Why Shanghai’s supply chain collapse is an opportunity for AI

Businesses around the world are bracing for another supply chain shock due to Covid lockdowns in China.

Since March, container dwell times have skyrocketed and cargo deliveries to and from the Port of Shanghai, among others, have been slowed or canceled. The number of container ships waiting outside Chinese ports is 195% higher today than in February.

Shanghai’s port system handles about one-fifth of China’s export containers. The volume of shipments to and from the port fell by 85%. The bottleneck means businesses around the world are experiencing significant delays in delivering goods. Waiting times for cargo at Shanghai’s sea terminals have increased by nearly 75% since the lockdowns began. Delays at the Shanghai terminal have sent ships to nearby ports of Ningbo and Yangshan, but these too are congested.

The disruption will have a significant impact on global shipping schedules this summer and fall. Businesses that depend on high volumes of freight are under pressure to fast-track the booking of supply chain lanes before congestion worsens in the coming weeks. Businesses are also bracing for inflationary conditions resulting from product shortages at a time when inflation in the United States is rising.

It is clear that disruptions such as the closure of the port of Shanghai will happen again and again. Unfortunately, businesses such as retailers and CPG companies are ill-equipped to deal with disruption on a global scale. Continued global supply chain disruptions, inflation, and the emergence of COVID-19 variants have continued to wreak havoc with critical functions such as demand forecasting.

This kind of disruptive market doesn’t seem to be going away anytime soon. It is therefore incumbent on companies to effectively plan for these disruptions by combining artificial intelligence with third-party and first-party data to monitor rapidly changing conditions in real time and adapt processes such as demand forecasting.

Third-party data such as weather forecasts and satellite maps of port traffic give companies real-time insight into conditions that can affect supply chain operations. For example, third-party shipping lane data (obtained from aviation intelligence companies) vividly illustrates the scale of the crisis in Shanghai:

Vasu 2.pngThird-party data, for example, gives a retailer in the United States more accurate visibility into the likely impacts of how congestion will slow down cargo ships that need a few weeks to reach their destinations at US ports. From there, the retailer can more accurately estimate the impact on supply over a period of weeks and months, and adjust their forecast accordingly. Merchandisers can more effectively weigh the impact on cost and pricing strategies.

Even better, retailers can combine third-party shipping and weather data with consumer-generated data like Google search trends to more accurately align supply with demand (always a moving target) regionally. They can weigh this information against their own first-party data on inventory levels and customer buying habits. A supply chain crisis does not affect all regions of the United States equally. A shortage of rainwear will impact Seattle retailers more in the summer than Phoenix retailers.

No human being can monitor, assimilate and analyze this data on a large scale. To do this, companies need to apply machine learning, a form of AI. Using machine learning, CPGs and retailers can go through third-party data and find patterns and associations that would not be detected by manual means.

Machine learning is particularly adept at finding nonlinear connections that are crucial for demand forecasting, such as search behavior, where purchase intent is not always clear. Even an automated platform would struggle to discover these nonlinear associations without machine learning.

Machine learning and real-time data can pack a mighty punch. Machine learning combined with real-time third-party and proprietary data can help businesses in several ways, such as:

  • Prepare for the next disruption through effective scenario planning. CPGs and retailers can do “what if” analyzes with computer simulations. For example, they can analyze the likely impact of a port closure long before it happens and be ready to take corrective action. They can also run scenarios on the ripple effects of a disturbance. How might a product shortage, combined with the rising price of gasoline in a city, affect a planned promotion for a non-essential CPG product, versus a commodity product in rural versus urban areas? This type of planning can be done with little investment.

Research has shown that by using machine learning and third-party data such as search trends and real-time data to detect demand throughout the pandemic, CPG companies have “reduced the error of forecast by more than a third, cut the volume exposed to extreme error in half, and increased the value realized from investments in planning people, processes and technology sixfold.

  • Get better real-time visibility. With real-time data, a business can identify inventory status anywhere in the supply chain. He can know precisely which trucks in which places no longer deliver goods to a crucial port. A retailer can find out which flat screen TV models are affected, how many, and for how long. With this level of visibility, they can more effectively tailor their in-store sales plans to major seasonal events. Businesses need to know where their goods are at all times if they are to successfully detect and respond to changes in demand and supply. Machine learning and third-party data can provide this.

Given a global conflict, a lingering pandemic, inflation and a shortage of gasoline, we need to define a new “status quo” model. By combining AI and machine learning, we have a few tools that will help businesses achieve more predictable results no matter what market chaos comes our way.

Vasudevan Sundarababu is Senior Vice President and Head of Digital Engineering at Pactera EDGE.

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