Simulation-based cost estimation under economic uncertainty using kernel estimators

Asiedu, Yaw; Besant, Robert W.

doi:10.1080/002075400188474

Cited by 28 publications

(15 citation statements)

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“…Such models represent organizations as fairly independent sets of processes with interacting entities and flows which can be (re)designed to meet corporate objectives (Ajaefobi 2004;Chatha and Weston 2005). In specific terms, some researchers in Cost Engineering (Johnson and Kaplan 1987;Beltramo 1988;Son 1991;Asiedu, Besant et al 2000;Agyapong-Kodua, Wahid et al 2007;Caputo and Pelagagge 2008) have proposed and tested several cost estimating, production monitoring, cost calculation and evaluation and cost modelling techniques which are currently being used in academia and industry. The following sections provide a detailed review of some of these techniques.…”

Section: Soa Survey Of Cost Modelling Methodsmentioning

confidence: 99%

“…Gray (Gray, MacDonell et al 1999) argues that cost judgements from experts and knowledgeable people cannot be done away with considering the fact that it takes a relatively long time to collect and analyse cost information. But Caputo (Caputo and Pelagagge 2008 (Asiedu, Besant et al 2000;Layer, Brine et al 2002;Caputo and Pelagagge 2008) into: 1) statistical; 2) analogous 3) generative or analytical and 4) feature based. They explained that statistical models adopt statistical criteria to identify causal links and correlate cost and product characteristics to obtain a parametric function with one or more variables.…”

Section: Soa Survey Of Cost Modelling Methodsmentioning

confidence: 99%

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Towards the derivation of an integrated process cost-modelling technique for complex manufacturing systems

Agyapong-Kodua

Wahid

Weston

2011

International Journal of Production Research

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Section: Soa Survey Of Cost Modelling Methodsmentioning

confidence: 99%

Section: Soa Survey Of Cost Modelling Methodsmentioning

confidence: 99%

Towards the derivation of an integrated process cost-modelling technique for complex manufacturing systems

Agyapong-Kodua

Wahid

Weston

2011

International Journal of Production Research

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“…Derived from the PESTEL 1 analysis, it is divided into an economic, sociopolitical, technological, and natural level. The economic level consists, inter alia, of labor rates (Asiedu and Besant 2000), competitors (Schmidt 2003), market demand (Kantola and Saari 2013), inflation and cost development (Cole and Sterner 2000), and discount rate (Hong et al 2007). The sociopolitical level includes demographic and social factors (Lai et al 2013), public acceptance (Troldborg et al 2014), and the influence of regulation and taxation (Greenberg et al 2004).…”

Section: Random Errors (Bevington Andmentioning

confidence: 99%

“…This lack of knowledge generally refers to existing or non-available data, model context, and modeling structure (Björklund 2002;Heijungs and Huijbregts 2004), which is adapted to parameter, model, and scenario uncertainty in this review. Uncertainty is present in every life cycle stage (Asiedu and Besant 2000) but alters in and over each stage depending on accumulated knowledge (Xu et al 2012a).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in life cycle costing for long-range infrastructure. Part I: leveling the playing field to address uncertainties

Ilg

Scope

Muench

et al. 2016

Int J Life Cycle Assess

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Purpose Life cycle costing (LCC) is a state-of-the-art method to analyze investment decisions in infrastructure projects. However, uncertainties inherent in long-term planning question the credibility of LCC results. Previous research has not systematically linked sources and methods to address this uncertainty. Part I of this series develops a framework to collect and categorize different sources of uncertainty and addressing methods. This systematization is a prerequisite to further analyze the suitability of methods and levels the playing field for part II. Methods Past reviews have dealt with selected issues of uncertainty in LCC. However, none has systematically collected uncertainties and linked methods to address them. No comprehensive categorization has been published to date. Part I addresses these two research gaps by conducting a systematic literature review. In a rigorous four-step approach, we first scrutinized major databases. Second, we performed a practical and methodological screening to identify in total 115 relevant publications, mostly case studies. Third, we applied content analysis using MAXQDA. Fourth, we illustrated results and concluded upon the research gaps. Results and discussion We identified 33 sources of uncertainty and 24 addressing methods. Sources of uncertainties were categorized according to (i) its origin, i.e., parameter, model, and scenario uncertainty and (ii) the nature of uncertainty, i.e., aleatoric or epistemic uncertainty. The methods to address uncertainties were classified into deterministic, probabilistic, possibilistic, and other methods. With regard to sources of uncertainties, lack of data and data quality was analyzed most often. Most uncertainties having been discussed were located in the use stage. With regard to methods, sensitivity analyses were applied most widely, while more complex methods such as Bayesian models were used less frequently. Data availability and the individual expertise of LCC practitioner foremost influence the selection of methods. Conclusions This article complements existing research by providing a thorough systematization of uncertainties in LCC. However, an unambiguous categorization of uncertainties is difficult and overlapping occurs. Such a systemizing approach is nevertheless necessary for further analyses and levels the playing field for readers not yet familiar with the topic. Part I concludes the following: First, an investigation about which methods are best suited to address a certain type of uncertainty is still outstanding. Second, an analysis of types of uncertainty that have been insufficiently addressed in previous LCC cases is still missing. Part II will focus on these research gaps.

