The themes of technological innovation, entrepreneurship, and organizing
The Roles of Cognitive Machines in Customer — Centric Organizations: Towards Innovations in Computational Organizational Management Networks
Federal University of Parana, Brazil
This chapter proposes innovative features of future industrial organizations in order to provide them with the capabilities to manage high levels of environmental complexity in the 21st century. For such a purpose the author introduces the concept of Computational Organization Management Networks (COMN), which represents new organizations whose principles of operation are based on the concepts of Hierarchic Cognitive Systems (HCS) along with those of Telecommunications Management Networks (TMN). Structured with functional layers and cognitive roles that range from technical and managerial to institutional levels of analysis, and also equipped with operational, managerial and strategic processes, the concept of Computational Organization Management Networks (COMN) plays an important part in the developments of future organizations where cognitive machines and Cognitive Information Systems (CIS) are prominent actors of governance, automation and control of the whole enterprise. It is in such a context that the new organization COMN will provide customers and the whole environment with innovations such as immersiveness for the production of services and goods that are most customer-centric.
DOI: 10.4018/978-1-61350-165-8.ch035
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This chapter mainly relies on principles of incompatibility, or non-equilibrium, existing between the continuous growth in the level of environmental complexity and the insufficient cognitive capacity of the organization to deal with higher levels of uncertainty, to operate in complex task environments, to attend new market demands, to manage new approaches to customers’ satisfaction and relationship, and to capture effectively information resources from the environment. Such a premise has motivated organizations to pursue higher degrees of cognition, intelligence, autonomy, and learning through principle s of organization de sign (Nobre, Tobias & Walker, 2009a, 2009b, 2009c, 2010; Nobre & Walker, 2011).
Therefore, this chapter focuses on the general picture of organizations pursuing high degrees of cognition in order to improve their capabilities of information processing and uncertainty management. It assumes that improvements in the degree of organizational cognition can lead the organization to achieve higher degrees of flexibility and agility, to operate through higher levels of mass customization (Pine, 1999), and to provide customers with immersiveness. In a broader sense, such improvements extend the capability of the organization to manage higher levels of environmental complexity. In such a context, flexibility means capability to reconfigure and to adapt to new operational and management conditions (Toni & Tonchia, 1998); and agility means the ability to manufacture a variety of products, services and goods, at low cost and in a short period of time (Lee, 1998).
This chapter supports existing works on manufacturing systems (Kusiak, 2000; Monfared & Steiner, 1997; Rao et al., 1993) and industrial organizations (Nobre et al, 2009a, 2009c), and additionally, it extends past and present concepts by proposing new technological, managerial and organizational capabilities which have to be developed in order to satisfy the requirements and to configure the new face of the industrial organization in the 21st century. F irst and foremost, this work aims to give insights and answers to the questions in the following whose responses are blended over this full chapter:
a. What is the nature of this new industrial organization?
b. What steps are required to design this new enterprise?
c. What would be the future of these organizations?
Chronologically, this work first introduces concepts of organizations and machines which are fundamental for the understating ofthis research. Such concepts comprise organizational cognition, intelligence, autonomy, and learning, along with uncertainty, environmental complexity, and cognitive machines.
Second, it proposes the concept and the features of Customer-Centric Systems (CCS) which were most developed through literature review and analyses of past and current industrial organizations as researched in (Nobre & Steiner, 2002; Nobre et al., 2009a, 2009c); whereas, in these works, the authors outlined the development of manufacturing systems and organizations, especially in the 20th century, through complementary perspectives of technology, management and organizational systems theory, respectively. As a result of the analyses, they indicate limitations of past and current manufacturing organizations which motivated them the proposal ofthe new frontiers, concept, and features of Customer-Centric Systems (CCS). CCS represent new organizing models of production that pursue high degrees of organizational cognition in order to manage high levels of environmental complexity, to operate through intensive mass customization processes, and to provide customers with immersiveness.
Third, from all these interdisciplinary backgrounds, this chapter mainly contributes by presenting the concept, structure and processes of Computational Organization Management Networks (COMN), which are new organizations with the capability to implement the features of Customer-Centric Systems (CCS). In COMN, cognitive machines and Cognitive Information Systems (CIS) are prominent actors ofgovernance, automation and control of the whole enterprise (Nobre et al, 2009a, 2009b, 2009c).
This subsection introduces the characteristics of Customer-Centric Systems (CCS) which concept was firstly touched in (Nobre & Steiner, 2002), and latterly it was further developed in (Nobre et al., 2009a, 2009c). Briefly, CCS represents organizational models with capabilities to:
1. Manage high levels of environmental complexity.
2. Operate through high levels of mass customization.
3. Pursue high degrees of organizational cognition, intelligence, autonomy, and learning, and consequently, high degrees of flexibility and agility.
4. And provide customers with immersiveness.
This chapter proposes that Customer-Centric Systems (CCS) are firm types which strategically organize their resources and competencies around customers’ values and needs, in order to involve customers into their business. By involving customers into their task environments and business, CCS-based organizations have the chance to understand and to produce the real needs, goods and services, to their clients.
