Using AI and GEO Analytics to Detect Regional Underperformance in Manufacturing
3 min read ● Silk Team
Traditional performance analysis typically hides regional issues. Aggregated dashboard views and quarterly reports mask variability, and therefore struggle to show which regions are actually failing.
Common challenges manufacturers face when trying to detect underperforming regions include:
- Average Data: Regional success can mask regional failures, because average regional data will “smooth” out differences between strong and weak performing regions.
- Static Regional Definitions: Typically, regional boundaries do not reflect the way products are being delivered to consumers, or how products are being demanded by consumers. Therefore, static regional definitions can hide the true nature of performance issues.
- Delayed Insights: Unfortunately, many organizations wait until there is an obvious financial problem (such as a decline in profit) before they recognize that one of their regions has a performance issue. By that time, it may already be too late to correct the problem without significant expense.
If a manufacturer does not have access to geographic intelligence and predictive analytic tools, they are likely to continue to act reactively rather than proactively.
AI is well-suited to find subtle patterns within large datasets — which is exactly what is required to perform regional performance analysis.
AI can:
- Compare actual performance to expected performance per region.
- Find outliers, such as declining output or order volume in a particular area.
- Identify trends that indicate potential performance issues in the future — prior to missing KPIs.
Therefore, AI enables companies to transition from looking at lagging indicators to finding early warning signs.
The role of GEO analytics is to provide a geographic basis to compare against the performance data collected by AI. In other words, while AI indicates where performance issues are occurring, GEO analytics explains why these issues are occurring.
When a company layers its operational data on top of geographic data, it can look at:
- How far away production sites or warehouses are from each region.
- Whether or not there are any issues with the transportation route, or whether or not the infrastructure of the region supports the movement of goods.
- Labor shortages in the region; or labor regulations.
- Whether customers, suppliers or competitors are close enough to the region to impact the cost of operations.
Having this spatial view converts what would otherwise be an abstract issue into a tangible issue based upon the location of the issue.
The benefit of combining the use of AI to predict where performance issues will occur with GEO analytics to provide a reason why those performance issues occurred enables companies to go beyond simply detecting performance issues and to take proactive steps to prevent them from becoming a larger problem.
For example:
- AI predicts that a company’s sales will consistently under-perform in a particular region.
- GEO analytics finds that this region is farther away from a company’s warehouse(s), thus increasing the cost of transportation.
- Management decides to adjust where it places inventory, or to modify shipping routes so that the region is closer to the warehouse(s).
Rather than addressing the symptom of under-performance, management addresses the cause of under-performance.
Examples of common scenarios for using AI and GEO analytics include:
- Demand Mismatch: Identify regions where a company produces more product than there is demand for in that region.
- Logistic Inefficiency: Find the regions with the highest increase in fulfillment costs.
- Service Level Gaps: Identify the regions where a company’s delivery times, or fill rates, fall below the standard established for the rest of the organization.
All three of these examples are made possible through the combination of having a clear geographic view of a company’s performance, combined with the ability to see patterns in data.
Important points for manufacturing leaders to remember about AI and GEO analytics include:
- There is a difference between a region and a global issue.
- AI can reveal patterns within your data that were previously unknown to you.
- GEO analytics provides the necessary context to explain why you are seeing those patterns.
- Collectively, AI and GEO analytics allow manufacturers to identify and address performance issues much quicker and with greater accuracy.
Summary:
Underperforming regions cannot be addressed at a high level summary. Manufacturers need to utilize AI and GEO analytics together to identify performance issues early, understand the reasons behind the issues and then respond quickly with precision.
Companies that adopt this model of decision-making are able to achieve more than clarity of operations; they are able to continually improve the performance of their regions by analyzing and optimizing performance on a region-by-region basis, before small problems turn into enterprise-wide risks.