Marcin Relich

The Use of Intelligent Systems for Planning and Scheduling of Product Development Projects

Abstract The paper investigates the use of intelligent systems to identify the factors that significantly influence the duration of new product development. These factors are identified on the basis of an internal database of a production enterprise and further used to estimate the duration of phases in product development projects. In the paper, some models and methodologies of the knowledge discovery process are compared and a method of knowledge acquisition from an internal database is proposed. The presented approach is dedicated to industrial enterprises that develop modifications of previous products and are interested in obtaining more precise estimates for project planning and scheduling. The example contains four stages of the knowledge discovery process including data selection, data transformation, data mining, and interpretation of patterns. The example also presents a performance comparison of intelligent systems in the context of variable reduction and preprocessing. Among data mining techniques, artificial neural networks and the fuzzy neural system are chosen to seek relationships between the duration of project phase and other data stored in the information system of an enterprise.

A Knowledge-Based Approach to Product Concept Screening

This paper is concerned with developing a knowledge-based approach for selecting portfolio of product concepts for development. The critical success factors for new product development are identified on the basis of information acquired from an enterprise system, including the fields of sales and marketing, project management, and production. The model of new product screening consists of enterprise functional domains and business information systems. The model has been described in terms of a constraint satisfaction problem (CSP) that contains a set of decision variables, their domains, and the constraints. Knowledge base is specified according to CSP framework and it reflects the company’s resources and relationships identified. The illustrative example presents the use of fuzzy neural network to estimating the success of new products and constraint programming to product concept screening in the context of the different search strategies.

A Declarative Approach to New Product Development in the Automotive Industry

The chapter aims to present a declarative approach for management of the new product development project portfolio in the automotive industry. A reference model of an automotive company and project portfolio planning is formulated in terms of a constraint satisfaction problem and implemented in constraint programming languages, facilitating the development of a decision support system that seeks a feasible set of alternatives for project portfolio completion. It is especially attractive in the case of a lack of possibility for continuing the baseline schedule, and supports managers in choosing an alternative schedule. As a consequence, project portfolio management is more efficient, the competitiveness of the automotive company increases, and the launch of new vehicle models containing technologies less harmful to the environment is faster. The chapter includes illustrative examples concerning new sustainable trends in the automotive industry.

An evaluation of project completion with application of fuzzy set theory

An evaluation of project completion with application of fuzzy set theory The project management contains such elements as management of time, cost, communications, procurement, quality, risk or scope of project. Each of these fields can be considered as a set of constraints, and then there is a possibility to verify their fulfillment in sense of an enterprises constraints and its environment. These constraints determine a completion of project activities and its success or failure, finally. The paper aims to present a problem of project management in terms of fuzzy constraints satisfaction problem, and then the using of constraint programming techniques to the evaluation of project completion. A fuzzy constraints satisfaction problem enables a description of data in distinct, as well as imprecise form, in a unified framework. It seems especially important in case of unique activities of project, when their estimation is based on linguistic information from experts. Ocena realizacji przedsięwzięcia z wykorzystaniem teorii zbiorów rozmytych W ramach zarządzania przedsięwzięciem wymienia się najczęściej takie elementy jak zarządzanie czasem, kosztami, komunikacją, dostawami, jakością, ryzykiem czy zakresem projektu. Każdy z tych obszarów zarządzania projektem można rozpatrywać w postaci zbioru ograniczeń, a następnie sprawdzać ich spełnienie w aspekcie ograniczeń wynikających z charakteru przedsiębiorstwa wdrażającego przedsięwzięcie oraz jego otoczenia. Ograniczenia te determinują realizację poszczególnych czynności projektu i ostatecznie to czy zakończy się on sukcesem, czy niepowodzeniem. Celem pracy jest przedstawienie problemu zarządzania przedsięwzięciem w postaci rozmytego problemu spełniania ograniczeń, a następnie wykorzystanie technik programowania z ograniczeniami do oceny realizacji przedsięwzięcia. Rozmyty problem spełniania ograniczeń umożliwia wyrażenie danych tak w postaci precyzyjnej, jak i nieprecyzyjnej w ramach jednego podejścia. Wydaje się to szczególnie istotne w przypadku planowania realizacji czynności unikalnych projektu, gdy ich szacowanie jest dokonywane przede wszystkim w oparciu o opinie ekspertów.

