New Frontiers in Industrial Organizations

Introduction

During the 20^th century, manufacturing organizations have evolved from mass to batch production systems. These systems entered the 21^st century moving towards a new production model called mass customization¹ (Pine, 1999). With such a new model, information management systems have been playing an increasing and dominant role. A representation of such a transition from mass and batch production to mass customization processes was presented by Monfared and Steiner (1997) through a mathematical model of paradigm shift. In their work, they argued that the current and dominant scientific principles of the time are incompatible with the emerging needs of the present and future of manufacturing systems. To support the new trends of their own manufacturing model, these authors have emphasized that some new dimensions of human intelligence need to be captured, represented, modeled and applied in activities such as design, inspection, planning, scheduling, decision-making and control. They proposed that fuzzy logic, neural networks and genetic algorithms are the main disciplines which can be used to design new tools with the capability to handle the levels of uncertainty and dynamics inherent to the new manufacturing systems of mass customization operations. In such a direction, Rao et al. (1993) has presented the concept, methodology and implementation techniques of an integrated and distributed intelligent system. He introduced applications of design, operation, control, planning and maintenance of what he called intelligent manufacturing and he put emphasis on the integration and the management of such activities. Moreover, Kusiak (2000) has presented new advances in model construction and application of computational intelligence approaches to solve problems across many areas of an enterprise, with emphasis on design and manufacturing. This author has focused mainly on knowledge-based systems for automated decision-making; planning, testing and diagnostic systems; treatment of ambiguous, complex, incomplete and conflicting data; technology integration, material handling and storage, information systems and knowledge management. As a result of such an application, Kusiak argues that the manufacturing enterprises of the future will be better represented with the attributes of adaptability, agility, modularity, standardization, collaboration, distribution, simplicity, knowledge orientation, human orientation and environment awareness, where the latter attribute is concerned with the use of less consumable resources and the generation of less waste. Latterly, Nobre et al. (2009a) examined limitations of past and current manufacturing systems and proposed new technological, managerial and organizational capabilities which have to be developed in order to satisfy the requirements of the new industrial organizations² in the 21^st century. In such a proposal, Nobre et al. introduced the concept of Customer-Centric Systems (CCS). Briefly, CCS represents new organizing models of production that pursue high degrees of organizational cognition, intelligence and autonomy³ in order to manage high levels of environmental complexity⁴, to operate through intensive mass customization processes, and to provide customers with immersiveness⁵.