In a recent blog, my colleague Alla Valente described the increased risk of late shipments in complex, interconnected supply chains. As she pointed out, any element may fail as a result of random events such as strikes or earthquakes. Project managers following critical chain methodology build project “buffers” to absorb random shock. But in logistics, you can engage demand-driven supply chain collaboration to synchronize supply with anticipated demand.
- Leading indicators of demand. You can’t rely on historical or time-series data in times of disruption. You need to combine multiple demand signals using machine learning to help understand the relative predictive value of point-of-sale data, epidemiological data, stringency indices, Google Trends, and regional economic data.
- Current logistics availability and costs. In Alla’s blog, she described the container imbalance and the soaring rates for transportation on popular routes. The SAS/C.H. Robinson partnership applies real-time analytics to the world’s largest database of transportation supply and demand trends and market rates. This helps shippers balance cost and lead time and use alternate routes to avoid congestion.
In Alla’s blog, she described the importance of timely, granular collaboration between shippers and carriers based on a current understanding of demand and logistics capacity. The SAS/C.H. Robinson partnership applies predictive analytics and machine learning to deliver real-time, integrated planning and execution dashboards that simplify collaboration with logistics partners in a demand-driven supply chain.
You can find out more here.
Until next time, good luck taming the supply chain tiger!