How Low Inventory, Yet High availability Saved the day for a Pharmacy Chain
Synopsys
Story of a rookie who made made money using Automated Replenishment Systems, In a market where every other Pharmacy retail chain had a bloody nose.
Story
It was a few weeks past April the hottest month in Hyderabad when I got a call from a consultant friend who wanted an ERP for his client who was starting a Pharmacy chain.
A meeting was scheduled and after understanding the business model my first question to him was how he planned to make money when everyone else in the market was losing it.
He did not even expect an IT Professional to even ask that question.
He did not have an Answer.
It got me thinking as to how the ERP could help.
I started analysing the root cause of all the players losing money in the market.
I noticed that they all had a pattern of operations.
They all had an inventory value average of Rs. 6-7 Lakhs per store.
They all had an average sale of Rs. 10-12K per day per store.
That would make the average inventory in days at 60 Days of inventory.
That would make the average inventory turns per year at about 6 Turns per year.
At ~10% Sale-COGS we were looking at about 60% ROCE.
This was the constraining factor as both the input price and sale price could not be changed due to Regulatory issues.
The only thing that we had control over was on:
What we bought
When we bought
How much we bought
Only if we could reduce our average inventory per store from Rs. 6-7 Lakhs to 2-2.5 Lakhs we could straight away look at a 3-fold increase in ROCE.
TOC to the rescue
The fundamental principle of the Theory of Constraints (ToC) is “Low inventory, High Availability”
This statement struck a chord.
This was exactly what we wanted.
The challenge with low inventory was we had to do frequent replenishments from a central warehouse and that would cost us an additional 1-1.5% in increased expenses. However, that was a small price to pay for the 3 fold increase in ROCE.
Low inventory also meant that we would lower our expired inventory as we would hold significantly lower inventory than our competitors.
The key was to do a 100% automated replenishment by removing human fear and greed from the decision-making process.
It took us a few iterations to optimize the algorithms, and when we were done with it our average per-store inventory was down to 2.5L and ROCE was 170% Vs. 60% which was the industry standard.