Lincoln C. Wood

The Event Study Method in Logistics Research: Overview and a Critical Analysis

Logistics researchers often want to understand how particular management changes or external factors influence a firm. While this can be accomplished using operational or survey data, we outline an alternative approach using the event study method where inferences are made with the estimated magnitude and direction of abnormal returns. The calculated abnormal returns can be used as a dependent variable in a cross-sectional regression to understand which managerial decisions may affect these outcomes. As the method remains little used by logistics researchers, we outline key assumptions and design considerations. We review recent articles and provide suggestions for logistics researchers improve the rigor of their research designs. This article aims to provide an overview of the method for logistics and supply chain researchers with a focus on developing the capability to design an effective study and to evaluate research articles to assess methodological weaknesses that may lead to untrustworthy results.

Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System

Every minute counts in an event of fire evacuation where evacuees need to make immediate routing decisions in a condition of low visibility, low environmental familiarity, and high anxiety. However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. This study aims to develop a fire evacuation routing model “Bee-Fire” using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. Bee-Fire is able to find the optimal fire evacuation routing solutions; thus not only the clearance time but also the total evacuation time can be reduced. Simulation shows that Bee-Fire could save 10.12% clearance time and 15.41% total evacuation time; thus the congestion during the evacuation process could be effectively avoided and thus the evacuation becomes more systematic and efficient.

Self-guided Exploration of Virtual Learning Spaces

Virtual learning spaces provide the opportunity to create authentic, immersive and high-fidelity experiences for learners; often enhanced with new technology to increase the interaction and perception with the learning space. Instead of creating mock-ups in classrooms, educators are able to recreate a controlled replica of the real world, i.e. scenarios and situations can be created that are difficult or impossible to achieve otherwise. However, an unrestricted and unsupervised exploration imposes challenges to monitor the learner, offer supportive guidance and provide formative feedback. Preliminary studies demonstrated that different approaches are able to engage the learner, create an intrinsic motivation and therefore provide curiosity to drive the self-paced learning; yet the use-case-based exploration is not transferred to a framework including a comprehensive tool for education. In this chapter, we demonstrate the prototype of the nDiVE framework, which combines authentic education, gamification, emerging technology and design principles used in the game industry to create an engaging learning space for students and workers.

Exploring Sentiment Analysis to Improve Supply Chain Decisions

Purpose: Traditional information sharing and supply chain decisions are examined in light of emerging technologies. Focusing on the bullwhip effect, implications of information asymmetry for upstream firms, distant from a market, are highlighted. A novel approach is presented as effective for sensing changes in market demand without reliance on supply chain partners.Design/methodology/approach: Remedies for the bullwhip effect based on traditional supply chain management techniques and technologies are discussed, providing a series of succinct hypotheses that summarise key relationships. These hypotheses are used to establish the usefulness of an emerging technology. Findings: The paper explains how the emerging technology of sentiment analysis can meet the same fundamental requirements for supply chain managers. Through rapid and real-time monitoring of aggregated opinions expressed in social media, upstream suppliers can gather additional external data that reduce reliance on supply chain partners to achieve the same objectives. Research limitations/implications: The effectiveness of the process is demonstrated by logically extrapolating that the approach is valuable to upstream companies, after showing that a consumer-facing firm can use the approach with accuracy. Practical implications: The practical value of textual data analysis is highlighted to emphasise how upstream firms can improve sensitivity to market demand changes, without requiring collaboration with supply chain members.Originality/value: Through the novel application of sentiment analysis to support supply chain management, the value of this paper is the unique opportunity logically afforded upstream firms to increase transparency and speed of response to market changes.

Action-Based Learning Assessment in Virtual Training Environments

Action-based learning plays an important role in today’s education, with assessment being an inevitable aspect that presently requires extensive financial and human expert resources. A major research area is dedicated to the development and implementation of systems to perform an automated or at least support formative assessment of human actions in action-based learning. That is, recognising and comparing the performed actions of the learner with the previously recorded actions undertaken by experts. Human actions are classified according to the developed taxonomy of human actions to facilitate a formative feedback for the learner. The action-based learning assessment method analyses the action sequences of the learners according to reference solutions (generated by experts) and automatically provides formative feedback to improve learner performance.

Factor Reduction and Clustering for Operational Risk in Software Development

Software development failures frequently emerge as a result of the failure to understand and identify risks. The aim of this paper is to identify the most salient risk factors present during a software development project’s life cycle, in terms of their occurrence likelihood and impact on cost overrun. A questionnaire was sent to 2000 software development companies, IT consultancy and management companies and web development companies in the United Kingdom, United States, mainland Europe, Canada, Australia, India, China, Japan and other Asian countries, asking respondents to evaluate a number of risk factors. Because many factors were closely related, we applied a factor reduction and clustering process to the results to allow a smaller number of crucial risk factors to be identified. The three main clusters of risk factors we identified are feasibility studies, project team management and technology requirements. While a feasibility study may be unlikely to take place, it can have a significant impact on project outcomes. In contrast, project team management is likely to occur but has a relatively small impact on outcomes in comparison with technology requirements. Professionals will need to apply checks and balances to these factors and generate risk mitigation plans to reduce the severity of project failures. Our results allow them to connect the probability of risk factor occurrence and overall impact, so they can focus their limited resources on reducing the most pertinent risks in their projects.