Harnessing AI & analytics to establish a smarter, adaptive Supply Chain
Businesses are looking to deploy adaptive Digital Supply Chains that are equipped to deal with uncertainties and can process and analyze vast quantities of fast-moving data from connected systems using AI/ML & analytics
Today’s globalized supply chains have evolved to become highly interlinked and continue to grow in complexity. Widespread disruptions such as COVID-19 have elevated the need for autonomous, agile, and resilient supply chains that provide end-to-end visibility and better operational control.
In addition to leveraging customer insights for marketing and promotions, organizations now also need to accelerate their exploration of analytics to create flexible digitally-enabled supply chain models. I believe there is significant value that can be realized to drive customer experience, revenue acceleration, risk prediction, and cost optimization. Artificial Intelligence (AI) and Machine Learning (ML) must be integrated with operations to draw actionable insights, predict events, and prescribe relevant actions.
I’m listing below some salient use cases on how AI-powered software can quickly process large volumes of data and define trends at a granular level. These insights can then be fed into robotics and immersive digital technologies to create customized, scalable, and secure Digital Supply Chains.
Gartner predicts that by 2024, 50 percent of supply chain organizations will invest in applications that support AI and advanced analytics capabilities. Forward-looking organizations can deploy adaptive Digital Supply Networks (DSN) that are equipped to deal with uncertainties and can analyze vast quantities of fast-moving data from connected systems. This will enable informed, timely and optimal decision-making leading to a smart value-driven network.
It is necessary to identify and include data sources that are closer to consumption. That’s because traditional forecasting techniques associated with time-series methods are no longer sufficient to derive actionable insights. The recent chip shortage incident is an example where improper demand planning caused a tremendous ripple effect across the supply chain – a variety of products from cars to gaming consoles and refrigerators were impacted. AI-driven Demand Sensing can increase visibility, accuracy, and reliability across the value network by estimating dips and spikes in customer demand using near real-time information such as e-commerce or Point-of-Sale (POS) data across short time horizons to create accurate demand forecasts. Thus, granular insights into customer behavior and preferences can be derived.
Leveraging advanced AI and ML in this manner enables SKU rationalization by considering numerous factors service levels, price points, bills of material, and unit costs – to better anticipate demand. These techniques can also use external data sources such as economic indicators, weather patterns, competitive pricing, and customer behavior to create daily forecasts that reflect current market trends and realities – thus delivering high service levels with an optimum inventory. Demand Sensing can allow strategic item placement and storage in forwarding Stocking Locations’ (FSLs) or other pockets of inventory near customers, optimizing last-mile delivery for a superlative customer experience.
An AI charter also leads to proactive risk management. It harmonizes supply chain data and applies self-corrections or learnings to both implement and enhance human intelligence. Automated solutions and prognostic AI models allow proactive risk assessment and business impact analysis by simulating what-if scenarios in real-time – such as identifying vulnerabilities and failure points in an organization’s infrastructure.
