Reducing Working Capital Using AI


Reducing Working Capital Using AI

This is Part 1 of our four part series: Inventory Management. This series addresses the most common pain points that arise in inventory management and will play out in the following order:



We will update the links to the above as they are released.


For Part 1, Reducing Working Capital Using AI, we are speaking to the accounts teams, the CFOs and anyone who looks at inventory management as a fiscal quandary first and foremost (as opposed to inventory managers, sales people, logistics and those more focused on getting the goods in/out in a timely, efficient manner).


Working Capital Issues:


Keep reading if you’ve looked at your balance sheet and either asked yourself, or been asked “why do we have so much money sitting in the warehouse?”


This question stems from a stock-standard financial metric ‘working capital’. The capital used in the day-to-day operations of the business. This can be simply calculated from the company’s balance sheet as:


Current Assets - Current Liabilities


In general, less is more. A higher-than-required amount held in the Inventory account equates to capital that is not being put to a more efficient use. This provides no end of headaches to CFOs around the world. Often, the problem arises because many firms see the risk of a stock-out as more dangerous than carrying the extra stock. While this is partially true, the impact of tying high levels of working capital up is equally, if not more damaging.


There are a range of A.I powered tools that are providing solutions to this problem, and while my business happens to be one of them, I’ll avoid the hard sell where possible and simply go into the benefits offered by the tools in a more general sense.

Typically, we find that the purchasing policies offered by AI (formed through the ingestion of all of the data that you have been accruing but haven’t been utilising as well as you could) can be altered such that the Inventory account can be reduced via an Auto-Supply Chain platform. This effectively provides a direct cash injection to the business.


Allow me to expand on this, using the well known financial metric “free cash flow to firm”:


Free cash flow to firm (FCFF) = Net Profit + Depreciation + Interest (1 - Tax rate) - Fixed Capital Investments - Change in Working Capital


Working Capital = Current Assets - Current Liabilities


Inventory on hand prior to the AI Auto-Supply Chain platform: $100m


Inventory on hand 6 months after implementation of the Remi AI Auto-Supply Chain platform: $80m


We see a $20 million reduction in the Inventory account effectively produces a reduction in working capital for the business by the same amount (assuming that Current Liabilities remain the same in Formula 2 above). Plugging this into formula 1 above, assuming all other terms remain the same and following the double negative, we can see that a reduction in working capital increases the Free Cash Flow to the firm by that same $20 million.

A cool $20 million cash injection to the business simply by altering the purchasing behaviour using an AI Auto-Supply Chain platform - damn good results if you ask me (or most people, I suppose).


Which I’m assuming leaves you wondering how does an A.I platform develop these purchasing policies. Here I cannot speak to the other players in the market, but at Remi AI, we have a built a platform that ingests huge (the more, the better) swathes of our clients historical data, including purchasing, sales, marketing, weather, and any other additional streams we feel relevant. The platform, which is viewed as a co-pilot, derives benefit from managing your entire inventory at an SKU level, and finding the balance between stock on hand to fill the predicted orders, without holding excess inventory.



What’s Next for AI and Inventory Management?


Our humble goal is to revolutionise supply chain management. While working towards solving the above 4 pain points (and coming damn close), we are simultaneously working to improve delivery scheduling, warehouse simulation, and global logistics simulation into our offering. The advantage of an entire supply chain being managed by the same AI platform is the benefits reaped from these different components speaking to each other. It truly is a hugely exciting time to be working in this space.


Stay tuned on the Remi AI blog as we build out the complete supply chain offering!



Who are we?


Remi AI is an Artificial Intelligence Research Firm with offices in Sydney and San Francisco. We have delivered inventory and supply chain projects across FMCG, automotive, industrial and corporate supply and more.