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Three Inventory Challenges Solved with AI and Machine Learning

Jeff Warren
Vice President, Solutions Management

Oracle Inventory Optimization on-demand webinar welcome slideConsumer shopping habits have changed in light of COVID-19, significantly impacting retailers over the last six months. We suspect that many of these new habits are here to stay. Modern retailers need to adjust to better anticipate what consumers will be buying, at what frequency, and through what channel. 

In some cases, grocery businesses are transitioning from a 98% foot traffic business to one that is having to fulfill orders across multiple channels. The volume puts a significant amount of stress on the operations and supply chain, but more specifically on a retailer's gross margin. Retailers are placing big bets and making strategic buys in anticipation of increasing and continued demand for specific categories and items. The trick is to create agility across your operations to respond quickly and maintain service levels while offsetting increased costs. 

How do you maintain the agility across all of your retail operations to respond quickly while preserving the operating margins? Moreover, how can you address all of the new consumer journeys cost-effectively? Where and how can you generate additional revenue? And how can you do all of this while placing the right product, in the right place, at the right time, with the right offer to meet the consumer demand? It's like changing the tire as you fly down a highway at 75 mph an hour. Grocers need to take out cost, drive revenue, and they need to do it quickly. Let’s break this apart and review the top challenges in more detail. 

Challenge 1: Carrying Too Much Inventory

Though we see this across many industries, health and beauty is one that typically sees over-stock by a multiplier of ten. While the consumer is always getting what they need, the impact against the gross margin can be a significant challenge. 

Solution: 

With pressure to reduce inventory and increase customer services levels, retailers need to leverage artificial-intelligence (AI) and machine learning (ML) to align business strategies by using smart parameter settings, settings that enable retailers to reduce inventory, optimize service levels and increase revenue. Ultimately, empowering retailers to eliminate manual processes, and drive scale and agility with science and automation. 

Challenge 2: Not Knowing How Much Inventory Is Needed

Generally, retailers operate with muscle memory and run smoothly. The industry works with a constant cadence of demand fed with a steady stream of supply. Then chaos strikes, and disruption occurs, resulting in disappointed customers, lost opportunities, or a glut of inventory, not to mention the rising costs to fulfill the unplanned demand. The wrong mix is just as costly as too much inventory. Some retailers would argue it is more costly in times of disruption.

However, consider slow turn inventory with expensive carrying costs. If a retailer has significant money tied up in excess inventory or doesn’t have enough stock, the effect is lost sales or a disappointed customer due to a slow reaction. By rebalancing inventory on just a few items from a particular department, a retailer could save thousands. You can hope that the consumer would be willing to return due to the need for that particular product, but ‘hope’ is not a strategy. 

Solution: 

The goal is to anticipate rather than react to changes in demand through adaptive forecasting and inform replenishment strategies with service-to-inventory trade-offs. Synchronized with all the other retail planning processes, Oracle Inventory Optimization Cloud Service helps retailers achieve revenue targets, and drive the right mix of inventory through:

  • accurate in-season forecasting

  • statistical demand modeling focused on slow movers

  • rule-based parameter management

  • optimal rationing 

Ultimately the solution plans the optimal assortment, optimizes promotions, increases full price sell through, and minimizes markdowns. 

Challenge 3: Having Inventory in the Wrong Place

Location, location, location. Retailers continue to struggle with the right inventory at the wrong place to meet demand. During COVID-19, companies can’t afford to miss critical items, or they risk loosing  a customer. It’s not enough for a retailer to say that they have it, but they need to have it at the location that the customer wants to get it from. Not at the store up the road, or in an alternate channel.

Inventory in the wrong place drives increased markdowns at locations with excess inventory, and lost sales at locations with no inventory.  

Solution: 

“Retailers’ end-to-end inventory management process is often manual, time-consuming, and does not adapt and learn.”

Today retailers' end-to-end inventory management processes are often manual and time-consuming, but necessary. Imagine if retailers could easily move inventory between locations and channels to meet customer demands – seamlessly. Customer demand is met, and inventory is strategically used. Optimized history, data correction services and a location planning strategy are three simple, automated processes, that will help retailers analyze and correct data for historical anomalies.  

Enter Machine Learning and Artificial Intelligence

30% reduction in inventory cost without service level impact

What if inventory management processes were adaptive or learning as time passed? Imagine a world where you can realize inventory reduction of up to 30%, while driving a revenue increase of 2%, without any changes to your existing replenishment or supply chain solutions.

 

Watch Jeff Warren's Navigating Uncharted Demand 20-minute Webinar for More Details 

 

 

 

 

 

 

 

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