Let us understand a few key challenges that the companies face in the traditional supply chain:

Siloed information sources – Different platforms are utilized to manage distributed information sources for the first-to-last-mile shipments and material visibility from the manufacturer warehouses to the distributors. Unification and integration of these platforms are essential to reduce shipment delays.

Massive amount of data – OT-IT convergence delivers a large amount of data from different field sources such as production, inventory, and warehouse systems. Management of this data and generating actionable insights from it is critical for modern supply chains.

Transportation Delay – Effective fleet management is one of the critical challenges for SCMs as it is the vital link between the supplier and the consumer. With shipment telemetry, driver, and weather condition information, precise and informed decisions should be taken to increase fleet management efficiency.

These challenges significantly impact the supply chain and pressure Chief Supply Chain Officers (CSCOs) to respond quickly to meet the dynamic customer demands.

Cognitive Supply Chain

The traditional supply chain has evolved from the connected to the predictive and now to the cognitive supply chain. AI and ML with complex algorithms are the new normal in the digital transformation journey of leading companies. They need inventory management and distribution systems that are intelligent, predictive, and adaptive. Artificial Intelligence in the supply chain ecosystem processes, including inventory, asset, fleet, and energy management, can help SCMs to improve efficiency, enable faster decision-making, and stay competitive in the marketplace.

In a recent survey, IBM reported that among the outperforming supply chain executives, 86% believe that cognitive computing will transform their forecasting and demand planning capabilities. With the tremendous benefits that the cognitive supply chain offers, CSCOs of financial outperformers are considering deploying AI to resolve multiple end-to-end supply chain challenges. A cognitive supply chain with an integrated platform analyzes data from internal (inventory, point of sale, production, raw material) and external (weather, market trends) ecosystems.

Let us take a deep dive into a couple of supply chain stages where cognitive decision-making can have an impact.

Transportation disruptions

Forward-thinking logistics companies realize that only conventional geo-data is now unreliable to plan freight distribution from one location to another effectively. They need to add more information sources from the streets, indoor locations, weather, current traffic, and more. Additionally, only locating a truck is not sufficient, and companies also need information from the integrated sensors about telemetry data, including the temperature, humidity, speed, fuel efficiency, driver availability, equipment performance, and so on. AI-enabled models can detect disruptions in the seas and weather conditions, which can help companies check for an alternate route and reduce the delay in vegetables and fruits reaching the port or the airport. This delay may reduce their shelf life, or they may become completely inedible before they reach the supermarket.

Driver behavior analysis is also critical in the road transport mode, where risky driver behavior can lead to fatalities and reduce driver safety. Cognitive analytics of driver habits such as drinking, eating, yawning, sleeping, texting, using mobile, over speeding, or changing the lane help alert the control center and prevent road accidents.

With engine telemetry data, the maintenance department can also intelligently predict engine performance, schedule maintenance, and reduce on-road truck breakdown. With self-learning algorithms, 3PL companies can also rate the suppliers, drivers and negotiate their future contracts with the actual performance data.

The entire cargo condition monitoring with goods, fleet, driver, surrounding ambiance, and traffic condition helps companies deliver critical merchandise on time.

Warehousing Management

Amazon, Walmart, Kroger, and other retail distributors have used cutting-edge cognitive tools to disrupt B2C markets and have accomplished growth. Distributors now should understand how AI can make them become order makers from order takers, resulting in improved business operations and customer experience in this competitive era.

Automated warehouse management systems using cognitive technology such as voice-enabled devices help the workforce to improve productivity. An intelligent camera network with an AI-based video analytics algorithm helps in improving inventory management efficiency and prevents theft. Intelligent video analytics software automatically monitors the video of the workforce, vehicles, objects on the floor and analyses the behavior associated with them. It can offer features such as counting people and vehicles present in certain areas, generating alerts for human presence in restricted areas, and so on.

Packaging and Palletization

As per Packing Revolution, currently, there are 10 billion shipping pallets in use worldwide. Connected and AI vision-enabled pallets improve workforce productivity, accuracy, and storage efficiency. Traditional pallets require manual barcode scanning and quality assurance for each pallet before and after loading the material. Edge AI-enabled cameras can capture, identify, and scan the material loaded on the pallet and inspect the quality of the packaging. The camera can identify and generate real-time alerts for poor packaging or handling of the goods. This AI video solution can also identify missed patterns while an individual is packing the material.

Concluding Thoughts

With its four-step process of predict, plan, control, and share, the cognitive supply chain can increase supply chain efficiency, reduce cost, and provide valuable insights for businesses so that they can respond in real-time to the dynamically changing conditions.