Predictive Analytics Requires Data

Predictive Analytics Requires Data

Why is data so important to leverage predictive-analytics for inventory optimization? Well, to accurately determine inventory levels, you can’t use algorithms or calculations because it’s impossible to account for all required supply chain factors. For example, real world process variation. That’s where predictive analytics comes into play. Predictive analytics, specifically Monte Carlo simulation, is able to contemplate all supply chain factors and therefore delivers accurate inventory settings. However, to utilize predictive analytics, data is inherently required. Right Sized Inventory utilizes actual client data to enable our simulations to accurately replicate the real-world behavior of their environment.

Two key inputs we require for an accurate and effective simulation are Demand Variability and Lead Time Variation. Within our simulation, demand variability drives the simulated consumption of an item for each day in the simulation. Daily re-supply of an item is also simulated, including the variability of lead times that happens in the real world. Ultimately, each day ends with that day’s demand – including carry-over past-due backlog if applicable – either having been met successfully or not. At the end of thousands of days of simulations, we finally look to see if the desired service level was met

Each input parameter has its own unique effect on the simulation results. For example, historical demand drives different statistical models.  We all know demand patterns can vary greatly by inventory item and we all know lead time varies in the real world. Therefore, it is critical to take both these elements into account to evaluate the probability of a ‘perfect storm’ scenario given demand spikes and longer than average lead time.

As you can see, accurate supply chain data is required for predictive analytics to simulate real-world processes in a client’s business. Algorithms or calculations can’t account for all these supply chain factors, it’s only possible with simulation.  The data RSI needs is easily available and important in order for RSI to provide the accurate results we do on a daily basis for our clients.

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