Supply chain leaders are aware that technology will have an effect on business in the next two or three years, but as research shows, only less than half (44%) currently have a strategy in place. The change will most likely come from predictive analytics, which is expected to impact the entire business, from demand prediction to after-sales modeling.
This technique has the advantage of enabling real-time decisions based on statistical estimates of future outcomes. It has the potential to enhance strategic thinking and overall performance. The difference is that it offers a proactive approach, compared with reactive actions, as deemed by historical reports.
Using predictive analytics is expected to have a clear and positive impact on improving the accuracy of forecasts, product tracing and offering growth paths. A study from 2017 identified and classified the use cases for supply chain analytics based on importance and level of adoption. These included:
Inventory level optimizations
Production and sourcing optimizations to reduce costs
Identifying and resolving quality defects and root causes
Identifying product cost variances
Analyzing customer service level performance
Tracking product traceability
Analyzing forecast accuracy and more
Although necessary, these could seem a bit too general for daily use. We will strive to examine straightforward ways of using predictive analytics, together with …