Predictive Analysis of Automation Performance on Logistics Management in Food Industries

Purpose: In this research study the researcher emphasized the Automation Performance on Logistics Companies Performance in food Industries. In this context pick and pack including delivery at doorstep is one the challenging issues in the real words. The automation technologies have a potential role to automate the business process and provide secure and transparent way perishable goods and services. In current business industries the companies looking for fact and secure automation system to operate the business process in logistics industries. Design/methodology/approach: To identify the automation performance of logistics system using the 395000 datasets with distinguish supervised machine learning algorithms such as logistics classifiers, Support Vector Machine (SVM), KNN, and Decision Tree Classifiers including ADA and XGB boosters for more accuracy. Findings: The researcher found that Logistics Classifiers training accurate87.97% and testing accuracy 87.62%, Random Forest Classifiers training accuracy 98.59% and testing accuracy 9.29%, Support Vector Machine (SVM) training accuracy 97.48% and testing accuracy 97.85%, KNN Classifiers training accuracy 53.37%and testing accuracy 43.51%, Decision Tree classifiers training accuracy 98.68%and testing accuracy 98.36%, ADA Booster training accuracy 74.3% andtesting accuracy 75.02%, XGB Booster training accuracy98.62%and testing accuracy 98.90%. Practical implications: Finally, the researcher concluded that decision tree classifier and XGB Booster predictive model are having the 98% accuracy level and that predictive model would be suitable to measure the automation performance on logistics companies. Originality/value: This research study is on “Predictive Analysis of Automation Performance on Logistics Management in Food Industries” original based on different machine learning algorithms and compiled by 395000 datasets which are taken from kaggle.com