Nathalie Cassaigne

Knowledge management tutorial: an editorial overview

We present a tutorial on knowledge management (KM) and a roadmap of this special issue around the knowledge life-cycle. Knowledge management is a discipline that provides a strategy, process and technology to share and leverage information and expertise that increases our level of understanding, to more effectively solve problems and make decisions. We address three key views: (1) codification (tacit and explicit knowledge types); (2) various knowledge environments; and (3) cognitive aspects, followed by detailed descriptions of features and technology, and how this capability is being leveraged in forward-looking organizations.

Predictive and reactive approaches to the train-scheduling problem: a knowledge management perspective

Predictive and reactive train scheduling are tactical and operational decision making, respectively, under constraints (e.g. resource capacity, managerial objectives) and under uncertainty (e.g. imprecise data and information, unforeseen events). Predictive scheduling produces timetables taking into account the market demand and resource utilization levels. Reactive scheduling challenges disruptions to timetables and schedules trains and operations with imprecise plans. Expert knowledge is indispensable for finding practical solutions for both predictive and reactive scheduling. Consequently, knowledge management strategies, processes and technologies can improve the decision-making process and outcomes. This paper focuses on the following issues. Five dimensions are introduced to distinguish predictive and reactive train-scheduling activities. The combined use of data and knowledge and the differences in uncertainty levels are used to comparatively position the two scheduling approaches. The intensity of reliance on explicit and tacit knowledge is highlighted via the elaboration and classification of knowledge used in either one or both scheduling environments. The significance of train-scheduling tacit knowledge elicitation is described by, first, presenting a real case analysis which resulted in the elicitation of rich and valuable tacit knowledge (timetabling heuristics) from explicit knowledge (timetable) and, second, generalizing lessons learned from this process. The contributions of the tacit knowledge elicitation process to the enhancement of the train-scheduling system which leads to better resource utilization and customer satisfaction are itemized.

Bid pricing-calculating the possibility of winning

For many years, those researching into the problem of bid pricing have attempted to determine the probability of a bidder winning a bid based only upon the price of its proposal. However, researchers have professed the need to consider the many other factors influencing the clients bid selection. We examine two novel pricing approaches which address this need. This has been done by modelling the three main actors within the bid pricing problem: the client, the competitors and the bidder. Multi-attribute utility theory is applied to capture the bid selection behaviour of the client, while possibility theory is used to model the performance of competitors in the bid. The resulting models determine the possibility of the bidder winning a bid at different bid prices, allowing the bidder to optimise its bid price. This is performed with the aim of enhancing the overall success rate and profitability of the bidder in real bidding environments. The authors are examining the novel bid pricing approaches outlined in this paper as a part of their research work within two major projects: (1) the DECIDE project, which is focused on one-of-a-kind or small-series manufacturing, where costs are important since the average margins on successful bids are often quite low; and (2) the ServPrice project, which aims to develop bid pricing tools for sophisticated high value-added service industries where costs are fixed and the marginal cost to provide the service is very small. This is especially true in the telecom and software industries, so ServPrice is developing the bid pricing tools with partners from these industries.

A critical analysis of bid pricing models and support tool

This paper presents the characteristics of widely known bidding strategy models. The main characteristic of such models is that they over-simplify the bidding situation and therefore are difficult to use to model real life bidding. A new model (ServPrice) is proposed to overcome the disadvantages of the current bidding models. The analysis of all these models is based on a number of criteria which have been selected from both a large literature review on bidding models, as well as from the authors field experience in bid pricing. The comparison of these models has been made using a real life case provided by the telecommunication industry: bidding for carrier services.

Decision support for the pricing of services in business to business sales

We examine the bid pricing problem from the perspective of practical bid preparation and the provision of intelligent support for it. The focus of the paper is on the redesign of the bid pricing process such that it is better able to incorporate information and knowledge (in house business expertise). The aim is to provide a competitive edge for services companies which make business to business sales and are obliged to bid for most of their business through responding to invitations to tender. We describe typical bid pricing processes as they are currently structured in services industries such as the telecommunications and software industries. We also describe how they could be re-structured in order to take advantage of the new generation of intelligent decision support tools arising from the ServPrice Project.

Decision support for effective bidding in a competitive business environment

For enterprises with products that are tailor-made to order, both the efficiency and the effectiveness of bid preparation are essential prerequisites to sustain competitiveness. Preparing bids efficiently means reducing effort and resources to a minimum, while effectiveness implies high average success rates at good profit rates. Various approaches and systems exist that focus on increasing efficiency in bid preparation, but support allowing one to improve the effectiveness of the process is lacking. In the face of this situation, an advanced decision support system allowing a sales executive to determine the optimum sales price has been developed within the DECIDE (DECIsion support for optimal biDding in a competitive business Environment) project (ESPRIT Project No. 22298). To ensure a sound scientific and technological base as well as the optimal practical use of the system, three research institutes and universities, two industrial companies with complex engineered-to-order products and a software vendor are co-operating in the project. This paper describes the approach and presents intermediate results.

Knowledge focused bid price setting process

This paper presents the strengths and weaknesses of the current bid price setting process of large telecommunication carrier operators and proposes a domain knowledge focused enhanced process supported by a knowledge based decision support system.

Providing intelligent support for the bid pricing processes

ABSTRACT In this paper we examine the bid pricing problem from the perspective of practical bid preparation and the provision of intelligent support for it. The focus of the paper is on the redesign of the bid pricing process such that it is better able to incorporate Information and Knowledge (In house Business Expertise). The aim is to provide a competitive edge for companies which make business to business sales and are obliged to bid for most of their business through responding to invitations to tender. In the paper we describe typical bid pricing processes as they are currently structured in both one of a kind manufacturing as well as in services. We also describe how they could be re—structured in order to take advantage of the new generation of intelligent decision support tools arising from the DECIDE and ServPrice Projects. The tools, which will support such processes, represent a wholly new class of IT systems and their novelty is explained in relation to existing categories of systems.

Bid pricing decision support: some preliminary considerations

In this paper we describe the problem of pricing bids in response to a tender offer and examine the information needs and decision support required for resolving this problem. For most bids, the bid pricing is not done by one person but rather by a multidisciplinary team so that the support for bid pricing needs to be for this process as opposed to for an individual decision maker. We examine the basic structure for a bid pricing decision support architecture to support the bid pricing process and what needs to go into each component module. Finally, we outline the research being done to develop prototype decision aids for the bid pricing process of both a large firm as well as of a small firm.

Experiences with a company—specific DECIDE adaptation

ABSTRACT Within the ESPRIT project 22298, DECIDE, an advanced decision support system for bid preparation of one-of-a-kind and small batch production was developed. To ensure its optimal practical use, two industrial companies with complex, engineered-to-order products were actively involved in the development process. The practitioners provided the system developers with insights into their daily business processes, with requirements as well as expectations. In addition, they tested the system and gave feedback to the developers. In this paper, the specific DECIDE adaptation for the Bremer Werk fur Montagetechnik GmbH (BWM) is described. Beginning with a company profile, the bidding process is shown and the requirements of a system supporting this process are outlined. The implementation of DECIDE for BWM is then outlined. Finally, the paper reports on the testing of the system in the companys environment and tries to quantify the benefits arising from its use at BWM.