COVID‑19 Accelerates Analytics‑Driven Supply Chain Management

From shortages of personal protective equipment to a wide array of consumer goods, the COVID‑19 pandemic has disrupted global supply chains in both expected and unforeseen ways. Experts estimate that a full recovery could take many months.

To rebound more quickly, supply‑chain leaders are turning to advanced technologies—IoT data, analytics, and machine learning—to gain real‑time insight into erratic supply and demand patterns.

“Having the right machine‑learning and AI tools enables firms to understand market dynamics and manage their supply chains more effectively,” said George Bailey, director of the Digital Supply Chain Institute.

The disruption began in China, the world’s manufacturing powerhouse. By 2010, China had overtaken the United States in manufacturing output. While the SARS epidemic of 2002‑2003 had China at 4.3 % of global GDP, MIT professor David Simchi‑Levi notes it now accounts for 16 %.

Globalization As We Have Known It Is Over

Companies that relied heavily on Chinese suppliers feel the impact acutely. According to 2020 Statista data, some retailers source more than half their inventory from China. The same survey found that 44 % of retailers expect supply‑chain delays, 40 % anticipate inventory shortages, and over half of electronics manufacturers foresee up to four weeks of disruption—an unacceptable lag in an era of two‑day delivery expectations.

While many firms are scrambling to reassess their networks, risk management has historically been a low priority. The Institute for Supply Management’s recent survey revealed that nearly three‑quarters of companies reported disruptions, yet 44 % had no formal mitigation plan.

“The urgency to diversify and build redundancies is now a reality,” said Alex Capri, senior fellow at NUS Business School. “The era of globalized supply chains as we once knew it is over.”

Bringing Analytics to Supply Chain Management

Manufacturers face cascading disruptions—from suppliers’ plant closures and inventory shortages to transportation delays and workforce absences. Tier‑1, tier‑2, and tier‑3 suppliers all experience setbacks, yet many firms lack visibility into these ripple effects.

“Because so many manufacturers depend on China—directly or through multiple tiers—factory shutdowns and understaffing have tightened capacity,” Bailey explained. A 2018 Statista survey indicated that 21 % of supply‑chain professionals view visibility as a significant organizational challenge.

“Today most companies still rely on Excel to model scenarios,” Bailey said. While Excel is useful, more sophisticated tools can deliver greater accuracy. A 2022 Supply Chain Quarterly study found that over 90 % of managers use Excel for analytics, whereas 82 % who employ advanced tools see marked improvement.

Deploying AI successfully requires ample data. “An AI system must be fed comprehensive datasets to learn how to respond,” Bailey noted. Ralf W. Seifert and Richard Markoff, in “Demand for AI in Demand Planning,” argue that inconsistent forecasts across sales and operations can bias AI outcomes.

AI in Demand Planning

Bailey emphasized that demand planners need to grasp shifting demand patterns during crises and influence them. IoT sensor data, for instance, helps tire manufacturers monitor pressure and tread wear, proactively alerting customers and informing inventory needs.

“Technology and analytics are essential for understanding what drives demand and for AI to forecast future needs,” Bailey said. While some firms must invest in new tech and talent, overall labor costs may decline as automation supplements planners’ work.

Gartner predicts that by 2023, at least 50 % of global companies will deploy AI‑driven transformations in supply‑chain operations.

However, many organizations must first clean and standardize their data. “A lot of data is untrusted—misformatted, inaccurate, or biased—so considerable effort goes into making it usable,” Bailey warned.

Data quality remains the biggest hurdle for AI in demand planning, Seifert and Markoff observed. In the short to medium term, supply‑chain managers will lean heavily on analytics to navigate scarcity and uncertainty.

Bailey suggested that companies may need to narrow their SKU portfolios, focusing on high‑value customers and making fact‑based prioritization decisions. “If you have 100 units of a product but need 500, you must decide which customers to serve first,” he explained.

Strategic sourcing will also shift. “Overconcentration in China is no longer viable,” Bailey said. Balancing risk and opportunity across geographic locations has become paramount.

Ultimately, the pandemic forces firms to adopt the technologies and practices they had postponed but now must embrace for supply‑chain resilience.

“COVID‑19 is a catalyst for change that many companies would have undertaken anyway,” Bailey concluded. “The good news is these changes are now inevitable.”