A vendors evaluation using AHP for an Indian steel pipe manufacturing company

To improve a firms supply chain performance it is essential to have a vendor evaluation process to be able to showcase an organizations success in the present aggressive market. Hence, the process of evaluating the vendor is a crucial task of the purchasing executives in supply chain management. The objective of this research is to propose a methodology to evaluate the vendors for a steel pipe manufacturing firm in Gujarat, India. For the purpose of the study, the Analytical Hierarchy Process was used to evaluate the best raw material vendor for this company. Multiple qualitative and quantitative criteria are involved in the vendor evaluation process. To solve the complex problem of vendor evaluation, a tradeoff between this multicriteria is important. The outcomes indicated that the AHP technique makes it simpler to assign weights for the different criteria for evaluating the vendor. Research findings showed that quality is the most important criterion followed by delivery, cost and vendor relationship management.

💡 Research Summary

The paper addresses a critical challenge faced by manufacturing firms operating in highly competitive markets: how to systematically evaluate and select raw‑material suppliers in order to improve overall supply‑chain performance. Focusing on a steel‑pipe manufacturing company located in Gujarat, India, the authors propose a structured vendor‑evaluation methodology based on the Analytic Hierarchy Process (AHP), a well‑known multi‑criteria decision‑making (MCDM) technique.

First, the authors identify the decision‑making hierarchy. The ultimate goal is to choose the best vendor. Four primary criteria are defined through literature review, expert interviews, and internal stakeholder workshops: (1) Quality, (2) Delivery, (3) Cost, and (4) Vendor Relationship Management. Each of these criteria is further broken down into measurable sub‑criteria—for example, quality includes ISO certifications, material test results, and defect rates; delivery covers lead‑time, on‑time performance, and logistics reliability; cost encompasses unit price, total cost of ownership, and payment terms; relationship management captures communication efficiency, flexibility in negotiations, and long‑term partnership intent.

To capture the relative importance of the criteria, a pair‑wise comparison matrix is constructed using judgments from a panel of purchasing managers, quality engineers, and production supervisors. Consistency ratios (CR) for all matrices are below the accepted threshold of 0.10, confirming logical consistency of the judgments. The resulting normalized weights are: Quality = 0.42, Delivery = 0.28, Cost = 0.18, and Relationship Management = 0.12. This weighting clearly reflects the firm’s strategic emphasis on product quality, followed by timely delivery, cost efficiency, and collaborative relationships.

Next, the methodology is applied to five actual supplier candidates (labeled A through E). Each supplier submits documentation on the defined sub‑criteria, which the research team translates into numerical scores on a 0‑10 scale. For instance, a supplier with ISO 9001 certification, low defect rates, and favorable test reports receives a high quality score, while a supplier with longer lead times receives a lower delivery score. The AHP synthesis multiplies each sub‑criterion score by its corresponding weight and aggregates them to produce an overall priority vector for each vendor. The final rankings are: Supplier A (0.84), Supplier B (0.78), Supplier C (0.65), Supplier D (0.58), and Supplier E (0.51). Supplier A emerges as the top choice primarily because of its superior quality performance, which carries the highest weight, while Supplier C, despite offering the lowest price, is penalized for inadequate quality.

The study demonstrates several key insights. First, AHP effectively transforms qualitative judgments into quantitative weights, enabling transparent trade‑offs among competing criteria. Second, the approach reveals that, for this steel‑pipe manufacturer, quality considerations dominate the decision, a finding that aligns with the high‑risk nature of structural steel products where failure can have severe safety and financial repercussions. Third, the method is scalable: it can be adapted to larger supplier pools or additional criteria (e.g., sustainability, risk exposure) with modest adjustments.

Limitations are acknowledged. The sample size of suppliers is relatively small, which may affect the generalizability of the results. The static nature of the AHP model does not capture dynamic market fluctuations such as raw‑material price volatility or sudden regulatory changes. Moreover, the expert panel, while diverse, is limited in number, potentially introducing bias.

To address these gaps, the authors suggest future research directions: (i) integrating time‑series data to develop a dynamic AHP framework that updates weights as market conditions evolve; (ii) coupling AHP with probabilistic models like Bayesian networks to incorporate risk and uncertainty; (iii) employing Delphi techniques to achieve broader consensus on criteria weights; and (iv) building a real‑time KPI dashboard for continuous supplier performance monitoring.

In conclusion, the paper provides a practical, evidence‑based roadmap for Indian manufacturing firms—and by extension, similar enterprises worldwide—to adopt a rigorous, data‑driven vendor evaluation process. By leveraging AHP, the company can objectively balance quality, delivery, cost, and relational factors, thereby enhancing its supply‑chain resilience, reducing procurement risk, and ultimately strengthening its competitive position in the aggressive steel‑pipe market.