Identifying Relationships Between Eco-innovation and Product Success

This chapter is concerned with the study of success factor identification in eco-innovation. Critical success factors are identified on the basis of Enterprise Resource Planning (ERP) database and questionnaires concerning the implementation of eco-innovation. The model of measuring eco-innovation includes indicators connected with fields such as research and development, production, logistics, sales and marketing, as well as eco-innovation implementation that consists of eco-organization, eco-process, and eco-product. The proposed methodology enables the merger of objective indices and subjective judgments with the use of fuzzy logic. In order to identify the relationships between product success and project environment variables, artificial neural networks are used. The proposed approach enables the identification of factors that significantly impact product success. The relationships sought can be further used to forecast the success of a new product that is in the development process and propose changes in the project variables that can increase the chance to develop a successful product.

A fuzzy weighted average approach for selecting portfolio of new product development projects

New product portfolio selection is a multi-criteria decision making problem including both qualitative and quantitative criteria. Determining the exact values for these criteria is often difficult or even impossible taking into account uncertainty and complexity associated with new product development projects. To assist managers in making portfolio selection decisions, this study proposes a new project portfolio selection model that uses a fuzzy weighted average approach for ranking new product projects and artificial neural networks for estimating project performance. New product development projects are evaluated according to criteria related to marketing, project team, project performance, risk, and strategy. The use of neural networks enables more precise evaluation of project performance criteria and provides additional information in portfolio selection. A case study of the evaluation of new product projects illustrates the usefulness of the proposed approach.

A Multi-agent System for Selecting Portfolio of New Product Development Projects

This paper is concerned with designing a multi-agent approach for evaluating new products and selecting product portfolio. In today’s companies it is widespread to execute many new product projects simultaneously. As these projects require resources that are available in the limited quantities, there is the need to select the most promising set of new product for development. The evaluation of new product projects involves many agents that analyse the customer requirements and information acquired from an enterprise system, including the fields of sales and marketing, research and development, and production. The company’s resources, performance metrics, and the identified relationships are stored in knowledge base that is specified according to the framework of constraint satisfaction problem. The relationships are sought with the use of fuzzy neural system and described in the form of if-then rules.

Knowledge Discovery in Enterprise Databases for Forecasting New Product Success

This paper presents the knowledge discovery process that aims to improve the forecast quality of the success of new product development projects. The critical success factors for new product development are identified on the basis of information acquired from an enterprise system, including the fields of sales and marketing, research and development, production, and project management. The proposed knowledge discovery process consists of stages such as data selection from enterprise databases, data preprocessing, data mining, and the use of the discovered patterns for forecasting new product success. The illustrative example presents the use of fuzzy neural networks for forecasting net profit from new products.

A case-based reasoning approach to cost estimation of new product development

Abstract New product development (NPD) is a crucial process in maintaining a company’s competitive position and succeeding in dynamic markets. One of contemporary trends in the global economy is mass customisation that bases on modifications of existing products instead of designing everything anew. The advancement of information technology helps today’s enterprises in managing business processes and collecting data in enterprise systems that can be a potential source of information. Specifications of previous products deliver information of design, cost and time of past NPD projects that can be the basis for developing new products. A promising methodology for assisting conceptual product design and monitoring a NPD project is case-based reasoning. This paper is concerned with developing a case-based reasoning approach towards using neural networks to estimate the cost of NPD in one-of-a-kind production companies.

A Knowledge-Based System for New Product Portfolio Selection

This chapter is concerned with designing and developing a knowledge-based system for evaluating concepts of new products and selecting product portfolio. The model of measuring the product success includes metrics identified by an expert, such as duration and cost of product development or net profit from a product. The model contains a set of decision variables, their domains, and the constraints that can be described in terms of a constraint satisfaction problem (CSP). Knowledge base is specified according to CSP framework and it reflects the company’s resources, performance metrics, and relationships identified. The presented knowledge discovery process consists of the stages such as data selection, data preprocessing, and data mining in the context of an enterprise system database. In order to identify the patterns, fuzzy neural networks have been used and compared with the results from artificial neural networks and linear regression. The illustrative example presents the use of fuzzy neural networks to the identification of patterns that are translated into rules understandable by users. The proposed knowledge-based system helps the managers in selecting the most promising product portfolio and reducing the risk of unsuccessful product development.