In 2020, the pandemic tested supply chains in a manner few have seen in our lifetimes, with businesses like Apple struggling to predict demand and keep factory lines moving. The weaknesses exposed by this crisis are not brand new, but they should be a wake-up call that current strategies are not sustainable.
The limitations of modern supply chains were becoming apparent last year when companies struggled to react to new tariffs and restrictions caused by Brexit and the U.S.-China trade war. The pandemic is again showing the danger of overly depending on a few distant suppliers for key resources such as food, medical equipment—and even phones.
Distributed global supply chains, combined with just-in-time inventories, have done an enormous amount to drive down cost and increase efficiency. But this model was implemented during a period of relative global stability that we can no longer take for granted. Whether it’s the increased threat of disease, political turmoil, or severe weather events caused by climate change, global supply networks need to be made smarter and more adaptable in the future. That’s why many executives now view supply chains as their biggest risk factor.
The solution isn’t to stop building efficient global supply chains. Rather, businesses need better visibility and the ability to pivot quickly when a crisis arises. The key to achieving this is to use data in smarter ways to spot disruption sooner, accurately gauge its impact, and make intelligent decisions about alternative sources of supply.
Here are some creative ways that businesses have been using data-driven solutions to make their supply chains more resilient, and the lessons they provide for navigating future disruptions.
In a crisis, businesses need to know what’s happening beyond their immediate supply chains to determine how they will be impacted. Third-party data can provide a more complete picture of events unfolding around the world and how they may affect deliveries, demand, and even the supply chains of competitors.
For example, geospatial analytics uses satellite imagery, cell phone pings, and other data sources to detect activity on the earth, such as when plants are closing or cargo ships are held up at ports. Unilever has been applying AI to this data to determine if its suppliers will be able to keep up with demand, so it knows quickly if it needs to look for alternatives.
Sometimes the required data is specific to a crisis. Blue Yonder, which projects demand for products such as dairy and produce, has integrated COVID-19 death statistics from the Centers for Disease Control and Prevention into its systems, to help measure the scope of the outbreak and make better predictions for clients in the food and agricultural industries. Businesses should determine which third-party data sources will help inform their own decision-making and integrate them into their operations.
When events are moving fast, sources such as weekly operational reports and government statistics are often out of date before they can be used. Businesses have invested heavily in streaming analytics to better market to customers, but in a fast-moving crisis, real-time data has immense value for logistics as well.
Kroger has used data from its intelligence unit to respond to the near-daily fluctuations in customer behavior during the pandemic, such as whether people are shopping more frequently on weekdays or buying certain products in bulk. Evidence Lab provides a data service that lets companies track, for example, how many coffee shops Starbucks has opened in China on a given day, which provides a proxy for how fast consumer behavior is returning to normal.
Applying machine learning to data from social networks and global newswires can also help to quickly identify where and how fast a disease is spreading, the severity of local political unrest, or the impact of a climate event such as flooding. These real-time insights buy valuable time in which companies can make critical sourcing decisions to offset supply chain disruption.
Modeling prices has been particularly tough during this pandemic. Supply and demand are both heavily impacted by infection rates, which affect how much people can shop and whether plants and factories remain open. Complicating matters, a patchwork of regulations govern how much businesses can charge for certain goods during a national emergency. The data practices described above should inform not only supply chain strategy but pricing and packaging decisions as well.
An intelligent pricing strategy that accounts for real-world conditions is essential even outside of a disruptive event. McKinsey has calculated that a 1% increase in price for a product can yield a 22% increase in profit margin, making it far more effective for boosting profit than an increase in sales volume. In a pandemic, a trade war, or another anomalous event, using data to accurately model pricing is even more critical.
Perhaps most fundamentally, businesses need better visibility to understand the dependencies that exist between suppliers and minimize risk in their supply chain analytics. Seeing beyond the first tier of suppliers and identifying weak links and hot spots shows where to focus attention when a crisis occurs. That means encouraging all suppliers to move away from paper invoices and PDFs and connect their operations digitally, which increases visibility for all parties.
Efforts to digitize supply chains have behind areas such as marketing and sales. A 2018 study found that only 28% of manufacturers had started to digitize their supply chains, and only 6% were part of an ecosystem in which every member could see the other members’ data. This digitization will be key to understanding where dependencies lie and not being caught by surprise when a supplier’s supplier is affected.
The current pandemic is unlikely to be the last shock to the world economy. Whether it’s disease, political conflict, or natural disaster, there is a potential for further disruptive events in the future. Globalization has created tremendous value for consumers and businesses, but an inability to foresee and react quickly to changing market conditions creates unacceptable risk.
Businesses must put data to work intelligently to help us all be better prepared for the next major disruption. Try ThoughtSpot’s consumer-grade search and AI-driven analytics today to make sure your organization is ready.