AI in Supply Chain Visibility (SCV) is often marketed as a game-changer, yet its real impact on efficiency, risk mitigation, and decision-making is questionable. While AI brings automation and predictive capabilities, the industry’s fundamental problems remain unsolved.
1. AI Generates a flood of information, not always actionable insights
- AI-powered SCV tools claim to improve efficiency at a strategic and tactical level, but in reality, they often generate data point spam. Countless alerts, ETAs and predictions may overwhelm users from an operational point of view - like a smartphone app whose notifications we don’t want to receive any more - rather than enabling better decision-making.
What are the real challenges? For me, there are three:
- How can we structure and prioritize information so that it translates into real operational efficiency gains, rather than just generating noise?
- How can we integrate AI into a new Risk Assessment Framework? Lack of visibility is a risk, as is excess of visibility. How do you redefine what leads to a real operational crisis? How do you frame the risk?
- Operational KPI reporting. Who cares, now that all the risks are shown on a map? What are the Key Performance Indicators that really matter?
2. AI can’t fix poor data quality. It amplifies it.
- AI-driven insights are only as good as the data they process. “Garbage in, garbage out”. But in supply chains, data inconsistency, missing events, and unreliable sources are common.
- If the data quality isn’t assured bottom-up, AI will only generate misleading forecasts and false confidence in unreliable outputs.
- Instead of chasing AI-first strategies, companies should invest in data integrity and governance as a priority.
3. AI can’t replace a responsive, bottom-up reordering process
- AI-powered demand forecasting still fails to fully adapt to real-time shifts in supply constraints, supplier reliability, and logistics bottlenecks. For example, with the current turbulence around tariff wars, regulation application dates can change so frequently that even an AI can’t keep up. Here again, a human touch is indispensable.
- Companies need bottom-up agility. This means decision-making that puts humans first; daily collaborative actionable workflows; and the importance of acting as a network. There isn’t “one supply chain for your company” that could be optimized and streamlined after revealing the risks on a global map; rather, as many supply chains as the number of products that your customers are ordering from you. In that context, building Supply Chain Resilience requires first and foremost a good understanding of your actual situation (your cash-to-cash cycle & transportation reliability), on which you can build scenarios, your re-supply plans and share forecasts with your trade partners.
Conclusion: “Data” is not the new gold; “High Quality Data” is the new gold.
In conclusion, the promises of AI shouldn’t be the end goal; measurable ROI and efficiency should be. Instead of focusing on AI as the differentiator, companies should prioritize:
- Operational efficiency, not AI hype. Start with the end in mind. AI must solve real business problems, not just generate alerts. Its output needs to be straightforward; such as ETAs with high accuracy and completeness; trusted by the Operational teams because the algorithm is well understood.
- Risk management & AI evolution. AI must be combined with your new Supply Chain Risk Frameworks; aligned with your standard operating guidelines and business continuity plans. Otherwise, like an excessive insurance policy, you will start seeing your carriers disengaged as the transport and delivery risk monitoring is considered handled by the Shipper teams; and your operational teams increase their “just in case” spending and internal PO requests.
- Data quality first. AI can only add value where the foundations are solid. You need to have a fact-based, transparent and trackable supply chain model. That requires clean master-data, clear goods availability confirmations, pick-up and delivery expectations vs capacity constraints, precise packaging and safety rules & shareable instructions; and of course: long-time partners ready to fully acknowledge your operational and information requirements. These partners don’t necessarily will take the same digitalization journey with you; but you do need to embark them on your journey offering a win-win strategy. Perhaps your competitors have already understood the importance of the data quality in the Supply Chain Visibility, before your organization has?
Hype often portrays AI as magical. But there is no button you can press to generate 100% accurate Door-to-door ETAs, with 100% of your customer orders tracked in real-time. You need to go step by step, with near real-time when it is enough, to 100% real-time when it is a must. You need to maintain the tracking levels and for that, review your freight service levels, communicate and collaborate with your carriers and choose the right partner in your journey. Then, and only then, can you start to leverage AI as a competitive tool to improve your supply chain, and your cash-to-cash cycle.
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