Predicting Lead Times, Throughput and Variability
As we’ve seen through events such as the Covid19 pandemic, Yantian port congestion, and Suez Canal blockage, real-world conditions are constantly changing the planning and execution of global supply chains.
Supply chain variability causes significant damage to shareholder value, operational efficiency, trading partner trust, and customer satisfaction.
What do we mean by variability? It comes in many forms.
So, how do global supply chain leaders improve their ability to understand and dynamically act upon real-time conditions?
One of the options is to accurately predict and avoid the disruptions that cause the transportation lead time variability in the first place. Companies can significantly reduce their lead time variability by learning and then predicting which carriers are more prone to make unscheduled port stops, and select carriers who don’t. Digital supply chain solutions do just that. They watch, model and learn the behavior of all ocean carriers on all lanes under varying circumstances, they know which carriers are more likely to make unscheduled port stops and on which lanes. The implications are significant…
The example described above (transportation lead time variability) is but one of many along the supply chain variability spectrum. These same principles apply to all other forms of variability. By continuously watching, modeling and learning end-to-end supply chain variability from supplier to end consumer, digital supply chain solutions help organizations to both avoid the root causes and to better plan downstream operations for the elements of variability that cannot be squeezed out. This means reduced costs, increased revenues and service levels, and improved capital efficiency – all hallmarks of digital supply chain solutions.
To learn how predicting lead times, throughput and variability dynamically right-size inventory, improve on-time and in-full performance and asset utilization efficiency, follow the link to download white paper.