Search results

1 – 2 of 2
Article
Publication date: 30 September 2020

Serhat Simsek, Abdullah Albizri, Marina Johnson, Tyler Custis and Stephan Weikert

Predictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and…

Abstract

Purpose

Predictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract.

Design/methodology/approach

This study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks.

Findings

There are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. “Age,” “Wins above Replacement” and “the team on which a player last played” are the most significant factors in determining if a player signs a new contract.

Originality/value

This paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.

Details

Journal of Enterprise Information Management, vol. 34 no. 2
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 31 January 2023

Christian Orgeldinger, Tobias Rosnitscheck and Stephan Tremmel

Microtextured surfaces can reduce friction in tribological systems under certain contact conditions. Because it is very time-consuming to determine suitable texture patterns…

Abstract

Purpose

Microtextured surfaces can reduce friction in tribological systems under certain contact conditions. Because it is very time-consuming to determine suitable texture patterns experimentally, numerical approaches to the design of microtextures are increasingly gaining acceptance. The purpose of this paper is to investigate to what extent the selected modeling approach affects optimized texturing.

Design/methodology/approach

Using the cam/tappet contact as an application-oriented example, a simplified 2D and a full 3D model are developed for determining the best possible texturing via a design study. The study explores elongated Gaussian-shaped texture elements for this purpose. The optima of the simplified 2D simulation model and the full 3D model are compared with each other to draw conclusions about the influence of the modeling strategy. The target value here is the solid body friction in contact.

Findings

For the elongated texture elements used, both the simplified 2D model and the full model result in very similar optimal texture patterns. In the selected application, the simplified simulation model can significantly reduce the computational effort without affecting the optimization result.

Originality/value

Depending on the selected use case, the simulation effort required for microtexture optimization can be significantly reduced by comparing different models first. Therefore, an exact physical replica of the real contact is not necessarily the primary goal when it comes to texture selection based on numerical simulations.

Details

Industrial Lubrication and Tribology, vol. 75 no. 7
Type: Research Article
ISSN: 0036-8792

Keywords

1 – 2 of 2