Lourdes Sáiz

DIPKIP: A CONNECTIONIST KNOWLEDGE MANAGEMENT SYSTEM TO IDENTIFY KNOWLEDGE DEFICITS IN PRACTICAL CASES

This study presents a novel, multidisciplinary research project entitled DIPKIP (data acquisition, intelligent processing, knowledge identification and proposal), which is a Knowledge Management (KM) system that profiles the KM status of a company. Qualitative data is fed into the system that allows it not only to assess the KM situation in the company in a straightforward and intuitive manner, but also to propose corrective actions to improve that situation. DIPKIP is based on four separate steps. An initial “Data Acquisition” step, in which key data is captured, is followed by an “Intelligent Processing” step, using neural projection architectures. Subsequently, the “Knowledge Identification” step catalogues the company into three categories, which define a set of possible theoretical strategic knowledge situations: knowledge deficit, partial knowledge deficit, and no knowledge deficit. Finally, a “Proposal” step is performed, in which the “knowledge processes”—creation/acquisition, transference/distribution, and putting into practice/updating—are appraised to arrive at a coherent recommendation. The knowledge updating process (increasing the knowledge held and removing obsolete knowledge) is in itself a novel contribution. DIPKIP may be applied as a decision support system, which, under the supervision of a KM expert, can provide useful and practical proposals to senior management for the improvement of KM, leading to flexibility, cost savings, and greater competitiveness. The research also analyses the future for powerful neural projection models in the emerging field of KM by reviewing a variety of robust unsupervised projection architectures, all of which are used to visualize the intrinsic structure of high‐dimensional data sets. The main projection architecture in this research, known as Cooperative Maximum‐Likelihood Hebbian Learning (CMLHL), manages to capture a degree of KM topological ordering based on the application of cooperative lateral connections. The results of two real‐life case studies in very different industrial sectors corroborated the relevance and viability of the DIPKIP system and the concepts upon which it is founded.

Constructing a Global and Integral Model of Business Management Using a CBR System

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system, which incorporates a novel clustering and retrieval method, has been developed for identifying critical situations in business processes. The proposed method is based on a Cooperative Maximum Likelihood Hebbian Learning model, which can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the model tries to generalise the hypothesis of organizational survival and competitiveness, so that the organisation that is able to identify, strengthen, and use key knowledge will reach a pole position.

Development of a Global and Integral Model of Business Management Using an Unsupervised Model

In this paper, we use a recent artificial neural architecture called Cooperative Maximum Likelihood Hebbian Learning (CMLHL) in order to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. We apply Maximum Likelihood Hebbian learning to an extension of a negative feedback network characterised by the use of lateral connections on the output layer. These lateral connections have been derived from the Rectified Gaussian distribution. This technique is used as a tool to develop a part of a Global and Integral Model of business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the model tries to generalise the hypothesis of organizational survival and competitiveness, so that the organisation that is able to identify, strengthen, and use key knowledge will reach a pole position.

Predisposition of Workers to Share Knowledge: An Empirical Study

Our objective is to determine the factors and obstacles that either contribute or complicate knowledge exchange between workers. This research area is new in Spain, as only three references have been found for firms in China and North America. In our approach to this topic, we apply concepts of a sociological, psychological, and motivational nature, which affect knowledge exchange and sharing and allow us to justify the theoretical basis of this study, as well as its purpose and its organizational benefits. A detailed survey was prepared for the empirical research with 21 questions given to a sample of 557 workers from firms in Burgos. Among the positive factors that contribute to knowledge sharing, the results highlight recognition and an appreciation of the worker’s contribution, the work environment and reciprocity. The most important barriers are the poor quality of employment contracts, fellow workers that do not wish to learn, and unfair and disloyal behaviour.

Analyzing key factors of human resources management

This study presents the application of an unsupervised neural projection model for the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. This work examines the critical role that the acquisition and retention of specialized employees play in Hi-tech companies, particularly following the configuration approach of Strategic HR Management. From the projections obtained through the connectionist models, experts in the field may extract conclusions related to some key factors of the HR Management. One of the main goals is to deploy improvement and efficiency actions in the implantation and execution of the HR practices in firms. The proposal is validated by means of an empirical study on a real case study related to the Spanish Hi-tech sector.

Hybrid Visualization for Deep Insight into Knowledge Retention in Firms

Neural projection models are applied in this study to the analysis of Human Resources (HR) from a Knowledge Management (KM) standpoint. More precisely, data projections are combined with the glyph metaphor to analyse KM data and to gain deeper insight into patterns of knowledge retention. Following a preliminary study, the retention of specialized employees in hi-tech companies is investigated, by applying the configurational approach of Strategic HR Management. The combination of these two aforementioned techniques generates meaningful conclusions and the proposal is validated by means of an empirical study on a real case study related to the Spanish hi-tech sector.

A Hybrid Solution for Advice in the Knowledge Management Field

This paper presents a hybrid artificial intelligent solution that helps to automatically generate proposals, aimed at improving the internal states of organization units from a Knowledge Management (KM) point of view. This solution is based on the combination of the Case-Based Reasoning (CBR) and connectionist paradigms. The required outcome consists of customized solutions for different areas of expertise related to the organization units, once a lack of knowledge in any of those has been identified. On the other hand, the system is fed with KM data collected at the organization and unit contexts. This solution has been integrated in a KM system that additionally profiles the KM status of the whole organization.

A beta-cooperative CBR system for constructing a business management model

Knowledge has become the most strategic resource in the new business environment. A case-based reasoning system has been developed for identifying critical situations in business processes. The CBR system can be used to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This technique is used as a tool to develop a part of a Global and Integral Model of Business Management, which brings about a global improvement in the firm, adding value, flexibility and competitiveness. From this perspective, the data mining model tries to generalize the hypothesis of organizational survival and competitiveness, so that the organization that is able to identify, strengthen, and use key knowledge will reach a pole position. This case-based reasoning system incorporates a novel artificial neural architecture called Beta-Cooperative Learning in order to categorize the necessities for the Acquisition, Transfer and Updating of Knowledge of the different departments of a firm. This architecture is used to retrieve the most similar cases to a given subject.

On the improvement of Knowledge Management status through case-based reasoning in a hybrid approach

From an enterprise point of view, Knowledge Management (KM) enables organizations to capture, share, and apply the collective experience and know-how (knowledge) of their staff. Up to now, little effort has been devoted to apply Artificial Intelligent techniques to KM systems. This paper proposes the application of case-based reasoning, in combination with a neural model, to develop a KM system. This combined approach profiles the KM status of the whole organization and automatically generates proposals, aimed at improving the KM situation of organization units. The system is fed with KM data collected at the organization and unit contexts. The outcome consists of customized solutions for different areas of expertise related to the organization units, once a lack of knowledge in any of those has been identified.

Optimization of Knowledge in Companies Simulating M6PROK© Model Using as Hybrid Methodology a Neuronal Network and a Memetic Algorithm

The pursuit of this paper is to give answers to the companies in order to know how profitable the knowledge they are acquiring, updating and transferring is. The scope of the application is carry out through a recent developed model, called Model of the Six Profitable Stages(M6PROK©) applied in twenty three companies of the service sector. Feasibility of the aforementioned model, results and conclusions are proved through the display of a Hybrid Architecture based in Neural Nets and Memetic Algorithms.