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How to Find OEM Manufacturers on Made-in-China.com Using Scraping Data

#webscraping #ecommerce #python #business

Finding the right OEM (Original Equipment Manufacturer) is one of the hardest parts of launching a custom product. Made-in-China.com hosts thousands of factories offering OEM services, but manually browsing through them is painfully slow.

In this tutorial, I'll show you how to use scraping data to systematically find, filter, and rank OEM manufacturers — turning weeks of manual research into minutes of automated analysis.

Why OEM Sourcing Needs Data

Traditional OEM sourcing looks like this:

  1. Search Made-in-China.com for your product category
  2. Click through dozens of supplier profiles
  3. Manually compare prices, MOQs, and certifications
  4. Hope you didn't miss a better option on page 15

With scraping data, you can:

  • Search across thousands of listings in seconds
  • Filter by OEM-specific signals (custom logo, packaging, design)
  • Rank manufacturers by verification level, response time, and production capacity
  • Cross-reference with other platforms for price validation

Step 1: Collect OEM Manufacturer Data

First, scrape Made-in-China.com for your target product category. The Made-in-China.com Scraper on Apify Store handles pagination, anti-bot measures, and data extraction automatically.

from apify_client import ApifyClient

client = ApifyClient("your-api-token")

run = client.actor("jungle_intertwining/made-in-china-scraper").call(
    run_input={
        "keyword": "custom phone case manufacturer OEM",
        "maxItems": 200,
    }
)

products = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    products.append(item)

print(f"Collected {len(products)} listings")
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Step 2: Identify OEM-Capable Suppliers

Not every supplier on Made-in-China.com offers OEM services. Here's how to filter for OEM capability using scraped data:

def is_oem_capable(product):
    """Check if a supplier likely offers OEM manufacturing."""
    oem_signals = [
        "oem", "odm", "custom", "customized", "customised",
        "your logo", "your brand", "your design", "private label",
        "custom packaging", "custom color", "made to order",
        "factory direct", "manufacturer"
    ]

    text = " ".join([
        product.get("title", ""),
        product.get("description", ""),
        product.get("supplier", ""),
    ]).lower()

    matches = [s for s in oem_signals if s in text]
    return len(matches) >= 2, matches


oem_suppliers = []
for p in products:
    is_oem, signals = is_oem_capable(p)
    if is_oem:
        p["oem_signals"] = signals
        oem_suppliers.append(p)

print(f"Found {len(oem_suppliers)} OEM-capable suppliers out of {len(products)}")
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Step 3: Build an OEM Manufacturer Scoring System

Once you have OEM-capable suppliers, rank them by reliability and fit:

def score_oem_manufacturer(product):
    """Score an OEM manufacturer on a 0-100 scale."""
    score = 0
    reasons = []

    # Verification level (0-30 points)
    supplier_type = product.get("supplierType", "").lower()
    if "audited" in supplier_type:
        score += 30
        reasons.append("Audited supplier (+30)")
    elif "verified" in supplier_type:
        score += 20
        reasons.append("Verified supplier (+20)")
    else:
        score += 5
        reasons.append("Basic supplier (+5)")

    # OEM signal strength (0-25 points)
    signal_count = len(product.get("oem_signals", []))
    signal_score = min(signal_count * 5, 25)
    score += signal_score
    reasons.append(f"{signal_count} OEM signals (+{signal_score})")

    # Price competitiveness (0-20 points)
    price = product.get("price")
    if price:
        try:
            price_val = float(
                str(price).replace("$", "").split("-")[0].strip()
            )
            if price_val < 1:
                score += 20
                reasons.append("Very competitive price (+20)")
            elif price_val < 5:
                score += 15
                reasons.append("Competitive price (+15)")
            elif price_val < 20:
                score += 10
                reasons.append("Moderate price (+10)")
            else:
                score += 5
                reasons.append("Premium price (+5)")
        except (ValueError, IndexError):
            pass

    # MOQ flexibility (0-15 points)
    moq = product.get("moq", "")
    if moq:
        moq_str = str(moq).lower()
        try:
            moq_val = int("".join(filter(str.isdigit, moq_str.split()[0])))
            if moq_val <= 50:
                score += 15
                reasons.append(f"Low MOQ {moq_val} (+15)")
            elif moq_val <= 500:
                score += 10
                reasons.append(f"Medium MOQ {moq_val} (+10)")
            else:
                score += 5
                reasons.append(f"High MOQ {moq_val} (+5)")
        except (ValueError, IndexError):
            pass

    # Location bonus (0-10 points)
    location = product.get("location", "").lower()
    oem_hubs = [
        "shenzhen", "guangzhou", "dongguan", "foshan", "zhongshan",
        "ningbo", "yiwu", "wenzhou", "quanzhou"
    ]
    if any(hub in location for hub in oem_hubs):
        score += 10
        reasons.append(f"OEM hub location (+10)")

    return score, reasons


# Score and rank all OEM suppliers
for supplier in oem_suppliers:
    supplier["score"], supplier["score_reasons"] = score_oem_manufacturer(supplier)

oem_suppliers.sort(key=lambda x: x["score"], reverse=True)

# Display top 10
print("\n Top 10 OEM Manufacturers:\n")
for i, s in enumerate(oem_suppliers[:10], 1):
    print(f"{i}. [{s['score']}pts] {s.get('supplier', 'Unknown')}")
    print(f"   Product: {s.get('title', '')[:80]}")
    print(f"   Price: {s.get('price', 'N/A')} | MOQ: {s.get('moq', 'N/A')}")
    print(f"   OEM signals: {', '.join(s.get('oem_signals', []))}")
    print(f"   Scoring: {'; '.join(s.get('score_reasons', []))}")
    print()
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Step 4: Export Your OEM Shortlist

