Now have a look at how your results compare to what our other users prefer:
- Product results based on your preferences
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Weighing it up
Your results based on your preferences
We've crunched the numbers and scored the products according to the preferences you just entered. The bar chart shows your product scores. Hover over the bars to see the drivers that most influenced the result for you.
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× Product performance
Product Information
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Find the product that's right for you
Multi-criteria decision analysis
This page helps you decide on the right type of product for your application. That may be easy to do if you're only looking at a single factor e.g. price or effectiveness, but it becomes exponentially more complex when looking at multiple factors in parallel.
Our comparison tool allows to analyse multiple factors such as cost, compliance, health, environment and sustainability metrics at the same time. These are based on predefined data taken from the models included in Makersite.
This alternatives assessment does not make a recommendation which product is the best - but once you have weighted which factors are important to you, we can suggest which products are best suited for you.
Product Performance
Normalized criteria utility scores (more = better)
Product sustainability, compliance, & cost at Makersite
At Makersite, we are building the platform that connects teams with the data, apps, and expertise they need to make, buy and sell better products, faster. Launched in 2016, Makersite is already the largest database on the web on how products are made and used, their supply chains, risks, and eco-impacts. Teams around the world use Makersite to reduce time-to-market, mitigate product risks, increase their margins, and making more profitable and sustainable product decisions along the value chain.
We have launched this website as a spin-off to showcase the power of the Makersite platform for customers. This page uses Makersite data and Makersite's multi-criteria decision analysis tool, which enables the comparison of arbitrary products across multiple dimensions. Contact us to see what Makersite can do for your business at team@makersite.de!
All rights reserved for productview.makersite.de @ Makersite
Our method
Multi-criteria decision analysis with pairwise criteria preference inference.
The product comparison presented here is a decision problem in multiple dimensions ({{d}},) . The model we use is a linear utility aggregation model.
To understand the model, have a look at its 3 main parts:
- For each dimension, each product's data is converted to a utility score between 0 and 100 (100 being the best).
- The user makes pairwise comparisons of the dimensions. The pairwise information is used to compute weights for each of the dimensions (the higher the weight, the more relevant the dimension).
- For each product, the product utilities are summed up across the multiple dimensions, weighted by the dimensional weights computed in the previous step.
This leads to the products' final utility scores (out of 100) that are displayed in the bar chart on the results page. Here are the details of the three steps above:
Utilities.
Except for the performance dimension, the smaller the raw data, the better score it receives. For example, smaller biocide exposure is ranked better than larger biocide exposure. In each of these categories, the "worst" input data (i.e. the largest) is assigned a utility of 1. The value 0 receives a utility of 100. Between 0 and the largest value, we use a linear map from raw data to utilities. The only exception is the performance dimension. Here, a score of 0 has utility 0, a score of 5 has utility 100, and everything in between is mapped linearly.
Pairwise comparisons.
Each of the pairwise comparison sliders allows the user to specify that one dimension is more important to them than the other by a factor of up to 8. These inputs are then combined with the existing setting (starting from a uniform prior) by computing the closest consistent set of dimension weights (computing the eigenvalues of the linear problem). In this part, we are following a method known as the Analytic Hierarchy Process. You can find some more information on it here.
Ranking.
In the last step, the products' dimensional utilities are combined into a total product utility by using the weights obtained in the previous step. This is just a simple weighted sum. The higher the total utility, the better the result. So we use the total product utility to determine your favorites.
What do other people prefer?
The publisher of this MCDA has not made other users preferences available. Please contact them
to get information on other users preferences.
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The data underlying the comparison
Our comparison tool is based off the data which has been entered into the Makersite system.
In order to make the various dimensions easily comparable, we have carefully aggregated the studies resulting data into the table below.
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