For businesses that sell physical products – online or in-store – inventory management is critically important. Miss the mark and you’ll either end up with surplus inventory that hurts cash flow, or you’ll have to turn customers away and leave money on the table.

Thankfully, new data-based strategies are improving inventory management.

Over the past three-plus years, we’ve seen some pretty significant changes in the way inventory is managed. This has largely been the result of more robust advances in how data is collected and stored. And now, as real-time data becomes the norm, industry experts are expecting to see even more positive growth in how companies handle this information and use it to make informed inventory management decisions.

When you study the changing face of inventory management, there are three big trends that stand out. Let’s check out how they’re fostering positive change:

1. Cloud-Based Inventory Management

“Knowledge is power. The company that has the most readily available data in the most comprehensible format will have a major leg up on the competition,” explains Boxstorm, one of the leaders in cloud-based inventory management. “Accessibility, security, and customizability are key traits of a good inventory management app.”

As an increased need for accessibility, security, and customizability as arisen, the industry has seen a swift uptick in the development of cloud-based inventory management apps and software platforms on the market.

Developers are particularly focused on developing software that aggregates data and creates centralized repositories that can be accessed and synced across multiple warehouses, channels, and locations. This lowers the chance of errors associated with redundancies and double-entry.

Integration with Enterprise Resource Planning (ERP) systems is also a key software development focus right now. As more and more businesses invest in software “stacks,” there’s tremendous value in simplification and integration between technologies.

2. Predictive Analytics and Machine Learning

It used to be that businesses looked at historical data and extrapolated it to identify future trends. While this does occasionally work in isolated situations, it’s certainly not the most accurate method. Thanks to machine learning and predicative analytics, this is becoming a thing of the past.

“Advanced machine learning and optimization algorithms can look for and exploit observed patterns, correlations, and relationships among data elements and supply chain decisions – e.g., when to order a widget, how many widgets to order, where to put them, and so on,” Morris A. Cohen writes for Harvard Business Review. “Such algorithms can be trained and tested using past data. They then can be implemented and evaluated for performance robustness based on actual realizations of customer demands.”

The result of this rise in machine learning helps reconcile the use of historical data and real-time data for greater accuracy and insights into what will be happening at any given moment in the future.

3. Inventory Forecasting

When you combine the power of cloud-based inventory management apps with predictive analytics and machine learning, you get superior inventory forecasting that couldn’t have even been dreamt of 10 or 15 years ago.

Theoretically, inventory forecasting will become so advanced that businesses won’t just be able to calculate the correct volume of inventory – but they’ll also be able to correctly account for numerous variables, such as size, color, materials, flavor, etc.

It’s important that businesses don’t underestimate or undervalue the importance of moving from manual, subjective strategies and investing in objective, data-driven solutions. These advanced inventory management solutions provide real-time visibility, increase accuracy, and reduce costs. In a business world where seconds and pennies matter, this shift is incredibly significant.

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