News and Events

A new robot cell can arrive on time and still fall flat once production starts. The install may go smoothly. Then a sensor fault hits, a handoff fails, or logic needs a quick adjustment. The system pauses. Everyone looks around for the person who can sort it out. 

That moment tells you a lot about automation readiness. The real bottleneck often sits with people, not equipment. Employers who want better results need Workforce Planning for Smart Manufacturing to start before automation expands across the floor.

Manufacturers are already feeling the pressure. The industry may need 3.8 million additional workers by 2033, with as many as 1.9 million roles left unfilled if talent gaps continue. That pressure hits operations, engineering, and technical support all at once. 

Strong planning starts with floor-level capability coverage. It does not start with a software field labeled “technician II.” Employers need people who can recover faults, interpret system feedback, support safe operations, and keep production moving.

The Roles You Need Before Automation Scales

Many teams think automation hiring starts and ends with technicians. That is too narrow. Smart manufacturing needs a fuller support structure long before the second or third system goes live. 

A robotics technician keeps the equipment running. Robotics specialists improve performance and help solve recurring problems. A robotics integrator or automation lead plans how the system fits into the plant in the first place. Those are different jobs, even if some plants try to squeeze them into one title. A better approach maps each role to the work ahead, then connects that work to career pathways and skill areas.

The human layer goes beyond robotics-specific titles. Process improvement support matters early because someone has to clean up the workflow before automation scales. Software and data support matter because connected systems create new dependencies fast. Operations and scheduling leaders matter because they turn digital visibility into daily decisions that affect uptime and throughput. 

This is where employers often feel stretched thin. They have pieces of the team, though not always in the right mix. A more comprehensive workforce planning approach for a smart manufacturing approach helps identify missing pieces before gaps slow output.

Why Job Titles Stop Helping

Job titles sound neat on paper, though they get messy fast inside real plants. One company’s technician may reset robots and replace sensors. Another company may use that same title for mainly mechanical maintenance. The label stays the same. The work changes a lot. That creates hiring problems early. 

Candidates apply to roles they interpret one way, while employers mean something else entirely. Screening takes longer. Onboarding gets bumpier. Productivity slips. Employers get better results when they define openings through actual work and skill needs, not broad titles. That’s one reason skills-based role mapping works better.

A practical fix is to hire based on applied capability. Search for people who have handled real tasks tied to automation systems. Look for experience with line recovery, controls awareness, safety procedures, or sensor checks. Those signals tell you more than a title ever will. 

RoboticsCareer.org supports this style of sourcing by helping employers search for candidates based on applied skills and role fit rather than loose naming conventions. That makes workforce planning for smart manufacturing more precise and a lot less frustrating. Create a profile to get started today.

Skills Change Faster Than Job Descriptions

Automation changes work in pieces. A role may keep the same name for years while the actual day-to-day tasks shift every few quarters. 

A maintenance employee who once focused on mechanical fixes may now spend time reading fault messages or reviewing data from connected systems. The title has not moved much. The work absolutely has. That gap is where planning falls behind. It also explains why skill coverage matters more than title accuracy. Employers need a system that reflects how work changes.

Research backs that up. The OECD found that even in jobs with high exposure to automation, only 18% to 27% of required skills and abilities are highly automatable. Deloitte reaches a similar conclusion from a planning angle: tasks reflect work, and skills reflect people more clearly than job titles or headcount alone. 

Think about a palletizing cell. The old version of the role may have centered on loading, clearing jams, and keeping product moving. The newer version often calls for sensor validation, fault recovery, controls awareness, and some comfort with system data. Job descriptions lag behind shifts like this all the time. 

Good planning tracks tasks as they change and updates hiring expectations based on capability. That is a much steadier route for workforce planning for smart manufacturing than waiting for titles to catch up.

Start With Work Design

The cleanest planning method starts with a use case, not a requisition. Pick the automation system you want to support. Break the workflow into core tasks. Define the skills needed for each task. Then compare those needs with what your current team already does. 

That process gives you something useful. It shows where hiring is needed, but also where upskilling makes more sense and where outside support may help for a while. Employers can turn that skill map into candidate searches and job posts far more easily than they can work from a vague title.

Take that same palletizing example. The work may include fault recovery, safety checks, upstream coordination, sensor review, and basic PLC awareness. That picture is much clearer than posting for an “automation technician level II” role and hoping the right person shows up. Yes, this mapping takes a little time at the start. It saves much more time later. It also provides a stronger foundation for workforce planning in smart manufacturing, as the plan aligns with work on the floor.

Build a Common Skills Language

Teams often describe the same capability in completely different ways. Operations may ask for someone who can recover a line. HR may call that troubleshooting. A training partner may describe it as controls diagnostics. Same need, different wording. That mismatch slows hiring and clouds development plans. It also makes internal mobility harder than it should be. 

A better system uses a single shared language for capabilities across hiring, training, and advancement. This is where role pathways and competencies help create consistency.