For the purpose of this chapter, manufacturing organizations are synonymous with industrial organizations; which are classes of organizations that satisfy the concept of open-rational systems (Nobre et al., 2009a; Scott, 1998) and also the perspective of economic organizations (Milgrom & Roberts, 1992). They are highly formalized organizations that pursue specific goals, innovation and sustainable competitive advantage. They produce goods and services. The elements of the organization include goals, social structure, technology, and the participants in the organization (Scott, 1998: 17-23). Moreover, the organization exists in a physical, technological, cultural, and social environment with which the organization interacts (Scott, 1998: 21-23). Participants are the agents who act in the name of the organization and they subsume humans and cognitive machines (Nobre, 2008; Nobre et al., 2009a, 2009b). Technology expands what organizations can do and it supports the connection of the organization to the environment. Goals and sub-goals are what organizations aim to achieve in order to satisfy people’s desires. Social structure refers to the standards and regularized aspects of the relationships existing among the participants in the organization, whereas it comprises normative and behavioral parts (Scott, 1998).
Contingency theory (Galbraith, 1973, 1977, 2002) has defined uncertainty as the variable which makes the organization contingent upon the environment. Hence, organization design, and thus organizational choice, depends on the concept of uncertainty. Briefly, uncertainty can be associated with propositions of bounded rationality theory (Simon, 1982a, 1982b, 1997a, 1997b), when carrying the meaning of (Nobre et al., 2009a: Chapter 2):
a. Lack of information, which leads the organization to unpredictability of outcomes.
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Figure 1. Uncertainty as lack of information |
b. And, insufficiency of cognitive capacity for general information-processing.
The former, lack of information, means that:
• Definition 1: Uncertainty is the difference between the total amount of information that the organization needs to have in order to complete a task, and the amount of information in possession of the organization.
The latter, insufficiency of cognition, means that:
• Definition 2: Uncertainty is the difference between the degree of cognition that the organization needs to have in order to complete a task, and the degree of cognition in possession of the organization.
These two approaches to uncertainty are complementary to each other since the greater the amount of information that the organization needs to have in order to perform and to complete a task, the greater is the degree of cognition that the organization needs to have in order to process and to manage this information for task execution and completion. Figure 1 and Figure 2 illustrate such concepts ofuncertainty using symbolic scales of measurement.
Therefore, the question which rises in our quest is: what to do in order to manage the level of uncertainty that the organization confronts and navigates in? Organizational cognition has an important part into such a perspective and therefore it is introduced in the next subsection.
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Figure 2. Uncertainty as lack of cognition |
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From such a context, this chapter proposes new principles, concepts and features of Customer - Centric Systems (CCS) which configure the new face of the industrial organization in the 21st century. These organizations are emerging in order to pursue higher degrees of cognition and greater capabilities ofgeneral information processing and uncertainty management. |
Research on cognition in organizations has its roots in the publications of Simon (1947) on Administrative Behavior, and March and Simon (1958) on Organizations. In these publications, the organization was associated with information processing systems whose picture resembles a nexus of cognitive agents and processes organized through lateral and vertical relations. In this perspective, the organization benefits individuals and groups by extending their cognitive limitations to more advanced models of rationality (Simon, 1997a, 1997b). However, the meaning ofthis perspective has been separated by some researchers in two main streams: the computational and the interpretive approaches (Lant & Shapira, 2001). The computational approach investigates the processes by which the organization manipulates information, and it associates the organization with information processing systems. In such a stream, the emphasis is on information and efficiency. This approach is grounded in cognitive psychology, cognitive science and artificial intelligence. The interpretive approach examines how meaning is created around information in a social context, and it is related to social collectives and knowledge systems. In such a stream, the focus is on knowledge and collectivities. This approach has been grounded in the sociology of knowledge, social psychology of organizations,
social cognition, and, most recently, in knowledge management and organizational learning, whereas this latter subject has also been associated with processes for creating, retaining and transferring knowledge in organizations (Argote, 2007).
Most of the perspectives on organizational cognition are placed somewhere in the continuous between such computational and interpretive approaches. In this chapter, the authors give special attention to the computational perspective and they use the metaphor of the organization as information processing systems.
In such a perspective, organizational cognition is concerned with the processes which provide agents and organizations with the ability to learn, to make decisions and to solve problems. The main agents of organizational cognition are the participants within the organization and the social networks which they form. In organizations, cognitive processes are supported by their goals, technology and social structure. Moreover, organizational cognition is also influenced by inter-organizational processes and thus by the environment. Therefore, the choice of the organization elements (participants, technology, goals, and social structure), and thus organizational design (Galbraith, 2002), plays a fundamental task in organizational cognition. The cognition of the organization can be represented as a matter of degree whose level depends on the choice of the organization elements. A borader review on organizational cognition is presented in (Nobre et al, 2010; Nobre & Walker, 2011).
In a first perspective, researchers in the field of organizational cognition have associated the concept of cognitive complexity with the degree or level of elaboration in which people, groups and organizations perceive their environment and construct their cognitive maps. In such a case, the degree or level of cognitive complexity can be attributed to the number of hierarchical or vertical levels (or deepness) and the number of horizontal constructs which are integrated into a cognitive map (Calori, Johnson & Sarnin, 1994; Nasser-Carvalho, 2004, 2005); whereas in this association, cognitive maps are viewed as systems (Hall & Fagen, 1956).
