We are excited to introduce the Parts.io Composite Price. The Composite Price provides a current, single price view across the electronic distributor landscape for each unique electronic component.
Why does Composite Price Matter?
Composite Price provides engineers with the ability to quickly scan and assess pricing trade-offs between parts. Additionally, we list Composite Price alongside the rest of the parametric data, which provides sort capability and quick views into which parts have your required specifications along with the best price. This provides a more consistent and relevant search experience, because searching by price is now normalized against the quantity of parts available.
On our detail page, we provide an upper bound (our estimated single unit price) and lower bound (estimated volume pricing) to provide you with a sense of the price range and quantity breaks. Combined with the Composite Price, engineers can estimate the top and bottom amount they will pay from a distributor, across all distributors. Outliers can happen, of course, but this estimate can help with “back of the napkin” calculations that engineers use to estimate best and worst case scenario for product pricing.
Why is it hard to compare prices in the first place?
Electronic component prices fluctuate for a variety of reasons:
- Market conditions
- New entrants into the marketplace
- Component demand
- Inventory levels
- Changes in business practice
Composite Price averages out these daily fluctuations and will provide a more stable view of current pricing. However, the fluctuation of the price is only one reason doing a true comparison is difficult. Another is the wide range of distributor pricing and stock that must be normalized, explained below.
How is Composite Price calculated?
Our data science team crafted the calculation of Composite Price for a specific manufacturer’s part number (MPN) listing to be independent of quantity levels from any particular distributor or manufacturer (who sell direct). All available distributor pricing information–including price breaks and stock information–form a log-linear regression model shown below. This model accounts for the steep drop off in prices at low quantity levels and a flattening of prices at higher quantity levels.
Additionally, we use an iterative re-weighting methodology that minimizes the impact of outlier prices. Once an optimal regression curve is constructed, we calculate the composite price by using the center of the region bounded by the fitted curve which accounts for the characteristics of the curve such as steepness and skew. Said another way, imagine the “area under the curve” and then finding the approximate center of that shape; this centroid is the number used for our Composite Price.
This calculation is done for any part that has pricing information, sourced from the FindChips API (you can sign up for access to that as well!). As with any set of data, the estimate of Composite Price improves with the availability of pricing data from distributors. If a part is available in the marketplace, we will attempt to create a Composite Price so engineers can compare and contrast it with other available parts.
What about other data?
The Composite Price pairs perfectly with the massive amount of parametric data, analytics (comparing price and parametrics) and the trends sections on our part detail pages. Our data is regularly updated and we continually update the Composite Price alongside that relevant auxiliary data. We always appreciate hearing from users, either in the feedback box on the lower right corner of any search page or on our Community site. We want to hear about what you are searching for and any new features they would like included in future versions of Parts.io.