Overcome Supply Chain Challenges with Artificial Intelligence
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The key word in any supply chain discussion in recent years has been “challenge”, as we all struggle to find stable ground in quicksand. There is no sure way to make the right decision every time, especially with the speed of change today. However, artificial intelligence (AI) tools are helping supply chain leaders analyze and understand more information and trends to improve their decision-making capabilities.
Risk planning, assessments, forecasting, partner and customer management, and creating a global outlook can all be enhanced with a mix of expertise and AI. Looking at the core challenges in the supply chain space, it’s easy to see that more risk awaits. However, clever modeling and some grit can make the next part of the journey easier to traverse.
Track carrier reliability
On-time, damage-free shipments are increasingly important to a company’s bottom line. Not only are buyers more willing to make cancellations and returns, especially in e-commerce, but competition for shrinking dollars is increasing. As economic recessions loom in the wings, every sale and dollar of revenue becomes more important.
For consumer-facing brands, carrier reliability significantly impacts profitability. AI tools can speed up much of this decision-making through real-time analytics. It’s common for AI and machine learning tools to make carrier product selections based on order needs — such as speed of delivery — and price.
Next-level tools will combine this with profitability assessments, as they sift through historical data to see on-time delivery and shrinkage rates relative to carriers. Some ML solutions offer sentiment analysis in reviews and social media mentions. By referencing it with order data, it can help companies build a profile of carrier reliability from the consumer’s point of view. When sentiment changes rapidly and selection must occur almost instantly, AI is one of the few reliable tools to make the best decision.
Refreshing predictions
Talk to almost any supply chain professional, and they’ll say they’ve had to throw out at least one long-term forecast in recent years. The 2019 data was largely worthless when the pandemic hit. The e-commerce boom in 2021 also saw brands big and small overstock in 2022. Long-term planning without efforts to predict risk and disruption has wreaked havoc on the logistics space.
AI—both in advanced forms and robotic process automation (RPA) tools that regularly collect data—has the ability to alleviate some of this burden. RPA efforts can automatically update freight locomotive rates, diesel prices, current carrier surcharges and more. Advanced AI tools can combine this with sales and geopolitical trends to look for disruptions.
When either flags a potential issue, supply chain management (SCM) professionals must begin working on ways to assess and mitigate this risk. This may mean additional suppliers, reducing safety stock levels, or reallocating inventory to be closer to active consumers. SCM leaders often do this laborious work, but AI can speed up the process and help spot trends sooner.
Reconsidering end-user options
Manufacturers have been relying on AI for a variety of decisions for years. One area that should have gained renewed importance, especially after the supply chain disruptions of recent years, is the search for flexibility in production.
Companies can turn to AI to look at surplus inventory and identify other potential use cases. Some work may identify alternatives to complete products, while others may highlight where existing parts can be used in new development. At the same time, AI models can look upstream and potentially identify alternative procurement options based on price and availability. This effort is becoming a best practice in semiconductor development and procurement where outages are common and inventory maintenance is difficult.
Maintain AI’s Helper Status
The essence of keeping AI as a practical support and reducing its threat is to treat it as a tool. These tools require a human hand to guide and review; resist the call to use them as a substitute. Supply chains rely on strong relationships and communication to succeed, especially during turbulent times.
Introducing AI into small decision trees and larger review processes can be incredibly powerful. However, companies must carefully monitor AI recommendations and actions to ensure they don’t harm partnerships or take actions that meet metrics-based goals while ignoring human goals. Keep your AI tools as a helper.
Image credit: metamorworks / Shutterstock.com
