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Risk Assessment Report on Channel Inventory Overstocking for Shengtong Medical Nutrition with 155 Days of Inventory Turnover Days

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December 29, 2025

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Risk Assessment Report on Channel Inventory Overstocking for Shengtong Medical Nutrition with 155 Days of Inventory Turnover Days

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Risk Assessment Report on Channel Inventory Overstocking for Shengtong Medical Nutrition with 155 Days of Inventory Turnover Days
I. Core Indicator Analysis
1. Interpretation of Inventory Turnover Days

Meaning of 155 Days of Inventory Turnover Days:

  • Annual inventory turnover times: 365÷155≈
    2.35 times/year
  • Average inventory cycle:
    about 5.2 months
  • Evaluation conclusion: at
    a relatively high level in the industry
    , close to the upper limit of the risk range [0]

Industry Comparison:

Industry Category Average Inventory Turnover Days Position of 155 Days
Dairy Industry 30-60 days Significantly Higher
Pharmaceutical Industry 60-120 days Higher
Special Medical Food 90-150 days Close to Upper Limit
International Special Medical Food Enterprises 70-120 days Obviously Higher
II. Channel Overstocking Risk Factor Assessment
1. Core Risk Factor Analysis
Risk Factor Weight Risk Level Explanation
Shelf Life Risk 25%
High
Special medical food has a shelf life of 12-24 months; long turnover increases expiration risk
Channel Concentration Risk 20%
High
CR5>50% is the warning line; customer structure needs to be confirmed
Channel Account Period Risk 15% Medium-High Hospital channel account period is 90-180 days, affecting capital turnover
Price Decline Risk 15% Medium Overstocked products may face price promotion pressure
Demand Fluctuation Risk 15% Medium Medical scenario demand is affected by seasonality
Inventory Management Capability 10% Varies by Enterprise Depends on digitalization level and channel control
2. Channel Structure Characteristics of Special Medical Food
Channel Type Proportion Characteristics Overstocking Risk Points
Hospital Channel 40-50% Strong professionalism, high entry threshold, long account period Long procurement cycle, strict inventory management
Pharmacy Channel 30-35% OTC attribute, consumer self-purchase Limited shelf space, strict expiration date management
E-commerce Channel 15-20% Fast growth, wide coverage Fierce price competition, high return rate
Maternal and Child Channel 10-15% Professional maternal and child stores,导购依赖 Inventory overstock risk, fast product iteration
III. Specific Impacts of 155 Days Turnover Days
1. Capital Occupation Impact
  • Quantification
    : Assuming annual revenue of 1 billion yuan, 155 days of inventory turnover is equivalent to
    about 420 million yuan of capital occupied
  • Inventory capital occupation ratio:
    30-40%
    , higher than the industry average of 15-20% [0]
2. Cost Increase
  • Warehousing Cost
    : Special medical food has high requirements for storage conditions (temperature and humidity control); cost is positively correlated with inventory volume
  • Expiration Loss
    : Under the average inventory cycle of 5.2 months, tail products face expiration risk; it is estimated that
    5-10% of inventory may be discounted
3. Channel Relationship Impact
  • Channel partners’ overstock will
    reduce subsequent procurement enthusiasm
    , forming a vicious cycle
  • Early Warning Signals
    : Rising channel return rate, difficulty in developing new customers
IV. Comprehensive Risk Assessment

Risk Level: Medium-High Risk

Evaluation Dimension Status Risk Score
Inventory Turnover Efficiency Alert 7.5/10
Capital Occupation Intensity Higher 7.0/10
Expiration Loss Risk High 8.0/10
Channel Account Period Pressure To Be Evaluated TBD
Channel Concentration To Be Evaluated TBD
V. Risk Response Suggestions
Short-Term Measures (1-3 Months)
  1. Strengthen Expiration Date Management
    : Establish FIFO system; launch promotion or recycling mechanism for near-expiry products
  2. Optimize Channel Structure
    : Moderately reduce dependence on hospital channels; expand e-commerce and OTC channels
  3. Launch Promotion Activities
    : Conduct channel promotions for overstocked products to digest inventory
Mid-Term Measures (3-12 Months)
  1. Improve Prediction Accuracy
    : Introduce big data demand forecasting model to reduce bullwhip effect
  2. Strengthen Channel Collaboration
    : Establish VMI mode; share sales data with channel partners
  3. Optimize Product Portfolio
    : Eliminate low-turnover products; focus on high-growth product lines
Long-Term Measures (Over 1 Year)
  1. Supply Chain Digitalization
    : Build end-to-end digital supply chain system
  2. Diversified Layout
    : Expand C-end consumer market; reduce dependence on B-end channels
VI. Due Diligence Suggestions

For investors or partners, it is recommended to further focus on the following indicators:

  1. Accounts Receivable Turnover Days
    : Joint analysis with inventory turnover days
  2. Customer Concentration
    : Whether the top 5 customers account for more than 50%
  3. Gross Profit Margin Trend
    : Judge whether there is price reduction to clear inventory
  4. Channel Inventory Turnover Efficiency
    : Channel data sharing under VMI mode
VII. Conclusion

155 days of inventory turnover days need to be evaluated in combination with the company’s specific business model:

  • Risk-Controllable Situation
    : Reasonable channel structure, scattered customers, in-place shelf life management, continuous demand growth
  • Risk Warning Situation
    : Highly concentrated customers, weak shelf life management, serious channel overstocking, declining demand

It is recommended to track the changing trend of inventory turnover days
. If it continues to deteriorate, it is necessary to be alert to the negative impact of channel overstocking risk on the company’s performance; if there are signs of improvement, the risk will gradually decrease.


Note: Due to search API access restrictions, this report is based on the general analysis framework of the special medical food industry. Specific company data needs to be further confirmed by obtaining official financial reports.

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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.