DATA-DRIVEN ANALYSIS OF LAST-MILE DELIVERY EFFICIENCY AND ITS IMPACT ON SERVICE QUALITY AND ORGANIZATIONAL PRODUCTIVITY: EVIDENCE FROM FOOD DELIVERY PLATFORMS IN CHENNAI
Keywords:
Last-Mile Delivery, Food Delivery Platforms, Service Quality, Organizational Productivity, Chennai, Route Optimization, Delivery Efficiency, Data-Driven Logistics, Gig Economy.Abstract
The emergence of online food delivery apps like Swiggy, Zomato and Dunzo has revolutionized the food service industry in India's urban cities. In Chennai - a densely populated urban agglomeration with a population of over ten million and high tech-savviness amongst consumers - last-mile delivery is the most critical and high cost aspect of the food delivery supply chain. This research conducts a quantitative analysis of the efficiency of last-mile delivery and the resulting effect on service quality and organisational productivity for the food delivery platforms in Chennai.
A structured questionnaire was distributed to 103 delivery partners of food delivery platforms such as Swiggy, Zomato, Zepto and Eatsure. The study used five statistical techniques: Percentage Analysis, Descriptive Statistics, Pearson Correlation, Regression Analysis and One-Way ANOVA. The study finds that workload, technology reliability and service design account for 67 percent of the variance in delivery performance and job satisfaction (R² = 0.670, R = 0.818). High workload is the strongest predictor of complaints (r = .461**), and reliability of GPS is the second most positive predictor of delivery performance. The research offers insights for food delivery platforms, logistics managers, and policymakers.
