Srinivas Bhashyam, Ki Hoon Shin and Debashish Dutta
Computer aided design systems are routinely used by designers for creating part geometries. Interfaces to computer aided analysis and manufacturing are also commonplace enabling…
Abstract
Computer aided design systems are routinely used by designers for creating part geometries. Interfaces to computer aided analysis and manufacturing are also commonplace enabling the rapid fabrication of the designed part. Thus far, however, the focus was on objects with homogeneous interior. Two recent advances use of functionally graded materials in parts, and layered manufacturing technology have brought to the forefront the need for CAD systems to support the creation of geometry as well as the graded material inside. This paper reports on such a system. We describe the need, the components and implementation of a CAD system for creating heterogeneous objects. Two examples illustrate the use.
Details
Keywords
Shrawan Kumar Trivedi, Jaya Srivastava, Pradipta Patra, Shefali Singh and Debashish Jena
In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must…
Abstract
Purpose
In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.
Design/methodology/approach
The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.
Findings
It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.
Originality/value
To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.