In a second perspective, cognitive complexity can also be associated with the concept of degree of cognition in the organization or degree of organizational cognition as introduced in (Nobre et al., 2009a, 2010); whereas degree of cognition can be symbolically associated with tangible and intangible measures of processes and representations. Through the participant observation approach, Nobre et al. (2009a: 113-162) presented a case study about an international telecommunications and software business corporation, where they associated the degree of cognition in the organization with levels of organizational process maturity and performance, along with organizational learning results. In a macro view, the level ofthe organization’s process maturity was defined by the level of elaboration, integration and specification of the technical, managerial and organizational processes, routines and norms (Nobre et al, 2009a: 122-132), which were most based on the Capability Maturity Model (CMM) policies, recommendations and guidelines for software process improvement (Paulk, Weber, Curtis & Chrissis, 1994). In this macro part, the degree of cognition in the organization could be associated with one of the five CMM maturity levels. In a micro view, the organization’s process performance was associated to concepts and measures of customer satisfaction and process quality, whereas these concepts were first socially constructed by a group of software project management and engineering experts in the corporation of study; second, these concepts were explicitly represented through mental models described by IF-THEN linguistic rules; and third, these concepts were mapped into a two-dimensional linguistic phase-plane which indicated the implications ofantecedents (i. e. independent constructs or variables) to the consequents of customer satisfaction and process quality (i. e. dependent constructs or variables). In this micro part, the organization’s process performance was associated to a set of quantitative indexes about customer satisfaction and process quality which were calculated through the computational modeling and simulation of the IF-THEN linguistic rules. Through these approaches, the authors achieved qualitative analyses and quantitative measurements which indicated that improvements in the levels of organization process maturity and performance were associated with improvements in the degree of organizational cognition; and also that improvements in organizational learning could be associated with improvements in the degree organizational cognition.
Similar methods have been adopted by other researchers who associated organizational performance and productivity gains with practices of organizational learning (Argote, 2007).
Therefore, in this chapter, the concept of degree of organizational cognition can be understood as synonymous with cognitive complexity at the organizational level of analysis. In such a case, degree of organizational cognition involves a whole picture about the cognitive processes and representations at the organizational level, and this macro picture is greater than the sum of the individual cognitions.
Human vs. Organizational Cognition
Organisms ofthe ecological system have evolved and improved their abilities and mechanisms for fitness and adaptation in the environment. Among such organisms, the human being is the specie that has found the highest probability to survive, to reproduce, and to continue evolving and developing. Such a predominance of humans is a particular privilege provided by the evolution oftheir brain, emotional, and cognitive processes (Heyes & Huber, 2000; Simon, 1983). Among the results of such a continuous evolutionary path are their abilities to search information, to organize knowledge, to make decisions, to learn, and to solve complex problems. Humans adapt to the environment, and they also change the environment to their own needs. In such a continuum, humans have been transferring some of their abilities to systems, and most important, to machines and organizations (Nobre et al., 2009a, 2009b). Certainly, one of the main rationales for organizing can be explained by the perspective that organizations benefit individuals and groups by extending their cognitive, physical, temporal, institutional, and spatial limitations (Carley & Gasser, 1999).
In such a perspective, while human cognition is part of a natural system, cognition in organizations is part of a symbiosis between natural (human) and artificial systems because it involves the art of design (Simon, 1996). Therefore, the cognitive ability in the organization can be changed and improved through processes of organization change and design. Hence, the degree of cognition in the organization is contingent upon the goals, the social structure, the participants, the technology and the environment of the organization.
Organizational Intelligence, Autonomy, Learning, and Complexity
Like organizational cognition, definitions of organizational intelligence, autonomy, learning, and complexity are proposed in (Nobre et al, 2009a, 2010; Nobre & Walker, 2011). Nevertheless, they are briefly defined in this subsection.
Intelligence is a general mental ability (Schmidt & Hunter, 2000), which depends on rational and emotional processes (Goleman, 1994). Rational process or rationality is the ability to follow procedures for decision making and problem solving in the pursuit of goals (Simon, 1997a). When rational processes lead individuals to satisfactory (satisfice) outcomes, rationality can be associated with intelligence. Emotional process (Scherer, 1982) is less procedural than rationality and it is less purposeful in the context of achieving goals. However, researchers have shown that emotions play an important part to motivate, to direct, and to regulate actions in the service of goal pursuit (Bagozzi, 1998; Keltner & Gross, 1999; Keltner & Haidt, 1999). When emotional processes lead individuals to excel in life, emotion can be associated with intelligence. Complementarily, while emotion influences cognitive processes such as attention, learning, decision making, and problem solving (Goleman, 1994), cognition is in the service of emotion when interpreting stimuli (Plutchik, 1982) and regulating emotional processes and states. Therefore, intelligence, and in particular, intelligent behavior, depends on cognitive and emotional processes.
Organizational intelligence can also be associated with degrees of intelligence in the organization. However, while organizational cognitions are associated with cognitive processes and representations in the organization, organizational intelligence is associated with the degree in which the organization satisfy or satisfice (Simon, 1997b) its goals and sub-goals. Therefore, the greater the degree of cognition in the organization, the greater is its chance to exhibit intelligent behavior (Nobre et al, 2009a, 2010).
