Performance Measurement: A Conceptual Framework for Supply Chain Practices

AbstractMe--asurement of Supply Chain (SC) performance with regards to key practices of SC paradigms is the area which is under research. Presently there are no guidance or set rules under which we can measure SC performance. The lack of clarity and comparability concerns in this area creates misunderstanding and makes it more difficult to formulate a clear strategy. The aim of this research is to identify antecedents of existing SC paradigm's practices, as well as antecedents for SC performance measurement to formulate a conceptual framework. Based on this research, new sustainable SC performance measurement conceptual framework is proposed for existing SC paradigms. The detailed analysis presented in this research paper offers a set of characteristics and structure that industry as well as academia could use it as a guidance framework to measure SC performance

Quantitative analysis of sustainable use of construction materials for supply chain integration and construction industry performance through Structural Equation Modeling (SEM)

This research focuses on the mediating role of construction materials, sustainable use between the construction supply chain integration and the construction industry performance. In this concern, the case of Pakistan was considered specifically. The research design employed in this study was quantitative and a close-ended survey questionnaire was used as a research instrument. The sample size used is comprised of 300 participants and analysis was performed through the Structural Equation Modelling (SEM). The results revealed that the effect of the components of supply chain integration on the construction industry performance was statistically significant. Moreover, outcomes also substantiate the mediation role of using construction material sustainably. The scope of the research was limited to the construction industry of Pakistan; however, future research would focus on other countries and industries

Prediction of compressive strength of cementitious grouts for semi-flexible pavement application using machine learning approach

This study involves the preparation of cement grout samples by varying proportions of superplasticizer (SP) and water-cement (w/c) ratio (0.25–0.45) which were tested for computing flow value and 1-day, 7-day, and 28-day compressive strength. With 75 data points a neural network model was developed to predict the 28-day compressive strength based on w/c ratio, superplasticizer percentage, flow value, and compressive strengths of 1-day and 7-day as input variables. Due to the better performance of Artificial Neural Networks (ANN) over other applied models, this study predicts a confident pattern of relationship between w/c ratio (indirect), superplasticizer (direct up to 3%), and the corresponding strength characteristics of the study samples. The prediction power of the developed model has been improved from R2 = 0.959 to R2 = 0.984 by optimizing the number of neurons, activation function, and optimizer. The best performance was observed in the case of Model45-R_A. The same model was used for assessing the effect of the percentage of superplasticizer and water cement ratio on 28 days compressive strength where the compressive strength of 73.941 MPa was recorded in the case of 3% superplasticizer and w/c of 0.25