Dynamic Knowledge: Diagnosis and Customer Service

Introduction

The main contribution of this chapter is centered in three aspects: showing using a model how an enterprise diagnosis could be done through logistics, how when performing this diagnosis a better management of dynamic diagnosis could be established and clarifying how through this generated Dynamic knowledge, enterprises could increase its knowledge of itself, while this new knowledge eases the generation of a better knowledge.

Starting from the position of Customer Services Manager, which constitutes the based rationale for the Logistic Model Based on Positions (LoMoBaP, in Spanish Modelo Logístico Basado en Cargos [MoLoBaC]) and using that position as a key element when performing enterprise diagnosis, which results are expressed through Matrixes of Weighing (MOW), it can be verified that a Dynamic knowledge management can be established, which enables the company on which the diagnosis is made to have a better Knowledge management, especially those aspects of management that are of most relevance to it.

The general objective of the chapter is to show that by making the diagnosis of an enterprise through the Customer Service Manager, which is one of the positions of the Logistic Model Based on Positions, and expressing this diagnosis through a Matrix Of Weighing, it is generated a Dynamic knowledge base that will allow the company an efficient Knowledge management, especially in the most important management aspects.

To facilitate its understanding, the general objective has been divided in five specific objectives:

• 1.

  Present the Logistic Model Based on Positions and thus one of their main positions, the Customer Services Manager,

• 2.

  Explain one of the easier to use multi-criteria models, Matrix Of Weighing,

• 3.

  Show how enterprise diagnosis can be performed through a position in the Logistic Model Based on Positions,

• 4.

  Express the result for enterprise diagnosis through a MOW and finally

• 5.

  Show how a Dynamic knowledge base, that will allow the company an efficient Knowledge management, is generated through the expression of this diagnosis.

To achieve the general objective and its specific objectives the methodology to follow will be the scientific method applied to operations research (Hernandez & Garcia, 2010; Hernández, García & Hernández, 2009; 2011), where instead of testing hypotheses a series of steps are followed:

To define the problem, as stated in the objectives is to show how through enterprise diagnosis Dynamic knowledge can be generated, supported by a specific position in MoLoBaC.

To find data, particularly on issues related to the generation of Dynamic knowledge, the MoLoBaC, enterprise diagnosis and MOW.

To establish alternatives, meaning the different kinds of enterprise diagnosis and different MOW based models to express them and that can be used to generate Dynamic knowledge.

To evaluate alternatives; or to see the feasibility of the alternatives proposed to achieve the desired goals.

To select the best alternative based on the appraisal and secondary objectives, whether tacit or explicit.

To implement the best alternative, in this case it will be done through a hypothetical case of Dynamic knowledge generation, by making an enterprise diagnosis supported by the position of Customer Services Manager and MoLoBaC.

To establish controls that would be mechanisms to recognize whether the proposed models retain their validity over time.

As a result this work will present a hypothetical example, to maintain a broader view of the Dynamic knowledge generation through enterprise diagnosis conducted with the support of the Customer Services Manager position, of MoLoBaC, which measurement was made through MOW.