Autonomy is the ability of individuals, groups, and organizations to act through the use of cognition. Autonomous organisms are continuously in the pursuit of intellectual independence and therefore they are continuously attempting to improve their cognitive abilities. Similarly to cognition and intelligence, autonomy is a matter of degree. The degree of autonomy of individuals, groups and organizations improves as much as they interact with the environment by capturing, processing, creating, storing, exchanging and managing new resources. In such a view, organizations with higher degrees of cognition have higher degrees of autonomy (Nobre et al., 2009a, 2010).
Organizational learning has been associated with the creation and management of knowledge in organizations (Argote, 2007; Dierkes, Antal, Child & Nonaka, 2003). In psychology research, learning is the process of making changes in the individuals’ mind and behavior through experiences along with cognitive, emotional, and environmental influences (Bernstein, Penner, Clarke-Stewart & Roy, 2008; Illeris, 2007; Lefran^oies, 1995; Minsky, 1986; Reed, 1988). In such a process, learning involves acquiring, enhancing, or making changes in one’s knowledge, skills, values, and world views. This work supports this definition and it puts forward the perspective that organizational learning is the process of making changes in the organization’s elements (goals, social structure, technology, and participants) and behavior through experience, cognition, emotion, and environmental influences, for the organization benefits. Such a perspective implies relations on the effect of organizational learning on organizational cognition, and vice-versa. On one hand, it is plausible to say that organizational learning affects organizational cognition, and more specifically, the degree of organizational cognition, by changing cognitive processes and representations in the organization. On the other hand, it is also plausible to state that organizational learning depends on organizational cognition, and more specifically, on cognitive pro - cesses and representations, for the corroboration of change, and for the creation and management of knowledge in the organization. The process of change in the organization follows mechanisms and models which are mostly based on principles of feedback control, adaptive and learning systems originated in the broad fields of cybernetics and general systems theory (Ashby, 1968; Bertalanffy, 1968; Buckley, 1968; Wiener, 1961). Well-known models of organizational learning include singleloop and double-loop types (Argyris & Schon, 1978) along with meta-learning which concept was introduced in Biggs (1985) to describe the state of being aware of and taking control of one’s own learning. Further studies on the concept of meta-learning and its distinction from deuteron and planned-learning are discussed in Visser (2007); and the use of organizational meta-learning for the construct of dynamic core competencies is presented in (Lei, Hitt & Bettis, 1996).
In such a view, cognition is what provides individuals, groups and organizations with the ability to learn. Therefore, organizations with higher degrees of cognition have higher capacity or degree of learning (Nobre et al., 2009a; 2010).
This chapter defines the level of complexity of the organization as contingent upon its degree of cognition. Therefore, the complexity of organizations are synonymous with their cognitions which are processes used to solve complex tasks. Hence, the greater the degree of cognition of the organization, the greater is its ability to solve complex tasks (Nobre et al., 2010).
Environmental Uncertainty and Complexity
Environmental uncertainty can be associated with the level of uncertainty that the organization, groups and participants perceive or sense from the environment (Ducan, 1972). The complexity of the environment is contingent upon the level of uncertainty that it represents to the organization. Similarly, the complexity of a task environment is contingent upon the level of uncertainty that it represents to the organization during task execution and completion. Therefore, it can be asserted that the greater the level of environmental complexity, the greater is the level of environmental uncertainty that the organization confronts and needs to manage.
Initial lines of contribution on the perspectives of cognitive machines in organizations were first touched in (Nobre, 2008; Nobre et al., 2009a, 2009b).
Cognitive machines are information processing and knowledge management systems which unify computational and cognitive strengths of humans and computers. They are necessary when we need to extend the reasoning or mental capacity of humans, groups and organizations to more advanced models of cognition. Cognitive machines are agents whose processes of functioning are mainly inspired by human cognition. Therefore, they have great possibilities to present intelligent behavior. When participating in organizations, cognitive machines are agents of organizational cognition and they contribute to improve the degree of cognition, intelligence, autonomy, and learning of the organization. Intensive and extensive research on the design and analysis of cognitive machines in organizations is proposed in (Nobre et al., 2009a, 2009b). The design of cognitive machines comprises theories of cognition and information - processing systems, and also the mathematical and theoretical background of Fuzzy Systems (FS), Computing with Words (CW) and Computation Theory ofPerceptions (CTP) (Zadeh, 1973, 1999, 2001). This class of machines has the capabilities to carry out complex cognitive tasks in organizations, and in particular the tasks which involve representation and organization ofknowledge via concept identification and categorization along with the manipulation of perceptions (percept), concepts and mental models. The ability of these machines to manipulate complex symbols described in the form of words and sentences of natural language provide s them with higher levels of information-processing than other symbolic - processing machines; and according to the theory of levels of processing in cognition (Reed, 1988) these machines can mimic, even through simple models, cognitive processes of humans.
Similarly to the definitions of organization intelligence, autonomy, learning, and complexity, it can be stated that the greater the degree of cognition of the machine, the greater is its chance to present intelligent behavior; the greater is its autonomy; and the greater is its ability to learn and to solve complex tasks.
The concept of cognitive machines plays an important role in the organizations proposed in this research. These machines participate in the organization and they provide the organization with higher degrees of cognition, intelligence, autonomy, and learning as investigated in (Nobre et al, 2009a, 2009b).
