Process and aiding tools for response to call for bids – OPERA
Process and aiding tools for response to call for bids
Process and aiding tools for response to call for bids
Challenges and objectives
OPERA works in a business-to-business (B2B) customer/supplier relationship, on the supplier side when drawing up a response to a call for tenders of the engineering-to-order type. In this case, the development and costing of the offer (cost, lead times, performance, etc.) is usually based on a preconception of the solution (a few key choices without detailed design). Once the offer has been accepted, the supplier must complete the design of the solution (BOM and realization process) before producing it. This can be tricky, as there is no guarantee of the validity of the choices made during pre-design. As a result, there is a risk of not being able to design and produce what has been sold to the customer. OPERA's objective is to develop a process and knowledge-based support tools to take into account, right from the design stage, the risks likely to affect the realization of the offer. Five issues emerged: 1) Model the confidence the supplier can have in an ETO offer, and derive an estimation model. 2) Identify the intensity of the ETO, i.e. whether it is more or less standard or customized. 3) Model the ETO product/service offering. 4) Identify risk typologies and treatments in response to calls for tender. 5) Aim for a multi-criteria choice aid to select the offer to submit to the customer.
The OPERA project aims to improve the tendering process by proposing an approach that enables bidders to define suitable offers without resorting to detailed design. To achieve this, a methodological framework is being developed, incorporating two key confidence indicators: OCS (overall confidence in the technical system) and OCP (overall confidence in the delivery process). These indicators combine objective elements, such as TRL (Technology Readiness Level) and AFL (Activity Feasibility Level), with subjective assessments based on human expertise. At the same time, OPERA adapts product and service configuration models to better respond to engineering-to-order (ETO) offers, based on constraint modeling. Another complementary approach is based on risk management, using a taxonomy to identify and deal with uncertainties in commercial offers. Finally, a multi-criteria decision support framework based on possibility theory and the Pareto-dominance principle is proposed, to select optimal technical solutions taking into account uncertainties and the confidence level of bidders.
The first contribution concerning confidence exploits extensions of TRL quantification principles in a new estimation model. The second contribution on ETO intensity exploits extensions to constraint-based configuration models. The third contribution on product/service modeling is based on an enrichment of the Tukker product/service model. The fourth contribution, concerning the development of a risk typology, is based on interviews with project stakeholders, analyses of past offers and the literature. The fifth contribution, concerning multi-criteria choice support, is based on the exploitation of the notions of confidence, possibility and Pareto dominance. As far as the results or spin-offs for companies or the general public are concerned, it appears that the models of confidence in the offer and the typologies of risk and/or treatment are the closest to the application field of responding to invitations to tender. Many of the results obtained have enabled our partners to increase their maturity with regard to bidding risks. Some of the results can be used quite simply in “paper procedures” and integrated into current tender response processes. The problem of visualizing results has arisen. All these proposals for tools and methods form the basis of the OPERA software demonstrator.
In terms of spin-offs for companies or the general public, it appears that the models of confidence in the offer and the risk and/or treatment typologies are closest to the application field of responding to calls for tender. Many of the results obtained have enabled our partners to increase their level of maturity with regard to bidding risks. Some of the results can quite simply be used in “paper procedures” and integrated into current tender response processes. The problem of visualizing the results has arisen.
Ayachi, R.; Guillon, D.; Aldanondo, M.; Vareilles, É.; Coudert, T.; et al. Risk knowledge modeling for offer definition in customer-supplier relationships in Engineer-To-Order situations. Computers in Industry. 2022, 138, 103608.
Guillon, D.; Ayachi, R.; Vareilles, É.; Aldanondo, M.; Villeneuve, E.; et al. Product?service system configuration: a generic knowledge-based model for commercial offers. International Journal of Production Research. 2021, 59 (4), 1021-1040.
