AI vs. Inventory Management: a revolution?

 In Inventory management, Technology

Artificial Intelligence – or AI – is emerging as a new factor of production, replacing the traditional factors of labour, capital, and innovation. In fact, it is estimated that AI technologies could boost India’s GDP by about 1.3 percentage points by 2035.

If this is what it can do to a country, imagine what it could do for your business.

However, people are still hesitant when it comes to adoption of AI in practices like inventory handling. Good inventory management in itself revolves around a contradiction – keep enough stock in the warehouse to make sure the business keeps progressing, but not enough stock that it ends up draining cash reserves.

On one hand, inventory management is highly sensitive. On the other, AI is unpredictable. Both together sounds like a match made in hell.

Can you bring the unexpected AI to the messy, fast-paced world of inventory management?

Yes, but human supervision must be heavily used – you don’t want the AI model to replace the system entirely.
So far, there are two main frontiers AI can be incorporated within inventory management:

Predictions for Inventory Management

As the name suggests, the general idea is to build a prediction model that can estimate what demand will be like for the coming days across all items in your inventory. The starting point would be to capture all possible data, and create a sort of “golden record”. Then, AI algorithms can analyse the data by giving insights on what action is most likely to impact the business. This is not only innovative but extremely informative – now a computer can give you the initiative to change business tactics when needed.

Reinforcement Learning systems for full inventory management

This is the more advanced approach to AI, where they can not only predict or classify but can also take actions to manage inventory. However, the AI must not be given complete liberty; for this reason, people set up either “rewards” for a good measure or “punishments” for an incorrect action. This way, an AI model constantly reinforces itself, moulded to help anticipate customer demand, leading to better forecasting as well as improved inventory prediction and management.

It is no doubt that AI can be an unstoppable force – it can not only generate its own models but can also minimise the need for workers due to complete automation of work functions.

In this modern age, it is the perfect time to start testing applications of AI within businesses – from there, the sky’s the limit.

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