Sustainability toolkit for simulation-based logistics decisions

There have been significant changes in the international trade policy regime in recent years, along with an increasing emphasis on policy alignment and sustainability-related issues. The paper aims to empirically examine the role of sustainability and policy measures in influencing export performance. The study uses the stochastic frontier approach to estimate gravity equation on the export performance of 10 leading world exporters from the Asia Pacific Economic Cooperation (APEC) region over the time period 2012–2017. While the absolute difference in real exchange rates shows trade-inhibiting effects, estimates for reduced tariffs and interdependence among economies suggest trade-enhancing effects and significance in all specifications. There is a conspicuous difference in the export performance from the perspective of intra-regional and inter-regional trade. The study underscores the important role of regional trading blocs in policy alignment and sustainable development.

Performance measures and metrics are essential for effectively managing logistics operations, particularly in a competitive global economy. The global economy is featured with global operations, outsourcing and supply chain and e-commerce. The real challenge for managers of this new enterprise environment is to develop suitable performance measures and metrics to make right decisions that would contribute to an improved organizational competitiveness. Now the question is whether traditional performance measures can be used and out of them which ones should be given priority for measuring the performance in a new enterprise environment. Some of the traditional measures and metrics may not be suitable for the new environment wherein many activities are not easily identifiable. Measuring intangibles and nonfinancial performance measures pose the greater challenge in the so-called knowledge economy. Nevertheless, measuring them is so critical for the successful operations of companies in this environment. Considering the importance of nonfinancial measures and intangibles, an attempt has been made in this paper to determine the key performance measures and metrics in supply chain and logistics operations. This is based on a literature survey and some of the reported case experiences. Suggestions for future research directions are also indicated.

Drawing on original empirical data comprising police interviews and case file analysis, this article seeks to better understand the policing responses to cases of rape and specifically, the ways in which officers assist victims in the context of two themes – encouraging engagement with the investigative process and ensuring victim safety and general welfare. This work – which we call ‘hidden work’ as it is often neglected in the research literature – involves victim care, multi-agency working, provision of practical assistance, along with efforts to protect victims from physical and psychological harm. This article reaffirms key observations in the existing literature that emphasise the importance of victim welfare and engagement as part of a police investigation. A focus on victim care can be seen as an end in itself but also as a strategy that may carry benefit to the police themselves in the pursuance of performance goals should a victim remain engaged with the criminal justice process. However, the central argument in this paper is that traditional measures of police performance, such as detection and arrest rates, miss a significant amount of police activity which are important to rape victims and that a broader range of considerations should be factored into discussion of police performance. This is suggested not to diminish or downplay the significance of traditional measures of performance, or indeed to minimise police shortcomings in these areas, but to facilitate a more nuanced discussion of some of the realities of investigative work in the complex field of sexual offences. The article concludes by arguing that much of this hidden work should be included in formal assessments of police performance alongside traditional key performance indicators.

This study examined the prospects of adopting contemporary performance measures and how it affects sustainability reporting in Deposit Money Banks in Nigeria. This was achieved by using the traditional measures of performance that are normally found on the banks published annual reports and comparing it to other contemporary measures of performance. Data for the study was collected from the annual reports of seven (7) listed deposit money banks for a period of seven (7) years that is 2014-2020. Data obtained was analyzed using two selected contemporary measures of performance (Tobins Q and Loan deposit ratio (LDR)) and two selected traditional measures (Earnings per share (EPS) and Return on Assets (ROA)) in Performance management. Findings revealed that the new measures are quicker to identify loss of value and underperformance in standard. This shows that the new measures are more reliable when it comes to early prediction and rescue of sudden failure of listed deposit money banks in Nigeria. The study therefore recommends among others that deposit money banks should endeavor to use and include the performance results from the selected contemporary measures in their annual published reports for early detection of threats to their going concern.
 Keywords: Performance Management, Performance Measurement, Profitability, Value, Standard.

This study provides a review of the main measures of portfolio performance. We discuss their weaknesses and distinguish between traditional performance measures and more recent conditional performance measures. We show that the conditional approach addresses one major shortcoming of the traditional approach (risk stability assumption). Conditional measures allow expected returns and risk to vary with the state of the economy. We also propose new avenues for future research and some improvements to the existing measures.

