scholarly journals Data Science and Traditional Engineering and Technology Programs—How to Improve Operational Excellence

2021 ◽  
Author(s):  
Vassilios Tzouanas
2015 ◽  
Author(s):  
Radian Belu ◽  
Richard Chiou ◽  
Tzu-Liang Tseng ◽  
Lucian Cioca

Author(s):  
Sima Zakani ◽  
Jake Kaupp ◽  
Roderick Turner ◽  
Brian Frank

Abstract – Inconsistent transfer policies, lack of articulated syllabi, and subsequent differences on the delivery of comparable courses are a few examples of the obstacles that Ontario students face when trying to change programs. This study sought to develop a framework to support transfer between engineering and engineering technology programs in Ontario using explicit and implicit course outcomes to help develop and define new pathways.  Primarily focusing on program expectations in introductory design courses this study compared the content and context of design projects in different institutions and programs across the province. The contextual framework, namely the “Outcome Comparison Framework for Engineering and Technology”, synthesized relevant elements from existing frameworks which can collectively identify the differences in the context of learning in engineering and technology disciplines. This framework looks into disciplinarity, use of tools and design thinking required to successfully finish a design project.  We collected design projects from 5 technology programs and 4 engineering programs across the province and coded them based on the content targeted by the project description and the three dimensions of the framework.  Content analysis for design courses showed an overall low percentage of alignment between the learning outcomes and the project descriptions across the board.  It was found that engineering design courses were more focused on principles of engineering design (problem definition, stakeholder needs, idea generation, decision making) and development of professional characteristics (workplace communication skills, ethics, etc.); but technology design courses, focused on the use of more “hands-on”' skills (building/implementation, troubleshooting, etc.)  


Author(s):  
Gaurav Nagpal ◽  
Gaurav Kumar Bishnoi ◽  
Harman Singh Dhami ◽  
Akshat Vijayvargia

With the increasing share of digital transactions in the business, the way of operating the businesses has changed drastically, leading to an immense opportunity for achieving the operational excellence in the digital transactions. This chapter focusses on the ways of using data science to improve the operational efficiency of the last mile leg in the delivery shipments for e-commerce. Some of these avenues are predicting the attrition of field executives, identification of fake delivery attempts, reduction of mis-routing, identification of bad addresses, more effective resolution of weight disputes with the clients, reverse geo-coding for locality mapping, etc. The chapter also discusses the caution to be exercised in the use of data science, and the flip side of trying to quantify and dissect the phenomenon that is so complex and subjective in nature.


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