Parametric estimation of supplier's plant construction costs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F22%3A43921917" target="_blank" >RIV/62156489:43110/22:43921917 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.52514/sier.v1i2.19" target="_blank" >https://doi.org/10.52514/sier.v1i2.19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.52514/sier.v1i2.19" target="_blank" >10.52514/sier.v1i2.19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parametric estimation of supplier's plant construction costs

Popis výsledku v původním jazyce

Cost engineers of buying enterprises perform detailed product cost calculations of externally manufactured components. The aim of these calculations is to determine what a product should cost and to support purchasing functions in fact-based negotiations. While product cost engineers have deep knowledge in the calculation of direct cost, they need support in the calculation of supplier&apos;s indirect cost categories. The calculation of industrial rent, which is expressed in annual cost per m2 of occupied plant building floor space can be improved by providing accurate construction cost estimates. Construction costs are strongly impacting the calculation of supplier&apos;s annual building depreciation, which is a crucial cost driver for the determination of the industrial rent. Academic literature is actually not providing an accurate and suitable cost model for product cost engineers, which is estimating construction cost per m2 depending on different industrial building categories and alternative supplier plant locations. The paper aims to close this gap by applying linear regression analysis on a set of European construction cost data considering two industrial building categories: &quot;warehouses/basic factory units&quot; and &quot;high-tech factories&quot;. By regressing construction cost against construction labor rates within different supplier plant locations it was possible to form suitable and accurate parametric regression functions with R2 values between 0.74 and 0.88. Next to high R2 values acceptable mean average percentage errors between 7.45% and 11.77% could be realized by comparing estimated with observed construction cost. The estimation of industrial construction costs based on the paper&apos;s results can be used to improve the calculation of industrial rent, which is one cost element, that has to be covered within product cost engineer&apos;s Should Cost Calculations.

Název v anglickém jazyce

Parametric estimation of supplier's plant construction costs

Popis výsledku anglicky

Cost engineers of buying enterprises perform detailed product cost calculations of externally manufactured components. The aim of these calculations is to determine what a product should cost and to support purchasing functions in fact-based negotiations. While product cost engineers have deep knowledge in the calculation of direct cost, they need support in the calculation of supplier&apos;s indirect cost categories. The calculation of industrial rent, which is expressed in annual cost per m2 of occupied plant building floor space can be improved by providing accurate construction cost estimates. Construction costs are strongly impacting the calculation of supplier&apos;s annual building depreciation, which is a crucial cost driver for the determination of the industrial rent. Academic literature is actually not providing an accurate and suitable cost model for product cost engineers, which is estimating construction cost per m2 depending on different industrial building categories and alternative supplier plant locations. The paper aims to close this gap by applying linear regression analysis on a set of European construction cost data considering two industrial building categories: &quot;warehouses/basic factory units&quot; and &quot;high-tech factories&quot;. By regressing construction cost against construction labor rates within different supplier plant locations it was possible to form suitable and accurate parametric regression functions with R2 values between 0.74 and 0.88. Next to high R2 values acceptable mean average percentage errors between 7.45% and 11.77% could be realized by comparing estimated with observed construction cost. The estimation of industrial construction costs based on the paper&apos;s results can be used to improve the calculation of industrial rent, which is one cost element, that has to be covered within product cost engineer&apos;s Should Cost Calculations.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

SCENTIA International Economic Review

ISSN

2748-0089

e-ISSN

2748-0089

Svazek periodika

1

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

27

Strana od-do

95-121

Kód UT WoS článku

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EID výsledku v databázi Scopus

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