A balanced scale comparing SAW and WP decision-making methods.

SAW vs. WP: Choosing the Right Decision Support System for Your Business

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Mira Elwood in Tech & Innovation September 2025 • 5 min read.

"Confused about which decision-making method to use? Compare Simple Additive Weighting (SAW) and Weighted Product (WP) to find the best fit for your needs."


In today's fast-paced business environment, making quick and accurate decisions is more critical than ever. Technology has revolutionized how we approach decision-making, especially in sectors like banking, where efficiency and precision are paramount. When it comes to granting business loans, for example, the ability to rapidly assess eligibility based on well-defined criteria can significantly impact operations.

Decision Support Systems (DSS) have emerged as invaluable tools, designed to assist decision-makers in navigating complex choices. These systems act as 'information sources' or 'second opinions,' providing a structured framework for evaluating options and formulating policies. Within the realm of DSS, various methods exist, each with its own unique approach to weighing criteria and arriving at a conclusion.

This article dives into a comparison of two popular DSS methods: Simple Additive Weighting (SAW) and Weighted Product (WP). Both methods utilize criteria and weights to evaluate alternatives, but they differ in their calculation processes and strengths. By understanding these differences, businesses can select the method that best aligns with their specific needs and goals.

SAW vs. WP: Decoding the Decision-Making Methods

A balanced scale comparing SAW and WP decision-making methods.

Simple Additive Weighting (SAW), often called the weighted summing method, operates on a straightforward principle: it calculates the weighted sum of performance ratings for each alternative across all relevant attributes. This approach requires normalizing the decision matrix to ensure comparability across different scales. SAW is known for its ability to perform judgments more precisely because it is based on pre-defined values and preference weights. The SAW method requires the decision maker to determine the weight of each attribute. Total score for the attribute is obtained by summing all the resulted of the rated multiplication and the weight of each attribute. The rating of each attribute must be dimensionless in the sense that it has passed the process of normalizing the previous matrix. The advantages of SAW method of its ability to do the assessment more precisely because it is based on predetermined criteria and preference weights.

Weighted Product (WP), on the other hand, employs multiplication to evaluate attribute ratings. In WP, each attribute rating is raised to the power of its corresponding weight. This process mirrors normalization and is similar to the SAW method in that it requires defining criteria and their weights. WP offers the advantage of providing both value and cost assessments for each alternative.

Here's a quick breakdown of the key differences:
  • Calculation Method: SAW uses addition, while WP uses multiplication.
  • Normalization: SAW requires explicit normalization, while WP's multiplication inherently accounts for scale.
  • Output: SAW provides a weighted sum, while WP yields a product-based value.
  • Interpretation: SAW's results are often more intuitive due to the additive nature, while WP requires understanding exponential relationships.
Choosing between SAW and WP depends largely on the specific application and the decision-maker's preferences. SAW's simplicity and clear weighting make it suitable for scenarios where transparency and ease of understanding are crucial. WP, with its multiplicative approach, may be preferred when interactions between attributes are significant or when a combined value-cost assessment is desired. While the WP method is a completion method by using multiplication to attribute attribute rating, where the rating must be raised first with the weight of the attribute in question.

Finding Clarity in Decision-Making: Concluding Thoughts on SAW and WP

In conclusion, both Simple Additive Weighting (SAW) and Weighted Product (WP) offer valuable frameworks for decision-making within Decision Support Systems. While both methods involve evaluating criteria and assigning weights, their distinct calculation approaches lead to different strengths and suitability for various applications. Ultimately, the choice between SAW and WP depends on the specific context, the desired level of transparency, and the importance of capturing interactions between attributes. By understanding the nuances of each method, decision-makers can harness the power of data to make informed and effective choices.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1051/matecconf/201821501003, Alternate LINK

Title: Comparison Analysis Of Simple Additive Weighting (Saw) And Weigthed Product (Wp) In Decision Support Systems

Subject: General Medicine

Journal: MATEC Web of Conferences

Publisher: EDP Sciences

Authors: Dede Wira Trise Putra, Adrian Agustian Punggara

Published: 2018-01-01

Everything You Need To Know

1

What is Simple Additive Weighting (SAW) and how does it work?

Simple Additive Weighting (SAW) calculates a weighted sum of performance ratings for each alternative across all relevant attributes, requiring normalization to ensure comparability. The total score for an attribute is obtained by summing the results of the rated multiplication and the weight of each attribute, where the rating of each attribute must be dimensionless after normalization.

2

What is the Weighted Product (WP) method and how does it work?

Weighted Product (WP) evaluates attribute ratings by raising each rating to the power of its corresponding weight. This multiplicative process inherently accounts for scale, eliminating the need for explicit normalization. WP provides both value and cost assessments for each alternative.

3

What are the key differences between Simple Additive Weighting (SAW) and Weighted Product (WP) in the context of decision-making?

The primary difference lies in the calculation method: SAW uses addition to aggregate weighted attribute ratings, whereas WP employs multiplication. Also, SAW requires explicit normalization, while WP inherently accounts for scale through its multiplicative approach. SAW yields a weighted sum, making its results more intuitive. WP yields a product-based value, useful when interactions between attributes are significant or when a combined value-cost assessment is desired.

4

When is Simple Additive Weighting (SAW) more appropriate than Weighted Product (WP), and vice versa?

SAW is suitable for scenarios prioritizing transparency and ease of understanding due to its simplicity and clear weighting. This makes it effective when stakeholders need to easily grasp how decisions are made. WP is useful when the interactions between attributes are significant, or when a combined value-cost assessment is desired. This method is helpful when the relationship is non-linear.

5

Besides SAW and WP, are there other decision-making methods available within Decision Support Systems (DSS), and how do they compare?

While both SAW and WP are valuable frameworks for decision-making within Decision Support Systems, other methods exist. The choice depends on the context, desired transparency level, and importance of capturing interactions between attributes. Other alternatives include AHP (Analytic Hierarchy Process), which allows for hierarchical structuring of criteria and pairwise comparisons, and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), which identifies the alternative closest to the ideal solution and farthest from the negative ideal solution.

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