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Accident emergency hospital (AEH) services require cohesive, collective, uninterrupted streamlined medical diagnostic and satisfactory patient care. Medical service efficiency in AEHs is difficult to quantify due to the clinical complexity involved in treatment involving various units, patient conditions, changes in contemporary medical practices and technological developments. This paper aims to show how to measure efficiency by eliminating waste in AEH system, identify service failure points, identify benchmark medical services, identify patient throughput time and measure treatment time when AEH services are nonstandard. The applications shown in this paper are distinct in particular; we the authors use nontraditional and systems engineering approach to collect data as the traditional data collection is difficult in real-time AEHs.

The authors show in this study how to measure overall patient treatment time from admission to discharge. Project evaluation and review technique (PERT) captures the inconsistencies involved in measuring treatment time, including measures of variability. The irregular treatment time and complexity involved in the emergency health-care services are usual. The research methodology illustrates how the time function map and service blueprint can improve value-added time in AEHs and benchmark services between similar AEHs.

The inconsistency in treatment time between AEH in public and private hospital is found to be in ratio of 1:20. The private hospital suggests variety of treatments and long stays for recovery. The PERT computations show that the average time a patient remains in a government AEH is about 10 days. The standard deviation of the AEH treatment time is about 0.043 per cent of the expected patient care time. The inconsistency is not significant as compared to the expected value. In 89.64 per cent of the cases, a patient may be discharged in less than 10 days’ time. The patient on average is discharged in 13 days in a private hospital.

The patient treatment time of an AEH is evaluated with PERT project management approach to account for inconsistencies in treatment time. This research makes new contributions in benchmarking AEH throughput time, identify medical service failure points with service blueprint, measure the efficiency with time function map and collect patient data with nontraditional methods. The inherent inconsistencies in a clinical process are identified by PERT analysis with the variance as a characteristic of the treatment time. Improvement of variability implies cost reduction in AEH system.

A new triage method, MBCE (Medical Bio Social Ethics), is presented with social justice, bio, and medical ethics for critical resource distribution during a pandemic. Ethical triage is a complex and challenging process that requires careful consideration of medical, social, cultural, and ethical factors to guide the decision-making process and ensure fair and transparent allocation of resources. When assigning priorities to patients, a clinician would evaluate each patient’s medical condition, age, comorbidities, and prognosis, as well as their cultural and social background and ethical factors.

A statistical analysis shows no interactions among the ethical triage factors. It implies the ethical components have no moderation effect; hence, each is independent. The result also points out that medical and bioethics may have an affinity for interactions. In such cases, there seem to be some ethical factors related to bio and medical ethics that are correlated. Therefore, the triage team should be careful in evaluating patient cases. The algorithm is explained with case histories of the selected patient. A group of triage nurses and general medical practitioners assists with the triage.

The MBCE triage algorithm aims to allocate scarce resources fairly and equitably. Another ethical principle in this triage algorithm is the principle of utility. In a pandemic, the principle of utility may require prioritizing patients with a higher likelihood of survival or requiring less medical care. The research presents a sensitivity analysis of a patient’s triage score to show the algorithm’s robustness. A weighted score of ethical factors combined with an assessment of triage factors combines multiple objectives to assign a fair triage score. These distinctive features of the algorithm are reasonably easy to implement and a new direction for the unbiased triage principle.

The idea is to make decisions about distributing and using scarce medical resources. Triage algorithms raise ethical issues, such as discrimination and justice, guiding medical ethics in treating patients with terminal diseases or comorbidity. One of the main ethical principles in triage algorithms is the principle of distributive justice.

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.

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.

The training algorithm is insensitive to the initial starting weights in comparison with the gradient‐based methods. Therefore, it can locate a relative local minimum from anywhere of the error surface. It is an important property of this training method. The algorithm is suitable for error functions that are discontinuous, ill conditioned or the derivative of the error function is not readily available. It improves over the standard back propagation method in convergence and avoids premature termination near pseudo local minimum.

Classifications problems are efficiently classified when using this method but the complex time series in some instances slows convergence due to complexity of the error surface. Different ANN network structure can further be investigated to find the performance of the algorithm.

The search scheme moves along the valleys and ridges of the error function to trace minimum neighborhood. The algorithm only evaluates the error function. As soon as the algorithm detects flat surface of the error function, care is taken to avoid slow convergence.

