Data Diligence: Leveraging Advanced Analytics for Actionable Inventory Management Solutions

Every retailer is looking for any edge that can help them stay ahead of the competition, and each has an opportunity to save time and money and improve efficiency with smart, data-driven inventory decisions.

While technology solutions for retailers have certainly gotten smarter in the last decade, one of the biggest obstacles remains siloed data. If retailers can achieve fully-connected systems and leverage advanced data analytics and machine learning, they can reach a new level of strategic advantage.

Data Silos in Inventory Management

Disconnected systems and data create operational inefficiencies and add road blocks to effective decision-making. Each system that a retailer uses can represent a separate data silo.

The OMS (Order Management System) may be unable to send or receive live data from other systems, which spells the potential for unoptimized fulfillment decisions.

eCommerce Platforms reflect only online inventory, creating discrepancies with in-store or warehouse inventory data.

The POS (Point of Sale) system only captures in-store sales data, which causes retailers to approach the brick-and-mortal sales channel as distinctly different from other channels.

The ERP (Enterprise Resource Planning) lacks granular-level inventory data and live updates.

The IMS (Inventory Management System), if not fully connected to order and sales data, is limited in its demand forecasting accuracy.

The TMS (Transportation Management System) is often disconnected from these other systems, yet it might have valuable information to help inform inventory decisions.

Connecting these systems enables a unified view of inventory to improve decision-making and efficiency across sales, fulfillment, and logistics.

Imagine if online orders were automatically rerouted to stores with excess stock, helping to reduce delivery times and avoid stockouts. POS systems should be connected to improve replenishment based on live, in-store sales, eliminating the issue of data lag. The TMS could contribute to greater visibility, for example, keeping these interconnected systems informed if there are any delays in transportation that could affect stock availability.

It’s important for all systems to be on the same page. Each one handles different parts of the retail process: orders, inventory, sales, shipping, etc. Without system synchronization, data is incomplete and inaccurate, effectively leading to inefficiencies. When systems are aligned, retailers get a reliable picture for smart decision-making.

Data Analytics: The Key to Actionable Inventory Management

With integrated systems as the foundation, let’s add data analytics to the equation. Analytics is the piece of the puzzle that takes these systems and turns visibility into insight and action. Retailers can identify trends, predict demand, and optimize stock levels across all channels. They can target inefficiencies like slow moving stock and work to continuously improve their inventory decisions. They can gain an edge over the competition by quickly processing emerging data and leveraging it for new predictions, even honing decisions on a micro-level within specific markets. Analytics enables retailers to move from reactive to predictive inventory strategies.

How do retailers achieve this? The data from various systems (OMS, POS, ERP, etc.) is sent to a single data lake or centralized repository. This is the first step in Dropit’s approach, which produces a single, unified view of inventory. Next, the data must be cleansed, standardized, and synchronized for accurate analysis.

However, at this point, data is still raw. Retailers are likely working with high volumes of data that pose a challenge for processing. Their technology solution must be able to handle this data volume and complexity and process it to shed light on current performance metrics and reveal opportunities for improvement.

Machine learning is where the data comes to reveal valuable insights. Taking in live and historical data, machine learning can factor in the many variables in the complex equation for inventory efficiency.

• Advanced algorithms can predict the need to stock more of a particular item in a specific region, for example, based on past sales during peak season, an upward trend, and an understanding of how fast the current stock may sell out.
• Advanced algorithms can decide how excess stock allocated to one location can be rebalanced efficiently to avoid stockouts at another location.
• Advanced algorithms can optimally route returns at the micro-decision level, helping retailers save time and money instead of handling all returns the same way.

This is all done with the goal of inventory efficiency for the retailer– helping inventory get to the right place, at the right time to sell faster and decrease the chances of markdowns.

Optimized Inventory Decisions Powered by Dropit

Dropit’s technology enables retailers to analyze inventory holistically and make calculated decisions toward inventory optimization– making the ideal allocation, restocking, and returns decisions for less waste, fewer markdowns, and increased profitability. Best of all, Dropit does this by sitting atop retailers’ existing technology stack, eliminating the need for costly rip-and-replacements.

Reach out to us today and learn more about achieving unified inventory visibility with Dropit and gaining actionable insights for inventory efficiency.