Affordances are possibilities for action offered by environment: objects, artifacts, technologies. This concept was created to complement the functional view with an emphasis on users’ actions, and help identify potential failures or negative effects which the other approaches have difficulty identifying. And though this approach will never provide the creator with all potential user actions, it helps change one’s viewpoint to a more reflective one, and devise organizational designs and technologies that are safer and easier to use.
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Robotic warehouses have transformed logistics, prioritizing speed and efficiency. However, traditional static priority systems often leave low-priority customers facing excessive delays, raising concerns about fairness. This research, based on Invia, a robotic warehouse company, proposes a dynamic priority allocation model to balance efficiency and fairness. By adjusting order priorities over time, this approach ensures that both high-priority and long-waiting low-priority orders receive timely fulfillment. Through stochastic modeling and simulations, we demonstrate that dynamic prioritization reduces delays compared to static and first-come, first-served (FCFS) models. Case studies in e-commerce and healthcare logistics illustrate the broader impact of fairness in automation. As industries increasingly rely on AI-driven decision-making, the balance between efficiency and equity becomes critical. This research challenges the assumption that robotic warehouses should optimize for speed alone and advocates for a future where fairness plays a central role in automated commerce.
- Research
- Digital Transformation, Logistics and Supply Chain
