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Product Cost Estimation:
product cost estimation PCE.
This paper presents an extensive hierarchical classification of
these techniques. The classification is based on grouping the techniques
with similar features into various categories.
This paper categorizes the PCE techniques into qualitative and quantitative.
Qualitative cost estimation techniques are primarily based on a comparison analysis of a new product with the products that have been manufactured previously in order to identify the similarities in the new one.
In general, qualitative techniques help obtain rough estimatesduring the design conceptualization. These techniques can further be categorised into intuitive and analogical techniques, which are discussed, in detail, in Secs. 2 and 3, respectively.
Quantitative techniques, on the other hand, are based on a detailed analysis of a product design, its features, and corresponding manufacturing processes instead of simply relying on the past data or knowledge of an estimator.
2.2 Decision Support Systems (DSS).
2.2 Decision Support Systems (DSS).
Shehab and Abdalla .14 developed knowledge-based cost
models for the PCE in early design stages, whereas Luong and
Spedding .15 developed a knowledge-based system by integrating
process planning into cost estimation. Another approach
adopted by Gayretli and Abdalla .16 focused on developing a
prototype system for manufacturing process optimization. The
system assisted designers to create real-time cost estimates and
feasible process plans by retrieving manufacturing form features
and parameters from the feature database.
Gayretli and Abdalla .17 developed a rule-based algorithm for the selection and optimization of feasible processes to estimate process time and cost based on parts features. A detailed description of part features with possible processes and constraints was given. Process times were calculated using a standard formula as Process Time = Form Feature Volume Material Removal Rate 1. The process time is then used to calculate lot time, which is based on a form feature quantity. The total process cost is subsequently calculated as follows: Total Process Cost = Lot Time . PHC 2. where PHC is the productive hour cost given by a cost estimation database .18. The total cost is then calculated as follows: Total Cost = Material Cost + .Lot Time . PHC. + Tool Cost + Setup Cost 3.
Parametric Cost Estimation Techniques
Parametric models are derived by applying the statistical methodologies
and by expressing cost as a function of its constituent
variables. These techniques could be effective in those situations
where the parameters, sometimes known as cost drivers, could be
easily identified. Parametric models are generally used to quantify
the unit cost of a given product.
A wide range of parametric models can be found in the literature. For example, Hajare .30 modeled parametric costing of components using the product specifications. Roberts and Hermosillo .31 used approximate tool paths and process parameters from available factory resources to estimate time and cost for small surface units. Boothroyd and Reynolds .32 adopted a parametric costing approach using the volume of typical turned parts as a parameter to estimate the cost in the early design stages. Unlike the detailed-breakdown approach, the method adopted by them could be used in the early design stage without the need of a process plan. Similar work can be found in .33.
Cost Estimation in the Early Phases of the Design Cycle
Cost Estimation in the Early Phases of the Design Cycle
The key to thrive for a manufacturing enterprise in the twenty- first century is based on product quality, competitive cost, fast delivery, and flexibility. On the other hand, factors such as globalization, and mass customization. put an extra pressure on a business enterprise to survive and remain profitable at the same time. Although an innovative approach and a new product development process may attempt to deal with issues such as flexibility and product quality, they may still be time consuming and less cost effective.
In addition, the prospective end user of a would-be product often demands a price quote as soon as possible, sometimes even unconcerned and oblivious of factors such as the extent of the customization, the nature of the data required, and the design complexity. To make matters worse, often a manufacturer ignores the significant factors, such as design module availability, manufacturability, and the level of accuracy required for processing time estimation.
The overall situation, therefore, could either lead to an underestimation resulting in a profit loss and a blow to operational targets or a more profound strategic damage caused by overestimation leading toward the loss of customer goodwill and market share. All the above highlights the ever-increasing importance of devizing methods to forecast the cost for a new product in the early design and development phases with accuracy. Since most of the product costs sustained during later in the production life cycle are determined during the conceptual design phase .8, the cost estimation in the early phase of the design cycle is crucial.
Many researchers have emphasized the importance of cost estimation at the early design stages when 70–80% of a total product cost is determined .9,16,25,44. Some researchers have developed methodologies with a special emphasis on early cost estimation .65,66. A framework for developing a cost database was suggested by Sheldon et al. .67 and aimed to serve different groups of DFC system users to determine appropriate cost structures by analyzing the information provided by a costaccounting system.
Knowledge representation in such a way facilitated the generation of cost estimates at an early design stage.
A framework to integrate design costs into quality function deployment QFD. was used by Bode and Fung .68. The approach adopted is a helpful tool for designers at the early stages of product design for making trade-off decisions between quality and cost prioritizing the attainment of technical attributes based on customer requirements. Within the specific context of the classification presented in the current study, qualitative techniques are generally favorable in making cost estimates in the early design stages. This is because these techniques make use of the past data to predict the cost of a new product without requiring detailed information, such as geometric design data or process planning results.
Although, the accuracy of these techniques is sometimes questionable, the rough estimates obtained in the early design phases still provide a good platform for decision making, which is employed during all the stages of the design and development process. Thus, product cost can be controlled from the conceptual design stages when the design alternatives have a direct bearing on the product cost to the shop floor execution stages when the process alternatives are the results of the choices made during the early phases of the design process.
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