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One of the most basic pieces of information useful to hospitality operations is gross sales, and the ability to forecast them is strategically important. These forecasts could provide powerful information to cut costs, increase efficient use of resources, and improve the ability to compete in a constantly changing environment. This study tests sophisticated, yet simple‐to‐use time series models to forecast sales. The results show that, with slight re‐arrangement of historical sales data, easy‐to‐use time series models can accurately forecast gross sales.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The purpose of this paper is to investigate the association between corporate governance mechanisms and quality of forward-looking information in the Chinese stock market which presents a mandatory disclosure environment for forward-looking information.

Using sales forecasts to proxy forward-looking information and using precision and accuracy to measure the quality of information disclosure, the authors investigate the impact of corporate governance attributes on the precision and accuracy of sales forecasts made by listed Chinese firms in their 2010 annual reports, using logistics and ordinary least squares regressions.

The authors find good corporate governance has a positive and significant impact on the precision choice of sales forecasts disclosure. Firms with good corporate governance are more likely to disclose more precise sales forecasts than providing qualitative discussions on firms’ sales trend. In addition, good corporate governed firms are found more likely to provide precise non-financial information. The authors also find that good corporate governance is positively associated with making more conservative sales forecasts disclosure. However, the authors find no significant relationship between good corporate governance and smaller forecast error.

The study makes significant contributions to corporate disclosure literature. The authors investigate the determinants of the quality of forward-looking information in a mandatory disclosure regime while most forward-looking information disclosure literature have been conducted in a voluntary-based disclosure environment. The authors examine whether in a mandatory disclosure regime, corporate governance mechanisms can play a positive role in precision choices and accuracy of forward-looking information. Further, the study is the first to examine corporate governance and the quality of non-financial forward-looking information (sales target and production goal). The research findings therefore extend forward-looking information disclosure research from financial information to non-financial information.

The empirical findings will provide regulators with evidence on the quality of forward-looking information in a mandatory disclosure regime and the influence of corporate governance on forward-looking disclosure. The properties of forward-looking information disclosure in China should be of interest to policy makers, investors and financial analysts in other international jurisdictions.

The study investigates forward-looking information in a mandatory disclosure regime while most extant forward-looking information studies have been conducted in a voluntary disclosure environment. The study is the first to examine the quality of non-financial forward-looking information such as operational goals and plans, and to investigate the association between the quality of non-financial forward-looking information and corporate governance mechanisms. The research findings extend forward-looking information disclosure research from quantitative financial information to quantitative non-financial information.

Traditionally, statistical time series methods like moving average (MA), auto‐regression (AR), or combinations of them are used for forecasting sales. Since these models predict future sales only on the basis of previous sales, they fail in an environment where the sales are more influenced by exogenous variables such as size, price, color, climatic data, effect of media, price changes or campaigns. Although, a linear regression model can take these variables into account its approximation function is restricted to be linear. Soft computing methods such as fuzzy logic, artificial neural networks (ANNs), and genetic algorithms offer an alternative taking into account both endogenous and exogenous variables and allowing arbitrary non‐linear approximation functions derived (learned) directly from the data. In this paper, two approaches have been investigated for forecasting women's apparel sales, statistical time series modeling, and modeling using ANNs. Four years' sales data (1997‐2000) were used as backcast data in the model and a forecast was made for 2 months of the year 2000. The performance of the models was tested by comparing one of the goodness‐of‐fit statistics, R², and also by comparing actual sales with the forecasted sales of different types of garments. On an average, an R² of 0.75 and 0.90 was found for single seasonal exponential smoothing and Winters' three parameter model, respectively. The model based on ANN gave a higher R² averaging 0.92. Although, R² for ANN model was higher than that of statistical models, correlations between actual and forecasted were lower than those found with Winters' three parameter model.

