How AI and GEO Insights Reveal Regional Performance Gaps in Manufacturing & Distribution
3 min read ● Silk Team
Manufacturers and distributors operate in a very dynamic and geographically sensitive marketplace that impacts all aspects of an organization including revenue, cost management and ultimately customer satisfaction. Demand varies from one location to another; transportation costs vary depending on the route being used; and market conditions can rapidly change the outcome of a company’s plans. In spite of this, most companies continue to generate and review high level reports that do little to help them understand why performance differs in different locations. This is where the combination of AI (artificial intelligence) and GEO (geospatial) insights can greatly assist organizations in determining not only what is occurring in each location, but also what steps should be taken in response to those findings.
The Key Advantage: Linking Location Based Data To Decision Making
The key advantage of utilizing both AI and GEO is the direct connection that is made between performance metrics and geographic location. Rather than viewing sales, inventory and fulfillment data independently, manufacturers and distributors can now view how location impacts those results.
As a result, teams can now:
- Identify underperforming locations sooner
- Match inventory and production to localized demand
- Adjust distribution strategies to account for real world conditions
Ultimately, organizations will have a better understanding of how their various regions are performing and make quicker, more informed decisions.
Limitations of Legacy Reporting Tools
Legacy reporting tools were developed to address today’s multi-regional and fast-paced manufacturing and distribution environments. Many legacy reporting systems suffer from the following limitations:
- Lagging insights: Reporting on past performance does nothing to indicate future changes
- Static regional boundaries: Sales territories and service regions rarely represent the actual demand patterns within each area
- No consideration for external factors: Organizations typically ignore significant external factors such as infrastructure, weather, labor availability and competition when making decisions about their regions
Without layering intelligent processes above geography, organizations are unable to proactively plan for their regions, rather than simply reacting.
Advantages of Using AI For Enhanced Regional Performance
Artificial intelligence has proven itself to be capable of finding connections in large, complex datasets which makes it well-suited to regional analysis.
Using AI, organizations can:
- Find performance trends that exist between regions
- Utilize historical and real time data to forecast demand for specific regions
- Identify anomalies that may have occurred due to logistics or supply chain issues
Leaders are able to make more data-driven decisions regarding regional performance based on trends, forecasts and anomalies, as opposed to making decisions solely on instinct.
Value of GEO Insights
GEO (geospatial) insights brings geographic awareness to performance data. When organizations analyze data based on geography, they are able to identify relationships that would otherwise remain hidden in spreadsheet data.
By doing so, organizations are able to:
- Produce more accurate demand forecasts based on location
- Optimize warehouse placements and routing based on location
- Gain a clear picture of market penetration and potential coverage gaps by location
Adding geographic awareness to performance data allows organizations to transform raw numbers into actionable insights that are relevant to their physical operations.
Benefits of Combining AI and GEO Insights
While AI and GEO insights are individually powerful, together they form a multiplier effect.
For example:
- AI finds increasing demand for a product in a particular region
- GEO insights show that delays in delivering products are caused by long distances and poor infrastructure
- Teams proactively rebalance their inventory and adjust their routes to meet demand
Combining these two technologies provides manufacturers and distributors with a way to improve the performance of their regions while maintaining their operational efficiencies.
Examples of Real World Applications for Manufacturers and Distributors
Examples of common applications include:
- Optimizing territories: Rethinking sales and service regions based on performance data
- Optimizing inventory: Avoiding overstocked areas with low demand and avoiding stock outages in areas of high demand
- Anticipating risk: Identifying vulnerabilities in specific regions prior to impacting service levels
Each of these examples ties geographic-based understanding directly to quantifiable business results.
Conclusion: Regional Intelligence Is No Longer Optional, It Is Required for Competitiveness
Regional performance cannot be managed using static reports and manual analysis. The use of AI and GEO insights gives manufacturers and distributors the clarity, speed and foresight needed to compete in a regionally diverse marketplace.
Those who implement these capabilities go beyond using data to support historical reviews. Those who implement AI and GEO insights will have the ability to anticipate change, respond to events as they occur in each location and operate with confidence in each of the regions they serve.