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Product prototyping
Published in Fuewen Frank Liou, Rapid Prototyping and Engineering Applications, 2019
When designing a product or a prototype, it is very critical for a design engineer to learn about the cost factors—all costs that can contribute to the final cost of the project. Many opportunities for product cost optimization are embedded in the process of defining the project. Often it is too late to make a change in the later part of the product development stage. A cost estimate establishes the baseline of the project cost at different stages of the product development. Cost engineering is defined as that area of engineering practice where engineering judgment and experience are utilized in the application of scientific principles and techniques to the problem of cost estimation, cost control, and profitability. Cost estimation is also an art since sometimes cost estimates may be influenced by other factors, such as those influenced in a political way. For example, 12 months were given to do the job so that the project will be budgeted for 12 months. One wants to show the product at the trade show after 7 months, so the project needs to be budgeted for 7 months to prepare for the trade show. One knows that the competitor put in a bid of $1.5 million, so one will put $1.4 million to win the bid. The project needs 8 months, but the boss will not accept more than 6 months, so the project is budgeted for 6 months. This section will not discuss these factors, but one may need to consider these in many industrial projects.
What Is Cost Engineering?
Published in Chris Domanski, Cost Engineering, 2020
Cost engineering is defined by Wikipedia as “the engineering practice devoted to the management of project cost, involving such activities as estimating, cost control, cost forecasting, investment appraisal, and risk analysis.” In simpler terms, cost engineering (sometimes called design to cost) is the practice of engineering a company’s products to meet pre-defined cost requirements. It is really an umbrella of various methodologies (see Figure 1.1) that are often confused for cost engineering itself, but really only address portions of it that have something to do with cost estimating, cost control, or cost optimization.
Cost estimating and budgeting
Published in John M. Nicholas, Herman Steyn, Project Management for Engineering, Business and Technology, 2020
John M. Nicholas, Herman Steyn
In some projects, the project manager delegates responsibility for estimating to those responsible for the work, combines their estimates, and presents the final estimate to management or the customer. In others, the project manager coordinates estimating efforts among functional managers and aggregates the estimates. Although this typifies the estimating process, the actual approach depends on the project organization. It also depends on the information available and the required accuracy of the estimate. Most estimates are made using variants of four methods: expert judgment, analogy, parametric, and cost engineering.
Application of machine learning techniques for cost estimation of engineer to order products
Published in International Journal of Production Research, 2023
Mario Rapaccini, Veronica Loew Cadonna, Leonardo Leoni, Filippo De Carlo
CE is part of the broader domain of cost engineering, which is defined as the application of experience, scientific principles, and techniques for cost estimation and control, as well as for project planning and management (Hollmann 2006). On the other hand, CE is referred to as a process devoted to estimating the cost of an activity based on information that is currently available (Ou-Yang and Lin 1997). Accordingly, CE has a dynamic nature since it should be systematically repeated when new data emerge. Due to the importance of this topic, several techniques for CE have been suggested in the scientific literature. The classification upon which scholars commonly agree is that proposed by Niazi et al. (2006), who discriminate between qualitative and quantitative techniques. The former is used to elaborate rough estimates in early design stages, based on the estimator's experience and tacit knowledge (Duverlie and Castelain 1999). These approaches are relatively cheap and fast in their implementation (ISPA 2008), but usually produce estimates affected by a great deal of subjectivity (Ievtushenko and Hodge 2012). On the other hand, quantitative techniques employ either algorithmic or statistical models. In this context, an interesting work by Shen et al. (2017) proposes a three-stage framework to evaluate the cost of different configurations of product-service systems. The authors develop a model that considers process items and resource consumptions, along with the inclusion of uncertainty factors. In a more recent study by Salmi et al. (2018), a parametric approach for the early CE of an assembly line is presented considering tasks to perform and resource consumption. The authors also propose an integration with an optimisation stage, to test different solutions and choose the best one. Since the statistical or algorithmic models are grounded in explicit knowledge of manufacturing processes (Layer et al. 2002), the use of quantitative techniques is complicated in the early design stages of ETOPs, which, by definition, are affected by high variability and uncertainty. In addition, being based on analytical formulation, these models can neither handle exceptions nor consider drivers that cannot be expressed through numerical scales (Saleem et al. 2019). Considering the previous statements, it is possible to state that conventional techniques (both qualitative and quantitative approaches) have major drawbacks that strongly limit their application, especially during the early design phase.