Search results

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

Demand forecast methodologies have been studied extensively to improve operations in e-commerce. However, every forecast inevitably contains errors, and this may result in a disproportionate impact on operations, particularly in the dynamic nature of fulfilling orders in e-commerce. This paper aims to quantify the impact that forecast error in order demand has on order picking, the most costly and complex operations in e-order fulfilment, in order to enhance the application of the demand forecast in an e-fulfilment centre.

The paper presents a Gaussian regression based mathematical method that translates the error of forecast accuracy in order demand to the performance fluctuations in e-order fulfilment. In addition, the impact under distinct order picking methodologies, namely order batching and wave picking. As described.

A structured model is developed to evaluate the impact of demand forecast error in order picking performance. The findings in terms of global results and local distribution have important implications for organizational decision-making in both long-term strategic planning and short-term daily workforce planning.

Earlier research examined demand forecasting methodologies in warehouse operations. And order picking and examining the impact of error in demand forecasting on order picking operations has been identified as a research gap. This paper contributes to closing this research gap by presenting a mathematical model that quantifies impact of demand forecast error into fluctuations in order picking performance.

The purpose of this paper is to develop a framework for measuring the S&OP process performance.

The method used is a multiple case study of five companies from different industries based on data from 12 structured interviews.

The main result is a framework to measure the S&OP process. It includes concrete suggestions for organizations when developing measures to increase effectiveness and efficiency of the process. It will also help organizations to standardize measures and to enhance organizational transparency. Our results include measures for every step of the process as well as for the outcome of the process. The authors highlight the importance of cross-functional measures along with measures that focus on how to conduct the process. The framework is founded on a set of criteria on appropriate measures such as comprehensiveness, internal process efficiency, horizontal and vertical integration, internal comparability, and usefulness. The study contributes to performance measurement literature and the S&OP literature.

Validation of the framework is desirable in similar as well as other contexts. Implementation challenges should also be investigated.

The framework provides guidelines in order to measure, analyze and improve the effectiveness and the efficiency of the process.

This is the first framework for measuring the S&OP process that includes detailed measures for each step of the process, for the outcome of the process as well as how to conduct the process itself.

The main scope of the paper is to demonstrate the capabilities of PYTHIA forecasting platform, to compare time series forecasting techniques, which were used to forecast mortgage loans in UK, and to show how PYTHIA can be useful for a bank.

The paper outlines the methods used to forecast the time series data, which are included in PYTHIA. Theta, the time‐series used to forecast average mortgage loan prices, were grouped in: all buyers – average loan prices in UK; first‐time buyers – average loan prices in UK; and home‐movers – average loan prices in UK. The case of all buyers – average loan prices in UK, was presented in detail.

After the comparison of the methods, the best forecasts are produced by WINTERS and this is maybe due to the fact that there is seasonality in the data. The Theta method comes next in the row and generally produces good forecasts with small mean absolute percentage errors. In order to tell with grater certainty which method produces the most accurate forecasts we could compare the rest error statistics provided by PYTHIA too.

The paper presents the PYTHIA forecasting platform and shows how it can be used by the managers of a Bank to forecast mortgage loan values. PYTHIA can provide the forecasts required by practically all business situations demanding accurate predictions. It is designed and developed with the purpose of making the task of managerial forecasting straightforward, user‐friendly and practical. It incorporates a lot of knowledge and experience in the field of forecasting, modeling and monitoring while fully utilizing new capabilities of computers and software.

The increasing frequency and intensity of the extreme weather events could cause devastating consequences in tourism. Climate change–related extreme weather events and their relation to tourism is an emerging field for education and research. The purpose of this study is to categorize the impact of climate change on tourist destinations with regard to extreme weather-related risks in outdoor recreation and tourism. Managerial implications for policymakers and stakeholders are discussed.

To outline the risks from climate change associated with tourism, this study uses the Prisma analysis for identification, screening, checking for eligibility and finding relevant literature for further categorization.

