Reducing Inventory Imbalances Across Distribution Networks With AI and GEO Solutions
3 min read ● Silk Team
Distribution inventory is one of the biggest problems facing companies today — and it is one of the biggest expenses. Stock sitting around in some of your locations is tying up money (capital) — money that could be earning returns if used elsewhere. And stock outages in high-demand areas are costing you money in lost revenue and poor customer service. Many distributors have an Enterprise Resource Planning (ERP) system and still can’t keep their inventory in sync with their regional demand.
AI and GEO Solutions Solve This Problem
By combining Predictive Intelligence and Geographic Context, AI & GEO Solutions provide inventory decision-makers the ability to make decisions about placing inventory in the places that products will flow to next — not just historically where they flowed in the past.
The Main Advantage Of Using AI & GEO Solutions For Inventory Management Is Precision
Using historical average data or blanket safety stock rules, you’re going to miss where inventory needs to go. With AI and GEO Solutions, you can place inventory where demand for your products is forecasted to exist — whether it’s in a different region than where you currently hold stock, or a different part of a city or region.
This allows you to:
- Decrease stock holding in low demand areas
- Increase stock availability in high demand areas
- Increase your inventory turnover without adding risk
Ultimately, create a distribution network that responds faster to your customers’ needs.
Why Do Inventory Imbalances Continue To Exist In Distribution?
Most traditional inventory planning techniques are ineffective due to their failure to consider geographical variability.
Reasons why this occurs include:
- Aggregate forecasting: Demand is forecasted at the national or regional level, which masks variations by location
- Static replenishment rules: The reorder point does not adjust based on changes in the demand pattern
- Disjointed logistics data: Lead times and transportation constraints are not considered when making inventory decisions
With the lack of geographic intelligence, inventory placement is typically reactive and non-efficient.
How Does AI Improve Forecasting and Allocation?
AI is able to identify demand patterns within large datasets; however, these patterns may vary greatly depending on the region.
AI models are able to:
- Predict demand at a granular, location-based level
- Use real-time sales data, promotions, and seasonal factors to modify its prediction
- Identify early signs of a shift in demand prior to experiencing stock-outs or over-stocking
This enables inventory teams to transition from static planning to continuously allocating inventory.
What is the Role of GEO Solutions in Achieving Inventory Balance?
Whereas AI is predicting demand, GEO provides insight as to where the inventory should reside within the network.
Through GEO, distributors can analyze various aspects of the network including:
- Proximity of warehouse(s) to customer clusters
- Regional delivery time and transportation costs
- Service-level expectation by location
- Feasibility of cross-region transfer
With this spatial knowledge, distributors can ensure that inventory is both available and placed strategically for speed and cost effectiveness.
Combining AI Forecasts with Geographic Intelligence: The Real Value
When using AI to predict demand and GEO to understand the network, the true benefit lies in the combination of these two forms of intelligence.
Example:
- AI indicates there is an increase in demand for a particular product in a specific metropolitan area
- GEO indicates the closest warehouse is currently constrained
- Inventory is proactively relocated from another slow moving region
As opposed to waiting until service levels drop before rebalancing inventory, distributors can proactively rebalance their inventory to avoid being visible to their customers during periods of imbalance.
Use Cases in Practicing Multi-Warehouse Optimization
Distributors can utilize AI and GEO solutions to optimize inventory across multiple warehouses and facilities in order to allocate stock based on regional demand and proximity to customer locations.
Another example is inter-facility transfers: determining when and where to relocate inventory in a cost-effective manner.
Additionally, distributors can utilize AI and GEO to improve seasonal planning and accurately anticipate the geographic demand for products during peak seasons and proactively position inventory accordingly.
Finally, distributors can utilize AI and GEO to determine the optimal initial inventory location for new product launches and place that inventory in the regions where the products will likely be adopted the fastest.
These use cases all work to remove friction between the supply and demand side of the business.
Important Takeaways for Distribution Executives
- Inventory imbalances are geographically driven, not systemic
- AI improves demand accuracy at the regional level
- GEO Solutions enable inventory placement to reflect actual world constraints
- Collectively, these technologies eliminate waste while improving service levels
Closing Thoughts: Inventory Balance Requires Geographic Intelligence
Inventory balance is no longer possible using static rules and historical averages in today’s dynamic distribution networks. Combining AI and GEO Solutions provides the foresight and spatial awareness necessary to synchronize inventory with regional demand in real-time.
Distributors who implement this approach will experience greater control over their working capital, improved service levels, and a network that adapts as quickly as their customers do.