Behavioral Drivers of Routing Decisions: Evidence from Restaurant Table Assignment

In this paper, we employ large‐scale sensor data to examine the impact of data‐based intelligence and work‐related experience on the time efficiency of individual taxi drivers, measured by their propensity of choosing the fastest routes. The identification strategy is built on 1) a unique exogenous policy shock banning taxi‐hailing apps with embedded GPS system, and 2) a measure of Non‐Recurring Congestion (NRC) avoidance, enabled by the real‐time sensor data, which serves as a proxy for GPS usage. Our empirical model provides evidence that data‐based intelligence improves taxi drivers’ routing decisions by close to 3%, as measured by trip speed. Our results further demonstrate that inexperienced drivers have a higher chance of choosing the fastest route, as they are more likely to rely on the real‐time traffic information from GPS technology than experienced drivers. The general implications of our findings on the adoption and utilization of data‐based performance‐enhancing technology are discussed in closing.This article is protected by copyright. All rights reserved

Section: Taxi Routing and Scheduling Decisionsmentioning

confidence: 99%

The role of data‐based intelligence and experience on time efficiency of taxi drivers: An empirical investigation using large‐scale sensor data

Lu,

Wang,

Chen

et al. 2023

“…Similarly, Hueter and Swart (1998) discuss uses of customer arrival forecasting, labor simulation, and crew optimization at Taco Bell. Other researchers examine how analysis of worker knowledge and performance data can improve staffing efficiency (He et al., 2019; Kawaguchi, 2020; Smirnov & Huchzermeier, 2020), labor assignment, scheduling, and scaling (Kamalahmadi et al., 2021; Tan & Netessine, 2019; Tan & Staats, 2020).…”

Section: Background and Framingmentioning

confidence: 99%

Analytics applications, limitations, and opportunities in restaurant supply chains

Swink

Zhao

2022

Technology, market, and competitive dynamics are requiring firms in restaurant/food service supply chains to improve their analytics capabilities, which have tended to lag behind other comparable industries. The global COVID‐19 pandemic has further encouraged industrial leaders to evaluate new challenges and opportunities. Our research provides insights into current applications of analytics technologies and organizationally integrates these insights for decision‐makers in restaurant supply chains. The study applies decision theory as a framing perspective to this phenomenon, thereby advancing the academic literature on the interface between data management, analytical techniques, and computing. We combine data drawn from interviews of leading players in U.S. and Chinese‐based restaurant chains with insights from trade publications and social media posts to identify best practices for analytics usage and supporting organizational changes. Our analysis provides examples of ways in which business leaders are applying analytics technologies to structured and unstructured data to address targeted objectives for demand/supply processes and to foster higher order organizational learning. In keeping with the stated objectives of this special issue of Production and Operations Management, this study provides an overview of both current state‐of‐the‐art and next‐generation capabilities for analytics in the restaurant industry. We further identify specific limitations of current processes, opportunities for development and theory‐based research, and challenges to implementation.

“…Based on this insight, they derive the optimal staffing policy in terms of workers per shift. Tan and Staats (2020) focus on the behavior of hosts when seating customers. Customers in a restaurant are traditionally seated by the host based on simple rules, such as the round-robin (RR) rule.…”

Section: Restaurant Table Management In Research and Practicementioning

confidence: 99%

Restaurant analytics: Emerging practice and research opportunities

Roy

Spiliotopoulou

Vries

2022

Smart technologies and increased data availability enable restaurateurs to gather more information about customers and their behavior. These data can be combined with data from other sources to make a wide range of strategic and operational restaurant decisions, and can therefore generate tremendous value for restaurants and their customers. This study focuses on discussing the most promising research opportunities in restaurant operations that leverage data analytics. In particular, we focus on specific research questions across restaurant decision domains such as location, reservation and table management, queue management, menu design and engineering, and multichannel order management. For each research question, we motivate its practical and theoretical relevance, identify data sources, propose a methodological approach for analysis, and discuss actionable insights for practitioners. As a result, this paper aims to highlight data analytics opportunities for restaurateurs and inspire researchers to contribute in this domain.