Optimizing Inventory with Data Analytics

Optimizing inventory has always been challenging. Having the right products in stock at the right time (without overstocking) requires successfully navigating an increasingly complex supply chain while remaining responsive to the ever-changing whims of consumer demand. Events such as the global pandemic, severe weather, and geopolitical conflict have complicated these efforts, presenting new challenges for businesses seeking to optimize their inventory. But as obstacles mount, so do the opportunities presented by a rapidly expanding reservoir of new data sources. In this post, we’ll highlight how using data to power inventory optimization can increase revenue, improve efficiency, and provide a better experience for customers.  

Benefits of Inventory Optimization

Every manufacturer and retailer is looking for a competitive advantage that will help them excel. Optimizing inventory using data is an advantage that delivers significant benefits. 

Prevent overstocks and stockouts 

The goal of all businesses that hold inventory is to strike a Goldilocks-like balance, a perfect equilibrium between demand and available stock. If demand is too hot, customers will find their items out of stock. But if demand is too cold, business capital and warehouse space become locked up with excess inventory. Data analytics can provide businesses with valuable insights on key metrics, including inventory performance and replenishment speed, ensuring they have just enough product to satisfy demand and a small buffer supply to smooth out unexpected spikes in demand or difficulty restocking.

Lower operational costs

Excess product is costly to handle, warehouse, and insure. Inventory that goes unsold for too long risks becoming obsolete and often ends up being sold at a discount. In contrast, a well-optimized inventory adds business value. Stocking products that are sold quickly increases profitability and maximizes the use of working capital. 

Improve customer satisfaction

When customers find what they want, they’re more likely to return. With so many online and in-store retailers to choose from, consumer loyalty is at an all-time low. If a competitor does a better job of keeping popular items in stock, your customers are likely to switch. Beyond simply keeping items in stock, analytics can also help businesses identify the root cause of issues such as late shipments or damaged goods. 

Fulfill orders faster

Optimizing inventory can help retailers fill customer orders more quickly. By analyzing data, companies can find inventory at the closest warehouse to a customer’s address, identify the most efficient locations to store items in each facility, and streamline fulfillment center operations.

Prevent or prepare for supply chain issues

Supply chains are highly vulnerable to disruption, from adverse weather events to port congestion. Predictive analytics helps businesses better understand how adverse events such as the bankruptcy of a key parts supplier or a customs delay will impact product availability. Prescriptive analytics can help planners identify the optimal course of action to lessen or avoid the negative impacts of a disruption.

How Data Analytics Assists with Optimizing Inventory

Data analytics allows manufacturers and retailers to bring facts and figures to their inventory management programs, rather than relying on guesswork. Here are several ways today’s companies are using data to optimize inventory.

Demand forecasting

Modern data analytics tools can analyze data from a variety of sources to help manufacturers and retailers better understand how factors such as historical sales data, emerging consumer trends, weather patterns, supply chain disruptions, and holidays will impact demand for specific goods. Improved demand forecasts allow businesses to realize greater efficiencies in areas such as manufacturing flow management, raw materials sourcing, marketing campaigns, order fulfillment, and logistics.

ABC analysis

Using ABC analysis to identify the most popular and least popular products and those with the highest and lowest profitability has historically been done with backward-looking sales data. But the past doesn’t always predict the future. AI-powered analytics programs enrich historical sales data with additional data sources, resulting in more-accurate analysis. 

Predictive analytics to identify supply chain risks

Because supply chains are so complex, there are many different factors that can impact them. Predictive analytics helps businesses anticipate delays and disruptions before they occur, allowing them to create contingency plans for events before they occur and to respond more quickly to unanticipated disruptions. 

Challenges When Using Data Analytics for Optimizing Inventory

As data sources become more numerous and diverse, using legacy tools to ingest, analyze, and report on data makes gleaning insights increasingly difficult. Here are a few of the top challenges facing manufacturers and retailers looking to use data for optimizing inventory.

Siloed data

When data is locked away in disparate systems, gaining a complete picture is all but impossible. Storing all relevant data in one place creates a single source of truth that provides decision-makers with a complete view across all business operations and the supply chain. 

Unstructured data and semi-structured data

Storing unstructured and semi-structured data that comes from valuable sources such as mobile device activity and social media posts is costly and complex for businesses using on-premises storage. Today’s manufacturers and retailers must have the ability to use data related to purchase intent, customer sentiment, and other key metrics.

Inability to access all types of data needed 

Public data and data from vendors, industry partners, and other third-party sources play an important role in supporting a comprehensive inventory optimization strategy. Manufacturers and retailers must have a platform that can quickly make this data accessible. 

Inability to collaborate

Sharing data within the organization and with vendors and industry partners is vital for optimizing inventory. But it’s not possible to share data without a secure platform that has collaborative features and that supports robust governance. 

Optimizing Inventory with Snowflake

The Snowflake 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 manufacturers and retailers 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.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.