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Lean Procurement
Published in Paul Myerson, Lean Demand-Driven Procurement, 2018
Needless to say, many industries are adopting technology to automate supply processes, bringing themselves significant bottom-line benefits through greater productivity, visibility, and cost efficiency. In fact, e-procurement—the business-to-business purchase and sale of supplies and services over the Internet—provides the opportunity to increase efficiencies in business transactions and enhances negotiation leverage for firms. Use of technology is a key metric used to benchmark companies as a measure of the maturity of their procurement organizations.
Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/liquid paraffin mixture properties
Published in Engineering Applications of Computational Fluid Mechanics, 2022
Xiaoluan Zhang, Xinni Liu, Xifeng Wang, Shahab S. Band, Seyed Amin Bagherzadeh, Somaye Taherifar, Ali Abdollahi, Mehrdad Bahrami, Arash Karimipour, Kwok-Wing Chau, Amir Mosavi
Cost efficiency is the act of saving money by optimizing a process. This is done by decreasing experimental costs and improving efficiencies across the process (Abidi et al., 2021; Du et al., 2020; Nguyen, Rizvandi et al., 2020; Sun et al., 2021). Liu et al. (2019) presented a new approach of ‘Recursive Least Squares Fuzzy' neural network. They optimized the thermal conductivity measurement of Graphene Oxide/Water nanofluid by R-Squared of 0.99. Alsarraf, Malekahmadi et al. (2020) used the neural network to optimized the thermal conductivity measurement of Graphene/Water nanofluid by R-Squared of 0.99. Xu et al. (2020) used Levenberg Marquardt (LM) algorithm to optimize the viscosity measurement process of Graphene Oxide/Water nanofluid by R-Squared of 0.997 for RMPs of 10 and 100. Li et al. (2021) presented a novel neural network algorithm (Orthogonal Distance Regression (ODR)) to optimize the thermal conductivity measurement of Carbon Nanotube-Titanium Dioxide/Water-Ethylene Glycol nanofluid by R-Squared of 0.9999. Malekahmadi et al. (2021) compared two kinds of neural network algorithms, LM and ODR, to find the best optimization method for thermal conductivity measurement of Carbon Nanotube-Hydroxyapatite/Water nanofluid. They reported that the ODR is a better method to model the process and to reduce the cost of experiments.
Financial winners and losers since the privatization of the English and Welsh water and sewerage industry: a profit decomposition approach
Published in Urban Water Journal, 2020
Andrés Villegas, María Molinos-Senante, Alexandros Maziotis, Ramón Sala-Garrido
The cost efficiency of the input allocation between period and period is measured by the distance between and . A positive cost-efficiency effect indicates improvements in productivity and contributes positively to profit changes. The progress or regression of the input set is measured by technical change, i.e. the difference in the distance between and compared to the technology of period and . Technical change contributes positively to productivity and profit changes. The scale effect is measured along the surface of from to and shows the impact of scale economies on the productivity effect. A positive scale effect implies either an expansion in the presence of increasing returns to scale or a contraction in the existence of decreasing returns to scale, either of which contributes positively to the quantity effect and increases changes in profit (Grifell-Tatjé and Lovell 2008).