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This paper rates a series of factors that managers believe influence the on‐time delivery of rail transportation for shippers of grain and grocery products. These variables are empirically tested through a series of statistical techniques. On‐time performance is found to be dependent on a combination of route, situation, and patronage variables. Railroad efforts to reduce or simplify terminal operations are supported by the findings of this study. The feeling expressed by some distribution managers concerning the hopelessness of affecting on‐time performance is partially confirmed although several positive guidelines for action are offered. Finally, claims that larger carrier size yields better service are not supported by this study.

Service quality has become an important area for competition among Chinese carriers. This paper focuses on studying the relationship between customer satisfaction measured by customer complaints and their expectation of the on-time performance of Chinese carriers and how the customer complaints affect the financial performance of carriers. By using a quarterly balanced panel data set covering six large listed carriers, the empirical results show that an increase in actual on-time performance reduces customer complaints. However, an increase in expected on-time performance significantly raises customer complaints. An increase in customer complaint reduces the yield measured as revenue per revenue ton kilometer (RTK) of carriers.

The purpose of this paper is to propose an approach to evaluate “on time” performance for a class of Indian railways (IR). This approach is build-up on the theme of six sigma level computation for continues data. The arrival data obtained from a class of IR called “Rajdhani Express” exhibited a unique characteristic: neither did the data followed a distribution nor could it be transformed to fit into a distribution. In this work, the authors present an approach to evaluate on time performance of IR, given such “unruly” data.

An attempt is made to develop a lucid approach, given an unruly data. Initially, the authors plot a histogram using Scott’s method. Later, the authors use Taguchi’s quality loss function to assign weights to each of the bins in histogram. Weights to each of the bins are assigned based on the predefined rules. Finally, sigma level is computed by using weighted defect per million opportunities (DPMO) approach. In this paper, the authors discuss the proposed algorithm, an illustrative example with an emphasis on a class of IR.

Given the unique characteristic (unruly data) of arrival data of IR, the proposed methodology helps in quantifying it is on time performance. This method extends the conventional DPMO approach of computing the sigma level. The proposed method is validated using various data sets of different distributions. Further, this approach can be generalized and applied to data set of any distribution. Since distribution of the data is not the pre-requisite, the proposed approach can be applied to compare (and benchmark) data sets of different distributions. This methodology, thus, paves path to develop newer approaches in quantifying and benchmarking the sigma levels.

In this manuscript, the authors present a case of Rajdhani Express trains, a class of IR. A practicing manager can use this approach to compare the performance of various classes of railways and benchmark their performance. Such an approach, with suitable modifications (if any) can be applied to evaluate performance in various service industries.

Usually, if the data are unruly, the sigma level is computed by using ad-hoc methods that may provide compromising solutions and/or inaccurate results. The developed methodology proposes a unique approach to quantify sigma level, given such an unruly nature of the data. This approach thus fills the long needed gap in addressing such situations. This approach can be applied in various similar situations. A case of IR is presented.

A product or service usually has multiple measurable characteristics, and its performance on different measures may vary and may change over time. Multi-criterion and multi-period performance benchmarking presents a challenge for management to determine performance gaps among comparable products or services. The purpose of this paper is to propose a new performance benchmarking method to address this challenge.

The author develop this method by formulating two benchmarking functions: a differentiation function based on Shewhart average and standard deviation charts to distinguish the performance of products or services on a single measure, and a categorization function to classify each product’s or service’s overall performance across all measures. By systematically removing the lowest-performing products or services from comparison, the author use these functions iteratively to detect performance gaps.

Using this method, the author find performance gaps in each of three benchmarking applications of airports, hotels, and minivans, although a number of performance gaps are not obvious from the raw data.

This benchmarking study focuses on the quantifiable outcome performance of products and services.

This benchmarking method is generic and applicable to most products and services. It is robust not only for discovering performance gaps, but also for providing useful insights for managers to prioritize improvement efforts on individual performance measures.

The novelty of this benchmarking method lies in that it can not only find the “best overall” products or services for all performance measures, but can also pinpoint the “best-in-class” products or services as well as performance gaps for each performance measure. In addition, this paper presents several original ideas for performance benchmarking, including: using the control limits of Shewhart control charts to categorize performance gaps, systematically removing the lowest-performing products or services from comparison for the purpose of detecting hidden performance gaps, and using symbolic expressions to integrate benchmarking results from all measures and to show all performance gaps intuitively.

Important evaluation criteria as they are perceived by quality managers in American and US‐based Japanese firms are examined. For this study, three different groups of companies contained within four industries were considered. They included American firms using a traditional approach to manufacturing management, Japanese firms operating in the United States, and American firms attempting a Japanese approach to manufacturing management. This study identified price, on‐time delivery, and the supplier′s product quality as the three major criteria for evaluating vendors. The attitudes of quality managers concerning the importance of these variables were counter to the impressions portrayed in the academic and managerial press. Also differing from the literature was how much the managers in these different types of firms linked the evaluation criteria and overall organisational performance.