All communities, regardless of their location or size, face the need to re-think and plan their transportation futures. In recent history transportation planning has begun to move from the public hearings to more input and consensus based processes. To facilitate this trend it has become necessary for transportation officials to communicate technical information in a manner that is accessible and understandable. One technique that has come into increasing use is that of graphic visualization. This method, while very effective in presenting a variety of planning and design options to a community, runs the risk of closing down dialogue by being too formal or too final in its appearance. To bridge the gap between information and public dialogue the Minnesota Department of Transportation has begun using a process called the Transportation Action Model (TAM). The TAM, initiated and designed by a national consortium led by the U.S. Department of Agriculture, was created with two guiding principles. First, sound transportation systems and the decisions behind them are critical to the social and economic well-being of communities. Second, informed community participation creates better transportation decisions. By linking technical information with a citizen-driven decision process, the TAM helps communities produce their own blueprint for local action. In 21 weeks, with a minimum of 25 participants from a broad range of transportation stakeholders and citizens, a community can become an active voice in their transportation future. Two rural communities in Minnesota, Two Harbors and Nisswa, challenged themselves and their planning histories by using the TAM. Both communities, each with unique transportation histories and development, sought this model as a way to deal with existing congestion impacts and future development pressures. Through public dialogue and graphic visualization, each community could frame and prioritize local transportation issues and develop potential solutions, creating a Transportation Action Plan that will assist the transportation plans and decisions of the future.

Life cycle assessment in support of sustainable transportation

Dissatisfaction with traditional accounting-based performance measures has spawned a number of alternatives, of which Economic Value Added (EVA) is currently the most prominent. How can we tell which performance measures best capture managerial contributions to value? There is currently a heated debate among practitioners about whether the new performance measures have a higher correlation with stock values and their returns than do traditional accounting earnings. Academic researchers have relied instead on the variance of performance measures to gauge their relative accuracy. To formally address the above debate, we use a relatively standard principal-agent model in which contracts can be based on any two accounting-based performance measures plus the stock price. Rather than model detailed differences between EVA and traditional measures such as earnings, we focus on the problem that while the variability of each measure is observable, its exact information (signal) content is not. The model provides a formal method for researchers to ascertain the relative value of alternative accounting-based measures based on two distinct uses of the stock price. First, as is well known, prices provide a noisy measure of managerial value-added. In our model, stock prices also can reveal the signal content of alternative accounting-based performance measures. We then show how to combine stock prices, earnings, and EVA to produce an optimally weighted compensation scheme. We find that the simple correlation between EVA or earnings and stock returns is a reasonably reliable guide to its value as an incentive contracting tool. That is, a firm could reasonably gauge the merits of adding a measure like EVA by examining its correlation with the firm's stock price. This is not because stock returns are themselves an ideal performance measure, rather it is because correlation places appropriate weights on both the signal and noise components of alternative measures. We then calibrate the theoretical improvement in incentive contracts from optimally using EVA in addition to accounting earnings. Specifically, we empirically estimate the value-added of EVA by firm and industry. These estimates are positive and significant in predicting which firms have actually adopted EVA as an internal performance measure.

One of the key functions of an effective Advanced Traveler and Management Information System is the ability to model short-term predictions of traffic conditions on major freeways and arterials with reasonable accuracy. The impetus of forecasting traffic information rather than relying on real-time information is to allow the traveling public to be proactive in their travel decisions at both pretrip planning stage and en route. Traffic information is usually reflected by the traditional performance measures such as travel times and delays. Such measures are well perceived by the traveling public and, hence, are adopted in most short-term traffic prediction studies. When disseminated to the traveling public, forecasted travel time information will help the travelers make better trip decisions in terms of departure time, route selection, and mode selection, when transit becomes competitive. Several traffic prediction models have been developed in the past using a wide spectrum of modeling techniques. Most studies showed that prediction accuracy is often compromised by the underlying mechanism of prediction methods more than other influencing factors. The main objective of this study is to investigate the factors that have a significant impact on the forecasting accuracy of travel times using a nonlinear time series traffic prediction model. The ultimate goal is to identify the operational settings and the anticipated forecasting accuracy of the model before it can be fully implemented. The study was conducted using extensive amount of real-time data collected from the 62.5 km corridor of Interstate-4 in Orlando, Florida. Various scenarios were generated from a combination of model parameter settings and different traffic conditions in order to test the performance of the model. Relative travel time prediction errors were used as a measure of performance and were based on speed predictions at freeway detector stations. Statistical analysis was conducted to identify the parameters with a statistically significant effect on the model's performance.

The rapid growth of data generation and the advancements in artificial intelligence (AI) have opened up new opportunities for data-driven decision-making in transportation management. This article presents a comprehensive review of the applications of AI in transportation, focusing on machine learning techniques, optimization algorithms, and predictive analytics. The article proposes a novel AI-based decision-making framework that integrates data preprocessing, AI modeling, and performance evaluation to address complex transportation challenges. A case study on traffic congestion management is conducted to demonstrate the effectiveness of the proposed framework in reducing travel times and improving system efficiency compared to traditional methods. The results highlight the potential of AI in optimizing transportation operations and supporting informed decision-making. However, the article also discusses the limitations and challenges of implementing AI-based decision-making in transportation, such as data quality, privacy concerns, and computational requirements. Future research directions, including transfer learning, integration with emerging technologies, and explainable AI, are identified to facilitate the widespread adoption of AI-based decision-making in transportation management. The findings of this article contribute to the growing body of knowledge on data-driven intelligent transportation systems and provide valuable insights for researchers, practitioners, and policymakers in the field.

