Computational Intelligence for Estimating Cost of New Product Development

Relich, Marcin

doi:10.1515/fman-2016-0002

Cited by 13 publications

(10 citation statements)

References 40 publications

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“…the number of prototype tests, project duration, project team members) that are supposed to have a significant impact on NPD project performance and the cost of a new product. Parametric estimation techniques often base on regression analysis [6][7], artificial neural networks [8][9] or hybrid systems (e.g. neuro-fuzzy and genetic fuzzy systems).…”

Section: Fig 1 a Framework Of The Proposed Methodsmentioning

confidence: 99%

Cost Projections for the Product Life Cycle at the Early Stages of Product Development

Relich¹,

Bocewicz²,

Banaszak³

2021

IFIP Advances in Information and Communication Technology

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The paper is concerned with predicting the total cost of a new product and searching for cost reduction at the early stages of product development. The costs of a new product development project, product promotion, production and after-sales service are predicted using parametric models. The identified relationships are also used to searching for possibilities to reduce the cost of faulty products and after-sales service through increasing prototype tests. As a result, the trade-off between the cost of a product development project and costs of production and after-sales service are sought. Company resources and product specification are formulated in terms of variables and constraints that constitute the systems approach for a problem related to cost optimization. This problem is described in the form of a constraint satisfaction problem and implemented using constraint programming techniques. An example shows the applicability of the proposed approach in the context of searching for the desirable level of the cost related to prototype tests, faulty products and after-sales service. This study develops previous research in the context of adding the cost of after-sales service to a model of total costs of a new product. Moreover, the proposed method of predicting cost has been developed towards using the similarity value to data selection.

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Section: Fig 1 a Framework Of The Proposed Methodsmentioning

confidence: 99%

Cost Projections for the Product Life Cycle at the Early Stages of Product Development

Relich¹,

Bocewicz²,

Banaszak³

2021

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Linear or nonlinear modeling belonging to regression analysis allows the user to make predictions and inference of causal relationships. In turn, ANNs are part of nature-inspired methodologies called computational intelligence [18]. These methodologies are dedicated to solving complex real-world problems, for which the use of traditional models is often restricted.…”

Section: Predictive Analyticsmentioning

confidence: 99%

“…Constraint satisfaction modeling is effectively used, for example, in conceptual design [17] and product cost evaluation [18,19]. However, this type of modeling is so far very rarely used in the field of assessing and improving sustainability performance.…”

Section: Introductionmentioning

confidence: 99%

Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing

Relich

2023

Sustainability

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Environmental issues and sustainability performance are more and more significant in today’s business world. A growing number of manufacturing companies are searching for changes to improve their sustainability in the areas of products and manufacturing processes. These changes should be introduced in the design process and affect the whole product life cycle. This paper is concerned with developing a method based on predictive and prescriptive analytics to identify opportunities for increasing sustainable manufacturing through changes incorporated at the product design stage. Predictive analytics uses parametric models obtained from regression analysis and artificial neural networks in order to predict sustainability performance. In turn, prescriptive analytics refers to the identification of opportunities for improving sustainability performance in manufacturing, and it is based on a constraint programming implemented within a constraint satisfaction problem (CSP). The specification of sustainability performance in terms of a CSP provides a pertinent framework for identifying all admissible solutions (if there are any) of the considered problem. The identified opportunities for improving sustainability performance are dedicated to specialists in product development, and aim to reduce both resources used in manufacturing and negative effects on the environment. The applicability of the proposed method is illustrated through reducing the number of defective products in manufacturing.

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“…They have used a feed-forward artificial neural network with backpropagation to estimate the maintenance cost in the usage phase. Artificial neural networks have also been successfully used to estimation of the NPD and unit production cost [10,38].…”

Section: The Total Product Costmentioning

confidence: 99%

Reducing the Total Product Cost at the Product Design Stage

2022

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Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are inadequate to provide simulations for verifying a possibility of reducing the total product cost through changes in the product design phase. The proposed approach provides a framework for identifying possible variants of changes in product design that can reduce the cost related to the production and after-sales phase. This paper is concerned with using business analytics to cost estimation and simulation regarding changes in product design. The cost of a new product is estimated using analogical and parametric models that base on artificial neural networks. Relationships identified by computational intelligence are used to prepare cost estimation and simulations. A model of product development, production process, and admissible resources is described in terms of a constraint satisfaction problem that is effectively solved using constraint programming techniques. The proposed method enables the selection of a more appropriate technique to cost estimation, the identification of a set of possible changes in product design towards reducing the total product cost, and it is the framework for developing a decision support system. In this aspect, it outperforms current methods dedicated for evaluating the potential of a new product.

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Computational Intelligence for Estimating Cost of New Product Development

Cited by 13 publications

References 40 publications

Cost Projections for the Product Life Cycle at the Early Stages of Product Development

Cost Projections for the Product Life Cycle at the Early Stages of Product Development

Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing

Reducing the Total Product Cost at the Product Design Stage

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