Engineering AI Systems: A Research Agenda

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that we have studied. The main contribution of the paper is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.Comment: 8 pages, 4 figure

A Knowledge Network and Mobilisation Framework for Lean Supply Chain Decisions in Agri-Food Industry

Copyright ©2017 IGI Global. Reproduced with permission from IGI Global. All rights, including translation into other languages reserved by the publisher. No part of this article may be reproduced or used in any form or by any means without written permission from the publisher, except for noncommercial, educational use including classroom teaching purposes.Making the right decisions for food supply chain is extremely important towards achieving sustainability in agricultural businesses. This paper explores that knowledge sharing to support food supply chain decisions to achieve lean performance (i.e. to reduce/eliminate non-value-adding activities, or “waste” in lean term). The focus of the paper is on defining new knowledge networks and mobilisation approaches to address the network and community nature of current supply chains. Based on critical analysis of the state-of-the-art in the topic area, a knowledge network and mobilisation framework for lean supply chain management has been developed. The framework has then been evaluated using a case study from the food supply chain. Analytic Hierarchy Process (AHP) has been used to incorporate expert's view on the defined knowledge networks and mobilisation approaches with respect to their contribution to achieving various lean performance objectives. The results from the work have a number of implications for current knowledge management and supply chain management in theory and in practice.Peer reviewe