Supply chains are not usually popular topics of conversation around the dinner table, but these complex networks of procurement, production, logistics, and transportation touch most of our daily lives—from healthcare and fashion to dining and communications. To put it simply, they’re how we get our stuff. So, a breakdown at any point in the supply chain can be felt, often acutely, down the line. Fortunately, emerging AI applications are giving companies effective new ways to steel themselves against disruptions that threaten supply-chain health. In this blog, the first of a two-part series about supply chain challenges and AI technologies that can help address them, we will dive into the need for more reliable and resilient supply chains. We will also look at how AI solutions can help meet this need through improved supply management and demand forecasting.
When supply chains fail to work as expected, the consequences can be inconvenient—such as a reduced selection of cereals at the grocery store—or dire—such as a dearth of critical pharmaceuticals. For a time, lumber shortages stalled construction projects and a scarcity of computer chips vexed the auto industry. These challenges underscored supply chain vulnerabilities that became critical flaws in the novel business climate precipitated by the COVID-19 pandemic.
The pandemic made us all painfully aware of the impact supply chains have on our lives. Aside from the scarcity of high-demand items such as paper towels and hand sanitizer, we witnessed struggles to procure sufficient supplies of personal protective equipment for healthcare workers, ventilators, and other healthcare necessities. Compounding those shortages was the explosion of online commerce that accompanied pandemic lockdowns and further taxed supply chains. Supply chains struggled to keep up with the evolving circumstances that complicated management of both supply and demand, including stalled supplier activities, rising demand in some areas, and reduced consumption in other areas. As we emerged from the most stringent phase of pandemic lockdowns, supply chains were strained by a surge in demand while supply processes remained complicated by ongoing restrictions. The pandemic presented a dramatic picture of supply chain disruption and brought to light existing limitations related to both supply and demand.
Of course, the pandemic is just one particularly stark example of supply chain disruption. In fact, according to McKinsey, supply-chain disruptions lasting at least one month can be expected once every 3.7 years. In addition, 54 percent of firms responding to an Economist Intelligence Unit survey said that organizations would have to make significant changes to address supply-chain disruptions in the next five years.
Other internal and external challenges that introduce friction into supply chains include trade wars, blocked shipping routes, weather events, labor shortages, and pandemics that force production shutdowns. As our industries become more and more interconnected, supply chains become more complex, and the need for supply chains to be agile, resilient, and nimble likewise rises.
By integrating the components of the supply-chain system and increasing end-to-end visibility into all links along the way, companies can meet these needs. Anticipating disturbances, forecasting the often fickle dynamics of demand, and responding quickly to fluctuations along the supply chain has become more important than ever, requiring capabilities to capture real-time metrics and perform predictive analytics. As a Forbes article points out, companies that had multiple sources of real-time data throughout the supply chain had fewer errors and saw their forecasts adjust more readily than companies that lacked these technologies.
According to McKinsey, early adopters of AI-enabled supply-chain management have improved logistics costs by15 percent, inventory levels by 35 percent, and service levels by 65 percent, as compared to their less AI-driven peers. AI capabilities, increasingly aided by IoT devices, equip supply chains to respond quickly and effectively amid volatility. For example, companies can automate many manual processes to safeguard against operational slowdowns due to labor shortages. In addition, IoT devices with predictive maintenance capabilities can support timely repairs and aid companies in planning for equipment breakdowns to minimize downtime. Advanced analytics can generate executable insights across the supply chain and can even automate decision-making processes. AI is also improving supply and demand management in several areas, including:
Demand Forecasting - The pandemic highlighted the limitations of relying exclusively on historical data to forecast demand under unprecedented circumstances. Fortunately, machine learning tools can help supply-chain managers adapt quickly to new circumstances by providing real-time insights into indicators such as retail activity, customer behavior, and other consumption trends.
Inventory Management - Monitoring real-time data about inventory levels at various points along the chain can drive forecasting that learns from and reflects current patterns and trends. AI technologies can also autonomously order new supplies when current supplies are low. In addition, with sensor-driven data analytics, AI tools can monitor temperature and other environmental metrics and autonomously adjust conditions to prevent inventory loss or damage.
Procurement and Supplier Selection - Companies can use AI to generate insights that help them make sourcing decisions, manage their parts supply, and optimize product costs. AI-enabled tools can also mitigate risks associated with supplier selection by analyzing data about their financial health and even their reputations.
The complex journey of the supply chain is usually only noticed when roadblocks and delays result in price increases, scarcity, and even diminished healthcare treatment options. Fortunately, by equipping companies to anticipate and respond rapidly to future turbulence, AI technologies offer solutions that help supply chains prevent the failures that we all feel. We will follow this dive into how AI can help organizations navigate the dynamics of supply and demand with the second part of our series, which will explore how AI tools can improve supply-chain performance in the increasingly important areas of enterprise sustainability.
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