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Demand Forecasting Accuracy: Inventory Management Pain Points

Demand Forecast Accuracy

This is Part 4 of our four-part series: Inventory Management Pain Points. This series addresses the most common problems that arise in inventory management. The series will play out in the following order:

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

For Part 4, Demand Forecast Accuracy, we are speaking to everyone involved in the supply chain, from those in charge of inventory management, to back-of-house re-ordering, to CFOs and COOs.

Demand Forecasting Accuracy:

As mentioned, demand forecasting goes well beyond purchasing. CFOs and COOs draw on this information when undertaking long term planning, and it is fast becoming a linchpin of efficient business. In an inventory context, demand planners typically run forecasts for the upcoming purchasing cycle which is then reviewed and acted upon by the supply planning and replenishment teams. Given that these forecasts dictate the amount of money that a business will invest in inventory, it is easy to understand why accuracy is key.

The following quote has been shamelessly lifted from this Forbes article:

“a rough rule of thumb is that 1% forecast improvement leads to a 2.5% reduction in the amount of inventory that needs to be held.”

The problem that arises is that Demand Forecasting and accurate Demand Forecasting are two different things. If you’re looking to implement Demand Forecasting, how do you know which method to apply, and what sort of measures should you use to ensure the greatest degree of accuracy?

For Remi AI, the solutions to both of these problems are clear: Our Demand Forecasting suit uses an Ensemble Method, and deploy our Quality Assurance Team to audit accuracy at regular intervals. To expand on this, an ensemble method runs multiple predictive models in simulation mode and then consensus is reached between the algorithms for which result will be best for each SKU depending on its demand profile. Our models also draw insight from a variety of external data sources and works to ensure that we provide only the most accurate forecasts for our clients. On top of that, our Quality Assurance Team (QAT) are routinely implementing and improving processes to safeguard against the risks inherent in forecasting. This way you can be comfortable and confident that we’re generating the best results for your business with regular safety checks to ensure no surprises.

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!

Or, if you're ready to start seeing the benefits of A.I-powered inventory management, start the journey here.


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.


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