Data is everywhere. Today's supply chain managers have more access to data than ever before. But what are we doing with all that data? Sure, it's great to be able to see how many orders you have for a certain item each day of the week, but what's that really telling you? What's missing is an actionable insight from that data.
One way supply chains are achieving real insights is by incorporating new, unusual, and often external data sources to provide additional visiblity into their operations. What are some of those data sources, you ask? Continue reading to find out.
Weather is one of the most unpredicable things we all deal with on a daily basis. When was the last time weather predictions were accurate for more than a day or two in a row? But incorporating the latest weather data in real time gives you insights that let you make decisions about your supply chain.
For example, what if you knew about a bad storm happening along one of your primary shipping routes before your trucks were stuck in the rain? Weather delays can add hours if not days to delivery times, and when customers are demanding one and two day shipping, those hours matter.
By integrating real-time weather data with your shipment routing data, you could recieve an alert before the storm impacts your deliveries. And with that information, you can make informed decisions to transition to another shipping route or mode to avoid the stormy conditions.
Incorporating traffic data into your operations can have a similar impact. By connecting into one of many real-time traffic data sources, you can gain visibility into problems before they happen. Instead of your trailers being stuck in gridlock unable to react, your systems can predictively see changing traffic patterns, and you can make decisions that have a real impact on your shipments.
But you can take it a step further as well. Not only does current state traffic data give you additional flexibility, but modern systems allow you to incoporate historical traffic data to make better routing decisions. Machine learning algorithms can be connected to past traffic information, and over time provide insights that help speed up delivery times.
For example, systems can automatically suggest a better waving or routing schedule based on learned patterns along your normal shipping lanes. Furthermore, your systems can even suggest more optimal shipping modes in order to avoid traffic congestions altogether. And if you want, these decisions can be made automatically without the need for human intervention.
Another unexpected data source that can provide valuable insights are external sources like port crane scheduling data. While many supply chain managers can access AIS data to see where their containers are on the water, that doesn't help if the boat your goods are on ends up sitting anchored in port for two weeks before being unloaded.
By incoporating not just AIS locational data, but port scheduling data as well, you can gain insights that let you make better decisions and get ahead of problems before they happen. Systems can be configured to send alerts when port schedules predict your goods may be sitting for more than 24 hours.
With these alerts in hand, you could route additional inventory from domestic suppliers to keep your production lines running until your containers arive. The decision you make and how you handle the situation is up to you in the end, but combining data sources to give you real visibility is the key to keeping up with the modern marketplace.
These are just a few examples of how supply chain managers are incorporate new data to gain valuable insights. And with all these new data sources comes the inherent challenge of integrating this data with your existing systems and processes. And moreso than just connecting your systems with data, your company needs a platform to drive continued visibility into your supply chain operations.
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