Processing's Weekly Mixer: Physical AI for Mid-Market Manufacturers, and more
Welcome to the latest installment of Processing's Weekly Mixer, which highlights recent content from EndeavorB2B brands relevant to process manufacturers.
This week's entry features content from Automation World, Chemical Processing and Control Global, as well as this week's content from Processing.
Physical AI: New Possibilities for Mid-Market Manufacturers
From Automation World: Physical AI is giving mid-market manufacturers a practical way to make operations smarter, safer, and more responsive to constant change.
Wolfe Tone and Tim Gaus of Deloitte write:
For many mid-market manufacturers, automation has long meant investing in fixed systems designed to deliver repeatable output at scale. That model can drive efficiency, but it also assumes relatively stable conditions: predictable production runs, consistent labor availability and environments that do not change too quickly. In practice, many mid-market operations do not have that luxury.
Product mixes change. Workforce availability fluctuates by shift or site. Equipment footprints evolve over time. And when even a small disruption hits the line, mid-market manufacturers often have less excess capacity, fewer specialized resources, and less margin for delay than their larger peers. Each of these circumstances is where physical AI is beginning to matter.
Read the entire article HERE.
Barry on Batteries: Why Battery Materials Demand Tighter Process Integration
From Chemical Processing: From graphite to electrolytes, delivering battery-grade quality requires a level of cross-company coordination that goes beyond traditional process industry practice.
Barry Perlmutter writes:
Throughout my career, I have supported chemical and pharmaceutical projects by applying a systems-based approach to process definition, technology selection and project execution. The process involved using tools such as Responsible, Accountable, Supportive, Consulted, Informed, or RASCI, to assign accountability. In the RASCI approach, the responsible engineer is the one who is performing the task. The supportive and consulted engineers provide guidance, as necessary, while the accountable and informed levels ensure the overall project requirements are met, such as design, budget and schedule.
In a complex project, each task may have multiple people from different organizations. A specification document index (SDI) for each task in the RASCI approach ensures coordination among the vendors and engineering companies to meet every process, mechanical, electrical and performance requirement. Since these projects are typically developed in-house, a holistic systems approach combined with strategic project management (SPM) reliably delivers success. SPM is the high-level control that includes the RASCI and SDI systems but adds the end-user objectives and specific process details such to ensure a full understanding of the process and project scope.
However, the story for lithium-ion batteries is more complex for several reasons. First, the scope of responsibilities stretches across many players—from chemical suppliers, automotive OEMs, cell manufacturers with the different battery types to recyclers and refiners. In addition, geo-political external market forces and battery demand and supply chain shifts add volatility to the market, which impacts large-scale projects and foundational partnerships.
Read the entire article HERE.
How to build a strong process engineering workforce
From Control: In the latest episode of Control Amplified, executive editor Jim Montague spoke with Brian Romano, technology development director of Arthur G. Russell. They discussed engineering workforce development issues, the ensuing brain drain, and how to build, recruit and retain operators and engineers.
Listen to the episode below.
Recapping the week on Processing
Articles
Bulk solids moisture reduction without heat: A guide to pugmill back mixing
Moisture’s major impact on pulp and paper
Pressure budgets, hold-up maps and recovery logic
A retrofit framework for cutting waste, energy loss and operating risk in existing process plants.




