ANN) problem. The numbers of function evaluations are about 40 times less with the improved method against the Nelder and Mead (1965) method to train an ANN problem with 36 variables.

Simulation Method to Improve Hospital Service Quality

This article presents the results of a simulation model designed to reduce patient waiting time in the emergency department of a hospital in the United Arab Emirates. The process-oriented simulation model shows how the resources in the hospital are inter-related. The model depicts the hospital operating system and its performance and management issues with regards to allocation of human and material resources. Based on results of the simulation, optimized response surfaces are developed to explain patient waiting time and the total time a patient spends in the hospital for treatment. Results of the study can be used by hospital management to reduce patient waiting time and improve service quality by using a mix of operational strategies and resource allocations.

Multi‐directional search to optimize neural network error function

Purpose – The proposed algorithm successfully optimizes complex error functions, which are difficult to differentiate, ill conditioned or discontinuous. It is a benchmark to identify initial solutions in artificial neural network (ANN) training.Design/methodology/approach – A multi‐directional ANN training algorithm that needs no derivative information is introduced as constrained one‐dimensional problem. A directional search vector examines the ANN error function in weight parameter space. The search vector moves in all possible directions to find minimum function value. The network weights are increased or decreased depending on the shape of the error function hyper surface such that the search vector finds descent directions. The minimum function value is thus determined. To accelerate the convergence of the algorithm a momentum search is designed. It avoids overshooting the local minimum.Findings – The training algorithm is insensitive to the initial starting weights in comparison with the gradient‐ba...

Degenerated simplex search method to optimize neural network error function

Purpose – The purpose of this paper is to present a degenerated simplex search method to optimize neural network error function. By repeatedly reflecting and expanding a simplex, the centroid property of the simplex changes the location of the simplex vertices. The proposed algorithm selects the location of the centroid of a simplex as the possible minimum point of an artificial neural network (ANN) error function. The algorithm continually changes the shape of the simplex to move multiple directions in error function space. Each movement of the simplex in search space generates local minimum. Simulating the simplex geometry, the algorithm generates random vertices to train ANN error function. It is easy to solve problems in lower dimension. The algorithm is reliable and locates minimum function value at the early stage of training. It is appropriate for classification, forecasting and optimization problems.Design/methodology/approach – Adding more neurons in ANN structure, the terrain of the error functi...

Performance of a coordinate search ANN training algorithm

A coordinate direction search algorithm is designed to train artificial neural network error function. The algorithm searches all possible directions in the error space. An acceleration step is introduced for quick convergence. The step is taken when successive search by the algorithm reduces the function value. The repeated successful search directions provide information for orthogonal move. This direction of search is defined as leap-frog step. The algorithm is suitable when complex geometry of the error surface is present in the form of stiff ridges, valleys, contours, or flat surfaces. Quite often derivative-based training algorithm terminates in local minimum. The leaf-frog step allows the algorithm to escape local minimum. The algorithm is derivative free and is convenient when the derivative information of an error function is not available. The algorithm converges to minimum value and is robust. This algorithm is a different class and is not a random search or a heuristic optimization method. It is quite different from the first- and second- order derivative-based training methods. The algorithm finds optimized neural network weights. It is tested with seasonal time series and classification problems.

Forecasting Exchange Rate of Kina against AUD Using Artificial Neural Network and Time Series Models

In this paper we propose an Artificial Neural Network model for forecasting exchange rate of Kina against AUD. We use daily exchange rate data during the period of 2 January 2008 to 30 November 2012. The proposed model is compared with autoregressive time series model, exponential smoothing with trend; and Holt-Winter multiplicative and adaptive models. The performance of the models was measured by using various error functions such as root square mean error, mean absolute error, mean absolute deviation, and mean absolute percentage error. The results reveal that the proposed ANN model is an efficient tool for forecasting Kina against AUD more accurately.

Process analysis and capacity utilization in a dental clinic in Kazakhstan

– The purpose of this paper is to investigate the service delivery system of a dental clinic in Kazakhstan to maximize the clinic’s efficiency. , – The study uses process analysis to determine the capacity utilization and areas of bottlenecks in the dental clinic’s system. , – The analysis shows that the most severe bottleneck is identified in step 16 of the 20-step patient flow process. The system efficiency is approximately 62 per cent. , – The study will help similar health-care organizations identify areas of bottlenecks in their operational system. This would allow management to deploy optimal resources that would improve systems’ performance. , – The paper provides a framework for health-care managers to identify how to reduce patient throughput time and increase patient satisfaction.