Proposing a new approach to the selection of material portfolio using a combination of data mining and optimisation methods

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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Collective Losses of Low Power Cage Induction Motors—A New Approach

Energies ◽

10.3390/en14061749 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1749

Author(s):

Elzbieta Szychta ◽

Leszek Szychta

Keyword(s):

Control System ◽

Experimental Studies ◽

Complex Problem ◽

Accurate Estimation ◽

New Approach ◽

Water Pumping ◽

Efficiency Control ◽

Interacting Systems ◽

Cage Induction Motor ◽

Selection Of

Energy efficiency of systems of water pumping is a complex problem since efficiency of two distinct interacting systems needs to be combined: water and power supply. This paper introduces a non-intrusive method of calculating the so-called “collective losses” of a cage induction motor. The term “collective losses”, which the authors define, allows for accurate estimation of motor efficiency. Control system of a pump determines operating point of a pumping station, and thus its efficiency. General estimated performance characteristics of a motor, components of a control system, are assumed to serve selection of a range of pumping speed variations. Rotational speed has a direct effect on motor load torque, pump power and head, and thus on motor performance. Hellwig’s statistical method was used to specify characteristics of estimated collective losses on the basis of experimental studies of 21 motors rated at up to 2.2 kW. The results of simulations and experiments are used to verify validity and efficiency of the suggested method. The method is non-intrusive, simple to use, and requires minimum data.

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Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysisThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽

10.1139/g10-080 ◽

2010 ◽

Vol 53 (11) ◽

pp. 1002-1016 ◽

Cited By ~ 59

Author(s):

B.R. Cullis ◽

A.B. Smith ◽

C.P. Beeck ◽

W.A. Cowling

Keyword(s):

Pedigree Information ◽

Environment Interaction ◽

Correlation Matrices ◽

New Approach ◽

Genome Wide ◽

Selection Indices ◽

New Varieties ◽

Oilseed Brassicas ◽

Analytic Models ◽

Selection Of

Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I of this series, we presented an approach to modelling V × E interaction in the analysis of complex multi-environment trials using factor analytic models. In this paper, we develop a range of statistical tools which explore V × E interaction in this context. These tools include graphical displays such as heat-maps of genetic correlation matrices as well as so-called E-scaled uniplots that are a more informative alternative to the classical biplot for large plant breeding multi-environment trials. We also present a new approach to prediction for multi-environment trials that include pedigree information. This approach allows meaningful selection indices to be formed either for potential new varieties or potential parents.

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Facing disruptive technologies: aligning purchasing maturity to contingencies

Supply Chain Management An International Journal ◽

10.1108/scm-03-2018-0087 ◽

2019 ◽

Vol 24 (1) ◽

pp. 147-169 ◽

Cited By ~ 1

Author(s):

Britta Søgaard ◽

Heather Dawn Skipworth ◽

Michael Bourlakis ◽

Carlos Mena ◽

Richard Wilding

Keyword(s):

Strategic Alignment ◽

Future Research ◽

Research Directions ◽

Company Strategy ◽

New Approach ◽

Content Type ◽

Disruptive Technologies ◽

Future Research Directions ◽

Exploratory Case Studies ◽

Selection Of

PurposeThis paper aims to explore how purchasing could respond to disruptive technologies by examining the assumptions underlying purchasing strategic alignment and purchasing maturity through a contingency lens.Design/methodology/approachThis study uses a systematic review across purchasing maturity and purchasing strategic alignment literature. This is supplemented with exploratory case studies to include practitioners’ views.FindingsThis research demonstrates that neither purchasing maturity nor purchasing strategic alignment are suitable approaches to respond to disruptive technologies. Purchasing maturity does not allow purchasing managers to select relevant practices. It also shows no consideration of any contingencies, which practitioners highlight as important for the selection of practices. Purchasing strategic alignment includes the company strategy as a contingency but does not provide any practices to choose from. It does not include any other contextual contingencies considered important by practitioners. The findings indicate that linking the two research streams may provide a more suitable approach to responding to disruptive technologies.Research limitations/implicationsThis research demonstrates the requirement to develop a new approach to responding to disruptive technologies, by linking purchasing maturity and purchasing strategic alignment to contextual contingencies. This is a currently unexplored approach in academic literature, which refutes the generally accepted premise that higher maturity unilaterally supports a better positioning towards technological disruption. This research also highlights a requirement for practitioners to shift their approach to “best practices”.Originality/valueThis is the first research to systematically review the relationships between purchasing maturity and purchasing strategic alignment. It adds to contingency theory by suggesting that purchasing maturity models can support the achievement of strategic alignment. Also, future research directions are suggested to explore these relationships.

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