I. Industry Risk Analysis

(1) Policy Risk

The unmanned supermarket industry faces significant risks at each stage of the policy life cycle: During the policy formulation period, as the industry is in the stage of innovation and exploration, there is a lag in legislation by regulatory authorities regarding core aspects such as unmanned cash – registers and privacy data collection. Entrepreneurs may face a sharp increase in compliance costs due to sudden policy tightening (such as mandatory installation of facial recognition warning signs). During the policy implementation period, the differential interpretation of the implementation rules of the Data Security Law in different regions leads to conflicts with local standards when operating across regions (for example, Shanghai requires local storage of data while Shenzhen allows cloud storage). During the policy evaluation period, the accumulation of industry accident cases (such as the regulatory upgrade after a brand in Hangzhou was hacked due to system vulnerabilities in 2023) may trigger an increase in national operating standards, resulting in pressure for equipment transformation. During the policy termination period, there is a risk that the existing tax incentives (such as the immediate refund of value – added tax for new retail enterprises) may not be sustainable.

(2) Economic Risk

The unmanned supermarket industry currently faces the dual risks of demand contraction and cost squeeze under economic cycle fluctuations. During the period of slow economic growth, consumers tend to be more cautious about non – essential spending. The demand for instant retail with lower customer unit prices may be diverted by traditional supermarkets. The large upfront heavy – asset investment in smart devices and Internet of Things (IoT) technology leads to a sharp increase in depreciation and amortization pressure. Coupled with the intensified price fluctuations in the supply chain during the economic downturn, the gap between the high and persistent operation and maintenance costs and the weak revenue growth continues to widen. In the capital winter, the financing window for the new retail track is narrowing. An overly high proportion of debt financing will increase the liquidity risk. During the economic recovery period, the lag in consumer recovery may lead to insufficient cash flow to support the technology iteration cycle.

(3) Social Risk

From the perspective of entrepreneurs, the unmanned supermarket industry faces social risks caused by generational consumption differences: The penetration rate of the market among the middle – aged and elderly groups is insufficient due to technical operation barriers and lack of trust. Although the younger generation has strong technical adaptability, their consumption habits are more dependent on social scenarios and the sense of experience, and their emotional value recognition of unmanned services is limited. Frequent technical failures are likely to trigger a systematic trust crisis among the public regarding privacy leakage, payment security, etc. The generational digital divide magnifies the ethical controversy in public opinion about “technology replacing human labor”, and intensifies the public’s anxiety about the unemployment risk of traditional retail practitioners. Regulatory lag and vicious competition may induce destructive social events (such as malicious damage to equipment), further weakening the industry’s social recognition.

(4) Legal Risk

The legal risks currently faced by the unmanned supermarket industry mainly include: Data collection and storage must comply with the Personal Information Protection Law. If consumption records or facial information are leaked, fines will be imposed. Product quality must comply with the Food Safety Law. Selling sub – standard food will trigger administrative penalties. The layout of monitoring equipment may infringe on customers’ privacy rights, and excessive tracking of behavior trajectories is likely to lead to litigation disputes. If the self – checkout system uses pirated software or counterfeit trademarks, it will bear the liability for intellectual property infringement.

II. Entrepreneurship Guide

(1) Suggestions on Entrepreneurship Opportunities

Currently, entrepreneurship opportunities in the unmanned supermarket industry are concentrated on the refined operation of high – frequency scenarios and the integration of intelligent technologies. The key is to explore areas with insufficient coverage of community convenience stores (such as mid – to high – end communities and industrial parks) and night – time consumption scenarios (such as 24 – hour office buildings and transportation hubs). By adopting a scenario – based product selection strategy (a combination of fresh food and high – frequency daily necessities) and using small – scale pre – warehouse equipment, the operation and maintenance costs can be reduced. Combining with AI visual dynamic pricing technology (automatically adjusting prices during peak hours) can increase the space utilization efficiency by more than 30%. At the same time, developing enterprise – level solutions (such as connecting to the office building management system to realize automatic meal subsidy verification) can form an ecological closed – loop. When promoting in emerging business districts and smart communities in second – tier cities, it is advisable to cooperate with property management in a profit – sharing model to lower the entry threshold.

(2) Suggestions on Entrepreneurship Resources

Focus on the integration of core technology resources. Prioritize obtaining stable suppliers of intelligent recognition, IoT devices, and big – data analysis systems. Reduce hardware investment through equipment leasing or profit – sharing models. Establish direct supply channels with leading brands in food, beverages, and daily necessities, and exchange the number of SKUs for preferential supply chain payment terms. Focus on connecting with community property resources and adopt a venue cooperation model of fixed rent plus a percentage of turnover. Integrate local government resources to obtain 24 – hour business licenses and innovation subsidies for unmanned retail. At the same time, connect to the risk control system of third – party payment to reduce the risk of fund security. Establish a sharing mechanism for backup operation and maintenance teams, and cooperate with regional convenience stores for emergency services. Achieve rapid light – asset replication through a combination of resources.

(3) Suggestions on the Entrepreneurship Team

Entrepreneurs in the unmanned supermarket industry need to recruit three core types of members: The technology team should have a background in R & D of IoT and AI software and hardware to ensure the stable operation of intelligent shelves and automatic settlement systems. The operation team must have experience in convenience store supply chain management. It is preferable to select data analysis talents with the ability to select fresh food products. Among the partners, there must be a “government – connected person” who is proficient in local retail policies to quickly respond to the differences in approval policies for unmanned vending cabinets in different regions. It is recommended that the founder personally oversee the RFID tag cost control team. The current pain point in the industry is that the hardware cost accounts for a relatively high proportion (about 60% of the total investment). A supply chain team with experience in bulk procurement of electronic components should be formed. At the same time, an operation officer with practical experience in supermarket loss control should be introduced to control the inventory loss rate below the 3% warning line.

(4) Suggestions on Entrepreneurship Risks

Entrepreneurs in the unmanned supermarket industry should focus on balancing technological stability and user experience. Select intelligent shelves and seamless payment systems that have been verified by the market. Prioritize the layout in high – density pedestrian flow scenarios such as communities and industrial parks. Reduce the risk of system downtime through redundant design of hardware equipment. Establish a dynamic inventory warning mechanism and connect to the emergency supply chain of surrounding supermarkets to prevent losses due to out – of – stock situations. Adopt a dual anti – loss mechanism of AI behavior recognition plus real – name authentication, and connect to the credit information platform to reduce the probability of malicious non – payment. In terms of compliance, a professional legal team should be configured to focus on preventing compliance risks in biological information collection and consumption data application. At the same time, design a product price gradient (20% promotional products + 50% regular products + 30% high – margin products) to offset the initial equipment depreciation cost.