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Good Component Selection Bridges Gaping Sourcing Gap

In an electronics system design, the final product is only as good as the quality, price, and availability of the components that comprise it. Engineers and sourcing professionals continually work to improve their part selection techniques, since an error in the component selection can cause costly redesigns, or simply kill a product before it even gets to market.

In recent years, parametric search engines have facilitated this task. Narrowing down options by applying tangible filters (figure A) has become an alternative–and a much more scientific–method to vendor meetings and individual knowledge. Search engines do a great job at indexing data fields and returning component information according to specific filters such as the value or tolerance of a resistor. 

However, selecting a component after researching and comparing specifications, constructions, and functionalities doesn’t necessarily make it the right part for a product . It certainly makes it the right part for a design . And there’s a tremendous difference between the conceptual stage and the physical item.

Figure A

When passed to the sourcing or purchasing team, the list of selected components needs to be sourceable and, in most cases, for the cheapest possible price.

Here are a few questions that need to be answered:

  • Is each part available in the requested quantity?
  • Which suppliers have it in stock?
  • Which supplier has the best price?
  • Is there an alternative component that matches the desired form, fit and function that is easier to get or cheaper to purchase?
  • What are the risk levels associated with each component (end of life, compliance, etc.)?

Finding the answers to these questions requires more than parametric information. Machine learning and artificial intelligence allow a new generation of sourcing tools to identify bottlenecks before they happen and enable proactive recommendations. This is when analytic search delivers incredible value in the product development cycle.

Let’s look at a concrete example.

Figure B

Looking for a Texas Instruments buffer/driver (SN74LVC1G07DCKT) triggers two warnings:

  • No single distributor has enough inventory to match my requested quantity. Digi-Key is the closest but still short of  by1,500
  • The current price is above market average.

At this point, I can quickly visualize the inventory landscape (figure C) and decide to split the quantity between

Figure C

 

I see that Texas Instruments has a stock of 2,227 at a price that beats the market. (figure D)

Figure D

I could split my quantity between these two suppliers, but for the sake of this example, let’s assume that I am not inclined to buy any components at a price above market average.

Instead, I’m going to fetch all cross-references matching Form (figure E), Fit and Function to quickly see if we can replace the original SN74LVC1G07DCKT. From there, I can find a sourcing scenario that doesn’t require me to multi-source this specific component, and possibly get a better pricing option in the end.

Figure E

Selecting the first option, Texas Instruments’ SN74LVC1G07DCKR (figure F) allows me to move forward with a component that I can purchase at a competitive price without breaking my requested quantity between multiple suppliers.

Figure F

 

So what’s the point?

Component selection made solely from parametric data doesn’t consider market environment. Parts need to execute a function in a system, but they also need to make sense from a business perspective. 

Designing with the right parts requires both a functional and an economical match, but the information needed to select a component from a functional standpoint is very different from the knowledge required to pick the best option at a market level.

Accessing this market knowledge means bridging the gap between engineering and sourcing departments. Normalizing data from a wide range of sources in the electronic component market and translating incoming data streams into a comprehensive view of a component’s market data allows the part selection process to take “real-life” factors into account.

Before we move forward and automate the entire supply chain, we need an easy way to answer a single, critical question, without spending countless hours on Google or distributors’ websites: Is this bill of materials (BOM) sourceable?

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