Reality doesn’t always reflect your intended supply chain design
Closing the gap between how you designed your supply chain to work, and how it’s actually performing improves your ability to predict revenues, satisfy customer demands and maximize financial performance. Just look at assumed lead times. When assumptions don’t reflect the as-demonstrated state, there are big impacts on crucial metrics like revenue-at-risk, customer satisfaction and inventory costs. But for most, finding ways to detect, measure and correct this vast divide isn’t only painful, it’s virtually impossible. Correcting inaccurate supply chain design assumptions means you’ll be able to:
- More reliably model revenue and profit so you have a better picture of your company’s health
- Spot performance degradation faster so you can take action before it impacts the bottom line
- Empower your team to focus on exceptions and stop wasting time chasing down the everyday
Machine learning can help — if you can cut through all the hype
When it comes to fake news, some machine learning supply chain solutions are full of it. Whether it’s over-hyped promises with no practical application, or functionality that’s been around for years dressed up to look like something new. Real-world solutions are out there, though. You just have to look for them. To avoid making a big investment that only yields small results, choose a solution:
- Built with a practical use case in mind Advanced algorithms won’t benefit you if they aren’t performing a function that improves your supply chain performance.
- Developed with customer input Academics in ivory towers can only provide so much insight. There’s something to be said for hands on testing by peers with similar goals.
- Designed to focus on data accuracy first If the data’s wrong, the results will be too. It’s as simple as that.
Revolutionize performance with a Self-Healing Supply Chain
Implement machine learning in a way that actually provides value. The Self-Healing Supply Chain™, makes it easy to compare your as-designed and as-demonstrated supply chain performance and then automatically corrects any inaccurate design assumptions.
- Detect inaccurate design assumptions, such as lead times, using historical data so you know where to focus your attention first
- Alert you when discrepancies are discovered, providing a summary of the findings and recommended actions so you can get moving on next steps
- Analyze and provide a visualization of potential impacts on critical business metrics so you understand how making corrections can help your success
- Suggest actions for supply chain design inconsistencies outside the automatic correction thresholds so you can collaborate with stakeholders and suppliers to quickly address outstanding issues
- Automatically monitor and adjust design inputs based on personalized tolerance thresholds so your supply chain is always in top health, letting you see increasing value over time
Get started with your own Self-Healing Supply Chain, part of Kinaxis® RapidResponse®. Visit Kinaxis.com to learn how.