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.

Reimagining product portfolios with real-time demand insights

From the outset of the pandemic, organizations have had to regroup quickly and reinvent how they connect with their targeted customers. Understanding product demand is crucial at any time, but it has become even more so due to the unprecedented market disruptions. An understanding of customer demand in real time is required in order to refocus and realign products across channels, regions, and stores with the goal of preventing revenue losses and ensuring satisfying customer experiences.

To solve these challenges, AI’s analytics capabilities provide an effective means for accurately identifying rapid shifts in product demand and turning the pandemic’s challenges into new sales opportunities. AI models’ capability to deliver what-if-analyses (counterfactual simulations of scenarios), along with their explainability and interpretability of inferences and insights, can help organizations efficiently capture demand data and attain real-time insights into consumer behavior. Brands can also identify product gaps in existing portfolios, as well as prioritize and undergo a product shifts, if required, based on demand and market disruptions. With these benefits in place, companies can develop the repeatable ability to deliver the right product at right time and drive business growth.

Mitigating risks and price surges by monitoring vendor readiness

Multiple suppliers, third-party vendors, and cross-border operations are common in global supply chains. Companies work closely with suppliers to source raw materials, handle production ramp-ups, and manage product quality and costs. Yet the reliance on suppliers and vendors across the world comes with its own risks. For example, a U.S. auto manufacturer with production in the U.K. and raw materials supplied from China must have an alternate business continuity strategy developed in case a trade war disrupts its supply chains.

AI’s deep learning capabilities can augment existing supply chain management (SCM) operations by accurately monitoring the multifaceted relationships between demand and the various factors influencing it. The technology integrates external factors to discover and track unfolding global events, analyzing the economical, geopolitical, and disruption risks associated with suppliers and vendors. AI also offers the ability to provide recommendations on alternate vendors to mitigate price surges, enabling faster response times and business continuity. As a result, companies can deliver optimal prices to consumers without compromising on profit margins.

Expanding demand forecasting to a granular level: Channel, store, or SKU

For years, AI-powered demand forecasting has been making waves in the supply chain. Although the solution market is crowded, many demand-prediction solutions only work at the regional or city level. Granular forecasting is increasingly important, as markets are seeing unprecedented changes in where people shop, what they purchase, and how much they buy. This means certain products stay on the shelves longer than expected and make it more likely that these products will go out of style or become obsolete, forcing businesses to sell them at reduced margins. By expanding demand forecasting down to a granular level (targeting a channel, store, or product stock-keeping unit [SKU]), such scenarios can be minimized.

Conclusion

As supply chain complexities and disruptions continue to grow, it is critical that businesses build agile, cognitive, and resilient supply chains capable of managing challenges now and in the future. Advanced AI-based algorithms with tools such as Wipro Holmes enable businesses to create smarter, more agile supply chains with a greater ability to manage disruptions, reimagine product portfolios, mitigate business risks, and forecast demands at a granular level. Whether facing pandemic-related challenges or adjusting to industry trends, AI offers an effective way to transform business operations and get the most value from gathered data.

Rohan Madhusudhanan

Rohan is an industry leader with over two decades of experience covering solutioning as solution architect, account management, pre-sales and business development. Currently, Rohan is a lead solution architect for Holmes Outcome Shaping and Human Centered AI themes.