Today businesses are facing ongoing challenges when managing the growing complexity of their supply chains due to a variety of factors, including expanding customer expectations, increased cost pressures, the scarcity of truck drivers, and pressing geo-political risks. According to a recent GEP survey, more than $4 trillion in revenue may have been lost as a result of supply-chain disruptions, which continue to be a hot issue for every business regardless of its industry.
The need to strengthen supply chains and increase resiliency has never been more important. Resiliency in the supply chain begins with demand planning, as inaccurate demand forecasts have ramifications throughout the entire value chain, resulting in consumers making product choices based on whatever items are available. In a recent McKinsey study of the consumer packaged goods (CPG) sector, researchers have found that 76% of consumers “experimented with new shopping behaviors,” and that 37% of that group switched to new brands primarily because of their availability in 2020. As reported by Forbes, in the fashion industry alone, “$120 billion worth of excess fabric sits in warehouses and 87% of fiber input used for clothing ends up burned or in landfills,” ultimately costing the consumer and the environment. Companies must be able to forecast demand in an agile, flexible, and timely manner in order to adapt successfully to changing market conditions. Doing so can allow them to more effectively handle disruptions, avoid losses, minimize waste, and adopt sustainable business practices.
Advanced AI techniques can put these goals in reach, with the potential to reshape supply chains, and leading organizations are increasingly adopting AI platforms to drive agility and flexibility. As companies seek to strengthen supply chains with cognitive solutions, they can employ AI beyond its obvious and well-known applications (such as inventory optimization) to drive smarter supply chains and stronger resiliencies in the following four key areas.
Managing supply-chain shocks
Today’s global business reality calls for supply chains that not only minimize everyday risks, but can also absorb, adapt, and recover from disruptions. The ripple effects of unexpected events such as natural disasters, epidemics, pandemics such as Covid-19, and cyberattacks are multifold, and such shocks, according to a recent McKinsey study, are becoming more frequent and severe. When these adverse events occur, business leaders must make decisions swiftly to sustain business operations, continue serving customers and clients, and avoid financial losses. Responding effectively to these kinds of scenarios requires the ability to make informed, data-driven decisions and utilize a forward-looking predictive model that can adapt to changing environments.
By leveraging advanced techniques such as AutoML-based time series, reinforcement learning, causality algorithms, Bayesian inference, and meta learning, AI can help companies sense risks and recognize failure modes. It can also enable them to gain insights on realistic customer demand, provide contingency management, and mitigate supply-chain shocks.