Based on a thoroughly examination of relevant literature, risks and threats posed by climate change could be categorized into following four areas: reduced experiential value in outdoor winter recreation; reduced value in beach scenery and comfort; land degradation and reduced biodiversity; and reduced value in personal safety and comfort in tourism. It also focuses on the significance of using big data applications in catastrophic disaster management and risk reduction. Recommendations with technology and data analytics to continuously improve the disaster management process in tourism education are provided based on findings of this study.

Primary contributions of this study include the following: providing a summarized overview of the risks associated with climate change in terms of tourist experiential value for educational implications; and revealing the role of data analytics in disaster management in the context of tourism and climate change for tourism education.

The purpose of this study is to provide a framework of managerial responses to the Industry 4.0 phenomenon, which has impacted the productivity of Indonesian manufacturing companies while revolutionizing global industries.

The study employs qualitative research using the Grounded Theory Method since research in this area is still in its preliminary stages. The study elicits insights from 12 operation managers through a semi-structured interview and a focus group discussion. Using content analysis, the study formulates relationships among Industry 4.0 initiatives, its driving factors and challenges as well as critical success factors and the expected benefits.

The findings reveal that Indonesian manufacturers have engaged in Industry 4.0 initiatives: cyber-physical systems, the internet of things, Big Data and cloud computing. These initiatives require managers to adopt best practices, appoint champions as change agents, conduct training and even tailor the job qualifications of their subordinates to suit the current technology.

The qualitative method allows an in-depth investigation that is synthesized into a conceptual framework, but this framework still needs to be empirically tested. The study is currently based on informants from large manufacturing companies. Future studies could scale up the research and validate the findings.

This exploratory framework could guide managers in their strategic and operational decisions while embracing the Industry 4.0 transformation inside the organization.

Prior studies examining the adoption of Industry 4.0 principles by Indonesian manufacturing companies are rare. Furthermore, conceptual studies dominate the existing literature related to the Industry 4.0 concept. This study attempts to fill the gap and provides a framework that is based on grounded empirical data of manufacturing companies in Indonesia, a newly industrialized economy.

This paper aims to conduct a comparative analysis of the impact of market uncertainty on the degree of accuracy and bias of analysts' earnings forecasts versus four model-based earnings forecasts.

The study employs panel regression analysis on a sample of Egyptian listed companies from 2005 to 2022 to examine the impact of market uncertainty on the accuracy and bias of each type of earnings forecast.

The empirical analysis reveals that market uncertainty significantly affects analysts’ earnings forecast accuracy and bias, while model-based earnings forecasts are less affected. Furthermore, the Earnings Persistence and Residual Income model-based earnings were found to be superior in terms of exhibiting the least susceptibility to the impact of market uncertainty on their forecast accuracy and biasness levels, respectively.

The findings have important implications for stakeholders within the financial realm, including investors, financial analysts, corporate executives and portfolio managers. They emphasize the importance of considering market uncertainty when formulating earnings forecasts, while concurrently highlighting the potential benefits of using alternative forecasting methods.

To our knowledge, the influence of market uncertainty on analysts' earnings forecast accuracy and bias in the MENA region, particularly in the Egyptian market, remains unexplored in existing research. Additionally, this paper contributes to the existing literature by pinpointing the forecasting method, specifically distinguishing between analysts-based and model-based approaches, whose predictive quality is less adversely impacted by market uncertainty in an emerging market.

To the authors’ knowledge, this paper is the first to use Detail I/B/E/S to study directly the timeliness of security analysts’ next-year earnings-per-share (EPS) estimates relative to the SEC filings of annual (10-K) and quarterly (10-Q) financial statements. Although the authors do not prove a causal relationship, they provide evidence that the average time from firms’ filings of 10-Ks and 10-Qs to the release of analysts’ annual EPS forecasts during short timeframes (for example, 15-day timeframe from a 10-K’s SEC file date) subsequent to the 10-K and 10-Q filing dates significantly shortened with XBRL implementation and then remained relatively constant following implementation.

