Catálogo de publicaciones - libros

Enterprise resource planning (ERP) projects are highly complex information technology (IT) based business initiatives that should be grounded on a strategic infrastructure decision adding value to the firms’ IT infrastructure capability. Not every ERP project is seen in this sense and research has placed a focus on the more technical viewpoint of ERP. This article provides an analysis based on a case study of a strategy driven ERP decision with an emphasis on the organizational fit of ERP. It shows the organizational impact of the ERP project, especially pertaining to project costs and utility, in comparison with the common practice situation in terms of the stages of ERP implementation as well as ERP operation. Results show an example of a highly successful ERP project grounded on the high decision making quality observed, in particular, in terms of team building or evaluation procedures, and the positive consequences for ERP implementation and operation.

This paper presents the conception, modeling, and implementation of a decision support system applied to the operational planning of solid waste collection systems, called SCOLDSS. The main functionality of the system is the generation of alternatives to the decision processes concerning: (a) the allocation of separate collection vehicles, as well as the determination of their routes, and (b) the determination of the daily amount of solid waste to be sent to each sorting unit, in order to avoid waste of labor force and to reduce the amount of waste sent to the landfills. To develop the computer system, a combination of quantitative techniques was used, such as: simulation of discrete events and algorithms/heuristics for vehicle allocation and routing. The system was validated using a field test in Porto Alegre, Rio Grande do Sul.

The next generation Web, called the Semantic Web (SW), is receiving much attention lately from the research and development communities globally, Many software designers, developers, and vendors have recently begun exploring the use of SW technologies within the context of developing intelligent Web-based Decision Support Systems (DSS) since they provide an attractive, application-neutral, platform-neutral, Web environment that operates on top of the existing Web without having to modify it. They are envisioned to provide machine interpretation and processing capability of the existing Web information. With these powerful potential advantages, there is a need for the DSS designers and developers to not only understand the SW technologies in terms of what they are and do, but also how and where they could be effectively used in Web-based DSS to enhance its environment, This study addresses that and characterizes a semantic DSS environment to assist the researchers and developers to investigate in more detail the SW technology applications and challenges within the context of DSS.

Modelbases and databases are complementary to each other in Decision Support Systems. Hence, there exists a need for a framework to uniformly represent modelbases and databases in order to facilitate information exchange and integration between them. Because of the advancement in Web technologies and business globalization, such a framework must be Web-based, promote reuse and share modelbase and database information among branch locations, and be able to express user-defined rules, which are culturally diverse. This paper proposes a unified framework for modelbases and databases. It comprises two major components: Ontology and Schema. The former provides the meanings of specific terminology widely agreed upon by concerned communities; the latter, contains purely generic schematic descriptions of specific information. The framework is represented by means of OWL Declarative Description (ODD)—a language with well-defined semantics and expressive means for direct and uniform representation of database and modelbase components, as well as inferences and rules.

In this paper, we present a hybrid, two-round procurement auction that can be used when a buyer wants to procure a single unit of a multi-attribute item. In such cases, bids are measured on many attributes like price, quality, reliability, past history of the bidder, geographical distance between the locations of the bidder and the buyer. The problem is even more acute for Global Enterprises where additional attributes like tax and tariff structures of the country of the supplier become important as well. While such multi-attribute bids are commonplace in sealed bid tenders where the analysis of the bids can be carried out after all of them have been placed to determine the winner; it is difficult to handle such multi-attribute bids in other auction formats like English and Dutch auctions. The difficulty arises because in holding multi-attribute forms of English and Dutch auctions, the buyer needs to communicate information about his true preference amongst attributes to the participating suppliers. But by passing the information on preference between various attributes (termed as the preference structure), the buyer risks revealing sensitive strategic information to the suppliers. In this paper, we present a two-phase auction mechanism that guides the multi-attribute bidding of the participating suppliers, but ensures that only limited information about the buyer’s preference structure can be reverse interpreted by the buyers. We also provide results relating to proper choice of the amount of information that should be disclosed in such manner.

Knowledge-driven decision support systems (DSS) rely significantly on the currency of it’s back-end knowledge-base for improved quality of support that it can provide to the decision making process. In this paper, a model has been developed and presented to support this purpose i.e. making the knowledge base of a knowledge-driven DSS as self-incrementing. First, the necessity of such a model for maintaining knowledge currency and the importance of knowledge currency in the context of DSS supporting operational decision processes, are discussed. Then, a generic model is presented using customer-email as input knowledge sources, frames as a knowledge representation scheme, and Bolzmann machine as the seif-incrementing mechanism. The model can be further extended or fine-tuned both in terms of the input options and process options. The input options can include other types of knowledge inputs with varying degrees of structuredness. The process options may include other algorithms and machine learning processes.