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Analysis of Quality Control Risk Prevention Under Engnice Holdings' OEM Model

#婴幼儿辅食 #品控管理 #代工模式 #供应链管理 #食品安全 #质量管理体系 #BRCGS认证
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December 29, 2025

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Analysis of Quality Control Risk Prevention Under Engnice Holdings' OEM Model

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Analysis of Quality Control Risk Prevention Under Engnice Holdings’ OEM Model

According to public information, as a leading enterprise in China’s infant complementary food industry, Engnice Holdings adopts an ‘independent production + entrusted production’ model, with a high dependence on OEMs [1]. The infant complementary food industry has extremely high requirements for product quality and safety; an excessively high proportion of OEMs does exist certain quality control risks. The following is an analysis of risk assessment and prevention strategies from multiple dimensions.

I. Identification of Core Risks in the OEM Model

1. Supplier Management Risk

The production qualifications, quality management systems, and standardization of personnel operations of OEM manufacturers are uneven, making it difficult to fully control [2]. If OEM factories have problems such as lax raw material control and non-compliance with production hygiene standards, it will directly affect the quality of end products.

2. Information Asymmetry Risk

There is an information gap between the brand and OEM factories, and the transparency of the production process is insufficient, making it difficult to achieve full-process real-time monitoring.

3. Difficulty in Responsibility Traceability

Infant complementary food products involve many links from raw material procurement, production and processing to logistics distribution; once quality problems occur, it is difficult to define responsibilities.

4. Delayed Emergency Response

Under the OEM model, the recall cycle for problematic products is longer, and the speed of emergency response is limited.

II. Analysis of Engnice Holdings’ Existing Quality Control System

From public information, Engnice Holdings has built a multi-level quality control system [3]:

Supply Chain Collaborative Management Mechanism

The company innovatively built the ‘Five Joint Quality Management System’, integrating the upstream and downstream of the industrial chain into a ‘Quality Community’ through the model of ‘Joint R&D, Shared Rewards and Punishments, Joint System Construction, Shared Standards, and Joint Goal Adherence’. This system has been recommended by the Hunan Provincial Administration for Market Regulation to be included in the ‘National Enterprise Chief Quality Officer Quality Reform and Innovation Good Practices (2024)’.

Production Base Certification Endorsement

Engnice’s Liuyang Factory was fully put into operation at the end of 2024, and it won the dual certifications of BRCGS Grade A and IFS Advanced in its first application within less than a year of operation, which is known as the ‘Olympics of Food Safety’ international recognition. The factory is equipped with advanced facilities such as a 100,000-class GMP clean workshop, a central laboratory, and an intelligent logistics system.

Strict Testing Standards

All products strictly comply with the three national standards for infant complementary foods, and implement ‘zero tolerance’ for pathogenic bacteria. Each batch of products needs to undergo 17 foreign matter tests, 42 physical and chemical index tests, and multiple pathogenic bacteria tests. The sampling report from the third-party organization ‘Daddy Lab’ shows that 810 pesticide residues were not detected in the 3 randomly sampled Engnice rice flour products.

III. Recommendations on Prevention Strategies for OEM Quality Control Risks

1. Strengthen Supplier Access and Elimination Mechanisms

Establish a strict supplier qualification review system, conduct regular on-site audits, include quality indicators in supplier performance evaluations, and implement a last-place elimination system.

2. Improve Supply Chain Transparency

Learn from the ‘Cloud Supervision’ model of the Liuyang Factory, deploy real-time monitoring equipment in key production links of OEM factories, and realize the visualization and traceability of the production process.

3. Establish a Quality Co-responsibility Mechanism

Deepen the interest binding with OEM factories through the ‘Five Joint Management System’, write quality responsibilities into contracts, implement a quality risk deposit system, and achieve benefit sharing and risk co-responsibility.

4. Increase the Construction of Independent Production Capacity

The commissioning of the Liuyang Factory marks that the company is optimizing its production capacity structure and gradually reducing its dependence on OEMs. It is recommended to continue to promote the expansion of self-owned production capacity and increase the proportion of independent production.

5. Build a Rapid Response System

Establish a product quality early warning mechanism and an emergency recall plan, shorten the time window from problem discovery to disposal, and reduce the risk of negative impact spread.

IV. Key Points for Investors’ Attention

For investors focusing on Engnice Holdings, it is recommended to focus on tracking the following indicators:

Focus Dimension Specific Content
Changes in OEM proportion Progress of independent production capacity release
Quality problem incidents Regulatory sampling results, complaint status
Supply chain management Number of new/eliminated OEM manufacturers
Certification qualifications Acquisition status of international and domestic quality certifications

References:

[1] Tencent News - “Infant Complementary Food Leader’s IPO: Can Growth from Marketing Spending ‘Please’ the Capital Market?” (https://news.qq.com/rain/a/20251223A05TWR00)

[2] Sina Finance - “Engnice Holdings’ Bid for ‘First Stock of Complementary Food’: Model Dilemmas and Governance Concerns Under High Growth” (https://cj.sina.com.cn/articles/view/5953466437/162dab04506709s4xu?froms=ggmp)

[3] Sina Finance - “Winning International Dual Certifications: Exploring Engnice Holdings’ ‘Super Factory’” (https://finance.sina.com.cn/jjxw/2025-12-19/doc-inhcipuc7230900.shtml?froms=ggmp)

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Insights are generated using AI models and historical data for informational purposes only. They do not constitute investment advice or recommendations. Past performance is not indicative of future results.