The next section demonstrates the application of some of the new features of Customer-Centric Systems and it enhances the roles of cognitive machines, Cognitive Information Systems (CIS) along with the concept of immersiveness in the new Computational Organization Management Networks (COMN).
COMPUTATIONAL ORGANIZATION MANAGEMENT NETWORKS (COMN)
This section introduces a new kind of organization that implements the main features of Customer - Centric Systems. It contributes by presenting the definition, the structure and the processes of Computational Organization Management Networks (COMN) as proposed in (Nobre et al, 2009a). COMN are new organizations whose principles of operation are based on the concepts of Hierarchic Cognitive Systems (Nobre, 2008) along with those of Telecommunications Management Networks (ITU-T, 2000). Structured with functional layers and cognitive roles which range from technical and managerial to institutional levels of analysis, and also equipped with operational, managerial and strategic processes, the concept of Computational Organization Management Networks (COMN) plays an important part in the developments of future organizations where cognitive machines and Cognitive Information Systems (CIS) are prominent actors of governance, automation and control of the whole enterprise. Moreover, this section introduces the concept of immersive systems in order to provide the new organization with the capability of immersiveness.
The Scope of the New Organization
Computational Organization Management Networks (COMN) fall in the class of organizations that pursue high degrees of organizational cognition, intelligence, autonomy, and learning, and consequently, high degrees of agility and flexibility, in order to manage high levels of environmental complexity, to operate through intensive mass customization, and to provide customers with immersiveness (Nobre et al., 2008, 2009a).
This chapter advocates that such a kind of new organization has to be equipped with high levels of automation in order to pursue the necessary capabilities to govern, to coordinate and to control cognitive tasks of technical, managerial and institutional levels in the whole enterprise. Hence, it focuses attention to the conception of organizations ofthis type. Therefore, the creation of COMN requires intensive investments in information technology, artificial intelligence and knowledge management systems. This section shows the steps of design of such new organizations.
Cognitive Information Systems (CIS)
The processes with the new organization are managed by Cognitive Information Systems (CIS):
• Definition 3: Cognitive Information
Systems (CIS) are Knowledge Management Systems (KMS) that pursue high degrees of cognition, intelligence, autonomy, and learning. They are particular classes of cognitive machines, and they are designed to participate in the organization by performing cognitive tasks of all levels and by fulfilling managerial roles in all the layers of the whole enterprise (Nobre et al, 2008, 2009a, 2010).
Participation of CIS in the Organization
Cognitive Information Systems (CIS) participate in the organization by performing cognitive tasks and by fulfilling roles of technical, managerial, and institutional levels. From this point of view, this chapter identifies four major areas of CIS application in the whole enterprise. These areas are classified into four organizational layers:
a. Element Layer: The operational level
b. Network Management Layer: The primary managerial level
c. Service Management Layer: The secondary managerial level
d. Business Layer: The strategic level
Functional Layers of the New Organization: Steps of Design
Functional layers play the fundamental part in the definition of the structure and processes for the new organization of COMN. Their concepts are based on the definition of Hierarchic Cognitive Systems (HCS) as introduced in (Nobre, 2008) along with the principles of Telecommunications Management Networks (TMN) architectures which have been proposed by International Telecommunication Union (ITU-T); where ITU-T is the designation ofthe United Nations Specialized Agency in the field oftelecommunications (ITU-T,
2000) . In the organizational architectures ofTMN, agents execute tasks in all hierarchical layers of the organization. Similarly, agent technology (Bradshaw, 1997; Watt, 1997) plays important tasks in the functional layers ofthe new organization of COMN; where in this chapter, agents are also synonymous with cognitive machines and Cognitive Information Systems (CIS).
This subsection proposes four functional layers for the new organization. It also introduces the roles of the agents that participate in the COMN by governing, controlling and coordinating cognitive tasks of all levels in all the layers of the whole enterprise.
Step 1: CIS in the Element Layer: The Operational Level
The Element Layer (EL) comprises a Network Element Layer (NEL) and an Element Network Layer (ENL). The former part (NEL) comprises functional elements that work upon an individual basis, and, therefore, each individual element carries its own motives and fulfils micro-roles. The latter part (ENL) comprises a set of interconnected functional elements that work in group, and, therefore, they carry common motives and sub-goals, and they also fulfill micro-roles. In this kind of organization, an element is synonymous with an agent, and an agent is synonymous with a cognitive machine; and thus, a group of interconnected elements is synonymous with a group of agents that has the same meaning of a group of interconnected cognitive machines. Figure 3 illustrates the two parts of an Element Layer (EL), where a0 n) denotes agents, for n integer.
The roles of Cognitive Information Systems (CIS) in the Element Layer (EL) are concerned with the execution of cognitive tasks for operation, control and coordination of individual elements as well as of groups of interconnected elements. These elements, as individuals and groups, participate in the whole organization by performing cognitive tasks of technical, managerial, and institutional levels. Therefore, in this particular case, the CIS provide operational, control and coordi - native processes to individual agents and group of agents that participate in the organization.