10 - Performance measurement and transfer pricing in divisionalized organizations

PurposeThe purpose of this paper is to examine the claim of economic value added (EVA) proponents about its superiority as a corporate financial performance measure, compared to traditional performance measures in non‐financial Indian companies and provide empirical evidences.Design/methodology/approachThe paper uses a sample of 873 firms‐year observations from the Indian market and applies pooled ordinary least square regression to test the relative and incremental information content of EVA and other accounting‐based measures in explaining the market value added.FindingsThe results about relative information content test reveal that NOAPT and OCF outperform EVA in explaining the market value of Indian companies. Incremental information content test shows that EVA makes a marginal contribution to information content beyond traditional performance measures such as NOPAT, OCF, EPS and RONW, etc. Overall the authors' results do not support the hypothesis that EVA is superior to traditional accounting‐based measures in association with market value of the firm.Originality/valueThe authors conclude that non‐financial variables such employees, product quality and community satisfaction should be considered in order to capture the unexplained variation in the market value of the firm.

BackgroundWhile machine learning (ML) models are increasingly used to predict outcomes in health care, their practical effect on health care operations, such as bed capacity management, remains underexplored. There is a variety of traditionally used evaluation metrics to analyze ML models; however, decision makers in health care settings require a deeper understanding of their implications for resource management. Traditional performance measures often fail to provide this practical insight.MethodsIn this work, we conduct a simulation study to evaluate the impact of ML-driven length-of-stay (LOS) predictions on intensive care unit (ICU) bed capacity management. Two classification models differing in terms of explainability and interpretability, logistic regression (LR) and extreme gradient boosting (XGB), are applied to predict ICU-LOS. We use the HiRID dataset containing high-frequency data of more than 33,000 patients. The predictions of the ML models are integrated into a simulation framework that replicates real-world ICU bed management, allowing for the assessment of the practical implications of using these algorithms in a clinical setting.ResultsThe application of both classification models results in improved capacity control regarding the key performance indicators in the simulation study, with XGB outperforming LR. While LR leads to slight overoccupancy in the ICU, slight underoccupancy can be observed when XGB is applied.ConclusionOur study bridges the gap between predictive accuracy and practical application by emphasizing the importance of evaluating ML models within the context of ICU capacity management. The simulation-based approach offers a more relevant assessment for health care practitioners, providing actionable insights that go beyond classical performance measures and directly address the needs of decision makers in clinical practice.HighlightsWe apply multiple classification models for ICU-LOS prediction using time-series data. This approach enables an update of the initial prediction resulting in the possibility of efficiently managing intensive care capacities.We present a simulation-based approach to evaluate ML algorithms and their impact on bed capacity management in real-world clinical settings.Our work provides in-depth insights into the impact of using ML techniques as decision support systems in the ICU and can lead to increased acceptance in practice.

Even though remarkable progress has been made over recent years in the design of performance measurement frameworks and systems, many companies are still primarily relying on traditional financial performance measures. This paper presents an overview of the more common, more modern approaches to performance measurement and attempts to identify whether they have in fact addressed the limitations of traditional ways of measuring performance. The paper suggests that the modern frameworks have indeed addressed the underlying conceptual issues, but have rarely addressed the practicalities of measurement in ways that render them meaningful to practitioners. What is needed is further work to explore how these conceptual frameworks can be translated and tailored to fulfil the unique measurement needs of a specific company, especially at the operational level.

The rationale behind the study lies in the facts that about superiority of EVA over traditional matrices is mixed and were focused on manufacturing sectors.Hence, relationship between the Market Value Added (MVA), EVA, and accounting performance measures such as net earnings (PAT), earning per share (EPS), return on investment (ROI), return on net worth (RONW), and return on capital employed, Economic Value Added (EVA), and Market value Added (MVA) has not been grossly researched to Indian Banking Sector.The data has been collected through most worthy 'PROWESS' database of Centre for monitoring Indian Economy (CMIE).The reference period of the study is fourteen financial years i.e. 2000-2013.The multiple regression and Biddle 's et.al. (1997), Bao and Bao (1998) approach is selected for studying relative and incremental informational of EVA vis-à-vis other traditional financial performance measures.The result of cross-section regression reveals that earning (PAT) having highest explanatory power than EVA, ROI and EPS to explain market value added by sample banks.Fourth, the relative and incremental informational test present that The PAT having highest relative explanatory (66.7 percent) power do explain market value added as compared to EVA, ROI, EPS, ROCE alone and the relative informational content of EVA is lower than PAT, which, accounting for only 57.4 present of variation in market value added (MVA).Thus, the usefulness of EVA for decision making can't be ruled out based on the results and it can be used as an internal as well as external performance measurer.As Solomon (1965) suggested that residual income be used as an internal performance measure and Anthony (1973, 1982, and 1982) suggested that it is suitable to measure external performance.