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Heat maps can be a useful visualization tool for big data analytics in procurement, because they provide a visual representation of large data sets. We use heat maps to visualize past supplier delivery trends and as a tool to help predict future supplier deliveries issues by providing a visual representation of delivery patterns over time.

In the above image we track 3 suppliers patterns over a time span of a year:
1. % of late PO lines / Total number of open po lines

2. % of late parts/ Total number of parts under open PO's

3. Total number of red parts (Per our machine learning parts prediction algorithm - parts that are past due or expected to be late in the next 90 days)

4 Steps in building this:
1. Gather data on supplier deliveries: Collect data from different systems and log them on a daily basis for future analysis.

2. Create a heat map: Create a heat map that visualizes the delivery data over time. The cells of the heat map are color-coded to indicate the pattern - The red the cell - the worse the supplier performance is.

3.Analyze the heat map: Examine the heat map to identify patterns. Look for clusters of red colors to identify patterns of delivery issues.

4. Use the analysis to predict future delays: Based on the patterns identified in the heat map, you can predict which suppliers are likely to have future delays and a negative OTD score. You can then take proactive measures to address these potential delays, such as ordering from alternative suppliers or adjusting delivery schedules.

Overall, heat maps can be a powerful tool for visualizing supplier delivery data and identifying patterns that can be used to predict and prevent future delays.

Stop chasing your parts, past dues and line stoppages. Its time to become proactive!