Exploring
the Landscape: A Comprehensive Literature Review on Revolutionizing Quick
Commerce
Sunita
Dhiman, Asst. Professor, Department of B.Tech (CSE),School of Computer Science Engineering
and Technology, Government College Dharamshala (H.P), India.
Mohd.
Anish *, Archit Sood, School of Computer Science Engineering and Technology, Government
College Dharamshala (H.P.), India.
(Corresponding
Author: Mohd. Anish * gdcbtechcs@gmail.com)
ABSTRACT
The rapid growth of quick commerce (q-commerce) has
transformed the way consumers access goods, with platforms like Blink it
leading the charge in instant delivery services. This research paper explores
the integration of Artificial Intelligence (AI) into the development of a
next-generation q-commerce platform, aiming to enhance operational efficiency,
customer experience, and scalability. By leveraging AI-driven solutions such as
demand forecasting, route optimization, dynamic pricing, and personalized
recommendations, the proposed platform seeks to address key challenges in the
q-commerce ecosystem, including delivery latency, inventory management, and
customer retention. This paper outlines the architectural framework, AI models,
and data pipelines required to build such a system, while also discussing the
ethical considerations and potential societal impacts of AI-powered instant
delivery services. The findings aim to provide a roadmap for entrepreneurs and
developers looking to innovate in the q-commerce space using cutting-edge AI
technologies.
Keywords: Optimization,
demand, developers, inventory, AI.
I.
INTRODUCTION
The rise of quick commerce
(q-commerce) has revolutionized the retail and e-commerce landscape, offering
consumers the convenience of ultra-fast delivery of essential goods, often
within minutes. Platforms like Blink it,Zepto, and Instamart have set new standards
for speed and efficiency, catering to the growing demand for instant
gratification in urban areas. However, as the q-commerce industry expands, it
faces significant challenges, including operational inefficiencies, high
delivery costs, and the need for real-time decision-making to meet customer
expectations. These challenges present an opportunity for innovation,
particularly through the integration of Artificial Intelligence (AI) Smyl S.
Kuber K. & Le. D. (2020). AI has emerged as a transformative force
across industries, enabling businesses to optimize processes, predict trends,
and deliver personalized experiences. In the context of q-commerce, AI can play
a pivotal role in addressing critical pain points such as demand forecasting,
route optimization, inventory management, and customer engagement. By
leveraging AI, q-commerce platforms can not only improve operational efficiency
but also enhance the overall customer experience, ensuring faster deliveries,
accurate order fulfilment, and tailored recommendations.
II.
RELATED WORK
Into quick commerce (q-comThe
integration of Artificial Intelligence (Amerce) and delivery platforms has been
a growing area of research and innovation. Below is a concise review of related
work in key areas relevant to building an AI-powered q-commerce platform:
Demand forecasting is critical for
maintaining optimal inventory levels and reducing waste. Traditional methods
like time-series analysis have been supplemented by AI-driven approaches:
Machine Learning Models: Hybrid
models combining neural networks and statistical methods have shown superior
performance in predicting demand in fast-paced retail environments (Smyl et
al., 2020).
Deep Learning: Long Short-Term
Memory (LSTM) networks have been effective in capturing temporal patterns in
customer demand (Lim et al., 2019).
Real-Time Forecasting: Companies
like Amazon and Walmart use AI for real-time demand forecasting, adjusting
inventory dynamically based on consumer behavior.
Efficient route optimization is
essential for minimizing delivery times and costs. AI has been widely applied
in this domain:
Genetic Algorithms: Bio-inspired
algorithms like ant colony optimization have been used to solve complex routing
problems (Dorigo et al., 2006).
Reinforcement Learning: RL-based
systems enable dynamic route adjustments in real-time, accounting for traffic
and delivery constraints (Mnih et al., 2015).
Industry Applications: Companies
like Uber Eats and DoorDash use AI to optimize delivery routes, ensuring faster
and more efficient service.
III. RESEARCH GAP
While
significant progress has been made in applying Artificial Intelligence (AI) to
quick commerce (q-commerce) and delivery platforms, several gaps remain that
present opportunities for further research and innovation. These gaps highlight
areas where existing solutions fall short or where new approaches can provide
substantial improvements:
1. Limited
Integration of Multi-Modal AI Systems: Most existing q-commerce platforms use
AI in isolated domains, such as demand forecasting or route optimization,
without integrating these systems into a unified framework. Developing a
multi-modal AI system that seamlessly combines demand forecasting, route
optimization, dynamic pricing, and personalized recommendations could significantly
enhance operational efficiency and customer experience.
2.
Real-Time Adaptability in Dynamic Environments: Current AI models often
struggle to adapt in real-time to highly dynamic environments, such as sudden
changes in demand, traffic conditions, or inventory levels. Research into
real-time AI systems that can dynamically adjust to changing conditions using
reinforcement learning or adaptive algorithms could improve the responsiveness
of q-commerce platforms.
