Case Study:

Made-In Cookware

Made In - a fast growing digitally native cookware brand - uses Atomic to automate inventory optimization and replenishment planning.

Atomic was able to optimize Made In's inventory plan for 50% working capital savings.


Automating Planning for the Next Stage 

The Challenge

“Coming into this year, we made it our mission to automate the 'turn of the crank' analytics and let the team focus on insights and actions.”

“We found that, at best, the existing solutions were going to be an incomplete solve for us. So I didn't really feel that justified a multi-month system implementation and incrementally more software spend to take a percentage of the workload off of our plates.”

About Made In Cookware

  • Company size: 75-100 employees
  • Revenue: $100M+
  • SKUs: 350+
  • Channels:
    ○ DTC eCommerce
    ○ Amazon
    ○ Walmart
    ○ Wholesale
    ○ Retail
  • Vendor lead times: 30-150 days
  • PO frequency: Bi-monthly

Business Impact After Onboarding to Atomic 

Made In transitioned from indvidual spreadsheets to unified control over their inventory.


"We need better processes in place - we rely on millions of Google Docs to determine whether we need to place a purchase order or not"


"We had been trying to put these kind of analytical capabilities in place for almost 2 years. The amazing thing that's come out of working with you guys - we got a tailored solution in weeks.

There are some really obvious KPIs to measure success here in terms of like improving turns, improving in-stock rate. The impact we’re seeing makes it a no brainer.

The scenario planning is a really powerful capability. For example - I know we prefer net 60 terms vs. cash on delivery, but what does that actually mean to the amount of cash we need for peak inventory builds? I couldn’t answer that question without Atomic to do modeling on the fly”



50% Inventory Turns Improvement
Optimized purchase order plan and safety stock levels.
Millions $$ in working capital savings. 



20+ Hours Per Month Saved
With automated demand simulation and purchase plan generation.



Data Quality and Accuracy
Establishing consistent data pipelines to remove planning errors caused by spreadsheets




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