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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 variety of the subjects examined in the preceding chapters reflects the vastness of the contemporary conflict resolution field. To discuss contexts that encompass them requires relying on basic conflict processes. I have chosen such processes related to three fundamental matters: (1) that conflicts are interlocked with each other, (2) that various kinds of inducements are used in conflicts, and (3) that how conflicts are waged and settled is related to conflict outcomes.

Dependence on access to European markets through subcontracting relationships with European firms has exposed subcontracting clothing and textile producers in less developed economies to the vagaries of international market competition. This paper aims to examine the problems that such exposure creates and the requirements for developing marketing activities through the concept of the alliance life cycle as a viable solution for a sample of Tunisian clothing and textile firms.

The inductive reasoning of this research was implemented through qualitative research based on a range of tools derived from a case study and a dual ethnographic approach.

The main results showed that life cycle stages of the subcontracting firms reflected Schumpeter's creation and destruction cycle of innovation. Therefore, subcontracting firms could learn from their activities with their business customers so that they developed marketing competences in innovative processes. The findings also demonstrated that some of the firms in the Tunisian clothing and textile industries were more successful than others.

This research focused mainly on subcontracting alliances with implications for future study of other alliances for different industries.

The life cycle approach could be of great interest to subcontracting managers in the post Multi Fibre Arrangement era. This approach is relevant mainly for decision makers in providing them with a framework within which they might optimise their marketing strategies and their implementations.

The research originality resides in its Schumpeterian perspective in considering business‐to‐business relationships. The value of the paper is to focus on the evolutionary aspects of relationships between contractors and subcontractors and the patterns of the marketing development within these relationships.