How Cognitive Supply Chains are Unlocking New Levels of Efficiency and Collaboration

Supply chains are fragile, and even minor glitches can trigger significant ripple effects. A recent system upgrade within a major enterprise led to unexpected disruptions in its production schedules. With a significant portion of their products out of stock, consumers quickly turned to alternative brands. This tale is far from unique. Businesses world over face supply chain issues daily, and it costs them big money. Who can forget the Suez Canal blockage of 2021 that lasted six days and held up $9.6 Bn of trade per day or the supply chain disaster that was the COVID-19 pandemic.

In the face of an increasingly volatile world, linear supply chains are failing. It is like sticking to a one-way road in a networked world! You can only go from Point A to Point B with no alternative routes or detours. If there is a parade, a roadblock, or a sudden event in the city, you are stuck. You cannot divert, adapt, or explore new avenues. In a landscape where market demands fluctuate rapidly, disruptions are frequent, and consumer behaviour is unpredictable, this rigidity is fatal. Companies must have the flexibility to adapt, re-route, or even reverse their processes via dynamic, interconnected pathways. And that is only possible at scale with a cognitive supply chain.

What exactly is a cognitive supply chain?

If businesses were the human body, the supply chain would be the central nervous system. Just as our nervous system helps us sense and respond to environmental cues, cognitive supply chains - infused with Artificial Intelligence (AI) and Machine Learning (ML) - allow businesses to anticipate changes, dynamically adapt to new realities, and grow efficiently. Now, with generative AI in the mix, the user cases are expanding rapidly.

At the heart of a cognitive supply chain is unhindered visibility to data. This data must come not just from within the company but from an extended ecosystem of partners, suppliers, and third-party sources, creating a value network. And not just historical data that has little relevance in a dynamic world, but real-time, contextual, and quality data that helps cognitive supply chains describe, diagnose, and predict supply chain events - all while continuously learning and improving.

Improving business outcomes with connected, cognitive value chains

Imagine if a key supplier for a major smartphone brand suddenly faces production issues and is unable to deliver essential components in time. Fortunately for the smartphone manufacturer, they have a connected, cognitive supply chain. This intelligent system picks up the supply delay and analyses it against historical data to understand how it would impact production. It then predicts potential shortfalls, sends alerts to activate alternative suppliers, recalibrates manufacturing schedules, and adjusts shipping and logistics plans. The system even recommends tweaking marketing campaigns to manage customer expectations. All these adjustments are orchestrated rapidly, seamlessly, and almost like a reflex action, ensuring minimal disruption in the brand's operations and customer experience.

While this might be an ideal scenario, cognitive supply chains are already making a drastic impact on business outcomes by enabling:

• Dynamic Demand Sensing: Cognitive supply chains adapt to real-time market shifts with demand sensing. The agile cousin of demand forecasting, demand sensing does not just look backward; it looks around. It captures the market's current mood and nuances, interpreting this data and then making near-term predictions about demand to improve sales and reduce out-of-stocks. A global sporting goods company leveraged this capability to increase inventory turnover by 30%.

• Touchless Operations: Eliminating non-core human intervention is the holy grail of business operations. By connecting end-to-end systems, cognitive supply chains can help automate the entire order-to-cash process. No more manual order entries or human-generated invoices; it is all autopilots. Even route planning for deliveries gets an upgrade, optimizing in real-time for the fastest paths. An Agro-tech leader connected and automated its supply chain operations and now acquires 90% of its primary orders digitally. Some companies can process 100% of invoices digitally, significantly reducing cost and scope for errors.

• Optimized Marketing Performance: Tracking campaign performance is a costly challenge. Yet, 43% of CPG manufacturers are dissatisfied with their ability to manage trade promotions. Connected partners across the value chain can deliver granular product-level visibility into promotion performance. A cosmetics major was able to leverage this to track coupon-based sales promotions performance within 72 hours and respond to market demand instantly and effectively.

• Improved Business Resilience - McKinsey found that “supply chain leaders who increased end-to-end visibility were twice as likely to report having no challenges from 2022 supply chain impacts.” No wonder companies are making big bets on AI and cognitive value chains. Intelligent, generative AI-imbued systems can help companies run what-if scenarios to predict the impact of disruptions and suggest several courses of action to minimize adverse business impact. Maersk, for example, is planning to use AI-based recommendations to decide alternatives to congested shipping routes and Large Language Models to understand their sales process better.

What CTOs should know?

In a world where disruptions are on the rise, building a cognitive supply chain is not the question of if, but when and how soon. However, complex processes, legacy technology, and siloed systems throw a wrench in most businesses’ plans to connect their supply chain. A short-sighted approach of implementing quick-fix solutions makes the situation worse, especially in the AI-first era. AI solutions aren’t meant to be plug-and-play at scale. While they may work in isolation, scaling them at an enterprise level requires unhindered access to data and then the ability to integrate AI insights into every workflow.

So, to conclude, in order to get started on this journey, companies need to focus on digitizing all their data, discover how their supply chain operations are run and uncover opportunities for optimization and automation, connect with partners to enable data exchange in near real-time. Businesses should also  contextualize this data to enable AI systems to process it so as to integrate the insights into your workflows.

While this might look like a Herculean task, an incremental approach, keeping in mind the larger picture, will simplify the journey while self-funding the transformation efforts.

(Sateesh Seetharamiah is the CEO of Edge Platforms, EdgeVerve)

Sateesh Seetharamiah