A shared skills language also improves external partnerships. Employers can talk with educators and workforce groups using the same terms. That keeps programs closer to actual plant needs. It also makes candidate evaluation more straightforward because everyone is working from the same picture of readiness. Without that consistency, teams keep translating for one another and lose time. Strong workforce planning for smart manufacturing depends on reducing that friction.

Evaluate Readiness Through Applied Skill

Resume keywords can only tell you so much. Employers need proof that a candidate has handled work that resembles the real environment on the floor. Applied skills offer that proof. 

Did the person recover faults in a live system? Have they worked with sensors or safety procedures in a meaningful way? Can they explain what they did when a system stopped? Those signals matter because they connect directly to performance after hire. This is why candidate search by applied skill gives employers a much sharper view.

The same logic applies to the training background. Candidates from endorsed programs often have more direct exposure to realistic equipment and job-focused scenarios. That does not mean other programs lack value. It means the endorsement badge gives employers one more quality signal while sourcing. In a hiring market full of vague language, that extra clarity helps. It also makes workforce planning for smart manufacturing easier to execute because the hiring process connects to real evidence of readiness.

Build the Pipeline Before the Hiring Rush

A skills-based plan needs a skills-based talent pipeline behind it. Hiring alone will not keep pace with automation growth, especially once multiple systems need support at once. Employers need to connect talent sourcing to endorsed training, local programs, and internal upskilling. 

RoboticsCareer.org helps with that by enabling employers to review training options, identify endorsed programs, and search for candidates whose backgrounds align with real work needs. That makes pipeline building on RoboticsCareer.org more than a one-time recruiting move. Create a profile and start searching for the right candidate today.

Regional variation matters here, too. Workforce training gap analysis shows that skill shortages vary by region. One plant may find stronger maintenance talent nearby. Another may have better software support in its market. That means pipeline design should reflect local supply, not assumptions copied from somewhere else. Strong workforce planning for smart manufacturing is built on actual talent conditions in the market and within the company.

Timing Matters Because Skill Disruption is Speeding Up

Skill change is moving quickly. Employers already expect 39% of core skills to shift by 2030, and many workers will need to be reskilled or redeployed along the way. In manufacturing, future demand continues to grow around computer science, automation, process improvement, data analysis, and tooling. Those are not narrow specialist skills buried in one corner of the plant. They touch maintenance, operations, support functions, and leadership. That pace of change makes static title planning feel slow almost immediately. Employers need a system that updates more easily. A stronger workforce planning for a smart manufacturing model does exactly that.

Planning by skill makes adaptation simpler. You can update hiring targets and build training plans without rewriting the org chart every few months. That makes a huge difference once automation starts spreading from one area of the plant to another. 

The more connected the operation becomes, the more valuable that flexibility gets. Employers who act early usually have a smoother path through scale. The ones who wait often end up reacting under pressure. That’s a rough way to build a team.

Plan Capability Before You Scale Technology

Automation works better when the human layer is in place from the start. Employers need people who can plan systems, support uptime, improve workflows, interpret data, and guide adoption on the floor. Those boxes need coverage before automation expands. One person may cover several of them in a smaller operation. A larger operation may split them across teams. The structure can vary. The capability coverage cannot.

That is the core of workforce planning for smart manufacturing. Plan around what the work requires. Build around applied skill. Use tools that help you source candidates and training based on real capability.

Create an employer profile on RoboticsCareer.org, post roles tied to actual skills, search by applied experience, and review endorsed training options to build a talent pipeline that fits the work on your floor.

About The Author

Lisa Masciantonio 

Chief Workforce Officer

Advanced Robotics for Manufacturing (ARM) Institute 

Lisa Masciantonio is the Chief Workforce Officer for the Advanced Robotics for Manufacturing (ARM) Institute. She joined the ARM Institute in May 2017 as the Director of Membership and Outreach.  She moved to the position of Chief Workforce Officer in 2019 and she is responsible for driving the Education & Workforce Development vision for ARM in conjunction with the ARM membership, the federal and state government partners, and other expert stakeholders. 

Lisa brings with her over 25 years of experience as a performance-driven leader with notable success in cultivating and executing business strategies and formulating long-term strategic client relationships.  She has proven success in developing business solutions, commercialization of products, technology transfer, and technological initiatives that have supported organizational growth, improved staff productivity, and increased value to many communities of practice. Critical to her success is the ability to increase awareness and drive thought leadership position by designing and executing innovative programs as well as developing and launching new, value-add offerings for ongoing competitiveness. Lisa received a Bachelor’s degree from the Pennsylvania State University and 2 Master’s degrees from Carnegie Mellon University. 

In 2021, Lisa was recognized as one of 20 world-wide Exceptional Women in Robotics and Automation by SME. In 2022, she was recognized by the Pittsburgh Business Times as a Women of Influence and was also part of the inaugural Technical.ly Pittsburgh RealLIST Connectors list, which recognizes the top 100 influential leaders in Pittsburgh tech.