OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao. In a bidding process, the bidder must define and evaluate potential offers in order to propose the most suitable one to the potential customer. Proposing attractive but also realistic offers to various potential customers is a key factor for the bidder to stay competitive. In order to achieve this, the bidder needs to be very sure about the technical specifications and the constructability of the proposal. However, performing a detailed design is resource and time-consuming. This article proposes the foundation of a new framework which can help bidders to define the right offer: (i) in the context of a non-routine design process, while avoiding a detailed design and (ii) taking into account two new indicators that reflect the bidder's confidence that they can meet the commitments once the offer is accepted. The first indicator (OCS) characterises the Overall Confidence in the technical System, while the second one (OCP) gives the Overall Confidence in the delivery Process. Both OCS and OCP are based firstly on two factual objective indicators, Technology Readiness Level (TRL) for OCS and Activity Feasibility Level (AFL) for OCP, and secondly on two human-based subjective indicators, Confidence In System (CIS) for the OCS and Confidence In Process for the OCP. An illustrative application shows how this framework can really help bidders define an offer, while avoiding detailed design and enable them to evaluate the confidence level in each potential offer.

The bidding process is one of the most important phases for system contractors. A successful bid implies defining and implementing attractive and realistic systems solutions that fulfill customer expectations. An additional challenge arises with the increase in systems diversity resulting from growing customization needs. As a result, for standard customizing offers, bidders find good quality support with configuration software for assemble/make-to-order situations. But when requirements exceed the standard offers, bidders need extended support to fulfill Engineering-to-Order requirements. In this context, this article shows how configuration knowledge models, which support configuration in assemble/make-to-order situations (AMTO), can be extended and used in engineer-to-order situations (ETO). Modeling is achieved assuming that the configuration problem is considered as a constraint satisfaction problem. Six key requirements that differentiate ETO from AMTO are identified and modeling extensions are proposed and discussed. An example illustrates all the contributions.

Nowadays, in customer/supplier relationship, suppliers have to define and evaluate some offers based on customers' requirements and company's skills. This offer definition implies more and more some design activities for both technical solution and its delivery process. In the context of Engineering-To-Order, design and engineering activities are more important, the uncertainties on offer characteristics is rather high and therefore, suppliers bid on the calls for tender depending on their feelings. In order to provide suppliers with metrics that enable him/her to know about the confidence level of an offer, we propose a knowledgebased model that includes four original metrics to characterize the confidence level of an offer. The offer overall confidence relies on four indicators: (i) two objectives ones based on Technology Readiness Level and Activity Risk Level, and (ii) two subjective ones based on the supplier's skills and risks aversion. The knowledge-based model for offer definition, offer assessment and offer confidences is based on a constraint satisfaction problem.

Successful bidding involves defining relevant technical bid solutions that conform to the customers' requirements, then selecting the most interesting one for the commercial offer. However, in Engineer-To-Order (ETO) industrial contexts, this selection process is complicated by issues of imprecision, uncertainty and confidence regarding the values of the decision criteria. To address this complexity, a Multi-Criteria Decision Making (MCDM) support approach is proposed in this study. This approach is based on possibility theory and the Pareto-dominance principle. It involves three main stages. First, a method is proposed to automatically model the values of the decision criteria by possibility distributions. Second, four possibilistic mono-criterion dominance relations are developed to compare two solutions with respect to a single decision criterion. Finally, an interactive method is devised to determine the most interesting technical bid solutions with respect to all the decision criteria. The method is applied to the design of a technical bid solution of a crane. The results show that this approach enables bidders to select the most interesting solution during a bidding process, while taking into account imprecision, uncertainty and their own confidence regarding the values of the decision criteria.

No abstract

No abstract

No abstract

No abstract