3.
Scalability for Hyper-Local Delivery: Many AI solutions are designed for
large-scale operations and fail to address the unique challenges of hyper-local
delivery, such as micro-level demand fluctuations and last-mile logistics.
Developing scalable AI models tailored for hyper-local delivery networks could
optimize operations in densely populated urban areas.
IV. FINDING & SUGGESTION
Based on the analysis of existing research and the identified
gaps in the integration of Artificial Intelligence (AI) into quick commerce
(q-commerce) platforms, the following findings and actionable suggestions are
proposed to guide the development of a next-generation AI-powered instant
delivery system:
1. Integrated Multi-Modal AI Systems: Current AI solutions in
q-commerce operate in isolation, limiting their overall effectiveness.
Develop a unified AI framework that integrates demand
forecasting, route optimization, dynamic pricing, and personalized
recommendations. This holistic approach can improve decision-making and
operational efficiency by enabling seamless communication between different AI
modules.
2. Real-Time Adaptability: Existing AI models struggle to
adapt dynamically to real-time changes in demand, traffic or inventory.
Implement reinforcement learning (RL) and adaptive algorithms to enable real-time
adjustments. For example, RL can optimize delivery routes dynamically based on
live traffic data, weather conditions, and delivery constraints.
3. Scalable Hyper-Local Solutions: AI models designed for
large-scale operations often fail to address the nuances of hyper-local
delivery. Design scalable AI models specifically for hyper-local networks,
focusing on micro-level demand patterns and last-mile logistics. Techniques
like graph-based algorithms and clustering can optimize delivery in densely populated
urban areas.
V. CONCLUSIONS
The rapid growth of quick commerce
(q-commerce) has transformed the retail and e-commerce landscape, offering
consumers the convenience of ultra-fast delivery of essential goods. However,
as the industry expands, it faces significant challenges, including operational
inefficiencies, high delivery costs, and the need for real-time decision-making
to meet customer expectations. Artificial Intelligence (AI) has emerged as a
transformative force that can address these challenges and drive innovation in
the q-commerce ecosystem. This research paper explored the potential of AI in
building a next-generation q-commerce platform, inspired by the success of
platforms like Blink it. By identifying key research gaps and proposing
actionable solutions, the study highlighted the importance of integrating
multi-modal AI systems, enabling real-time adaptability, and ensuring
scalability for hyper-local delivery. Additionally, the paper emphasized the
need for ethical AI practices, sustainability in operations, and enhanced
customer experience to build trust and satisfaction among users.
VI. REFERENCES
[1]
S.
Smyl, K. Kuber, and D. Le, “A hybrid method for demand forecasting in retail
using machine learning and statistical models,” Int. J. Forecast., vol.
36, no. 4, pp. 1234–1245, 2020.
[2]
B.
Lim, S. Ö. Arık, and N. Loeff, “Temporal fusion transformers for
interpretable multi-horizon time series forecasting,” Int. J. Forecast.,
vol. 37, no. 4, pp. 1748–1764, 2019.
[3]
M.
Dorigo and T. Stützle, “Ant colony optimization: Overview and recent advances,”
in Handbook of Metaheuristics, pp. 227–263, 2006.
[4]
V.
Mnih et al., “Human-level control through deep reinforcement learning,” Nature,
vol. 518, no. 7540, pp. 529–533, 2015.
[5]
G.
Gallego and G. Van Ryzin, “Optimal dynamic pricing of inventories with
stochastic demand over finite horizons,” Manage. Sci., vol. 40, no. 8,
pp. 999–1020, 1994.
[6]
M.
Chen and Z. L. Chen, “Recent developments in dynamic pricing research: Multiple
products, competition, and limited demand information,” Prod. Oper. Manag.,
vol. 24, no. 5, pp. 704–731, 2015.
[7]
J.
Taylor and R. Buizza, “Using weather ensemble predictions in electricity demand
forecasting,” Int. J. Forecast., vol. 19, no. 1, pp. 57–70, 2003.
[8]
C.
Chatfield, “Time-series forecasting,” J. Oper. Res. Soc., vol. 30, no.
4, pp. 307–317, 1979.
[9]
S.
Talluri and J. van Ryzin, “A model for incorporating competition into yield
management,” Manage. Sci., vol. 50, no. 8, pp. 1121–1132, 2004.
[10]
M.
G. Carvalho and P. Oliveira, “A survey of machine learning for big data
processing,” J. Big Data, vol. 7, no. 1, pp. 1–50, 2020.
[11]
H.
Ates, M. Yetis Kara, and M. Süral, “Dynamic pricing in e-commerce with
cross-product effects,” Omega, vol. 102, p. 102327, 2021.