Today’s supply chains are facing a continuous challenge to minimize disruptions and ensure that goods are delivered to customers on time. However, there is one form of disruption that supply chain leaders cannot predict and can potentially strike anywhere in the world: natural disasters. Over the past few years, there have been numerous earthquakes, tsunamis and other forms of natural disasters, such as the wildfires currently burning throughout California, that have severely impacted global supply chain operations.
For example, a major earthquake took place in April 2016 in Kumamoto, southern Japan, a location packed full of automotive and high tech industry suppliers. One Japanese OEM located in the UK was impacted two weeks after the earthquake and was forced to shut down production lines for a couple of days. In addition, four GM plants in North America had to stop production for a few days due to a shortage of key parts being sourced from suppliers. The earthquake had damaging effects to the automotive supply chain relating to these key Japanese suppliers, with just over a month’s worth of inventory moving through their customer’s supply chains. Although the earthquake occurred in Japan, it largely impacted production operations in several regions across the globe.
In the ideal world, an OEM, whether in the high tech or automotive sector, would ensure suppliers are all connected to the same business-to-business (B2B) integration or business network. This way, an OEM has end to end visibility into all inbound shipments. But is there a way to provide a deeper level of visibility across a supply chain when disaster strikes?
Today’s supply chains are beginning to embrace a multitude of different disruptive technologies such as IoT, AI and even blockchain. But how can these technologies help a company during or after a natural disaster has taken place and how do they complement the capabilities already provided by a business network that is exchanging electronic transactions relating to each and every shipment? Let’s discuss a scenario based on the following concept for an AI powered disruption management dashboard.
The AI platform above is monitoring pre-configured news feeds from around the world. These include streams from traditional news outlets but could also include feeds from government agencies monitoring natural disasters. One such feed is shown here, which monitors and publishes a tweet every time an earthquake is detected around the world. This information is then ingested into the AI platform.
For example, when an earthquake takes place in Thailand, the technology can immediately detect the scale of the earthquake and its exact location, allowing responders to highlight the disruption zone and quickly determine which locations are likely to be impacted. The address details of all suppliers can then get cross checked in a central database with the location of the earthquake. The slider bar shown at the bottom left of the dashboard can quickly determine which suppliers have potentially been impacted 25 miles, 50 miles or 75 miles away from the epicentre of the disaster. Not only can these be highlighted in red on the map, the platform can identify the shipments and purchase orders that could potentially be impacted.
The AI platform can also pull in information feeds from local transport agencies and monitor the condition of local airports, highways and railways via the transport health check part of the dashboard. It can also connect into the service availability feeds from the 3PL carriers to provide a visual check on the condition of their respective services. The traffic light dashboard, shown on the right-hand side, quickly identifies which 3PL carriers have been impacted. Supply chain or logistics managers can use this information to identify not only the impacted suppliers but the impacted shipments, and quickly identify second source suppliers to help avoid production line stoppages.
Although this is just one illustration, all of the information relating to this particular earthquake would be held within the AI platform and, over time it will capture details from many other natural disasters. Based on this historical information, it can then predict the likely outcome, in terms of level of supply chain disruption, anywhere in the world. Combining this information along with the real time B2B transaction flows moving across a business network can provide a level of visibility and disruption management that can ensure business and supply chain stability during periods of disruption.