The Element Layer (EL) demands high degrees of cognition, intelligence, autonomy, and learning from the individual machines as well as from the groups of machines. For these requests, the technology of cognitive machines, along with the methodologies of Soft Computing (SC) (Zadeh, 1994), Fuzzy Logic (FL) (Zadeh, 1973), Computing with Words (CW) (Zadeh, 1999), and Computational Theory of Perceptions (CTP) (Zadeh,
2001) , play an important part in the conception of Cognitive Information Systems (CIS).
Applications at the level of Element Layer (EL) have received some attention, for instance,
Figure 3. NEL as a controller of individual agents a(1 n} and ENL as a controller of a group of integrated agents
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Ґ NEL ) |
С ENL j |
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(аГ) (jiT) (aT) |
0*0 (a0~CaP |
by researchers who have developed information and decision-support systems for manufacturing operations through the background of fuzzy logic, neural networks and genetic algorithms (Kusiak, 2000; Monfared & Steiner, 1997; Rao et al., 1993; Wu, 1994). Nevertheless, despite achieving some successful results, these managerial and decision - support tools of mathematical and computational background have been constrained by the limitations of cognition, intelligence, autonomy, and learning of the existing machines which are mostly encountered in the organizations of today. The application of these machines in Flexible Manufacturing Cells and Systems (FMS) and their coordination through Computer Integrated Manufacturing (CIM) technology, have reached thresholds and limitations of contributions because of their insufficient degrees of cognition, intelligence, autonomy, and learning (Nobre et al., 2009a, 2009b).
Step 2: CIS in the Network Management Layer: The Primary Managerial Level
The united work of individual agents and groups of agents in the Element Layer (EL) forms a set of patterns or clusters which represent the main macro-roles in the organization. Each pattern or cluster is synonymous with a functional network.
The Network Management Layer (NML) comprises the set of individual functional networks in the organization; and it is equipped with an organizing system constituted by normative structure, processes, technologies, agents and sub-goals, in order to provide management to each functional network upon an individual basis. Therefore, the NML provides the individual functional networks ofthe organization with coordination, control and management of processes, operations and information that flows through the clusters of agents and groups of agents that participate in the whole enterprise. Figure 4 illustrates a NML managing individual Functional Networks FN,, ,, for m
(1...m)’
integer.
The roles of Cognitive Information Systems (CIS) in the Network Management Layer (NML) is concerned with the effective and efficient use of the NML’s organizing system resources in order to execute cognitive tasks for coordination, control and management of the functional networks upon an individual basis; where, in this case, a functional network is synonymous with a network of agents and also with a network of cognitive machines. In such a perspective, functional networks (and thus networks of cognitive machines) participate in the organization by performing cognitive tasks of technical, managerial and institutional levels; and they fulfill operational, management and strategic roles in the whole enterprise.
It is important to emphasize that while Cognitive Information Systems (CIS) participate in the Network Management Layer (NML) by managing each individual functional network in the organization, they participate in the Element Layer (EL) by operating and controlling individual agents and groups of agents that participate in the functional networks ofthe organization. Therefore, the NML comprises the management of the EL in the organization.
The performance of managerial roles in the organization is contingent upon the capabilities of the managers and also upon the capabilities of the individuals and groups that the managers supervise. Therefore, it can be stated that the higher
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Figure 4. NML as the manager of individual |
the degree of cognition of Cognitive Information Systems (CIS), the higher is their capability to manage Functional Networks (FN) in the organization; and that the higher the degree of cognition of the elements of a Functional Network (FN), the higher is the capability of CIS to manage the FN.
Step 3: CIS in the Service Management Layer: The Secondary Managerial Level
The set of functional networks in the organization forms vertical and horizontal processes and involves sub-goals and goals, where sub-goals represent means for the achievement of more complex goals. Therefore, a managerial system is needed in order to coordinate, to control and to mediate all the operations, processes and information that flow between the functional networks in the organization.
The Service Management Layer (SML) comprises the set of functional networks in the organization; and it is equipped with an organizing system constituted by normative structure, processes, technologies, agents, goals and subgoals, in order to provide management for the set of functional networks. Therefore, the SML provides the organization with a managerial system with the capability to coordinate, to control, to integrate, and to mediate all the operations, processes and information that flows between the functional networks in the whole enterprise. Figure 5 illustrates an SML managing a set of integrated Functional Networks FN(1
The roles of Cognitive Information Systems (CIS) in the Service Management Layer (SML) is concerned with the effective and efficient use of the SML’s organizing system resources in order to execute cognitive tasks of integration, coordination, control and thus management of the relations, operations, processes and information that flows through and between the functional networks in the organization; where, in this case, the set of functional networks is synonymous with the set of networks of agents and consequently with the
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Figure 5. SML as the manager ofintegrated FN^ m) |
set of networks of cognitive machines in the organization. In such a domain, each functional network can be synonymous with a cluster of services, or in short, a service. Therefore, the Cognitive Information Systems (CIS) in the Service Management Layer (SML) can also be viewed as agents of management of the whole services in the organization.
It is important to emphasize that while CIS participate in the Service Management Layer (SML) by managing the operations, processes and information between all functional networks in the organization, they participate in the Network Management Layer (NML) by managing each functional network upon an individual basis. Therefore, the SML comprises the management of the NML in the organization.
