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The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies.

The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review.

The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized.

Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries.

This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia.

This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.

With the aim of discovering the important factors in training staff to use newly installed automated circulation systems, the appropriate libraries in New Zealand were sent a questionnaire in 1988. This asked how libraries had trained their staff, and the outcome of the training in terms of satisfaction with their decisions, sparseness of mistakes after implementation and time taken for staff to attain efficiency after implementation. The most important factors in training were that the vendor should be heavily involved in organizing and carrying out training; training should be given before the system went live; staff should be given detailed training in the whole system; and a number of different training methods should be used. These factors were especially important for libraries installing their first automated system, those installing integrated systems, those with few staff, and those installing their systems in the earlier years of automation; that is, those libraries whose staff have the least background in the system being installed, have the greatest need for excellent training.

Increasing the service quality in administrative areas is a critical factor facing the service industry today. This is supported by the latest version of the international quality standard ISO 9001, which emphasizes that customer satisfaction should have a complete and objective evaluation method and index. Uses the service quality defect concept in the PZB model to measure the time characteristic of service quality in processing an administrative job. At the same time, establishes the best estimates of service efficiency for each service unit and the whole department. Establishes a non‐center t distribution and a test procedure by p‐value to evaluate if the service process fits the customer‐defined service efficiency index. The result can then be used to judge the service efficiency of both individual service units and the whole department. According to the test procedures presented here, provides an objective evaluation criterion for administration and service industry.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

The paper aims to enrich previous findings for an emerging banking industry such as Vietnam, reporting the difference between the parametric and nonparametric methods when measuring cost efficiency. The purpose of the study is to assess the consistency in issuing policies to improve the cost efficiency of Vietnamese commercial banks.

The cost efficiency of banks is assessed through the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA). Next, five tests are conducted in succession to analyze the differences in cost efficiency measured by these two methods, including the distribution, the rankings, the identification of the best and worst banks, the time consistency and the determinants of efficiency frontier. The data are collected from the annual financial statements of Vietnamese banks during 2005–2017.

The results show that the cost efficiency obtained under the SFA models is more consistent than under the DEA models. However, the DEA-based efficiency scores are more similar in ranking order and stability over time. The inconsistency in efficiency characteristics under two different methods reminds policy makers and bank administrators to compare and select the appropriate efficiency frontier measure for each stage and specific economic conditions.

This paper shows the need to control for heterogeneity over banking groups and time as well as for random noise and outliers when measuring the cost efficiency.

Company intellectual capital (IC) is nowadays considered as a key resource that can transform a company’s value. For this reason, the efficiency of IC is crucial for all stakeholders. Evaluating efficiency is difficult, because IC is partly unobservable and its efficiency varies across time. The aim of this study is to suggest a methodology for estimating the dynamic efficiency of a company’s intellectual resources.

The panel data model suggested by Kneip et al. (2012) is used to estimate dynamic efficiency. The main feature of this model is that the unobservable component has a multi-dimensional factor structure. Taking advantage of the ability of this model to control for unobserved complex heterogeneity, the authors use the results in further stochastic frontier analysis. A data set containing information about Russian companies for the period from 2001 to 2010 is used.

In this paper, the dynamic efficiency of Russian companies is estimated. It is shown that, using the traditional efficiency estimate, companies can be overestimated.

The main limitation of the suggested methodology is that it is necessary to have a long panel data structure.

Taking advantage of time-varying efficiency, one can estimate the efficiency growth rate as a measure of performance, standard deviation as a measure of risk and autocorrelation as a measure of stability.

This is the first study to present clear evidence of the time-varying nature of IC efficiency. On the methodological side, the paper presents a fairly simple method capable of estimating various indicators of a company’s efficiency.

The measurement of construction performance is a vexed problem. Despite much research effort, there remains little agreement over what to measure and how to measure it. The problem is made even more complicated by the desire to benchmark national industry performance against that of other countries. As clearly construction cost forms part of the analysis, the mere adjustment of cost data to an “international currency” has undermined past attempts to draw any meaningful conclusions. The paper aims to discuss these issues.

This paper introduces a new method for comparing international construction efficiency, tested on a data set of 337 modern high-rise buildings in both Australia and the USA, and in so doing demonstrates that the ratio of cost over time is capable of ranking the efficiency of projects, building contractors, cities and even entire industries – not only today, but retrospectively over time.

It is concluded that, based on data from the largest five cities in each country, efficiency on site is improving in both countries. The growth in baseline cost/m2 suggests a possible rise in project complexity over time. While the trend in efficiency improvement is similar, there is evidence that base costs in Australia have outstripped the USA, meaning that “real” construction efficiency in Australia is relatively less. If Australia held an advantage in the past, then it seems that advantage might be disappearing. The USA is outperforming Australia in terms of construction efficiency by 1.10 per cent per annum.

Cost is measured as the number of standard “citiBLOC” baskets necessary to construct a project, where a standard basket comprises common and globally applicable construction items priced in each city in local currency, removing the need to apply currency exchange rates that otherwise introduce volatility and erroneous outcomes. Time is measured as the number of months between commencement on site and handover, inclusive of delays related to the construction process on site. Construction efficiency is defined as the ratio of construction cost per month, and is used to comment on the relative performance of the procurement process in different locations.

A lean manufacturing metric called Takt time is used as a benchmark evaluation measure to evaluate service quality at fast food restaurants. The metric is applied to find the relative efficiency between three fast food restaurants belonging to different chains. The purpose of this paper is to help guide management through ways to improve customer service and increase performance.

The customer lead time (the time taken by a customer from arrival at the service queue until their order is fulfilled) is the focus of this study. Takt time is used to find the relative efficiency of service time between three fast food restaurants.

It is shown that Takt time can be used effectively to measure the level of efficiency of the services provided. It measures the relative efficiency and identifies bottlenecks among different entities providing the same services.

The results can be used as a guide to rank the efficiency of the length of service time of different entities by taking the whole system into consideration rather than just measuring and comparing the service time itself between the entities. The results show the effectiveness of using lean manufacturing practices in pinpointing the relative inefficiencies between different service provider facilities.

This research presents a procedure to measure relative efficiency between different service providers to enhance their services. It can be applied to any service management systems that deal directly with walk-in customers.

Nowadays, diversity management in business organizations becomes so imperative in a time-sensitive business. However, diversity should not be measured as a static number. To better understand the fast-changing terrain, the paper aims to strongly propose using the tracking signal method to dynamically monitor the efficiency performance in regard to the future of diversity and best practices. This will allow diversity to play a more strategic role in cultivating sustainable business growth.

The paper employs the tracking signal, widely used to monitor the performance of a forecasting model, with some extension to dynamically monitor the technical efficiency due to its ability in alarming out-of-control signals.

The approach is considered as a quantitative, objective and reasonable evaluation system because it can quickly alert employees with inferior performance and promote those with persistently outstanding performance. It also provides managers the right timing to find assignable causes affecting their workers' performance. Most importantly, it can be unsophisticatedly implemented in many aspects of efficiency evaluation for assisting organizations to improve their competitive power through efficiency gains and well expedite their broadly managerial decisions.

The majority of previous researches aggregate multi-dimensional efficiencies by different weighting strategies into a single index before making their judgments. These approaches mainly focus on the first phase of the decision-making procedure by considering the efficiency in a static-state. As efficiency is time-sensitive, it should be evaluated dynamically. Thus, serving as the second phase of the procedure, the method is a complement of previous researches.