Fix Data Foundation Before Next ERP or AI Upgrade
Your ERP May Not Be the Problem. Your Data Might Be.
For manufacturers, the push toward artificial intelligence (AI) is exposing a problem that has been around for years: unreliable enterprise resource planning (ERP) data can undermine even the best technology. In his SDCExec.com article, Daniel Taylor of LeanDNA argues that companies are often too quick to blame their ERP when supply chain and AI initiatives fail. The bigger issue, he says, is often inconsistent, outdated and poorly governed data sitting underneath the system. The problems can be surprisingly basic. Different plants may use different part numbers, lead times may no longer reflect actual supplier performance, and master data can vary between ERP systems. When employees have to reconcile all of this manually in spreadsheets, it becomes difficult for AI tools to produce recommendations people can actually trust. That becomes especially important as manufacturers add more suppliers, plants and product variations. AI may confidently recommend reducing inventory or ordering a part based on data that is simply wrong. The technology isn’t necessarily failing—the information it’s working with is.
Taylor recommends starting small: pick one plant, product line or supplier group and clean up the underlying data first. Companies should validate information against what’s actually happening on the factory floor, establish clear ownership for maintaining it and prove a small, measurable win before expanding. The bigger takeaway is that AI readiness starts with data discipline. Replacing an ERP or adding another AI tool won’t necessarily solve the problem if the underlying information can’t be trusted.
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