Search

According to a recent trend of research, there is a growing interest in applications of machine learning techniques to business analytics. In this work, both supervised and unsupervised machine learning techniques are applied to the analysis of a dataset made of both family and non-family firms. This is worth investigating, because the two kinds of firms typically differ in some aspects related to performance, which can be reflected in balance sheet data. First, binary classification techniques are applied to discriminate the two kinds of firms, by combining an unlabeled dataset with the labels provided by a survey. The most important features for performing such binary classification are identified. Then, clustering is applied to highlight why supervised learning can be effective in the previous task, by showing that most of the largest clusters found are quite unequally populated by the two classes.

Part 8: Modular Design and Products; International audience; In business to business manufacturing, a major competitive advantage comes from the personalization of the product for the customer. In order to customize a product, companies go through a long process of customer interviews and specialized product development processes: this results in a time-consuming design phase and in a highly variable production process. In this paper, a method to improve the efficiency of product development and manufacturing, keeping a high degree of customization, is presented. A standardization effort is performed to identify a set of interchangeable components and to define a set of functional constraints. The consequences of such standardization are a dramatic reduction of the time expected to design and produce an item, as well as in lowered degree of variability of both the manufacturing process and the warehouses content. The presented methodology has been applied to a manufacturer of ink dispensing systems.