Selection Guide for Non-Standard Automation Equipment: 5 Dimensions and 30 Evaluation Indicators for Clients
Introduction: The Price of Choosing the Wrong Equipment —— Lessons from “800K Investment” to “Two Years of Idleness”
A smart home enterprise in Zhejiang customized an “intelligent lock detection line”. Due to blindly choosing a low-cost supplier (ignoring core technical maturity), the equipment had issues such as “poor multi-model compatibility” (only adapting to 30% of products), “unstable detection accuracy” (15% missed detection rate), and “after-sales response exceeding 72 hours” after delivery. It was eventually forced out of use, with the 800K investment wasted. Selecting non-standard equipment is a multi-dimensional game of “technology + business + risk”. This article constructs 5 evaluation dimensions + 30 core indicators to help clients build a scientific selection framework.
I. Requirement Matching: Precision Alignment from “Theoretical” to “Scenario 落地”
▶ Core Logic: Equipment is a “problem-solving” tool, not a “technology-displaying” toy
| Evaluation Dimension | Core Indicators | Evaluation Method | Case Reference |
| Depth of Requirement Understanding | 1. Whether requirement documents are quantified (e.g., “cycle ≤12s/piece”, “yield rate ≥98%”) 2. Accuracy of production line pain point positioning (e.g., identifying the bottleneck of “manual clamping accounting for 35% of man-hours”) |
Compare the supplier’s proposal with the on-site VSM value stream map, and check the indicator coverage rate (requiring ≥90% of core requirements to be addressed) | A auto parts factory required “oil seal assembly yield rate ≥99%”, but a supplier’s proposal only mentioned “improving efficiency” and was directly eliminated |
| Process Adaptability | 3. Whether it is compatible with multi-specification products (e.g., new energy equipment needs to support 166/182/210mm silicon wafers) 4. Environmental adaptability (cleanroom/ex 防爆 /low-temperature environment certification) |
Require the supplier to provide process parameter tables of same-scenario cases (e.g., actual measurement data of alignment accuracy for lithium battery pole piece laminators) | Medical device projects must select suppliers certified by FDA cleanrooms to avoid later compliance rectification (cost +500K) |
| Flexible Expansion Capability | 5. Number of reserved IO interfaces (≥20% redundancy, such as 20 inputs/15 outputs) 6. Degree of software modularity (whether it supports “one-click product model switching”) |
On-site demonstration of “model change testing”: record switching time (target ≤15 minutes) and parameter adjustment complexity (whether programming is required) | 3C electronics projects select equipment supporting “3-second quick-change fixtures + HMI parameter import”, improving model change efficiency by 80% |
II. Technical Maturity: Cutting Through the “Technical Concept” Fog to Focus on “Implementation Capability”
▶ Core Logic: Technology is not about being “newer” but “stabler”
| Evaluation Dimension | Core Indicators | Evaluation Method | Pitfall Avoidance Points |
| Core Technology Verification | 7. Whether core modules such as vision/force control/AI have mass production cases (requiring ≥10 same-type devices running for over 6 months) 8. Completeness of simulation reports (including mechanical/control simulation data, such as deformation ≤0.05mm) |
Obtain the supplier’s FAT test report and verify the compliance rate of key indicators (requiring ≥95%) | Beware of “laboratory-level technologies”: a supplier used self-developed AI algorithms without actual production line verification, resulting in a missed detection rate exceeding 10% |
| Precision Stability | 9. Repeat positioning accuracy (e.g., robotic arm ±0.02mm) 10. Long-term operation precision drift (1000 hours ≤±0.01mm) |
Require third-party inspection reports (such as China Metrology Institute certification) or on-site measurement of 300 times to take the average | New energy projects must test “pole piece lamination alignment accuracy”. A device 标称 ±0.02mm, but actual measurement was ±0.04mm, causing a sharp drop in yield rate |
| Control Algorithm Maturity | 11. Fault handling mechanism (e.g., over-travel emergency stop response time ≤50ms) 12. Self-diagnosis function (fault code coverage rate ≥90%) |
