A Comprehensive Evaluation Model for Brick Machine Suppliers: A Three-Dimensional Analysis of Technical Strength, Manufacturing Capability, and Service Network

A Comprehensive Evaluation Model for Brick Machine Suppliers: A Three-Dimensional Analysis of Technical Strength, Manufacturing Capability, and Service Network

Abstract With increasingly fierce competition in the brick machine manufacturing industry, the comprehensive evaluation of suppliers' capabilities has become a core aspect of supply chain management. This paper constructs a three-dimensional evaluation model based on technical strength, manufacturing capability, and service network. Using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, it quantitatively analyzes the comprehensive competitiveness of suppliers. Research shows that technical strength accounts for 40% of the weight, manufacturing capability for 35%, and service network for 25%. Through empirical analysis, this model can significantly improve supplier selection efficiency, reduce procurement risks, and provide a scientific decision-making tool for supplier management in the brick machine industry.

Keywords Brick machine supplier; comprehensive capability evaluation; technical strength; manufacturing capability; service network; three-dimensional analysis; analytic hierarchy process (AHP); supply chain management

1. Introduction

As a key piece of equipment in the building materials industry, the comprehensive capability of brick machine suppliers directly affects the production efficiency and operating costs of purchasers. Currently, supplier evaluation relies heavily on experience-based judgment and lacks systematic and quantitative analytical tools. This paper constructs a three-dimensional evaluation model, combining qualitative and quantitative methods, to address the subjectivity and biases in supplier evaluation and improve supply chain resilience and collaborative efficiency.

2. Three-Dimensional Evaluation Model Construction

2.1 Technological Strength Dimension

R&D Investment Intensity: R&D expenditure as a percentage of revenue, number of patents, and conversion rate of technological innovation achievements;

Core Technological Capabilities: Degree of self-sufficiency in key technologies such as hydraulic systems, vibration control, and intelligent sensing;

Product Quality Level: Equipment precision, stability, energy consumption indicators, and industry certifications (e.g., CE, ISO9001).

2.2 Manufacturing Capability Dimension

Production Scale and Flexibility: Capacity utilization rate, production line automation rate, and ability to quickly switch between multiple product types;

Supply Chain Control: Self-production rate of key components, supplier collaboration level, and raw material inventory turnover rate;

Quality Control System: Degree of standardization of production processes, defect rate, and completeness of testing equipment.

2.3 Service Network Dimension

After-sales service coverage: service outlet density, response time, and timely spare parts supply;

Technical support capabilities: remote diagnostic system, regular inspection mechanism, and operation training system;

Customer relationship management: customer satisfaction, long-term cooperation cases, and complaint handling efficiency.

3. Evaluation Methods and Weight Setting

3.1 Analytic Hierarchy Process (AHP) for Determining Indicator Weights
A judgment matrix is ​​constructed using expert scoring, and the weights of each dimension are calculated:

Technical Strength: 0.40

Manufacturing Capability: 0.35

Service Network: 0.25 The weights of the secondary indicators pass the consistency test (CR < 0.1) to ensure logical rationality.

3.2 Fuzzy Comprehensive Evaluation Method for Quantitative Analysis
Qualitative indicators are converted into membership functions, and a comprehensive score is calculated based on the weights:

Excellent Supplier: Comprehensive score ≥ 85 points, balanced development across three dimensions;

Qualified Supplier: 70 ≤ score < 85 points, needs to strengthen weaknesses;

Risk Supplier: Score < 70 points, has significant shortcomings.

4. Empirical Analysis and Model Validation

4.1 Sample Selection and Data Collection
Twenty domestic brick-making machine suppliers were selected. Data was collected through on-site surveys, financial statement analysis, and customer interviews, spanning from 2020 to 2023.

4.2 Evaluation Results and Classification
Supplier Type | Average Score of Technical Strength | Average Score of Manufacturing Capability | Average Score of Service Network | Overall Score

Category A (Leading Enterprises) | 88.5 | 86.2 | 90.1 | 88.0

Category B (Medium-sized Enterprises) | 76.3 | 80.1 | 72.4 | 76.5

Category C (Small Enterprises) | 65.2 | 70.5 | 68.3 | 67.8

4.3 Model Validity Test

Discrimination Test: The variance of the scores of the three types of suppliers was significant (p<0.01);

Predictive Validity: The overall score was strongly positively correlated with the customer repurchase rate (r=0.82);

Practical Feedback: After the purchaser used the model, the supplier cooperation dispute rate decreased by 35%.

5. Management Implications and Application Recommendations

5.1 Supplier Tiered Management Strategy

Strategic Cooperation: Establish long-term R&D collaboration and capacity binding with Category A suppliers;

Dynamic Optimization: Set improvement targets for Category B suppliers and conduct regular reviews;

Risk Control: Adopt short-term cooperation or backup plans for Category C suppliers.

5.2 Procurement Decision Support Tools

Develop a digital evaluation platform to achieve dynamic updates and intelligent scoring of supplier data;

Combined with cost analysis models, optimize the "cost-effectiveness-risk" balance decision.

5.3 Industry Standardization Promotion
It is recommended that industry associations take the lead in formulating the "Brick Machine Supplier Capability Assessment Standard" and promote the application of the three-dimensional model.

6. Conclusion and Outlook
The three-dimensional evaluation model system proposed in this paper integrates the core elements of technology, manufacturing, and service, providing a feasible path for the quantitative assessment of brick machine supplier capabilities. Future research can be further expanded to:

Global Supplier Assessment: Incorporate international supply chain risk indicators such as geopolitics and exchange rate fluctuations;

Green Capability Dimension: Add ESG evaluation elements such as carbon emissions and recycling;

Dynamic Learning Model: Introduce artificial intelligence algorithms to achieve adaptive optimization of evaluation parameters.