Technology development process and managing uncertainties with sustainable competetive advantage approach

The main purpose of this research work is to assist the decision-making process which is related to technology and knowledge factor within an organization. The data has been gathered and analysed from a particular multinational company that operates in the ceramic manufacturing industry within Malaysia. Four respondents were sought to answer the sense-and-respond questionnaire, including the part on technology sharing. The priority among technology types, including basic, core, and spearhead was decided by the maximum coefficient of the variance. The work has two main contributions: 1. It proposes and validates a tool for decisions and strategies related to technology focus in firms, and 2. expands the notion of technology types from focusing only on product development to one that focuses on both product and process development. The results of the study show that the proposed model which was previously applied in high tech start-ups and local medium-size enterprises is applicable in large industries involved in mass production.fi=vertaisarvioitu|en=peerReviewed

Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

This study aimed to develop accurate models for estimating the compressive strength (CS) of concrete using a combination of experimental testing and different machine learning (ML) approaches: baseline regression models, boosting model, bagging model, tree-based ensemble models, and average voting regression (VR). The research utilized an extensive experimental dataset with 14 input variables, including cement, limestone powder, fly ash, granulated glass blast furnace slag, silica fume, rice husk ash, marble powder, brick powder, coarse aggregate, fine aggregate, recycled coarse aggregate, water, superplasticizer, and voids in mineral aggregate. To evaluate the performance of each ML model, five metrics were used: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), coefficient of determination (R2-score), and relative root mean squared error (RRMSE). The comparative analysis revealed that the VR model exhibited the highest effectiveness, displaying a strong correlation between actual and estimated outcomes. The boosting, bagging, and VR models achieved impressive R2-scores in the range of 86.69%–92.43%, with MAE ranging from 3.87 to 4.87, MSE from 21.74 to 38.37, RMSE from 4.66 to 4.87, and RRMSE between 8% and 11%. Particularly, the VR model outperformed all other models with the highest R2-score (92.43%) and the lowest error rate. The developed models demonstrated excellent generalization and prediction capabilities, providing valuable tools for practitioners, researchers, and designers to efficiently evaluate the CS of concrete. By mitigating environmental vulnerabilities and associated impacts, this research can significantly contribute to enhancing the quality and sustainability of concrete construction practices