Using filing dates hand-collected from the SEC website for 10-Ks during 2009-2011 and filing dates for 10-Ks and 10-Qs during 2003-2014 input from Compustat along with analysts’ estimated values for next year EPS, actual estimated next year EPS realized and estimate announcement dates in Detail I/B/E/S, the authors study the days from 10-K and 10-Q file dates to announcement dates and the per cent errors for individual estimates during per- and post-XBRL eras.

The authors find that analysts are announcing next-year EPS forecasts significantly more frequently and in significantly shorter time in zero to 15 days immediately following 10-K and 10-Q file dates post-XBRL as compared to pre-XBRL. However, the authors do not find a significant change in forecast accuracy post-XBRL as compared to pre-XBRL.

Because this study uses short timeframes immediately following the events (filings of 10-Ks and 10-Qs), the relationship between 10-Ks and 10-Qs with and without XBRL and improved forecast timeliness is strengthened. However, even this strengthened difference-in-difference methodology does not establish causality. Future research may determine whether XBRL or other factors cause the improved forecast timeliness the authors’ evidence.

This improved efficiency may become critical if financial statement reporting expands as a result of new innovations such as Big Data and continuous reporting. In the future, users may be able to electronically connect to financial statement data that firms are maintaining on a perpetual basis on the SEC website and continuously monitor and analyze the financial statement data dynamically in real time. If so, then unquestionably, XBRL will have played a critical role in bringing about this future innovation.

Whereas previous studies have utilized Summary IBES data to assess the impact of XBRL on analyst forecasts, the authors use Detail IBES to study the effects of XBRL adoption directly by measuring days from 10-K and 10-Q file dates in Compustat to each estimate’s announcement date recorded in IBES and by computing the per cent error using each estimate’s VALUE and ACTUAL recorded in Detail IBES. The authors are the first to evidence a significant shortening in average days and an increase in per cent of 30-day counts in the zero- to 15-day timeframe immediately following the fillings of 10-K s and 10-Qs.

This paper aims to examine whether International Financial Reporting Standards (IFRS) adoption and corporate governance attributes increase the management earnings forecasts’ accuracy disclosed in prospectuses for French Initial Public Offerings (IPOs).

The analysis is based on cross-sectional regression explaining the absolute forecast errors by using 45 French firms that made IPOs between 2005 and 2016 in two French financial markets: Euronext and Alternext.

In agreement with the agency theory and the signaling theory, the authors find that the IFRS adoption and the effective corporate governance, proxied by the board characteristics, increase the accuracy of management forecasts. As a result, this latter gives a credible signal in constructing and sustaining shareholders’ trust on the transparency and the reliability of such financial information.

It is plausible that the limited size of the sample represents a limitation of this study. Another limitation is that no other corporate governance attributes such as board meeting frequency, audit committee measures and ownership structure are used.

Shareholders can take benefit from management forecasts accuracy to structure their investment portfolios efficiently to allocate their funds more effectively and mitigate the costs of adverse selection that they have to face. Furthermore, the authors expect the findings to be interesting to IPO firms, as this study highlights the efficiency of larger and independent boards in decreasing managerial discretion, increasing disclosure quality and supervising management. The results could encourage GAAP-adopters countries to move toward IFRS, as this research reinforces the role of IFRS in enhancing the quality of financial disclosure by offering the required information for shareholders.

This study is important because the potential investors should assess management earnings forecasts accuracy before they consider it when evaluating IPO firms. Also, this paper has some implications for the financial market. It is recommended that future investors pay more attention, when assessing the accuracy of management earnings forecasts, to the accounting regulations of the financial reporting along with the corporate governance mechanisms. Moreover, this study could incite French regulators to revise the AFEP-MEDEF code. Under this code, it could insist that larger and independent boards are more effective in performing their governing roles than smaller boards.