Building Smarter Supply Chains with AI – Part I

March 7, 2021

The global market for artificial intelligence in supply chain is expected to grow at a compound annual growth rate (CAGR) of 39.4% from 2019 to reach $21.8 billion by 2027.

More than ever, businesses are recognizing the need for solutions that allow them to be resilient in the face of uncertainty and proactive in anticipating future trends. Not surprisingly, this has led to an acceleration in the adoption of AI-based supply chain solutions to mitigate disruptions, drive agility, and maintain business continuity. A recent survey by MHI showed that nearly 60% of supply chain professionals anticipate that their departments will rely on AI in some manner by 2025.

Supply chains are comprised of multiple actors, relying on a tremendous amount of data to ensure smooth production and transfer of goods from manufacturer to customer. Businesses are quickly turning to AI-powered analytics to understand and forecast demand patterns, streamline inventory and order fulfillment, enhance visibility and coordination within the supply chain to predict and prevent risk as well as uncover new opportunities for cost savings.

AI opens limitless possibilities for supply chain enhancement, and this two-part article focuses on some transformational use cases that can help organizations build future-ready, intelligent supply chains. This list is by no means exhaustive, and your use case might vary based on your industry or specific business challenges. As a member of the Scale AI community, Adastra welcomes innovative, path-breaking ideas for application of AI in supply chains and can help secure funding for eligible projects.

Smart Supply Chain Predictions and Planning

While AI may not be able to forecast ‘black swan’ events like the pandemic, it can provide significant rapid response capabilities for different kinds of anomalous events, ranging from Adaptive Neural Network-based Flight Control taking over piloting to stabilize a damaged aircraft to quickly responding and initiating damage control in the event of a supply chain disruption.

The year 2020 attested to the fact that uncertain market conditions can greatly impact consumer demand. Notwithstanding the pandemic, market dynamics are rapidly changing, and demand forecasts based on historical trends and patterns often fall short of the real picture. Machine Learning solutions can analyze new sources of data to sense demand changes and allow businesses to plan proactively and intelligently optimize their demand and supply forecasts. The demand planning processes can be made smarter with predictive Machine Learning-based algorithms that can create forecasts at product or store level for different time frames. Moreover, these solutions can also incorporate data attributes like competitor pricing, store traffic, and weather, to make demand forecasts even more accurate. With smarter planning, organizations can seize new demand opportunities, leaving their competitors to play catch up.

However, fast, accurate, and quickly adaptable supply and demand forecasts are only few of the benefits that AI can provide for inventory management, and there are numerous other challenges where AI can play a crucial role. Improved inventory management can not only decrease storage and scrap related costs but can also ensure that your business minimizes stock-outs and is always prepared to meet changing demands.

Moreover, supply chain leaders have been able to replace manual data collection with AI-enabled automated workflows to accelerate time-to-insights, minimize resource utilization, and alleviate the risk of human errors in their forecast planning.

Machine Learning can predict future supplier interactions based on historical deliveries, delays, and accidents, and allow for better supplier selection.

Logistics Planning and Optimization

Disruptions and delays in supply chain logistics come at a heavy price to the business. Even small delays can mean empty shelves, lost customers, or unhappy channel partners. However, most supply chains have multiple moving parts and disruptions are often difficult to predict and mitigate.

With AI, organizations can leverage the power of data to predict and plan for future disruptions. Organizations are already using AI solutions to connect the dots between their logistics and different data sources, such as weather, flight logs, traffic, etc. to predict delays, identify next-best actions and respond quickly to mitigate disruptions.

AI and optimization models can be used to design better supply chain networks to reduce delivery costs, increase efficiencies and reduce shipping delays. AI-enabled solutions can help businesses reduce logistics costs with instant shipping quotes that reflect current market rates.

For organizations that manage their own fleets, AI-based fleet management solutions can analyze large amounts of real-time data and provide analytical insights for better, more efficient transportation management. In some industries, drones will likely become an important part of the supply chain in the near future. A combination of truck and drone delivery, together with the management and predictive capabilities of AI, will minimize downtime and greatly speed up deliveries in urban environments.

Shipment damage can put a dent on profits for many businesses. Advanced AI solutions can automatically determine the likelihood and expected amount of potential shipping damages based on past behavior as well as shipping conditions, so organizations can account for damages in the overall cost.

As a leading player in the data and analytics industry, Adastra can help you design and build customized AI solutions to enhance the efficiency and predictability of your supply chain. Our experts work in close collaboration with your team to understand your supply chain challenges and objectives and create tailor-made solutions that will provide you a winning edge. We leverage our deep technical expertise, coupled with two decades of academic and industry experience, to create valuable, specialized solutions geared towards your particular use case.

When it comes to the possibilities of using AI to transform supply chains, these use cases are just the tip of the iceberg. AI can also play a key role in improving supply chain transparency, strengthening collaboration with suppliers and vendors, and in ensuring safe and efficient storage and logistics. We will cover some of these use cases in our next blog. Stay tuned!

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