The algorithm is efficient due to incorporation of three important methodologies. The first mechanism is the momentum search. The second methodology is the implementation of directional search vector in coordinate directions. The third procedure is the one‐dimensional search in constrained region to identify the self‐adaptive learning rates, to improve convergence.

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.

Adding more neurons in ANN structure, the terrain of the error function becomes complex and the Hessian matrix of the error function tends to be positive semi‐definite. As a result, derivative based training method faces convergence difficulty. If the error function contains several local minimum or if the error surface is almost flat, then the algorithm faces convergence difficulty. The proposed algorithm is an alternate method in such case. This paper presents a non‐degenerate simplex training algorithm. It improves convergence by maintaining irregular shape of the simplex geometry during degenerated stage. A randomized simplex geometry is introduced to maintain irregular contour of a degenerated simplex during training.

Simulation results show that the new search is efficient and improves the function convergence. Classification and statistical time series problems in higher dimensions are solved. Experimental results show that the new algorithm (degenerated simplex algorithm, DSA) works better than the random simplex algorithm (RSM) and back propagation training method (BPM). Experimental results confirm algorithm's robust performance.

The algorithm is expected to face convergence complexity for optimization problems in higher dimensions. Good quality suboptimal solution is available at the early stage of training and the locally optimized function value is not far off the global optimal solution, determined by the algorithm.

Traditional simplex faces convergence difficulty to train ANN error function since during training simplex can't maintain irregular shape to avoid degeneracy. Simplex size becomes extremely small. Hence convergence difficulty is common. Steps are taken to redefine simplex so that the algorithm avoids the local minimum. The proposed ANN training method is derivative free. There is no demand for first order or second order derivative information hence making it simple to train ANN error function.

The algorithm optimizes ANN error function, when the Hessian matrix of error function is ill conditioned. Since no derivative information is necessary, the algorithm is appealing for instances where it is hard to find derivative information. It is robust and is considered a benchmark algorithm for unknown optimization problems.

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.

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.

Applications of systems like total quality management (TQM), total productive maintenance (TPM) and just‐in‐time (JIT) have been studied mainly in large industries with little attention being paid to small and medium industries (SMIs) in developing countries. This paper discusses the state of implementation of TPM in SMIs and the effects of lack of productive maintenance. The main hypothesis is to determine if SMIs have understood the importance of a productive maintenance system as a constituent of manufacturing management. A survey methodology has been applied for this test. The outcomes of some case studies are kept in mind. All these show that the implementation of TPM or preventive maintenance in SMIs is still low. Therefore, more effort should be given to developing a better understanding, motivation and participation for implementation of productive maintenance systems. Finally, an implementation methodology is proposed.

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.

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.

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.

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.

Since no studies to date have been conducted on the glass manufacturing industry in the Central Asian region, this paper could help glass manufacturers in the region to improve their production practices.

The paper is of value to those glass producers interested in the glass manufacturing industry in Central Asia.

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.

The aim of this paper is to present a generic model on using the total productive maintenance (TPM) concept in conjunction with ecology oriented manufacturing (EOM) and 5S focusing on their joint strengths in attaining organizational goals in furtherance to the equipment maintenance objectives.

A systematic implementation‐framework coupled with the standard tools, techniques and practices has been designed. The framework was applied in a large semiconductor manufacturing company.

It is evident that a well drawn TPM implementation plan not only improves equipment efficiency and effectiveness but also brings appreciable improvements in other areas such as reduction of manufacturing cycle time, size of inventory, customer complaints, and creates cohesive small group autonomous teams and increases the skill and confidence of individuals. The resulting system is found to be more productive in terms of both partial and total productivity measures. This is in line with the current need of manufacturing companies to have an integrated manufacturing management system (IMMS) in order to simultaneously increasing efficiency and improving effectiveness.

The applied framework can be mimicked by other manufacturing organizations and similar results could be brought about. As the implementation of TMP in conjunction with the EOM and 5S has come out successful, this can be combined with other manufacturing planning and control (MPC) systems (viz. JIT, MRPII/ERP) to develop an IT‐based IMMS.

The case study presented here shows that the applications of TPM through the fulfillment of its basic requirements can significantly enhance the accomplishment of organizational objectives beyond the equipment maintenance‐subsystem goals and add an array of benefits in the value chain across the various functional areas.