Despite investing heavily to improve demand forecasting tools in recent years — cited by three in four manufacturers (74%) —research shows that investment has not prevented the disruptions that often hit day-to-day factory operations, according to a study from LeanDNA, a provider of supply planning and inventory optimization solutions for discrete manufacturing supply chain organizations.
That’s because 75% of global discrete manufacturers say supply plan failures are most likely to occur at the factory-specific execution stage—not in the forecast itself.
In other words, the failure point is not a faulty forecast, but rather what happens after the plan leaves the planning system — the work of ensuring materials, suppliers, and production priorities are aligned and ready at the factory level, Texas-based LeanDNA said. The research comes from a study conducted in March by Wakefield Research among 150 senior-level decision-makers at global discrete manufacturers.
The problem is real: more than four in five manufacturers (83%) report supplier changes causing multiple production disruptions each quarter, with more than half (56%) experiencing them at least monthly. Nearly three in four (72%) discovered a material shortage only after production delays were already unavoidable — meaning the risk was present well before it became visible and the window to act had already closed.
Even worse, when disruptions are finally detected, the response compounds the damage. More than half of manufacturers (51%) take a week or longer to determine corrective action — a costly lag in environments where production schedules are measured in hours.
A main cause for the issue is shortcomings in today’s software systems, with 73% of manufacturers saying their enterprise resource planning (ERP) can provide visibility into required materials but cannot prevent execution failures. Nearly all (93%) report difficulty getting ERP visibility into actual manufacturing execution outcomes.
That’s because ERP systems and demand planning tools establish what materials should be ordered and when — but they are not built to manage how those decisions hold up against supplier conditions, material constraints, and shifting production realities at the factory floor. The result is a structural blind spot that no amount of better forecasting will resolve, LeanDNA said.
Artificial intelligence (AI) may offer a solution, the firm said. AI could help manufacturers shift away from treating supply planning as a scheduled process that produces a plan, and instead use supply planning as a continuous system that monitors every site, every supplier, and every buyer workflow — and is updated in real time as conditions change.
“Supply planning is not the output of the demand planning process — it is the first act of execution,” Andy Ellenthal, CEO of LeanDNA, said in a release. “The manufacturers who recognize that distinction and invest accordingly will compete differently: with lower inventory, higher delivery reliability, and supply chains that function as a strategic advantage rather than a constant source of firefighting.”
