Parts.io gathers data about electronics components from a variety of sources: composite pricing from distributor sources, popularity from searches across all of SupplyFrame, risk ranking data gathered from other 3rd party data sources. While we’re proud of how much data we have gathered into one space, it can be confusing to start wrapping your arms around all of it and making informed decisions for your design. This post will help in visualizing parts.io data, and show you the value behind the numbers.
The Risk Value Quadrant
Starting with a simple grid, let’s consider the two most critical pieces of information you use to quantify the value of a part: price and risk. All other specifications being equal (and those that match your design needs), you want to use the part that gives you the lowest risk for the lowest price. We’ll invert the axes so that low risk / low price components appear in the upper right of the graph (the happy quadrant). High risk, high price components will appear in the lower right of the graph (the fearful quadrant). Less risk moves you right, lower cost moves you up. On this chart, we’ll be adding some components using the composite price and the risk ranking associated with them. For this first example, we’ll just grab the first ten parts listed under the “Microcontroller” category within parts.io (search run on 14-AUG-2015)…
There are three micros clustered together in the upper right: the Atmel AT89C2051 and AT89S52, as well as the Zilog Z86E0812SEC. All three of these microcontrollers would provide the lowest cost for the lowest risk.
The Price Popularity Quadrant
When you’re looking for microcontrollers, you’ll have a lot of very fine tuned specifications that can back you into a corner on part selection; you may not have much choice other than to select the one that directly matches your specifications and needs. For common parts like 0.1uF bypass caps, this becomes an exercise in efficiency. You still need low risk and low price, but you also don’t want to spend much brain power on selecting something so common. Why not let the power of popularity ranking help guide you?
In this example, we’ll again take the top 10 search results (search run on 14-AUG-2015) and use their composite price. However, now we’ll use their popularity ranking in the search results as one of the axes, because all search results are returned in order of popularity. This will quickly show you how the price of a selection of parts would compare with the popularity of that part across all of SupplyFrame.
With very little effort, you can see that all you have to do is make a choice between the Vishay VJ0805Y104KXAAT, and the Kemet C0805C104K1RACAUTO. Both are in the top 5 of most popular 0.1 uF 0805 X7R capacitors across millions of searches daily, and both are in the lowest cost quadrant of the chart. Note the low risk rank for both of these parts as well. You won’t go wrong using either.
The Production Candlestick
Engineering has changed. Even at the outset of a design, you need to begin thinking about the end product. In the modern world, an engineer has to think about the feasibility of the supply chain; buy 100 of a key component every 36 weeks is a tenuous way to design your product.
For example: You’ve devised a novel way of measuring temperature with hardware, and your embedded engineer has come up with the algorithm of algorithms to swizzle that value into a human readable figure. The output will be shown on a very simple display. You’re busy focusing on the in-depth, hard core, technical stuff you love (getting the reading right). In doing so, you miss the fact that the simple display driver IC has become the pain point. Production time rolls around and you now have the equivalent of a car with a perfectly tuned engine but no windshield or tachometer.
Ideally, you need to quantify the risk. You’re still concerned about price, but not just in the short term. Has the price of the part increased or decreased over time? How is it trending? Are other engineers using that part? Those are incredibly useful data points when you’re trying to make sure you can still meet your production goals and budgets in eight months, as well as right now. (example search run 14-AUG-2015)
This candlestick chart is derived from a variety of data compiled by parts.io and only available here. The LED drivers are listed left to right in order of Popularity Rank. Each component shows a colored box with a small stick behind it. The top and bottom points of the box represent the current price and the price one year ago, while the color indicates if the price went up, green, or went down, red. The small stick behind the box shows what the highest prices and lowest prices for that component were over the previous 12 months. Think of it like a stock price: opening and closing price (the box) with the 52-week high price and low price (the stick). At the base of the graph are the labeled risk rankings for each part.
With this data, you can make very informed decisions about the parts you want to include your design. If you have a lot of budget, but absolutely need to ensure your product is delivered on time, then you can handle a larger price fluctuation, but must choose a low risk part. The MM5451YV (popularity #4) is the choice in that case because it has a low risk of 1.0, but has varied in price by nearly a full dollar over the last year.
If cash flow is king, but your production deadlines are flexible, then you want a price that has been very price stable over time: a small box with the smallest stick possible, but a moderate risk ranking can be dealt with. Now the STP16CP05MTR (popularity #2) looks attractive because the price has barely budged in a year, but at least you’re informed ahead of time that the risk is twice that of the MM5451YV.
Visualization is just the beginning
Ultimately, this data is a tool in helping you make decisions. The visualizations above show that the same data can be used to make different decisions, based upon your needs at production time. Compromises based on business and production needs rather than technical factors can be frustrating, but represent reality. Parts.io is here to help provide that data and ensure that when you need a replacement, you can easily find one. This will allow you to not only design the best product, but ensure it will can be made.