Applications at the SML and NML have received some contributions with the advances in Enterprise Resources Planning and Management Systems (EPR) that emerged from the 1970’s. ERP are classes of information technology and management systems which are applied to, and implemented in the whole organization with the purposes of integration, control and automation of data, information and processes. Examples of areas of application of ERP systems include: Manufacturing, Supply Chain, Financials, Customer Relationship Management (CRM), Human Resources, Warehouse Management and Decision Support System. Applications in the level of the Service Management Layer (SML) will receive greater contributions in the proportion of the continuous advancements in Cognitive Information Systems (CIS) of high degrees of cognition, intelligence, autonomy, and learning; and thus CIS will play an important role in the SML of new organizations.
Step 4: CIS in the Business Management Layer: The Strategic Level
The Business Management Layer (BML) comprises all the operations, management processes, strategies and services of the previous layers (i. e. the EL, NML and SML respectively); and it is equipped with an organizing system constituted by normative structure, processes, technologies, agents and goals, in order to provide the organization with capabilities to manage the environment. More specifically, the BML provides the enterprise with a managerial system with the capability to coordinate, to control and to mediate the operations, processes and information between the organization and the environment. Figure 6 illustrates the role of the BML in the organization.
The roles of Cognitive Information Systems (CIS) in the Business Management Layer (BML) are less obvious and less present in the organizations of today. It is concerned with the effective and efficient use of the BML’s organizing system resources in order to execute cognitive tasks for coordination, control and thus management ofthe relations, operations, processes and information in between the organization and the environment. To enhance this application, this chapter presents the concept of immersiveness which idea was first spoken in (Nobre & Steiner, 2002), and further developed in (Nobre et al., 2009a).
It was stated in this research that organizations have to be equipped with structure, processes, goals, agents and technologies which are able to provide them with the capability to pursue high levels of immersiveness, where:
• Definition 4: Immersiveness represents the ability of the organization to interact with agents of the market (either humans or machines) in a friendly way, by immersing them into the organization’s operations through approaches such as virtual reality, simulation or via real world protocols; and it aims to satisfy customers by capturing their exact needs, by customizing and managing the design, engineering and production of their goods and services, and by delivering their products with efficacy and efficiency.
More specifically, either manufacturing or service organizations, they can immerse their customers by providing them with the scope to interact with some of the life cycle stages of their processes of design, engineering and production, including those processes of requirements analysis, product design, test, prototyping, demand specification, volume and variety choice. Under this perspective, virtual reality will play an important task in the customer immersiveness; the technologies of cognitive information systems and cognitive machines will provide important contributions in the execution of cognitive tasks such as pattern recognition and vision, natural language processing, decision-making, problem-
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Figure 6. BML as the manager that mediates between the organization and the environment |
solving, learning, and management; additionally, the internet will play an important part in the connection of customers into the new organization. This perspective is illustrated in Figure 7 and it is assumed that such an illustrative immersive system can be configured to provide customers with different levels of access and interaction to the technical and managerial operations of the processes of design, engineering and production in the organization. The dotted lines symbolize the internet which connects customers within the organization; and the continuous lines denote the system operational levels that clients can interact with, in order to capture customers’ exact needs and even emotions, to customize and to manage the design, engineering and production of their goods and services.
• Definition 5: Computational Organization Management Networks (COMN) are organizations whose structure, processes, participants, goals and technologies are designed according to the concepts of Functional Layers which include Element Layer, Network Management Layer, Service Management Layer and Business Management Layer. COMN pursue high degrees of organizational cognition and their main participants subsume Cognitive Information Systems (CIS) and cognitive machines.
Structure and Processes of COMN
Figure 8 illustrates the structure of Computational Organization Management Networks (COMN) which is composed by Element Layer (EL), Network Management Layer (NML), Service Management Layer (SML) and Business Management Layer (BML) respectively.
Figure 7. Illustration of an immersive system
Customers
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_ / Payment / 7 Delivery |
Requirement Analysis / Design |
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Prototype / Production |
Test / J Simulation /— |
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Life Cycle Stages |
This chapter is the result of the analyses of past and current manufacturing organizations through three complementary perspectives of technology, management and organizational systems theory, as researched in (Nobre et al, 2009a, 2009c); whereas it was found that the convergence of manufacturing organizations to the new features of Customer-Centric Systems (CCS) is contingent upon the continuous growth in the level of environmental complexity. The chapter emphasized that Customer-Centric Systems (CCS) configure the new technological, managerial and organizational faces which industrial organizations need to have if they want to manage higher levels of environmental complexity in the 21st century.
The contributions proposed in this research were motivated by the principle of incompatibility, and the non-equilibrium state, existing between the continuous growths in the level of environmental complexity and the insufficient cognitive capacity of current manufacturing organizations. Therefore, this chapter focused on the general picture of organizations pursuing high degrees of cognition in order to improve their capabilities for information processing and uncertainty management. It assumed that improvements in the degree of organizational cognition can lead the organization to achieve higher degrees of flexibility and agility, to operate through higher levels of mass customization, and to provide customers with immersiveness. In its broader sense, it assumed that such improvements can extend the capability of the organization to manage higher levels of environmental complexity. In such a context, this chapter contributed by presenting the concepts of Customer-Centric Systems (CCS) and Computational Organizational Management Networks (COMN). COMN are new computational organizing models with the capability to implement the features of CCS.
