Leveraging Inventory Analytics For Better Inventory Planning

Good inventory planning is an essential part of operating a profitable retail business. But identifying optimal inventory levels can present a considerable challenge, especially for businesses with diverse product lines and large numbers of SKUs. Inventory analytics can significantly reduce the guesswork involved in planning. Using data from in-house and external sources, analytics tools help inventory planners see a complete picture of all the factors affecting planning and how they impact one another. In this article, we’ll explore why optimizing inventory planning is so important, the role inventory analytics plays in the planning process, and popular data analytics techniques.

Why Optimize Inventory Planning?

Excess inventory ties up valuable operating capital in unsold goods and the costs associated with storing them. But carrying too little product results in frequent stockouts and missed sales opportunities. Inventory planning seeks to balance these two competing factors, helping planners make more informed decisions on when to replenish stocks and in what quantities. By better understanding the profitability of individual products, how quickly they can be restocked, and the trends likely to impact future demand, retail businesses can maintain cash flow, improve profitability, and better meet the needs of their customers. 

Big data plays an important role in inventory planning. By analyzing historical and real-time sales data and demand data, analytics platforms can predict trends, optimize stock levels, eliminate excess inventory, and reduce the risk of stockouts. Data analytics can also be used to track and analyze other factors that impact inventory levels, such as supplier lead times, shipping delays, and weather patterns. Powerful machine learning algorithms can identify patterns and trends in the data that would be difficult or impossible to detect manually, helping inventory planners make more accurate predictions about future demand and adjust accordingly. 

Why Use Analytics for Optimizing Inventory Planning?

Inventory analytics has revolutionized inventory planning, increasing profitability while reducing the costs associated with carrying excess inventory. Here are five ways inventory analytics is benefiting today’s businesses. 

More accurate forecasting

Inaccurate inventory forecasting creates unnecessary surpluses and shortages. With too much or too little product on hand, businesses lose money both ways—incurring avoidable costs on warehousing or missing out on sales opportunities. Inventory analytics provides valuable insights that managers can use to better manage their inventory. 

Preserve operating capital

When businesses carry ideal levels of inventory, they minimize associated costs while still keeping enough product on hand to satisfy customer demand. Inventory analytics can help planners keep valuable capital free for use elsewhere and reduce the need to pay either for expensive expedited shipping for out-of-stock products or warehousing costs for stock overages.

Optimize supply chains

Data analytics can also identify bottlenecks in the supply chain and opportunities for improvement, helping businesses ensure a steady, reliable source of both finished goods and raw materials needed for manufacturing or other operations. 

Identify product improvements

Inventory analytics can help spot products that go unsold and languish in warehouses. This knowledge can help business decision-makers spot products ripe for innovation. When customers ignore certain product offerings, it can indicate an underlying issue with how those products perform. Additionally, analytics can identify popular items that show promise for special promotions. 

Improved customer experience

Customers rely on businesses to keep the products they depend on in stock. When inventories aren’t optimized, stockouts can become more frequent. When customers are unable to get vital products, they are much more likely to seek out an alternative supplier with better reliability.

Frequently Used Analytics Methods for Inventory Planning

Depending on the needs of the business, inventory planners may use a variety of analytics techniques. These methods help planners evaluate inventory using various metrics.

ABC analysis

ABC analysis is one of the most common analytics techniques used by retailers. This method ranks products by revenue and profit margins, using three buckets: A, B, and C. The highest-value products are placed into bucket A; middling products are assigned to bucket B; and the lowest-value products are relegated to bucket C. This value-based analysis helps inventory planners identify and focus on their highest-performing products. 

VED analysis 

VED analysis divides products into three categories: vital, essential, and desirable. Items are then placed in one of these buckets based on how essential they are to business continuity. Most often used in manufacturing, VED analysis assesses how important it is that an item remains in stock, helping planners to identify where their efforts need to be focused.

HML analysis

HML analysis is used to rank inventory by popularity: high, medium, and low. This technique helps planners gauge how often stock levels should be checked, as well as the optimal units of product that should be kept on hand. Ultimately, it tells retailers when current stocks should be replenished.

SDE analysis

SDE analysis helps planners assess how difficult it will be to replenish items once they’ve been sold or used to produce finished goods. Items that are challenging to replenish are categorized as scarce, while those that are slightly easier to obtain are categorized as difficult. Items that present no difficulty to replenish are classified as easily available. SDE analysis is a means of prioritizing the sourcing of key components. This method is especially useful in manufacturing where some components may be significantly more challenging to obtain than others.

EOQ analysis

Economic order quantity ranks items based on how quickly they sell and the costs associated with ordering and storing them. Used in tandem, these variables help inventory planners determine how often and in what quantities items should be ordered to maximize ordering and storage costs while still satisfying customer demand.

Moving Beyond Traditional Inventory Planning Analytics

The analytics methods described above have been used for decades to help retailers and manufacturers optimize inventory. With today’s cloud technologies, we have access to cost-effective storage and compute power that allows us to analyze massive amounts of data from a variety of different sources. We also have more advanced analytics tools and methods, such as predictive and prescriptive analytics. 

Predictive analytics leverages advanced machine learning algorithms that combine data from many different sources for more accurate predictions. This data may include marketing data, social media data, POS data, ecommerce sales data, warehouse data, weather data, transportation bottlenecks, and information on labor strikes in various regions. Prescriptive analytics takes things a step further, using similar data to tell inventory managers what specific actions need to be taken to optimize results. 

Optimize Inventory Management and Fulfillment with Snowflake

The Snowflake Retail Data Cloud allows retailers and manufacturers to access, govern, and share data seamlessly. With Snowflake, organizations can more accurately predict demand, identify risks, reduce costs, and streamline operations. The Snowflake Data Cloud provides retailers and manufacturers with a single source of truth where data from multiple sources can be stored and accessed in real time, empowering data-driven decisions based on a current, comprehensive view of all relevant data.