After identifying your top candidates, export to CSV for team review:

import csv
from datetime import datetime

def export_oem_shortlist(suppliers, filename=None):
    """Export top OEM candidates to CSV for team review."""
    if not filename:
        filename = f"oem_shortlist_{datetime.now().strftime('%Y%m%d')}.csv"

    fields = [
        "rank", "score", "supplier", "title", "price", "moq",
        "location", "supplierType", "oem_signals", "url"
    ]

    with open(filename, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=fields)
        writer.writeheader()

        for i, s in enumerate(suppliers[:20], 1):
            writer.writerow({
                "rank": i,
                "score": s.get("score", 0),
                "supplier": s.get("supplier", ""),
                "title": s.get("title", ""),
                "price": s.get("price", ""),
                "moq": s.get("moq", ""),
                "location": s.get("location", ""),
                "supplierType": s.get("supplierType", ""),
                "oem_signals": ", ".join(s.get("oem_signals", [])),
                "url": s.get("url", ""),
            })

    print(f"Exported {min(len(suppliers), 20)} suppliers to {filename}")

export_oem_shortlist(oem_suppliers)
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Step 5: Cross-Platform Price Validation

Smart sourcing means checking multiple platforms. Use the Yiwugo Scraper and DHgate Scraper to cross-reference your top candidates:

def cross_validate_prices(keyword):
    """Compare prices across Made-in-China, Yiwugo, and DHgate."""
    platforms = {}

    # Check Yiwugo
    yiwugo_run = client.actor("jungle_intertwining/yiwugo-scraper").call(
        run_input={"keyword": keyword, "maxItems": 50}
    )
    yiwugo_data = list(
        client.dataset(yiwugo_run["defaultDatasetId"]).iterate_items()
    )
    platforms["yiwugo"] = {
        "count": len(yiwugo_data),
        "avg_prig_price(yiwugo_data),
    }

    # Check DHgate
    dhgate_run = client.actor("jungle_intertwining/dhgate-scraper").call(
        run_input={"keyword": keyword, "maxItems": 50}
    )
    dhgate_data = list(
        client.dataset(dhgate_run["defaultDatasetId"]).iterate_items()
    )
    platforms["dhgate"] = {
        "count": len(dhgate_data),
        "avg_price": avg_price(dhgate_data),
    }

    return platforms


def avg_price(items):
    """Calculate average price from scraped items."""
    prices = []
    for item in items:
        price = item.get("price", "")
        try:
         val = float(str(price).replace("$", "").split("-")[0].strip())
            if 0 < val < 10000:
                prices.append(val)
        except (ValueError, IndexError):
            continue
    return round(sum(prices) / len(prices), 2) if prices else None
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Key OEM Manufacturing Hubs in China

When evaluating suppliers, location matters. Here are the major OEM hubs by industry:

Region Specialization Why It Matters
Shenzhen Electronics, IoT, PCB Fastest prototyping, huge component ecosystem
Dongguan Consumer goods, toys, packaging Massive factory density, competitive pricing
Guangzhou Textiles, garments, leather goods Fashion OEM capital, quick turnaround
Ningbo Auto parts, machinery, hardware Heavy industry OEM, export port access
Yiwu Small commodities, accessories Lowest MOQs, widest variety
Foshan Furniture, ceramics, lighting Quality manufacturing, design capability

The scoring system above gives bonus points to suppliers in these hubs because they typically have better supply chain access, more experienced OEM workflows, and competitive pricing from cluster effects.

Red Flags to Watch For

Not all OEM claims are gen. Use your scraped data to spot warning signs:

def check_red_flags(product):
    """Identify potential red flags in supplier data."""
    flags = []

    supplier = product.get("supplier", "").lower()

    # Too many product categories (trading company, not factory)
    if product.get("category_count", 0) > 10:
        flags.append("Too many categories - likely a trading company")

    # No verification
    supplier_type = product.get("supplierType", "").lower()
    if "audited" not in supplier_type and "vot in supplier_type:
        flags.append("No third-party verification")

    # Suspiciously low MOQ with low price
    moq = product.get("moq", "")
    price = product.get("price", "")
    try:
        moq_val = int("".join(filter(str.isdigit, str(moq).split()[0])))
        price_val = float(str(price).replace("$", "").split("-")[0].strip())
        if moq_val <= 1 and price_val < 0.5:
            flags.append("MOQ=1 with very low price - verify quality")
    except (ValueError, IndexError):
        pass

    # Generic company name
    generic_terms = ["trading", "import export", "international trade"]
    if any(term in supplier for term in generic_terms):
        flags.append("Trading company name - may not be actual manufacturer")

    return flags
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The Complete OEM Sourcing Workflow

Here's the full process:

  1. Scrape Made-in-China.com for your product category (200+ listings)
  2. Filter for OEM-capable suppliers using keyword signals
  3. Score each manufacturer on verification, price, MOQ, and location
  4. Export your top 20 to a CSV shortlist
  5. Cross-validate prices on Yiwugo and DHgate
  6. Check red flags before reaching out
  7. Contact your top 5 with specific OEM requirements

This data-driven approach typically reduces OEM sourcing time from 2-3 weeks of manual browsing to about 30 minutes of automated analysis.

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