Businesses around the world are bracing themselves for yet another supply chain shock due to Covid lockdowns in China.
Since March, container dwell times have soared, and cargo deliveries to and from the Port of Shanghai — among others — have been slowed or cancelled. The number of container vessels waiting outside of Chinese ports today is 195% higher than it was in February.
Shanghai’s port system handles about a fifth of China’s export containers. The volume of shipments to and from the port has dropped by as much as 85%. The bottleneck means businesses around the world are experiencing significant delays in the delivery of goods. Wait times for cargo at Shanghai marine terminals have increased nearly 75% since the lockdowns began. Delays at the Shanghai terminal have sent ships to neighboring ports in Ningbo and Yangshan, but those are getting congested as well.
The disruption will have a significant impact on global shipping schedules this summer and into the fall. Businesses relying on high volumes of freight are under pressure to accelerate the booking of supply chain lanes before congestion worsens in coming weeks. Businesses are also bracing themselves for inflationary conditions resulting from product shortages at a time when inflation in the US is increasing.
It’s clear that disruptions such as the Shanghai port closure will flare up time and again. Unfortunately, businesses such as retailers and CPG firms are ill-equipped to address disruptions on a global scale. Ongoing global supply chain disruptions, inflation and the emergence of COVID-19 variants have continued to wreak havoc with essential functions such as demand forecasting.
This type of disruptive market doesn’t appear to be going away any time soon. It’s therefore incumbent on businesses 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 a real-time snapshot of conditions that can affect supply chain operations. For instance, third-party data about shipping lanes (obtainable from aviation intelligence companies) vividly illustrates the scope of the crisis in Shanghai:
Third-party data gives, say, a retailer in the US more granular visibility into the likely impacts of how the congestion will slow down cargo ships that require a few weeks to reach their destinations in US ports. From there, the retailer can more accurately estimate the impact on supply over a period of weeks and months, and adapt its forecasts accordingly. Merchandisers can more effectively weigh the impact on costs and pricing strategies.
Even better, retailers can combine third-party shipping and weather data with consumer-generated data such as Google search trends to align supply more precisely with demand (always a moving target) at regional levels. They can weigh this information against their own first-party data on inventory levels and customer purchasing patterns. A supply chain crisis does not affect every region of the US in equal measure. A shortage of rain-repellant clothing is going to have more of an impact on retailers in Seattle in the summer than it will retailers in Phoenix.
No human being can possibly monitor, assimilate and analyze this data at scale. To do so, businesses need to apply machine learning, a form of AI. With machine learning, CPGs and retailers can shift through third-party data and find patterns and associations that would go undetected by manual means.
Machine learning is especially adept at finding nonlinear connections that are crucial for demand forecasting, such as search behavior, where the intent to purchase is not always clear. Even an automated platform would have difficulty uncovering those nonlinear associations without machine learning.
Machine learning and real-time data together can be a powerful one-two punch. Machine learning combined with real-time third-party and first-party data can help businesses in a number of ways, such as:
- Preparing for the next disruption with 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 with a corrective course of action. They can also run scenarios about the ripple effects of a disruption. How might a product shortage, combined with the rising price of gasoline in one city, affect a planned promotion for a nonessential CPG product, compared with a staple good in rural areas versus cities? This kind 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 sense demand throughout the pandemic, CPG firms have “cut forecast error by more than one-third, reduced the volume exposed to an extreme error by half and drove a six-fold increase in realized value from investments in people, processes and technology related to planning.”
- Getting better visibility in real-time. With real-time data, a business can identify the status of inventory everywhere across the supply chain. It can know precisely which trucks in which locations are no longer delivering goods to a crucial port. A retailer can discover which models of flat-screen TVs are being affected, how many, and for how long. With that level of visibility, they can more effectively adapt their in-store sales plans for major seasonal events. Businesses need to know where their goods are at all times if they’re going to successfully sense and respond to changes in demand and supply. Machine learning and third-party data can provide that.
Given global conflict, a continuing pandemic, inflation and a gasoline shortage, we need to define a new “business-as-usual” model. By combining AI with machine learning, we have a few tools that will help businesses realize more predictable outcomes no matter what market mayhem comes our way.
Vasudevan Sundarababu is senior vice president and head of digital engineering at Pactera EDGE.