Author: Sunder Prabhu
The Suez Canal crisis has brought the discourse on supply chain resilience back into focus. The incident comes at a time when global supply chains are inching back to normalcy in the hope that Covid-19 vaccinations will help the economy bounce back. Considering that the canal carries about 10% to 12% of global trade, logistics will take time to recover even though the crisis is now resolved.
The Cascading Impact
Despite the fact that the Suez Canal blockage may not be as significant as Covid-19 disruptions, it will take months to remove the pressure points in the global supply chain. In the world of the interconnected global supply chains, the choking of a significant artery such as the Suez Canal will have a cascading effect with delayed deliveries to consumers, rising prices due to shortage, loss of efficiency at factories due to short supply, and increased pressure on intermodal/road transportation when the traffic ramps up.
In the US market, the east coast ports will bear the brunt of the fallout. Data shows that nearly a third of imports into the east coast are routed via the Suez Canal. In the near term, there will be a lull period followed by an inbound rush when the backlog of delayed shipments arrives, stressing the logistics network.
This is not the first accident of its kind; it’s likely not the last either. Given this reality, companies would do well to build resilience in the supply chains proactively.
Strategies for Supply Chain Resilience
Companies have used several different strategies to mitigate the risk to supply chains. Multi-Geo Manufacturing – Developments such as the straining of the US/China relationship and the disruption caused by Covid-19 have led to many firms looking at alternate manufacturing locations outside China, such as India.
– Multi-Sourcing – Dual or more diversified supplier bases for critical raw materials or components.
– In-Sourcing / Near Shoring – Companies have started to build regional sourcing within the Americas or even in-house to mitigate the risk. One of our clients is exploring this option for critical products with much closer/tighter integration across the value chain.
– Inventory and Capacity Buffers – Moving away from the lean supply chain’s traditional mindset, customers are increasing the inventory and capacity buffers. One of our manufacturing clients had doubled down on stocks early last year to mitigate any supply risk due to Covid-19.
– Flexible Distribution – Companies are adopting multiple modes of transportation such as air and rail so that they have a backup in case of disruption of one of the modes of transportation. They are also moving warehouses closer to the customer.
 How Analytics can enable the resilience journey
The strategies elaborated in the previous section imply that there will be an additional cost of building the necessary redundancy rather than going with a lean principles approach. Most companies have accepted this additional expenditure since the risk of not doing it far outweighs the cost of redundancy. When supply chains become complex with multiple paths for product flow, analytics can help keep the operations nimble and make the right decision to balance cost and service levels. Analytics can enhance two types of capabilities:
–Â Operational Capabilities are primarily focused on risk containment. When the risk event is expected to occur or has occurred, machine learning models can generate real-time alerts and insights for the supply chain operations teams to take the next best actions. For example:
–Â Freight Pricing Impact: One of our logistics clients uses a pricing model to use truckload equipment. We designed this pricing model to look at the demand/supply imbalance at the origin/destination and predict the prices accordingly. It is expected that US East Coast ports will see a surge in Inbound containers once the Suez Canal blockage eases, and transportation prices will increase when the demand is higher than supply. Visibility into pricing helps our client secure the capacity upfront at non-peak pricing and ensure timely delivery to its customers.
–  On-Time in Full (OTIF) Risk Prediction: One of our manufacturing clients uses an ML tool that predicts ‘OTIF miss risk’ at each order level. We have built-in recommendations on what levers can be used to meet the SLA or reduce the penalty, e.g., Pick/Pack/Load priority in warehouse OR air freight.
–Â Â Risk Event Prediction: Risk data related to natural disasters, political strikes, labor disputes, financial events, environmental events, etc., can be tied to the enterprise supply chain. One of our clients uses risk models to simulate the impact of various risks on their supply chains’ better plan responses.
–Â Â Strategic Capabilities are focused on avoiding risk impact and enabling faster recovery. A component of this capability is a Digital Twin Supply Chain, which mirrors the physical network. Some of our clients use digital twins to do both mid-term and long-term risk planning involving some of the below activities:
–Â Â Assessing current network and identifying potential risk areas.
–Â Â Scenario planning and risk & cost analysis to provide inputs into Sales & Operation Planning.
–Â Â Planning and building long-term approaches such as multiple sourcing or multi manufacturing.
–  Revamping the supplier and distribution networks – Integrating supplier/carrier scorecard, cost, etc., into the network data to visualize multiple options’ tradeoffs.
–Â Â Pressure testing design choices at various levels. E.g., impact missed orders, delays, and inventory levels if a particular site went down, or how much it will take to initiate the contingency plan and interim impact.
 Conclusion
Recent developments have just acted as catalysts for an already growing affinity for AI and analytics. Gartner states that by 2023 at least 50% of large global companies will be using AI, advanced analytics, and IoT in supply chain operations gearing towards a digital twin model.
The companies which are agile and can respond to rapidly changing conditions are the ones that will survive increasingly frequent disruptions and add real value to customers and communities. AI & Analytics will be key enablers in building resilient supply chains that are proactive, agile, and maintain a balance between various tradeoffs.
