Whether your inventory is intended for manufacturing, retail, or day-to-day office operations, it always represents locked-in capital that must either be consumed or sold off. With markets and technology trends seemingly changing course every minute, getting precious operating capital stuck in an inventory you can’t get rid of is now a real risk that can slow down your business operations.

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Thanks to ongoing digital transformation, businesses of all sizes can now sidestep this risk and use data science to completely transform their inventory management. Thanks to software-based enterprise resource planning (ERP) systems like SAP Business One, decision-makers can use data science techniques like predictive analytics and data visualization to provide clear courses of action in a few moments rather than weeks.

However, while most business owners understand the value of ERPs, not everyone knows why a firm grasp of data science is so important for these tools. Let’s explore some specific ways applying data science can heighten your ERP’s capabilities.

1. Accurate Demand Forecasting

Using data science keeps decision-makers from relying on intuition and imperfect recollections of past events, enabling a far more accurate way to forecast demand. If set up correctly, ERPs will enable accurate demand forecasting by drawing from historical sales data to uncover market trends and seasonal fluctuations, faster than would be possible through manual analysis. 

Even better, next-generation machine learning enables new ERP products to predict future demand with good accuracy. Doing so helps businesses to consistently stock the right products in optimal quantities and  to free up more capital for growth.

2. Real-Time Inventory Tracking

ERPs can help businesses check inventory levels across multiple locations, avoiding the need for tedious back-and-forth communication between various branch managers. Many ERPs also allow the integration of sensors to collect and process live data, offering more transparency and preventing errors caused by stock pilferage or outdated information.

3. Consistently Optimal Stock Replenishment

Using data science principles enables inventory managers to always keep the right amounts of stock on hand and avoid expensive overstock and understock scenarios. This is done by analyzing consumption patterns, typical supplier lead times, and other factors that can affect when an item will arrive in the warehouse and how long it will stay there.

Thanks to the capabilities of newer ERPs, notifications can now be automatically sent to stock managers about critical stock thresholds or ideal order times. They can even be set up to automatically generate purchase orders when inventory reaches predefined thresholds, ensuring timely restocking while reducing warehousing costs.

4. Optimized Warehouse Management 

Speaking of warehousing costs, employing a data-driven approach in ERP usage helps reduce the expenses associated with safely storing inventory. When guided by integrated sensors, modern ERPs can create “heatmaps” that indicate high-traffic zones, guiding the rearrangement of inventory to minimize retrieval times and labor costs. 

Data science tools can also efficiently track volatile items and prevent losses through stock spoilage. Additionally, data science can help product and marketing managers identify slow-moving stock, enabling them to create promotional strategies to quickly offload low-performing items.

5. Fully Integrated of Sales, Marketing, and Inventory Data

Inventories don’t exist in a vacuum. They are always built within the wider context of a business’s needs. Using a data science approach with an ERP encourages the understanding of the linkages that connect inventories with other business areas—something that can get more difficult as a business grows.

For instance, businesses can use the approach to gain accurate insights into how promotions and pricing impact inventory. Likewise, they can use it to gain more objective forecasts of ideal stock levels, avoiding cases of overstocking or understocking that can happen from using just experience and intuition alone.

6. A Better Understanding of Supplier Performance

Data science helps businesses evaluate supplier reliability through delivery times, defect rates, and pricing trends. This makes it essential when a business depends on multiple suppliers to provide the same product or production input. With time, decision-makers can use this data to negotiate better contracts and secure better deals with more dependable suppliers, improving overall supply chain efficiency.

7. Improved Risk Mitigation and Customer Satisfaction

Lastly, using data science makes it much easier to identify risks such as supply chain disruptions or changes in customer preferences. Rather than wait multiple order cycles to understand customer sentiments, users can use predictive models to provide early warnings, allowing businesses to maintain ideal inventory levels during challenging times. The result is fewer avoidable losses for the business as well as an improved capacity to meet ever-changing customer needs.

Unlock the Full Potential of Your ERP through Data-Driven Decisions

Most decision-makers correctly understand that ERPs are necessary for keeping their business competitive in the digital age. However, integrating an ERP isn’t going to yield the desired results if it’s not wielded or selected properly. Any ERP integration must consider the application of data science to guarantee complete success.   

Businesses that adopt a data-driven approach to inventory management can also enjoy benefits in other areas. Having employees who understand the value of data in decision-making puts everyone on the same page, reducing the time it takes to get things done. With time, this approach will enable your staff to fully maximize the capabilities of your ERP, unlocking new levels of efficiency in other operational areas and driving profitability.