Simulate extreme working conditions (such as 20% material tolerance) and observe whether the equipment triggers protection and generates effective alarm codes | A non-standard bending machine had no “synchronous belt slack” warning, leading to batch product scrapping and losses exceeding 200K |
III. Supplier Strength: “Choosing Equipment” is “Choosing a Partner”, Avoiding “失联 After Delivery”
▶ Core Logic: The supplier’s “past” determines your “future”
| Evaluation Dimension | Core Indicators | Evaluation Method | Key Data |
| Industry Expertise | 13. Years of industry experience (recommended ≥5 years, ≥8 years for high-barrier fields like new energy/medical) 14. Number of same-type cases (recommended ≥30, with over 30% from leading clients) |
Field visit to the factory and review of the client list (e.g., Apple/Huawei cases in the 3C field, CATL cooperation in new energy) | A supplier established for 2 years undertook a medical device project. Due to unfamiliarity with GMP specifications, the equipment was rectified 3 times, causing a 6-month delay |
| Supply Chain Management | 15. Number of key component suppliers (recommended ≥2 alternative suppliers for key components, such as simultaneously certified Panasonic/Siemens servo motors) 16. Material readiness rate (requiring ≥90%, which can be inferred from past project delivery cycles) |
Require the supply chain risk assessment report, focusing on the procurement cycle of customized parts (recommended ≤45 days) | A project was delayed by 30 days due to a customized sensor procurement cycle exceeding 60 days, and the supplier had no alternative plan, resulting in heavy losses |
| After-Sales Response Speed | 17. 400 hotline connection rate (non-working hours ≤10-minute response) 18. Spare parts delivery cycle (vulnerable parts ≤3 days, customized parts ≤15 days) |
Call the supplier’s after-sales phone for testing or contact its old clients for real feedback (e.g., average on-site arrival time of 48 hours for an enterprise) | 3C electronics enterprises must select suppliers with “4-hour on-site + 7×24-hour remote support” to avoid production line shutdown losses |
IV. Cost-Effectiveness Ratio: Calculate the “Full Life Cycle Account”, Reject “Low-Price Traps”
▶ Core Logic: “Buying equipment” is the beginning, “using equipment” is the main battlefield for spending
| Evaluation Dimension | Core Indicators | Evaluation Method | Formula Tools |
| Budget Matching | 19. Quotation transparency (itemized listing of material/R&D/debugging costs, error ≤10%) 20. Reasonableness of payment nodes (recommended to pay ≤60% after prototype acceptance, avoid prepayment exceeding 30%) |
Require a cost breakdown sheet and compare with the average price of same-specification equipment (e.g., reasonable range for non-standard testing machines is 500-800K, beware of “loss-making quotations” below 400K) | Use the “equipment investment payback period formula”: Payback period = total equipment price / (annual labor savings + annual production capacity gain), recommended ≤1.5 years |
| Full Life Cycle Cost | 21. Energy consumption indicators (e.g., power consumption per hour ≤15kW・h) 22. Maintenance costs (annual spare parts expenses ≤5% of equipment total price) |
Obtain the operation report of similar equipment and calculate OEE overall efficiency (recommended ≥80%, each 10% lower causes annual losses exceeding 100K) | A hardware factory’s equipment energy consumption exceeded industry peers by 30%, resulting in an annual additional electricity cost of 80K, and over 400K more spent in 5 years, far exceeding initial low-price advantages |
| ROI Measurability | 23. Production capacity improvement commitment (e.g., from 50 pieces/hour to 120 pieces/hour, requiring attached calculation basis) 24. Yield rate improvement guarantee (e.g., from 85% to 98%, requiring provision of same-scenario data) |
Require signing an 《Indicator Compliance Agreement》 to clarify compensation clauses for non-compliance (e.g., deduct 1% of the contract amount for each 1% lower yield rate) | A auto project deducted 15% of the balance as per the agreement due to the equipment failing to meet production capacity commitments, avoiding risks of “data overstatement” |
V. Risk Control: Foresee “Black Swans”, Build “Safety Valves”
