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Activity Estimating
Published in Deborah Sater Carstens, Gary L. Richardson, Project Management Tools and Techniques, 2019
Deborah Sater Carstens, Gary L. Richardson
According to the PMBOK® Guide:Parametric estimating is an estimating technique in which an algorithm is used to calculate cost or duration based on historical data and project parameters. Parametric estimating uses a statistical relationship between historical data and other variables (e.g., square footage in construction) to calculate an estimate for activity parameters, such as cost, budget, and duration.(PMI 2017, 200) Work units or systems can be estimated using mathematical formulas with parameters based on size, functions, footage, or other variables. The process of creating a formal parametric model involves collecting data from many past similar efforts and, from this, produce a statistical regression-type model that quantifies the impact of each relevant variable. Parametric techniques are most useful in the early stages of a project when only aggregate data is available. These estimates are normally considered an order of magnitude in accuracy because they may lack detailed requirement precision (Kwak and Watson 2005).
Balanced Scorecard and Project Estimation
Published in Jessica Keyes, Implementing the Project Management Balanced Scorecard, 2010
The parametric estimation method relates the cost of a system to one or more parameters of the system such as physical or performance characteristics utilizing a mathematical model. Parametric cost modeling techniques were first developed in the 1950s by the Rand Corporation. The big advantage of this technique is that it is very sensitive to significant design changes, so a changing scope can be easily reflected in the estimation. This method also provides quick reproducible results, based on the “real world” experience of many systems. Parametric estimation might be the best approach to estimation for the purposes of aligning to a business strategy and being measured by balanced scorecard. NASA provides a good downloadable manual, which explains this technique in detail. It can be found at: http://cost.jsc.nasa.gov/pcehg.html
Introduction
Published in Len Holm, John E. Schaufelberger, Construction Cost Estimating, 2021
Len Holm, John E. Schaufelberger
Estimating can be defined as the process of determining the approximate cost of performing a work task based on the design information available, which is not always complete. For the general contractor (GC), estimating and their ability to do it accurately and reliably is a critical component to the success of each project and ultimately the success of the company. Many construction companies have suffered bankruptcy because of estimates that were not done properly or completely. Note we will utilize the terms general contractor and construction manager (CM) throughout this book, and although we define differences between the two terms in Chapter 4, we consider them synonymous with respect to the process of preparing construction estimates.
Artificial neural networks for intelligent cost estimation – a contribution to strategic cost management in the manufacturing supply chain
Published in International Journal of Production Research, 2022
Frank Bodendorf, Philipp Merkl, Jörg Franke
Niazi et al. (2006) divide cost estimation methods into qualitative and quantitative methods. While qualitative methods derive cost values from similar products manufactured in the past, quantitative methods analyse product specifications and simulate production processes in detail. Bottom-up cost calculation represents the most established quantitative cost estimation method. Here, the products are split up into components, also considering their production steps. For each component and production step, partial cost values are calculated. The total cost value results from summing up all the partial cost values and overhead surcharges. Especially because of the detailed simulation of production processes, bottom-up cost calculation is very time-consuming and knowledge-intensive. In contrast, many papers in literature show that qualitative methods from the field of ML have the potential to overcome the weaknesses of bottom-up calculation (Bode, Ren, and Shi 1995; Chan, Lu, and Wang 2018; Deng and Yeh 2009; Zhang and Fuh 1998). Among ML methods, literature very frequently investigates the use of ANNs for cost estimation (Bode, Ren, and Shi 1995; Geiger, Knoblach, and Backes 1998; Zhang and Fuh 1998). Most of these studies lead to the result that ANNs outperform simple ML methods in terms of cost prediction (Shtub and Versano 1999; Smith and Mason 1997; Zhang, Fuh, and Chan 1996).
Bottom-Up Resource and Cost Estimation for Restoration of Supply Chain Interdependent Critical Infrastructure
Published in Engineering Management Journal, 2021
Akhilesh Ojha, Suzanna Long, Tom Shoberg, Steven Corns
Since the construction processes vary with the type of infrastructure being restored, construction processes are analyzed independently for each infrastructure element. Using a bottom-up cost estimation technique, each construction process is analyzed and the amount of resources are estimated. After determining which materials are necessary for restoration, the cost of these materials is then calculated. Each piece of equipment uses a given amount of power and/or fuel to perform its activity. The number of man-hours required to construct an infrastructure element can be used to calculate the amount of potable water and food required. For example, if a person drinks 0.2 gallons of water per hour and works for five hours, the total amount of potable water needed would be a gallon. Similarly, other resources are calculated using a similar set of coefficients in a set of linear equations. Expert advice and historical data are used to determine these coefficients. Once the coefficients are estimated, the total cost of resources is calculated and compared with data available in the literature. Exhibit 3 shows the process used for collecting.
Understanding the impact of non-standard customisations in an engineer-to-order context: A case study
Published in International Journal of Production Research, 2019
Sara Markworth Johnsen, Lars Hvam
Statistical regression is also a well-known tool in quantitative parametric cost estimation models, and it has been applied in an ETO context (Brunoe and Nielsen 2012; Caputo and Pelagagge 2008). Parametric cost estimation methods are function-based; they identify the relationships between cost and functional parameters/product attributes, also referred to as cost drivers, and they can utilise historical data and statistical regression to define the exact relationship that is representative of a specific product family (Foussier 2006b; Niazi et al. 2006). Thus, statistical regression is seen to be an appropriate estimation tool in the early product lifecycles (Stewart, Wyskida, and Johannes 1995) or in an ETO context when the detailed design is unavailable (Caputo and Pelagagge 2008). These characteristics make it a more suitable approach for the purpose of this paper as opposed to a bottom up/activity-based cost analysis, an approach that depends on the availability of detailed cost data and a distinction between SC and NSC cost data within a project.