Guillon, D.; Villeneuve, E.; Merlo, C.; Vareilles, É.; Aldanondo, M. ISIEM: a methodology to deploy a knowledge-based system to support bidding process. Computers & Industrial Engineering. 2021, 161, 107638.
Sylla, A.; Coudert, T.; Vareilles, É.; Geneste, L.; Aldanondo, M. Possibilistic Pareto-dominance approach to support technical bid selection under imprecision and uncertainty in engineer-to-order bidding process. International Journal of Production Research. 2021, 59 (21), 6361-6381.
In the context of the development of commercial offers in increasingly fierce competition market, the following observations have been drawn:
- for companies, the number of direct solicitations or bidding is increasing and bidders companies must streamline, systematize and make more reliable their offer definition process,
- because of this increasing level of work, companies cannot longer realize detailed studies and then, take significant risks when the affairs are realized after acceptance.
These two findings justify the requirement about a formalized bidding process aided
by decision support tools in order to quickly propose ad hoc and precise offers with a high level of confidence.
Therefore, the OPERA project is based on the hypothesis that an offer is composed of a technical solution associated with a realization project. It proposes:
- the definition of a bidding process based on two key activities: (i) development of offers with regards to a global confidence indicator and (ii) risk analysis,
- the development of decision making tools (OPERA platform) based on knowledge and experience intensive reuse for offers definition and risk engineering,
- the definition of core concepts: (i) solution readiness, (ii) project maturity and (iii) confidences in order to define a global confidence indicator for an offer and then, reduce uncertainties and imprecision about its characteristics,
- the exploitation of global confidence indicator for the multi-criteria selection of promising offers.
In the OPERA project, when the confidence is higher and the risk assessment better, the effective realization of the affair will be much closer to the expected attempts. The imprecision and uncertainties about the offer characteristics (performance, delay, cost…) will be reduced. The bidder will have a higher confidence into the offer and potential negotiations will be easier to drive.
From a scientific viewpoint, the OPERA project is based on four requirements about the study, the definition, the formalization and the validation of:
- Four new key performance indicators (KPI) which characterize the confidence of an offer with different aggregation mechanisms,
- Principles of exploitation of these four KPIs in order to take into account imprecision and uncertainties about offers characteristics and to support the multi-criteria selection,
- The organization of experience and knowlede bases dedicated to risk associated with the definition of reasoning principles to exploit them,
- Principles of selection of the offer to submit to the customer.
The OPERA project is based on a well-balanced consortium composed of four industrial partners and three academic partners. The industrial partners are diversified following two activity sectors (services and systems development). The three academic partners are used to work together on research projects. The different prototypes which have been developed during the past years as well as the published scientific articles referenced into the web of science database are a proof of a high maturity level. The division of the project into five operational work packages, its 42-month duration and its agile development process based on four iterations lead to a low level of risk. In conclusion, the OPERA project proposes a methodology and a decision support tool which can support the bidding process. This tool, based on intensive exploitation of capitalized knowledge and experiences, will permit to develop offers and to evaluate them on original criteria and finally improve companies’ competitiveness.
Project coordination
Michel Aldanondo (Association pour la Recherche et le Développement des Méthodes et Processus Industriels Centre Génie Industriel de Mines Albi)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Partnership
ALTRAN ALTRAN TECHNOLOGIES
AES AUTOMATISMES-ETUDES-SERVICES
AXSENS
MécaNuméric MECANUMERIC
LGP-ENIT Laboratoire Génie de Production - École Nationale d'Ingénieurs de Tarbes
ESTIA-Bidart ESTIA, Ecole Supérieure des Technologies Industrielles Avancées (CCI Bayonne Pays Basque)
ESTIA Ecole Supérieure des Technologies Industrielles Avancées
ARMINES (CGI) Association pour la Recherche et le Développement des Méthodes et Processus Industriels Centre Génie Industriel de Mines Albi
Help of the ANR 765,851 euros
Beginning and duration of the scientific project:
September 2016
- 42 Months