This study on the airline industry covers the period from 1990 to 2006 and finds that “complaints” is a leading indicator of future financial performance as measured by return on sales (ROS) one-quarter ahead. Results also indicate that this effect persists into longer-term future performance (i.e., the average of one-quarter and two-quarter-ahead) as measured by return on assets (ROA) and ROS. Findings also indicate that service recovery effort in reducing mishandled baggage, is associated with higher future financial performance as measured by one-quarter-ahead ROA. Similarly, service recovery efforts, in reducing mishandled baggage and complaints, are found to be associated with both short-term and longer-term future financial performance as measured by ROA or ROS. Nevertheless, this relationship diminishes when flights have a higher “load factor” (or higher enplanements). Literature on service operations states that although service failure (such as flight cancellations, delays, misconnections, mishandled baggage, or over boarding) can negatively affect customer repurchase intentions, employees' ability to diagnose and respond to problems at the critical moment can overcome negative effects of a service failure. This suggests that management should consider having trained frontline employees and flight attendants provide comfort, assurance, empathy, support, and assistance to customers following service failures. This should help to enhance repurchase behavior and brand loyalty thereby improving future financial performance.

The temporality of performance measurement systems has been claimed to affect actors’ time orientation, such as that of listed company managers. The purpose of this paper is to explore this view.

The study uses constructivist data gathered from executives in one listed and one non-listed company.

The study shows that the research on performance measurement is based on a linear-quantitative view on time that assumes that humans orient towards the future from one point, the present; this view excludes other time-related constructs, particularly the past, and highlights a choice between the short term and the long term, idealising the long term. It is shown that the performance measurement of non-listed company executives is constructed through past-based, present-based and future-based rationalities: executives acknowledge the past as a basis for present and future performance, present actions as shaping future performance and future plans and performance targets as bases for present actions. Listed company executives’ performance measurement is constructed predominantly through the present-based time rationality.

“The orientation from the present” and the “short” and “long terms” could be enhanced with time rationalities.

The evaluation periods within performance measurement systems do not determine the time orientations of the actors subjected to those systems; time rationalities could be considered when designing such systems.

The paper provides a novel view on performance measurement and time.

The purpose of this paper is to analyse proposed cloud computing integration (CCI) and external integration (EI) effects on the relationship between the integration of supply chain and business performance of the organisation in the Indian context.

A two-stage, structural equation modelling (SEM) and artificial neural network (ANN) methodology are employed for the analysis, and for verifying the robustness of the developed model sensitivity analysis is performed.

The results of SEM revealed that out of 14 hypotheses, 12 hypotheses were supported. Furthermore output of SEM was used as input for the ANN model and the results highlighted that production flexibility is an essential factor for operational business performance (OBP) followed by customer integration, supplier integration, product quality, internal integration and on-time delivery (OD).

This study focussed on the emerging economies context and cannot be applied to all the countries, and there could be other derived variables from the real factors. This investigation is intended to guide various policy and decision makers of the case domain.

This study has introduced new factors such as CCI, EI and organisational business performance.

This paper aims to review the conceptual background for service quality as applied to the airline industry and use data from the Air Travel Consumer Report to examine airline quality performance on such key indicators as on‐time arrival, customer complaints, denied boarding, and mishandled baggage to determine the trend in airline service performance over the past two decades.

Data collected for this study included customer complaints on flight problems, ticketing, refunds, fares, customer service, advertising, and other problems, overall carrier on‐time performance, involuntary denied boardings, and mishandled baggage. Total yearly departures by carrier were obtained from the Bureau of Transportation Statistics and used to normalize the service data. Service quality rates were calculated for the industry overall and by individual carrier. The total quality rate represents the sum of the following data: the percentage of late flights, total number of consumer complaints, total number of involuntary denied boardings, and total number of mishandled baggage reports divided by total yearly departures for a particular airline. In a real sense, this rate is a measure of disquality and can be interpreted as the number of quality problems per departure.

The data show that during periods of retrenchment when airlines are reducing flight schedules due to economic downturns the level of on‐time arrival tends to improve while customer complaints, denied boardings, and mishandled baggage decline. Part of the explanation for the changes is the lack of airport and air traffic capacity to handle periods of higher traffic volume.

As a critical component of the transportation system, the ability of the airline industry to provide reliable, timely service is important. Understanding the factors that contribute to service problems can help guide airline decisions about process and government decisions about infrastructure needs.

Public agencies contract out to pursue a variety of goals. But, these goals cannot be realized if the performance of contractors is not assessed and monitored. This study examines the state of performance measurement and contract monitoring in the U.S. transit agencies. We focus on three research questions: (1) What monitoring capacity exists within transit agencies? (2) What monitoring methods are used by transit agencies? (3) What performance measures are tracked by transit agencies? We find monitoring units are common in a third of agencies in the study. Service and customer complaints are the most common performance measures, while penalties and liquidated damages are the most frequent form of penalties. Finally, we find that transit agencies utilize a variety of output and outcome measures to monitor contractors.