Randomization—An Interview with Ken Traub—Part 2: Properties of Randomization

This is the second of a five part interview with Ken Traub, GS1 standards expert and independent consultant, on GS1 serial number randomization.  The full series includes essays covering: GS1 Serial Number Considerations Properties of Randomization (this essay) Threat Analysis Algorithmic Approach Other Approaches to Randomization This week Ken introduces three properties of randomization.  — Dirk. _____________________________________

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3 thoughts on “Randomization—An Interview with Ken Traub—Part 2: Properties of Randomization”

  1. Hi Dirk and Ken
    Thanks for the explanation on Random numbers. Boy! must admit was heavy stuff.
    Can you elaborate when you mentioned this on sparseness “That would mean, overall, my serial numbers won’t be one in 10,000, but over any reasonable stretch of time I will have only used one serial number from each block of 10,000.”
    Because I thought the block will reduce over a stretch of time.

    Thanks
    Riz

  2. Dear Riz,
    Thanks for your question. Let me explain.
    Imagine all the numbers from 0 to 99,999,999,999 stretched out on a line – 100 billion numbers total. Now divide it into blocks of 10,000. Starting with the first block, pick one of the 10,000 numbers and use it on a product. Then go to the next block. When you have done this for 10 million products, you reach the end of your number line. Let’s say that takes you 5 years. But you’re still making products, so you go back to the beginning and choose a second number out of that first block of 10,000 (taking care to be sure it’s different than the first number you picked from that block), then you continue to the next block in the same manner. It takes you another 5 years to reach the end of the number line for a second time, assuming your average volume stays constant.

    Now, looking back, you don’t have 1 in 10,000 sparseness overall, because you’ve taken two numbers from each block of 10,000. So your sparseness is 2 in 10,000, or 1 in 5000. But if you look at any 5-year period, within that 5 year period the numbers you use are 1 in 10,000 sparse. That is what I meant.

    Make sense?

    1. Dear Ken
      Thanks for the lucid explanation. That helps in understanding. However I must say that sparseness will decrease over a period of time but that would be a fair trade-off given the time it takes to consume the 10m S/Ns(or whatever I assume on my number line!)

      Thanks again
      Riz

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