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“…Qualitative approaches rely on expert judgment or heuristic rules and will not be dealt with in this work (as they only state whether an alternative is better or worse than the other without specifying absolute values). Quantitative methods may be further classified into statistical models, analogous models or generative-analytical models (Asiedu et al, 2000). Parametric cost models belong to the family of statistical methods in that statistical criteria are utilized to identify the causal links and correlate costs and product characteristics in order to obtain a parametric function with one or more variables (Foussier, 2006b).…”

Section: Introductionmentioning

confidence: 99%

A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran

2011

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Potatoes are the single most important agricultural commodity in Hamadan province of Iran, where 25,503 ha of this crop were planted in 2008 under irrigated conditions. This paper compares results of the application of two different approaches, parametric model (PM) and artificial neural networks (ANNs), for assessing economical productivity (EP), total costs of production (TCP) and benefit to cost ratio (BC) of potato crop. In this comparison, Cobb-Douglas function for PM and multilayer feedforward for implementing ANN models have been used. The ANN, having 8-6-12-1 topology with R 2 = 0.89, resulted in the best-suited model for estimating EP. Similarly, optimal topologies for TCP and BC were 8-13-15-1 (R 2 = 0.97) and 8-15-13-1 (R 2 = 0.94), respectively. In validating the PM and ANN models, mean absolute percentage error (MAPE) was used as performance indicator. The ANN approach allowed to reduce the MAPE from -184% for PM to less than 7% with a +30% to -95% variability range. Since ANN outperformed PM model, it should be preferred for estimating economical indices.Additional key words: artificial neural networks; benefit to cost ratio; Cobb-Douglas production function; economical productivity; estimation error; Solanum tuberosum; total cost of production. ResumenEstudio comparativo entre enfoques paramétricos y de redes neuronales artificiales para la evaluación económica de la producción de patata en Irán La patata es el producto agrícola más importante en la provincia de Hamadan (Irán), donde se plantaron 25.503 ha de este cultivo en 2008 bajo condiciones de riego. Este trabajo compara los resultados de aplicar dos enfoques diferentes, un modelo paramétrico (PM) y redes neuronales artificiales (ANN), para evaluar la productividad económica (EP), los costos totales de producción (TCP) y el coeficiente beneficio/costo (BC) del cultivo de la patata. En esta comparación se han utilizado la función Cobb-Douglas como PM y el proceso "feedforward" multicapa para implementar modelos de ANN. Las ANN, con una topología 8-6-12-1 con R 2 = 0,89, resultaron ser el modelo más adecuado para estimar la EP. Del mismo modo, las topologías óptimas para TCP y BC fueron 8-13-15-1 (R 2 = 0,97) y 8-15-13-1 (R 2 = 0,94), respectivamente. Para validar los modelos PM y ANN, se utilizó como indicador de desempeño el error porcentual medio absoluto (MAPE). El enfoque de ANN permitió reducir el MAPE desde -184% para PM a menos del 7% con un rango de variabilidad de +30% a-95%. Dado que ANN fue mejor que el modelo PM, debe ser preferido para la estimación de los índices económicos.Palabras clave adicionales: coeficiente beneficio/costo; costo total de producción; error de estimación; función de producción Cobb-Douglas; productividad económica; redes neuronales artificiales; Solanum tuberosum.

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Simulation-based cost estimation under economic uncertainty using kernel estimators

Cited by 28 publications

References 0 publications

Towards the derivation of an integrated process cost-modelling technique for complex manufacturing systems

Towards the derivation of an integrated process cost-modelling technique for complex manufacturing systems

Uncertainty in life cycle costing for long-range infrastructure. Part I: leveling the playing field to address uncertainties

A comparative study between parametric and artificial neural networks approaches for economical assessment of potato production in Iran

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