Consumer direct delivery of packages ordered over the Internet has grown at well over 25 % per year over the past 10 years and now accounts for over $100 billion in sales in the U.S. alone. Retailers have rushed to capitalize on what has commonly been labeled multi‐channel retailing, while logisticians have faced a challenge in devising efficient methods of delivering billions of packages to customer homes. Inefficient deliveries in this “last mile” of the supply chain have led to numerous business collapses as well as a substantial increase in delivery costs. We present a study which examines the effect of two factors (customer density and delivery window length) on the overall efficiency of the delivery route. Data are collected based on empirically derived settings from interviews with several practicing managers. Results provide insight for logistics and marketing managers who must balance customer desires for convenience with business desires for efficiency. The data show that offering a 3 hour delivery window is 30–45% more expensive than offering unattended (9 hour delivery window) delivery. The results provide a tool for managers to address the tradeoffs between various settings for the independent variables (customer density and delivery window length) and the overall route efficiency.

No abstract

This chapter looks at emerging technologies and their use in supply management processes as a means to improve effectiveness through improved speed and accuracy, at a reduced cost. Many technologies are finding their way into supply management, with differing levels of penetration and application and with mixed results. It may be challenging for supply management professionals to understand how, when, and where these technologies are likely to yield positive results. This chapter reviews several technologies, including artificial intelligence/machine learning, big data/advanced analytics, blockchain, cloud computing, conversational things (e.g., chatbots), immersive technologies (e.g., virtual and augmented reality), and robotic process automation. Findings indicate that the primary advantages are achieved by improving current processes and workflows, rather than that these technologies are currently disrupting or will fundamentally change supply management. Another important finding is the importance of “clean data” inputs, something that artificial intelligence can help with and that is foundational for successful robotic process automation.