▶ Core Logic: The “uncertainties” of non-standard projects must be locked in by “systems”
| Evaluation Dimension | Core Indicators | Evaluation Method | Risk Control Tools |
| Requirement Change Management | 25. Whether a 《Change Agreement》 is signed (clearly defining change response time ≤48 hours and cost-sharing ratio) 26. Historical project change rate (recommended ≤10%, which can be calculated from the supplier’s past project documents) |
Review the supplier’s change management process and require the provision of 《Requirement Change Record Sheets》 from past projects | Adopt a “requirement freeze period” mechanism: freeze requirements within 45 days after plan confirmation, and changes require senior management approval from the client to reduce later rework risks |
| Project Management Capability | 27. Whether a PMP-certified project manager is assigned 28. Milestone node completion rate (recommended ≥90%, view past project Gantt charts) |
Require the project plan and verify whether key nodes (scheme/prototype/delivery) reserve 20% buffer time | A project had 失控 in the supply chain/debugging links due to the absence of a professional project manager, causing a 3-month delay. Risks could have been 预判 in advance through node buffering |
| Compliance Guarantee | 29. Completeness of industry certifications (e.g., ISO 13485 for medical devices, HACCP for food) 30. Data security commitment (local storage of equipment data or cloud transmission through ISO 27001 certification) |
Inspect original compliance certificates and verify their authenticity through official channels (e.g., query cleanroom certification numbers on the FDA website) | Equipment exported to the EU must select CE-certified suppliers. An enterprise suffered 500K losses due to ignoring EMC electromagnetic compatibility certification, leading to customs detention |
VI. Client Operation: 3 Steps to Build a Scientific Selection Process
1. Develop a 《Selection Evaluation Form》 for Quantitative Scoring
- Score each of the 30 indicators in 5 dimensions from 1-5 (e.g., “industry experience ≥8 years” gets 5 points, <3 years gets 1 point), with a total score ≥120 advancing to the next round;
- Set “one-vote veto” for key indicators (e.g., no mass production cases for core technologies or missing compliance certifications, direct elimination).
2. On-Site Inspection of “Three Must-Sees”
- See the factory: Observe production processes (e.g., whether the prototype debugging area is standardized and whether quality inspection equipment is complete);
- See cases: Require visits to same-type equipment in use and record actual operation data (e.g., lamination speed and yield rate of a lithium battery device);
- See the team: Communicate with technical leaders to judge their in-depth understanding of industry pain points (e.g., 能否 explain the root cause of “curved surface bonding bubbles” in 3C electronics).
3. Sign a “Risk-Sharing” Contract
- Clarify “indicator breach clauses”(e.g., deduct 10% of the balance for unmet cycle requirements, deduct 0.1% of the contract amount daily for delayed delivery);
- Agree on “intellectual property ownership”(e.g., algorithm models customized by the client belong to the client to avoid being kidnapped by suppliers during later upgrades).
Conclusion: Selection is Not a “Multiple-Choice Question” but a “Calculation Problem”
The essence of non-standard equipment selection is to find the optimal solution among “technical capability, supplier reliability, cost-effectiveness, and risk control”. When a new energy enterprise used the above indicators to eliminate a “low-price but no lithium battery case” supplier and chose a “20% more expensive but with CATL experience” partner, the equipment delivery cycle was shortened by 30%, the yield rate exceeded expectations by 2%, and the final ROI reached 2.5 times — this confirms that scientific selection is not about saving money but investing; not about avoiding risks but managing them.
(Next Preview: “The New Paradigm of Human-Machine Collaboration in Non-Standard Automation Equipment: The Efficiency Revolution from ‘Machine Replacement’ to ‘Human-Machine Collaboration'”, analyzing how force control technology, safety sensors, and AI algorithms achieve efficient collaboration between humans and equipment to solve the problem of “automation islands”.)
