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Supply Chain
Jake Rheude Dec 20, 2022
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The core word in any supply chain discussion in recent years has been “challenge,” as we all struggle to find stable ground in quicksand. There’s no surefire 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 improved with a mix of expertise and AI. Looking across the core challenges in the supply chain space, it’s easy to see that more risk awaits. Smart modeling and a bit of grit, though, may make the next part of the journey easier to traverse.
On-time, damage-free shipments are increasingly important to a company’s bottom line. Shoppers are not only more willing to make cancellations and returns, especially in e-commerce, but competition for shrinking dollars is growing. As economic recessions lay waiting in the wings, every sale and dollar of revenue become more important.
For consumer-facing brands, carrier reliability significantly impacts profitability. AI tools can speed up much of this decision-making through real-time analysis. 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 that with profitability assessments as they scour historical data to view on-time delivery and shrinkage rates relative to carriers. Some ML solutions offer sentiment analysis in reviews and social media mentions. Referencing that with order data can help companies build a profile of carrier reliability from the consumer standpoint. When sentiment changes rapidly, and selection must occur nearly instantly, AI is one of the few reliable tools to make the best decision.
Talk to nearly any supply chain professional, and they’ll say they’ve had to throw out at least one long-term forecast in recent years. The data from 2019 was largely worthless when the pandemic hit. The e-commerce boom in 2021 also led to brands large and small having too much inventory in 2022. Long-term planning without efforts to forecast risk and disruption has wreaked havoc on the logistics space.
AI — both in advanced forms and robotic process automation (RPA) tools that regularly gather data — has the capability to ease some of this burden. RPA efforts can automatically update freight spot rates, diesel pricing, current carrier surcharges, and more. Advanced AI tools can combine that with sales and geopolitical trends to look for disruptions.
When either flags a potential issue, supply chain management (SCM) professionals should begin working on ways to assess and mitigate this risk. That can mean additional suppliers, reducing safety stock levels, or reallocating inventory to be closer to active consumers. SCM leaders regularly do this arduous work, but AI can make the process faster and help spot trends sooner.
Manufacturers have relied on AI for a variety of decisions for years now. One area that should have 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 for complete products, while others might highlight where existing parts can be used in new development. At the same time, AI models can look upstream and potentially identify alternative sourcing options based on price and availability. This effort is becoming a best practice concerning semiconductor development and purchasing where disruptions are common and maintaining stock is difficult.
The crux of keeping AI as a practical support and minimizing its threat is to treat it as a tool. These instruments require a human hand to guide and review; resist the call to use them as a replacement. Supply chains rely on strong relationships and communication to succeed, especially during turbulent times.
Introducing AI into small decision-making trees and larger review processes can be incredibly powerful. However, companies should closely monitor AI recommendations and actions to ensure they’re not harming partnerships or taking actions that meet metric-based goals while ignoring human ones. Keep your AI tools as a helper.
Image Credit: metamorworks / Shutterstock.com
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