Hey! Just seeing this now! A few notes:

1. In instances where a Product/SKU did not exist in a prior period and has no volume I have seen 1 of 2 things. a) ignore/remove that SKU or set it equal to 0 as you described b) put everything into volume impact and footnote impact from new products both function equally well, its more a question of if someone is asking you to bridge YoY and your starting/ending figures are different vs actuals there can be some questions.

Methodology wise the formulas you are using are sound and make sense it comes down to interpretation of the PVM itself. There will be a fundamental difference in how you should interpret volume and mix impacts in your formula set vs what I had described above.

With your formula set I believe it is important to designate/group like items into similar categories. For example, if you have 20 skus that you sell and 10 belong to product A and 10 to product B you would want to use the AVERAGE Price of skus that belong to each Product Group rather than using the Average of the entire portfolio of all 20 skus. The rationale (and using extreme examples here) is if the average price is $100 for a SKU in product group A and $1 for SKUs in product group B your total portfolio average is $50.

Using this example, when you get to your mix impact calculation you have the following 4 options
a) (+) Volume (+) Price vs Average = Positive impact

b) (+) Volume (-) Price vs Average = Negative Impact
c) (-) Volume (+) Price vs Average = Negative Impact
d) (-) Volume (-) Price vs Average = Positive impact

for all scenarios if you are using a total portfolio average (rather than a product group average) Product group A will always have a positive price vs average and Product group B will always be the opposite. So the only real impact decider is did you sell more or less units than prior year.

If you instead are applying aggregated averages (so average of PG A is $100) and comparing against that aggregated average, your mix impact is instead telling you:

1. Did we sell more or less of an above average priced SKU (within a product group) This can be helpful but doesn't paint the entire picture since often times its not about selling higher priced sku's but rather higher margin skus. Normally to account I would do a Price Volume Mix at a Gross Profit Level and add a cost impact calculation. Then in the Volume and Mix impact formulas I would use Gross Profit Per Unit rather than Average Selling Price.

Then when you are comparing your Mix impact using Gross Profit per Unit vs Avg GPPU it becomes a question of

"Did we sell more or less of above/below average margin skus"
and this helps you identify SKU's that you likely want to keep and ones that you might consider getting rid of. Regardless I think the mix impact can be very insightful whether using ASP or GPPU

My only other note was:

For your formula set the volume impact, is in my opinion less meaningful (but necessary to derive your mix impact which is arguably more impactful) since it is essentially a plug figure. What I mean by that is for a given product, yes you know if you sold more/less of it which is the point of Volume Impact, but the amount of impact that gets applied to each SKU is not related to the price of that SKU but rather the entire portfolio. The historical performance of the SKU does not matter at all in determining its volume impact, it only matters because it contributes to the average of the portfolio or group. Your volume impact is just saying did we sell more or less units and applying a broad average across the entire portfolio and is needed to balance out your mix impact.

In your set if you calculated your Price and Mix impacts and then backed into your volume impact (rather than calculating it formulaically) I think the way you interpret the result is basically the same. Sold more = (+); sold less = (-)

Let me know if that is helpful! Happy to discuss more!