If you’re working with a new dataset, it isn’t too uncommon to also want to add mapping to your toolbox of analytical tools. The fact is tying your new data to maps can be a powerful way to look at your data and improve supply chain planning systems significantly. After all, the human brain is wired to find patterns and to arrange data spatially. As a result, it has the ability to facilitate connections a table or graph isn’t able to replicate.
Selecting the Right Maps
There are a number of maps to consider when you’re creating your dashboard, and there are data transformation decisions you have to make before you can display your creation and gather new supply chain planning systems. Some of the most common options include:
- Proportional or Choropleth Symbol
- Dot Density Maps
- Proportional Area Maps
When and How to Classify the Data
Think about a map of Europe in front of you with the top 100 ship-to data displayed on it. Are there 100 various sizes of symbols represented on the map? Would you be able to identify those quickly? Chances are this would be quite a challenge.
By adding a window into the data query and transforming the actual raw counts into quintiles, you can reduce the nuance, which means you can guide the end user to the type of analysis they need to best handle their set job.
Data maps aren’t a new concept, but when using them for supply chain intelligence, you need to make sure you get it right. The tips and information found here helps ensure you do that and there are no issues present.