How Predictive Analytics Made Inventory Management Better

Not matter if a business is a production, retail or wholesale focused, the inventory is a crucial piece of its smooth running. It is a challenge that has to be faced properly. If not, efficiency wouldn’t be gained, resulting in not acceptable business performance. Inventory management executed as it should mean less cash locked in a stock, on time deliveries and at the end of the day – happy clients. As we all know, they do matter.

Predictive analytics is applicable as efficiency booster in many business processes and inventory management is no exclusion. Optimizing inventory is ensuring the right SKU is available in the right quantities, at the right time and at the right location. Such optimization is leading to stock levels reduction, hence transportation costs reduction and write-down cost reduction. Relying purely on data, predictive analytics is the perfect tool for addressing issues like this. Combine demand prediction with sales forecasting and you`ll know what, when and where.

The most important inventory management KPI

GMROI or Gross Margin Return on Investment is the KPI any business manager should keep an eye on because it shows how efficient he or she is. You can calculate it by dividing the gross margin or gross profit to the average inventory cost. The result should be larger than 1.00 and demonstrates whether a business is able to make a profit on their inventory and to which extent.

By playing with the GMROI KPI on particular product groups and categories, anyone involved in inventory management easily will get aware of its slow-moving items as well as the stars within the SKU list.

Other inventory management KPIs

Sales to Inventory Ratio or SIR is showing the rate of inventory flow to customers. Close enough but not exactly the same is Days of Inventory or DOI. This metric is displaying how much cash is used for stored capacity. Total Inventory is another KPI that should be monitored closely at least because it could be the largest item on the balance sheet.

How predictive analytics is improving inventory management

There are no two businesses alike even though they could be a different dealership for the same manufacturers. This is why setting clear KPI goals in term of inventory management is the key for successful predictive analytics project in the field. By having business goals in mind they can be turned into business rules. This is when predictive analytics can take over decision making on what, when and where to be stocked.

Sales forecasting is significant part of such an equation. Not matter if its retailers sales forecasting or wholesalers sales forecasting, knowing your demand combined with projection of potential sales is pretty good foundation.

AI Inventory Management

By training data analytics models fueled by different data points, it is easy for analytics experts to create a tool projecting the potential output while accounting variables affecting inventory turnover. This way, businesses will easily avoid the excess stock. Having business rules ready, you are one-step closer to the creation of automated decision making on a granular level.

This might be a huge advantage since it is not just getting a business aligned with the desired KPIs but also because it gives time to inventory management to focus on growth and efficiency instead of day-to-day operation work.

Are you interested in boosting inventory management efficiency? At A4Everyone we have strong expertise in boosting inventory management efficiency, contact us to explore how we can help you.

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