Search results

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.

This study traces the internationalization strategy of Hering, a Brazilian-based apparel company active in manufacturing and retail.

The data set and analysis is presented in chronological order and decisions taken by the company are viewed based on internationalization theories. It presents two main internationalization frameworks, the production network and the value chain models.

While the company started the internationalization process in the 1960s and reached a consistent global presence in distinct markets, it has now retracted from main markets in Europe and North America and is focused on its own distribution network in South America. Hering has changed its strategy of a global production network player into a leader in the retail value chain model with regional distribution. This strategy change has been possible by valuing brand management and distribution instead of manufacturing capacity.

BRIC countries are known for exporting commodities but have poor performance in selling its own branded consumer goods abroad. This study provides such a rare case of a Brazilian consumer good company operating abroad.

The objective of this research is to develop a model to forecast sales for an ice-cream company. In order to achieve this objective, we evaluate sales data of three ice-cream flavors namely vanilla, chocolate, and Tally Ho (mixture of chocolate and vanilla) from January 2016 to 25 November 2019. To determine which model worked the best, we tested different models such as moving averages, simple exponential smoothing, Holt's method, Winters' method, method modeling seasonality and trend, and an ensemble method. We found Winters' method and modeling seasonality and trend performed well in terms of lowest error rates compared with other methods.

In this article, we propose a new class of flexible seasonal time series models to characterize the trend and seasonal variations. The proposed model consists of a common trend function over periods and additive individual trend (seasonal effect) functions that are specific to each season within periods. A local linear approach is developed to estimate the trend and seasonal effect functions. The consistency and asymptotic normality of the proposed estimators, together with a consistent estimator of the asymptotic variance, are obtained under the α-mixing conditions and without specifying the error distribution. The proposed methodologies are illustrated with a simulated example and two economic and financial time series, which exhibit nonlinear and nonstationary behavior.

In 2013, Chinese president Xi Jinping proposed the concept of “One Belt and One Road” economic cooperation. “The Belt and Road Initiative (B&R)” is the short of “The Silk Road Economic Belt” and the “21st-century Maritime Silk Road,” which has got a series of remarkable achievements and worldwide attentions in past five years such as Asian Infrastructure Investment Bank, China–Pakistan Economic Corridor, B&R Forum for International Cooperation, etc. Especially, cross-border EC has greatly strengthened the trade links between countries along the way, which is a rare chance for Chinese Export-oriented Cross-border EC’s rapid growth. Thus, the authors take DHgate.com as a typical example to do a big data analysis. This chapter analyzes vast data from 2013 to 2017 about seven kinds of commodities including Fashion accessories, Jewelry, Sports & Outdoors, Security & Surveillances, Car accessories, Watches, and Hair & Styling by using data mining related software and algorithms. The authors do some monthly sale charts and find a few counter-intuitive but useful conclusions such as by taking association analysis, the study shows that sports products and jewelry products have strong association rules. In addition, for potential products (such as Fashion accessories and Jewelry), although their sales have a certain shock, the overall selling line keep rising. It is possible to put forward some practical suggestions for Chinese Export-oriented Cross-border EC that actively respond to the One Belt One Road Initiative based on these analysis results.

The decision of whether or not to start a new business is a question pondered by many people and something that about .004% of the U.S. population decides to do every month (Kauffman Foundation, 2005). This decision becomes more complicated with the involvement of family members. One would be hard pressed to find any business enterprise without some sort of family influence and involvement at some point in the start-up or ongoing operations of the business. While most entrepreneurship research points to legal, environmental, regulatory, technological, or demographic changes as triggers that spur individuals into action, the role of family influence in new business founding is often overshadowed or not addressed at all (Aldrich & Cliff, 2003).