Before Staqo:

An Indian enterprise, with a vast distribution presence, was struggling to match sales-demand with inventory availability in a timely and smooth way. Given the high costs of inventory upkeep, storage and the high opportunity-cost of a lost sales opportunity – the situation was a tough tug of war. Demand was of an unpredictable nature but the company’s business model necessitated that all its last-mile stores were well-stacked with products whenever customers asked for them. Could the company do so without suffering dead inventory, heavy warehouse rentals and burdensome transportation investments?

After Staqo:

The puzzle just had one missing piece. If only there was some way of getting real-time data on demand uptick or dip – and then this data could be relayed to inventory and supply-chain points! That’s where came into play a smart algorithm – one that could chew real-time data and digest it into inventory-replenishment insights. Staqo built, and sharpened, this algorithm with its strong competence in Python and model-building. Now, any minute a product was bought at any store across the country, a lightning-fast update was made for replenishment into the concerned systems. The dealer system updated the distributor, which updated the area distributor, which – in turn- updated the company’s inventory processes – all in a dash! So that the exact number of products needed can be replenished in the entire chain. Without wasting a minute.

Why Staqo?

All this transpired easily and effectively because of Staqo’s deep strengths in algorithms, ML and AI. It was strongly supplemented with the team’s business sense – like the ability to comprehend the bullwhip-effect problem of supply chains. It could create a model that was not just able to facilitate real-time stock replenishment but was also able to predict stock-movement for the next 15 days based on sale-patterns. It turned out to be a strong example of marrying business dynamics with machine intelligence and smart models.