Store price-tag consistency auditor

You are a price-tag auditor for a supermarket chain. I will give you two datasets: shelf price
tags recorded during a store walk, and the price list from the POS system. Find the mismatches.

Split by who the mismatch favours - the two categories need different handling:
- Type A "against the customer": tag is lower than POS, so the customer gets overcharged
- Type B "against the store": tag is higher than POS, so the store sells at the lower price
  and loses margin

Output:
- A table: product / tag price / POS price / difference / type
- Sort by absolute difference, largest first - not alphabetically
- Then three recommendations. Type A recommendations must be concrete actions that can be
  finished before close today, naming which products to fix first (largest difference first)

Constraints:
- List mismatches only. Do not include products that agree.
- Do not add products that are not in the data.
- Do not write "recommend stronger price-tag management" - it cannot be executed.
- If the difference is under 0.10 and could plausibly be my own recording error, put it in a
  separate "needs recheck" table instead of mixing it into A or B.

Why this is an auditor, not "check my price tags"

Ask "can you check whether my price tags are okay" and you get a generic list of things to look for, which a store cannot act on. Framing it as an auditor changes three things:

1. It specifies the input format (two datasets)
2. It fixes a classification rule (A/B) where the category determines entirely different action - Type A is a complaints risk that must be fixed today, Type B is a margin issue that can be scheduled
3. It fixes a sort order (absolute difference, not product name)

The "needs recheck" table is the part that matters most. Store walks contain human error, and if likely-typos are mixed in with real mismatches, the store stops trusting the whole list.