Automation and artificial intelligence are advancing rapidly across manufacturing and supply chain operations, yet progress remains uneven. Large OEMs may have the capital and resources to launch sophisticated initiatives, while Tier 1 manufacturers and smaller suppliers often face limited technical skills, fragmented data, competing priorities, and resistance from employees whose work will be directly affected.
The technology itself is rarely the only barrier. Many automation and AI initiatives struggle because leadership teams are not aligned on the problem they are trying to solve, the results they expect, or how the new process will affect employees.
Start With a Clear Operational Outcome
Before introducing AI into supply chain planning, forecasting, inventory management, predictive maintenance, quoting, or quality inspection, organizations need to define a specific operational outcome.
A company might aim to reduce inventory carrying costs, improve forecast accuracy, accelerate customer quoting, decrease unplanned downtime, or improve first-pass yield. Without a clear target, teams can become focused on platforms and pilots rather than measurable business improvement.
Evaluate Readiness for Change
A useful way to evaluate readiness is to examine three factors: discomfort with the current state, clarity of the future vision, and the skills required to operate differently.
If employees do not understand why the current process must change, they may avoid or bypass the new system. If stakeholders have different definitions of success, departments may pursue conflicting priorities. If teams lack the necessary skills, even a successful pilot may fail to become part of daily operations.
Improve Supply Chain Visibility
These challenges become more significant when organizations attempt to connect OEMs, Tier 1s, and smaller suppliers. Greater supply chain visibility requires participants to share accurate, timely, and relevant information.
However, many companies still struggle to obtain reliable insights into supplier capacity, lead times, inventory availability, production constraints, demand changes, and delivery risks.
Technology can improve visibility, but only when companies agree on what data matters, who owns it, how it will be shared, and which decisions it should support. Poor data, disconnected workflows, and inconsistent definitions can limit the value of even the most advanced AI tools.
Read this article in full in the Integr8 Playbook, “Automation in the Real World: Aligning Supply Chain and Strategy,” here.
Ron Crabtree, CPIM, CIRM, CSCP, MLSSBB is a co-author or author of 5 books on operational excellence, including Driving Operational Excellence, and is published in multiple business publications including authoring APICS Magazine’s Lean Culture department for 13 years running. He has personally mentored thousands in getting great results in business generating
untold millions in benefits while improving everyone’s work life at the same time