The paper presents how a European pick‐and‐mix confectionery company has employed a new forecasting approach – assortment forecasting – to reduce significantly time spent on forecasting by working with an entire assortment at a time instead of producing a forecast for each product individually. The implementation of a less time‐consuming forecasting method has enabled the company to involve its salespeople in forecasting and in this way gain access to their product and market knowledge. The case company's implementation of the new forecasting method is described and its forecasting accuracy and time spent on forecasting before and after the implementation are measured. The results demonstrate a remarkable increase in forecasting efficiency as well as improved communication within the company.

This paper aims to examine how managers use planning meetings to coordinate their actions in light of an uncertain future. Existing literature suggests that coordination under uncertainty requires a “dynamic” approach to planning, which is often realized in the form of rolling forecasts and frequent cross-functional exchange. Not so much is known, however, about the micro-level process through which coordination is achieved. This paper suggests that a sensemaking perspective and a focus on “planning talk” are particularly helpful to understand how actors come to a shared understanding of an uncertain future, based upon which they can coordinate their actions.

This paper builds upon a qualitative case study in the Austrian production site of an international manufacturing company. Drawing on a sensemaking perspective, the paper analyses monthly held “planning meetings” in which sales and production managers discuss sales forecasts for the coming months and talk about how to align demand and supply.

The authors show how collective sensemaking unfolds in planning meetings and highlight the role that “plausibilization” of expectations, “calculative reasoning” and “filtering” of information play in this process. This case analysis also sheds light on the challenges that such a sensemaking process may be subject to. In particular, this paper finds that competing hierarchical accountabilities may influence the collective sensemaking process and render coordination more challenging.

The paper contributes to the hitherto limited management accounting and control literature on operational planning, especially its coordination function. It also extends the management accounting and control literature that draws on the concept of sensemaking. The study shows how actors involved in planning meetings create a common understanding of the current and future situation and what sensemaking mechanisms facilitate this process. In this respect, this paper is particularly interested in the role that accounting and other types of numbers can play in this context. Furthermore, it theorizes on the conditions that allow managers to overcome concerns with hierarchical accountabilities and enact socializing forms of accountability, which is often necessary to come to agreements on actions to be taken.

In order to predict the future we must examine the past in order to observe trends over periods of time and establish the degree of probability with which these trends are likely to repeat themselves in the future. All forecasts are wrong, and management must be aware of this fact and decide upon the degree of inexactitude that can be tolerated when planning for the future.

Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer‐based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.

The purpose of this paper is to investigate the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts.

The study uses a field study method to address the studied phenomenon in eight Finnish companies that have recently adopted PA systems. The data are primarily based on 19 interviews in the companies and five interviews with the PA consultants.

The authors found that initiators appear to play a major role regarding the degree of integration of financial and operational sales forecasts. The initiators from an accounting function have a tendency to pay more attention to the integration than the representatives from other functions, such as operations and sales.

The study also makes a practical contribution to companies in showing and discussing the important role of the accounting department as an initiator of a project if the target is to achieve a tight coupling of financial and operational forecast figures, i.e., “one set of numbers”.

Even though companies have increasingly adopted PA systems in recent years, we still know little about how the initiation affects the design of accounting information systems overall. The central contribution of the paper, therefore, is to show that if a PA project is initiated by the accounting department, data integration becomes more likely. It contributes also to the discussion related to the appropriateness of data integration in the context of forecasting.

This paper aims to examine whether corporate social responsibility (CSR) is related to management sales forecast accuracy.

Use KLD measures of corporate responsibility combined with forecast accuracy regression model, including controls for management skills and expertise.

Socially responsible firms commit forecast errors of lower magnitude and sales forecast accuracy is positively related to the level of CSR.

A strong motive for research on the field of CSR topic under the scope of reporting quality. Future research could focus on alternative measures of CSR; such as announcements included into the financial statements or separately disclosed expenses. Examine the magnitude of confirmed relation, among different economies worldwide.

CSR effect on manager sales forecasting activity, highlight the impact of brand awareness and customer loyalty, as created by implementing CSR strategies, on firm growth and sales expansion.

The research enhances the era towards more socially responsible firms, presenting evidence of such an adoption on corporate fundamentals.

To the knowledge there is no prior research examining the implications of CSR on sales forecast accuracy.