Daniel E. O’Leary

Using neural networks to predict corporate failure

Predicting corporate failure or bankruptcy is one of the most important problems facing business and government. The recent Savings and Loan crisis is one example, where bankruptcies cost the United States billions of dollars and became a national political issue. This paper provides a ‘meta analysis’ of the use of neural networks to predict corporate failure. Fifteen papers are reviewed and compared in order to investigate ‘what works and what doesn’t work’. The studies are compared for their formulations including aspects such as the impact of using different percentages of bankrupt firms, the software they used, the input variables, the nature of the hidden layer used, the number of nodes in the hidden layer, the output variables, training and testing and statistical analysis of results. Then the findings are compared across a number of dimensions, including, similarity of comparative solutions, number of correct classifications, impact of hidden layers, and the impact of the percentage of bankrupt firms.

Supporting decisions in real-time enterprises: autonomic supply chain systems

Supporting decisions in real time has been the subject of a number of research efforts. This paper reviews the technology and architecture necessary to create an autonomic supply chain for a real-time enterprise for supply chain systems. The technologies weaved together include knowledge-based event managers, intelligent agents, radio frequency identification (RFID), database and system integration, and enterprise resource planning systems.

Supply Chain Processes and Relationships for Electronic Commerce

In this chapter, I survey recent developments in supply chain relationships that have been designed to exploit technology. Firms are now in a position to more tightly link themselves with other firms through their use of supply chains in order to attain greater efficiencies.

Reengineering Assembly, Warehouse and Billing Processes, for Electronic Commerce Using “Merge-in-Transit”

This paper investigates merge-in-transit as an approach to reengineering assembly, warehouse and billing processes for electronic commerce. Merge-in-transit is defined and some examples are given to illustrate its use. Processes necessary to accomplish merge-in-transit are developed, while advantages and disadvantages of merge-in-transit are studied. Additional issues arising from merge-in-transit also are studied: Merge-in-transit software is discussed; new measures necessary for merge-in-transit are examined; the effects of merge-in-transit on others in the current supply chain are also examined.

Technologies for Knowledge Assimilation

Assimilation is a critical issue for knowledge management. Knowledge may be gathered, created or converted, but if it is not assimilated, the organization will not be able to take action on that knowledge or actualize all of its potential value. As a result, unless knowledge is assimilated it will have limited use and impact on an organization. Accordingly, organizations are concerned with how to facilitate assimilation. This chapter provides an analysis of some key technologies for assimilation, focusing on knowledge storage, massaging, structuring, integration, filtering, and navigation.

Is knowledge management dead (or dying)

Abstract Recently, Tom Davenport declared that ‘… knowledge management isn’t dead, but it’s gasping for breath.’ This declaration is investigated from an historical perspective, tracing some previous statements of knowledge management’s death back to the mid 2000’s. Then this paper investigates this declaration in the context of three emerging technologies and their potential contributions to knowledge management: social media / enterprise social media, crowdsourcing and IBM Watson-like systems. We also examine evidence that knowledge management creates value and the extent to which knowledge management actually is needed. If knowledge management is dead then some very similar idea is needed and would create value.

Evolution of Knowledge Management Towards Enterprise Decision Support: The Case of KPMG

Realizing that knowledge and its proper management are essential for effective decision support, this chapter traces the evolution of knowledge management within a major professional services firm – KPMG. By supporting decision making, computer-based systems for managing knowledge can impact organizational performance and the very nature of the organization itself. Here, we examine a progression of knowledge management systems at KPMG, beginning with the 1997 condition of having disparate or no knowledge management systems and culminating with an enterprise-wide integrated system accommodating both locally and globally managed knowledge. This chapter investigates why KPMG pursued the development and implementation of a global knowledge management system. Strategically, knowledge-management advances were used to transform the firm from a confederation of local enterprises to a global enterprise. In addition, it summarizes some of the key capabilities and technologies of the resulting knowledge management system, K-World. This chapter also examines some key implementation issues. Finally, the chapter investigates two key problems emerging from the use of the system after its introduction: search and client confidentiality, plus some of the emerging extensions for K-World.

Decision Support System Evolution: Predicting, Facilitating, and Managing Knowledge Evolution

Decision support systems (DSSs) need to evolve over time for many reasons, including changing user needs, technologies, and problem understanding. This chapter investigates what constitutes DSS evolution, taking the view that DSS evolution means that changes occur in all aspects of those systems, including hardware, databases, user interface, applications, and knowledge. This chapter summarizes and extends some of the literature on evolution and it also summarizes some approaches designed to help manage DSS evolution, including both the prediction and facilitation of evolution.

Measuring and Managing Complexity in Knowledge-Based Systems: A Network and Mathematical Programming Approach

Developing the knowledge base of an expert system is a complex process. The extent of that complexity is likely to impact the development time and cost of the system, quality of the system, validation and assessment efforts, and other development and maintenance issues. Thus, there is interest in analyzing the impact of complexity on those factors. One approach to that analysis is to develop some metrics for complexity of systems and compare those metrics to those factors for specific systems.

Knowledge Acquisition for a Diagnosis-Based Task

The basic research task discussed in this paper is the investigation of the relative effectiveness of alternative knowledge acquisition methodologies at eliciting different types of knowledge for a diagnosis task. In order to investigate this task, a field study was used.