The main contributions and further research are highlighted in the next paragraphs.
On Cognitive Machines, Organizations and the Environment
Cognitive machines are agents of organizational cognition and they contribute to improve the degree of cognition of the organization. Consequently, improvement in the degree of organizational cognition contributes to manage the level of environmental complexity and uncertainty that the organization confronts.
On Computational Organization Management Networks
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Business Management Layer BML (Strategic Level) |
Management and mediation between the organization and the environment |
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Service Management Layer SML (Secondary Managerial Level) |
Management and mediation between the organization's functional networks |
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Network Management Layer NML (Primary Managerial Level) |
Management of individual functional networks |
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Element Layer EL (Operational Level) |
Operation and control of individuals |
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Figure 8. Structure of computational organization management networks (COMN) |
Computational Organization Management Networks (COMN) implements the new features of Customer-Centric Systems. COMN are organizations whose structure, processes, participants, goals and technologies are designed according to the concepts of Functional Layers which comprise Element Layer, Network Management Layer, Service Management Layer and Business Management Layer. COMN pursue high degrees of organizational cognition and their main partici
pants comprise Cognitive Information Systems (CIS) and cognitive machines.
Such a kind of new enterprise will play a fundamental part in the processes of engineering, production, logistics and management of goods and services along with the processes of management of transactions, business and electronic commerce in the future organizations and markets. According to Nobre et al. (2009a), COMN will be legally supported with nexus of contracts that assign the responsibilities to, and define agreements between, the organization and the designer of the cognitive machines (and cognitive information systems) which are the main participants in the layers of the whole organization. The roles of these new participants will be defined in the normative structure of the organization.
The creation of COMN requires intensive investments in information technology, artificial intelligence and knowledge management systems. This chapter introduced the steps of design of such new organizations.
Nevertheless, some implications of Computational Organizational Management Networks (COMN) must be further investigated. COMN may also be used by some corporations and power-holders for their own benefits, who want to reinforce and to continue supporting the contemporary society, and a political and economic model of maximization of production and consumption which has generated cultural alienation and intense materialism. These have, in turn, destroyed environmental resources and eroded the values and social conditions of humanity.
On Cognitive Machines and Emotions
The topic of machines with emotions and emotional processes in organizations was left for further research. However, it deserves some comments due to its importance in the literature. Whether machines should exhibit emotional behavior, and whether they are able to have emotions or not, are controversial topics among the researchers of artificial intelligence, cognition and social sciences.
By assuming that machines may indeed be able to have emotional processes and emotional behavior, the question ofwhether emotions are important to machines or not depends on the motivations of their designers and upon the environment with which they relate. On the one hand, machines with emotions, or emotional machines, might form better relations and social networks with humans in organizations than other machines. In such a view, machine emotion would be relevant for researchers on organizational behavior. On the other hand, machines with emotions might have their own motives and might represent additional agents of dysfunctional conflicts in organizations. In such a view, machine emotion would be a problem for researchers of rational theories. Among the institutions which have been researching the field of emotional machines include The MIT Artificial Intelligence Laboratory at Massachusetts (Breazeal, 2000).
On Cognitive Machines vs. Humans in Organizations
Are cognitive machines better agents of organizational cognition and organizational learning than humans? Are they better agents of organization performance and productivity than humans? Such questions rely on the statement that: if we assume that the cognitive roles in organizations have performance and outcomes which can be attributed to either humans or machines, without any distinction, then we are ready to consider machines as participants within the organization similarly to people. This perspective involves a rational comparison between machines and human’s performance if we assume that they compete for the same roles in the organization. Such questions need to be further investigated in order to derive conclusions about the economic, political, social and technological implications of cognitive machines for the society (Nobre et al, 2009a).
Challenges and the Future of the Industrial Organization
While the characteristics of the elements of the organization will change, evolve and develop continuously towards higher levels of cognition and complexity, the purpose of existence of the organization will remain the same or will not change in the same proportion of its elements (Nobre et al., 2009a). The former part, which is concerned with the elements of the organization, will move towards high levels of automation, and it will include machines with high degrees of cognition, mainly in those areas at upper layers and levels of the organization; and thus they will provide organizations with more capabilities of computational capacity along with knowledge and uncertainty management. Therefore, new organizations of this kind will be able to operate in, and to manage higher levels of environmental complexity and uncertainty than organizations of today. These transformations towards new organizations will have implications for the society and this is a topic of further research (Nobre et al, 2008, 2009a, 2009b). The latter part, which is concerned with the purpose and the existence of organizations, will remain the same and for sure will not change in the same proportions to the evolutions in the organization elements. This is because the individual motives and the organizational goals which are pursued by human kind will not change over time into the political, economical and social facets of this society.
One day, perhaps not so far in the 21st century, worldwide organizations and their executives will have the ability to perceive, to sense, to decide and to act based on new models of organizing and management thought which are grounded in concepts of systemic sustainability; whereas these new models should require the reconciliation of environmental, social and economic demands—the “three pillars” of sustainability. It is in such a new context that organizations and their participants will be challenged to decide on whether they are ready to create competitive advantage without affecting the balance and equilibrium of such a triad. It raises the question about the endurance and survival of the human species.
