Why the collapse of the Shanghai supply chain is an opportunity for artificial intelligence

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

Since March, container waiting times have gone up, and cargo shipments to and from the Shanghai port have been slowed or canceled — among other things. The number of container ships waiting outside Chinese ports today is 195% higher than in February.

Shanghai’s port system handles about a fifth of Chinese export containers. The volume of shipments to and from the port decreased by up to 85%. The throttling means that companies around the world are experiencing significant delays in delivering goods. Cargo waiting times at Shanghai marine terminals have increased nearly 75% since the start of the closures. Delays at the Shanghai terminal have sent ships to neighboring ports in Ningbo and Yangshan, but those ports are also getting crowded.

This disruption will have a major impact on global shipping schedules this summer and into the fall. Companies that rely on large volumes of freight are under pressure to speed up booking supply chain lanes before congestion worsens in the coming weeks. Businesses are also preparing for inflationary conditions caused by product shortages at a time when inflation is rising in the United States.

It is clear that unrest such as the closure of the Shanghai port will flare up again and again. Unfortunately, businesses such as retailers and consumer goods companies are ill-equipped to deal with disruptions on a global scale. Ongoing global supply chain disruptions, inflation and the emergence of COVID-19 variables have continued to wreak havoc on essential functions such as demand forecasting.

It doesn’t look like this kind of turbulent market is going away any time soon. Therefore, companies need 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 provide companies a real-time snapshot of conditions that can affect supply chain operations. For example, third-party data on shipping lanes (which can be obtained from aviation intelligence firms) clearly illustrate the scale of the crisis in Shanghai:

Vasu 1.png

Vasu 2.pngThird-party data, for example, gives a US retailer a more accurate view of the potential impacts of how congestion will slow 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. Merchants 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 like Google Search Trends to more closely align supply with demand (an always-moving target) at regional levels. They can weigh this information against their own first-party data about inventory levels and customer buying patterns. The supply chain crisis is not affecting every region of the United States to the same extent. The lack of rainwear will have a greater impact on retailers in Seattle in the summer than it will on retailers in Phoenix.

No human being can observe, absorb and analyze this data on a large scale. To do this, companies need to apply machine learning, which is a form of artificial intelligence. Using machine learning, CPGs and retailers can go through third-party data and find patterns and correlations that wouldn’t be discovered by manual means.

Machine learning is particularly adept at finding non-linear connections necessary to predict demand, such as search behavior, where the intent to purchase is not always clear. Even an automated platform would have a hard time detecting those nonlinear correlations without machine learning.

Machine learning and real-time data together can be a huge blow. Machine learning combined with real-time third-party and first-party data can help businesses in many ways, such as:

  • Prepare for the next disruption through effective scenario planning. Consumer goods groups and retailers can perform “what if” analyzes using computer simulations. For example, they can analyze the potential impact of a port shutdown long before it occurs, and prepare with a corrective course of action. They can also run scenarios about the ripple effects of disruption. How might product shortages, combined with the high price of gasoline in one city, affect the planned promotion of a non-essential CPG product, compared to the staple in rural versus urban areas? This type of planning can be done with little investment.

research show up That by using machine learning and third-party data such as search trends and real-time demand-sensing data throughout the pandemic, CPG companies have “reduced forecast error by more than a third, reduced volume at risk of severe error by half and led to a sixfold increase in value realised from investments in People, processes, and technology related to planning”.

  • Get a better view in real time. Using real-time data, a company can determine the status of inventory everywhere across the supply chain. It can specifically tell which trucks locations are no longer delivering goods to a crucial port. The retailer can find out which models of flat screen TVs will be affected, how many, and for how long. With this level of visibility, they can more effectively adapt in-store sales plans for major seasonal events. Businesses need to know where their goods are at all times if they are to feel successful and respond to changes in demand and supply. Machine learning and third-party data can provide this.

Given the global conflict, the ongoing pandemic, inflation and gasoline shortages, we need to define a new “business as usual” model. By combining artificial intelligence and machine learning, we have a few tools that will help companies achieve more predictable results no matter what market chaos comes our way.

Vasudevan Sundarababu is Senior Vice President and Head of Digital Engineering at Bactera Edge.