Ongoing Research Projects

Future of Work: Ongoing Research Projects

 

Do It Right the First Time: Vehicle Routing with Home Delivery Attempt Predictors

Research Team: Stanley Lim (Michigan State University, Supply Chain Management)

Research ideas/interests: Up to 20% of all business-to-consumer deliveries fail on the first attempt. Despite its economic significance, research has paid little attention to delivery attempt as an operational outcome or seldom accounted for its effects in routing models. We explore the value of accounting for failed delivery attempts in routing models, using transaction data from an ecommerce retailer in South America. We propose a “two-stage VRP optimization” algorithm. Our analysis indicates that not accounting for the probability of failed attempts in routing models may create a significant downward bias in the total cost of delivery. The analysis also suggests that manipulating the sequence in which packages in a route are delivered can be a cost-efficient lever that firms can employ at almost zero cost to profoundly affect delivery outcomes. We replicate the prediction model to a new sample from a delivery company in Singapore and calibrate it for a randomized field experiment to validate our algorithm’s performance. Packages and drivers are randomly assigned to either our algorithm or the focal company’s existing algorithm. Results suggest that our algorithm, on average, reduces the share of failed delivery attempts by 10% and total cost of delivery by $13 per route. We further propose drivers’ discretionary work effort and the goal-gradient hypothesis as a mechanism for the efficacy of our algorithm. Controlling for time of day and other fixed effects, we empirically find that packages assigned to slots later in the route tend to have a lower failure rate because drivers display a higher degree of discretionary work effort towards the end of a route. Our approach can be applied to other firms that manage last-mile delivery operations to improve their delivery execution.


Enhancing Team Effectiveness Via Wearable Devices

Research Team: Andrew Mason (Michigan State University, Electrical and Computer Engineering) and Angela Hall (Michigan State University, Human Resources and Labor Relations)

Research ideas/interests: Our research interests lie in the use of sensing and AI technology to enhance team effectiveness, the employee experience, and employee performance. Specifically, through the use of wearable devices (such as a necklace-style ID badge), data will be gathered on employees’ professional social interactions, and these employees will receive real-time, unobtrusive feedback on whether their actions are conducive to (or hinder) team effectiveness, including whether their behaviors promote or discourage inclusion (such as when teammates are interrupted when talking or when one or more teammates dominates a conversation). This technology will also sense physiological parameters and emotional cues (such as increased heart rate associated with anxiety) and will help employees to recognizing their own emotions and can offer micro-interventions (e.g., a signal to engage in calming techniques) for emotion regulation.


Human-Robot Symbiosis (HRS): From Labor-replacing To Labor-reinstating Robots In Manufacturing Settings

Research Team: Hee Rin Lee (Michigan State University, Media & Information) & Tariq Iqbal (University of Virginia, Engineering Systems and Environment)

Research ideas/interests: As robots are incorporated into a wide variety of factory settings, human workers are increasingly dis- placed. The goal of this proposal is to confront this detrimental consequence of new technologies on low-level workers in the workplace and establish symbiotic relationships between human workers and robots that avoid human displacement and strengthens human autonomy. By employing a participatory research approach, the research team seeks to address these problems with an overarching goal to empower low-level workers in the decision-making process of workplace automation. The three key contributions of this project are: 1) developing human-aware algorithms based on a negotiation process where low-level workers have a chance to integrate their expertise into robot design; consequently, robots will enable human workers to decide when, how, and to what degree they want to work with robots; 2) creating a human-robot symbiosis design framework that empowers low- level workers in the workplace; 3) generating practical insights for future human-robot collaborations in manufacturing organizations. These insights will help guide labor distribution between humans and robots, and maintain the psychological well-being of human workers.


Next Generation Manufacturing Leaders

Research Team: Cheri Speier-Pero and Sriram Narayanan (Michigan State University, Broad College of Business)

Research ideas/interests: Working with Apple executives to produce a white paper in partnership with the World Economic Forum focused on the Next Generation Manufacturing Leaders (NGML). This research/white paper has several goals:

  • Highlight the value/impact associated with a career in manufacturing
  • Address aspects of global vs. local; social/economic/sustainability impact that leaders can have in the manufacturing space, and the role of technology in radically changing the nature of manufacturing
  • Develop a pathway to enhance gender diversity in these leadership roles

Studying the Impacts of Autonomous Vehicles on the Workforce

Research Team: J. Kevin Ford (Michigan State University, Department of Psychology), John Verboncoeur (Michigan State University, College of Engineering), Peter Savolainen, (Michigan State University, College of Engineering), and Troy Hale (Michigan State University, School of Journalism)

A multidisciplinary research team from Michigan State University will use a $2.49 million grant from the National Science Foundation to conduct a four-year study examining the impacts of autonomous vehicles on the future workforce. This project will help us understand human interactions with autonomous machines and the impact of these interactions on driving jobs, which is one of the first waves of workplaces expected to be impacted by this new wave of technologies. This research project will help determine the specific skills and skillsets needed to ensure that members of the current workforce, as well as the future workforce, are prepared for this transition. Researchers will also determine how willing and able workers are to adapt to the changing nature of driving jobs, and whether the changing nature of jobs will disadvantage some groups of workers more so than others. As part of the project, skills maps will be shared with education and workforce groups, who can develop new training and certificate programs, in order to mitigate job displacement.