Leveraging Analytics to Manage Through Disruption

Editor’s Note: Noha Tohamy, Distinguished VP Analyst, Gartner Supply Chain Practice

Like many, amid the coronavirus pandemic I find myself constantly checking the news and trying to stay current on the latest developments. With every news source imaginable covering COVID-19 to some degree, it's not hard to become overwhelmed by the volume of information. It can become quite exhausting to try and make sense of what's happening in the world.

Supply chain leaders navigating these unprecedented times face a similar challenge. They are constantly scouring for more data — publicly available data about the outbreak, changing economic indicators, enterprise data about their own supply chain performance, trading partner data. They are tasked with making sense of it all. This extends beyond solely addressing the immediate impact of the pandemic, but also evaluating the long-term implications it will have on society, global economies and supply chains.

For example, consider the potential long-term implications of COVID-19 on consumer products manufacturers and retailers. Personally, my grocery shopping habits have already changed considerably. I previously enjoyed going to the grocery store multiple times a week, getting what my family needed for a few days at a time. Now, I've adapted to planning for a few weeks of groceries at a time. I'm buying fewer perishable items and willing to try alternatives for out-of-stock first choices.

Questions loom about how these changes to shopping habits happening in households around the world will impact supply chains. Ultimately, there is no clear answer. Countless interdependencies and unknowns will shape the future. However, through careful analysis, leading organizations can still plan effectively for the future. They can first sense and respond to disruptive events in the short-term, recover in the mid-term and design and position for long-term patterns. This can be accomplished through a series of analytics competencies, including:

• Real-time Analytics: Supply chain organizations can leverage publicly available data to track how disruptive events unfold. For example, Johns Hopkins Coronavirus Resource Center data offers up-to-the-minute updates of coronavirus cases and deaths. Supply chain organizations can rely on this information to start gauging the impact of the pandemic on its employees, suppliers, trading partners and locations.
• Visualization Mapping: Visualization analytics help organizations gauge the impact of disruptions on the supply chain. This is especially critical for companies with global, complex supply chains that involve thousands of suppliers for millions of parts and components. Overlaying the span of the disruptive event onto locations, multiple tiers of suppliers and customers can highlight supply chain vulnerabilities; critical fail points risk exposure.
• Machine Learning and AI: Combining machine learning and artificial intelligence allows organizations to track unfolding events and take quick actions to minimize the immediate impact on their supply chains. For example, a consumer products manufacturer can use a machine learning algorithm to sense changes in demand patterns and autonomously reallocate available supply to avoid a service disruption to a key customer.
• Predictive Analytics and Simulation: Once short-term actions are taken to dampen the immediate impact of the disruption, supply chain leaders can focus on understanding mid- and long-term scenarios to predict the extended impact on their business. For example, due to a port shutdown from a labor strike, a high-tech manufacturer can use predictive analytics and simulation to analyze the likelihood of port strikes and simulate their potential impact on their ability to fulfill customer demand.
• Scenario Modeling and Prescriptive Analytics: Using optimization, companies can evaluate different supply chain policies, balancing the trade-offs of managing a lean supply chain while building a level of resilience that allows them to quickly recover from or altogether avoid a disruption. This could include companies opting to carry higher levels of inventory to build more resiliency in the supply chain.
• Forecasting and Optimization: Using long-range forecasting, organizations can predict the long-term impact of disruptions on their business under different scenario conditions. They can leverage optimization modeling to increase supply chain resiliency. This was used 2019, when China-U.S. tariff wars prompted many organizations to evaluate shifting manufacturing and production capacities from China to other locations. Modeling their network, the objective of additional network resilience and agility was considered against the reality of additional costs incurred to achieve them.

As we all start to consider what our new normal may look like, now is the time for supply chain leaders to evaluate their use of analytics. Today's supply chains are highly complex and without a robust analytics foundation, organizations will be challenged to make sense of unfolding events. The COVID-19 pandemic reaffirms the role analytics can play in understanding available data and helping supply chain leaders manage through disruptive events. More notably, beyond understanding short-term implications, without analytics companies will fail to understand the mid- and long-term impact, which can have lasting implications.

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