As manufacturing demands continue to evolve, companies are increasingly expected to push the boundaries of efficiency, productivity, and innovation. The pressure to modernize operations and reduce human intervention is intensifying, with a focus on achieving "lights-out" manufacturing — where production continues seamlessly with minimal human oversight.
Ahead of his session at the American Manufacturing Summit, we’re thrilled to spotlight Mike Blaney, Vice President of Engineering, Reliability, and Automation at Newell Brands. In his session, titled "Moving From Smart Operations to Dark Operations: Advancing Manufacturing To Lights-Out," Mike will explore how Newell Brands is leveraging automation and smart technologies to transform manufacturing processes, taking operations from smart to autonomous, and paving the way for the future of 24/7, fully automated facilities.
Get an inside look at Mike's upcoming session below!
Can you introduce yourself and share more about your career and journey leading up to your current role as the VP of Engineering, Reliability and Automation at Newell Brands?
I've always been in technical work. I began my career as a cryptologist in the U.S. Navy, where I worked on cryptographic equipment and was responsible for maintenance and technical services. That experience taught me early on what strong maintenance and reliability practices look like.
After leaving the Navy, I spent a few years as a commercial lobsterman off the New England coast. I then attended UMass Lowell and earned my degree in Electrical Engineering.
From there, I built on my technical background through roles across semiconductors, oil and gas, and multiple robotics and automation companies, before moving into the consumer packaged goods space with Newell Brands.
What are the biggest bottlenecks that you've encountered in automating end to end from the shop floor to data capture to action? How do you address them?
In most facilities, the biggest bottleneck is legacy infrastructure. Manufacturing plants often have existing data systems and a range of assets - from brand-new equipment to very old machines that can’t be connected easily. That creates fragmentation on the shop floor, where multiple “languages” are effectively being spoken across different systems and equipment types.
The goal is to create a single language, and that starts with standardization. You need engineering and data standards, supported by clear policies and organizational structures. From there, you deploy an IIoT framework to unify data across the plant.
Training is also essential. You need strong internal capability-building programs for robotics, PLCs, sensors, and predictive tools. Ultimately, everything must be tied to clear outcomes - service, cost, and safety - with defined ownership. You’re rarely walking into a brand-new factory, so the program must be flexible enough to accommodate a wide range of maturity levels.
What governance or operating model helps ensure that technology and data efforts actually translate into sustainable performance improvements?
You need a pragmatic continuous improvement program that blends TPM, Lean, and Six Sigma. The key is aligning digitization with continuous improvement. This convergence becomes your governance model.
That’s where you build cascaded KPIs, drive accountability down into individual performance goals, and power the operating model through continuous improvement. It shouldn’t be a separate initiative; it should be the way you run the business.
Where have you seen digital tools strengthen LEAN/TPM execution and where can they unintentionally create complexity?
You can’t introduce tools just for the sake of having tools. Digital solutions need to be built around clear business needs. When done well, digital tools strengthen Lean and TPM by enabling real-time OEE visibility, downtime reporting, and immediate insight into factory performance - so teams can react faster.
If you can identify waste and loss sooner, and empower the employees closest to the work with the right information and decision-making support, the impact is strong.
However, if you roll out a large number of tools without focusing on culture change and skill development, you create complexity. You end up with the equivalent of 150 apps on your phone and no clarity on which one to use.
When implemented correctly, digital doesn’t replace continuous improvement - it enhances it. It strengthens visual management, standard work, and root-cause analysis. Instead of being filled out manually, those practices become digitized and stored in databases, which can then enable AI to support the work. Ultimately, culture matters just as much as technology - culture really does “eat strategy for breakfast” - so the cultural transformation must match the tools you deliver.
How do you translate business priorities - growth, service levels and margin, into an automation and engineering portfolio that leaders can clearly support?
It comes down to effective master planning. You need at least a five-year vision to set sequencing and priorities. A strong master plan aligns work to strategic and organizational measures, all the way down to team KPIs.
Each workstream should have clear measures, and those measures must connect directly to the business’s priorities and values. Then you drive the portfolio through a measure-and-improve cycle.
You don’t change the master plan every week or month. You adjust it periodically as strategies evolve, but you use it consistently to guide tactical execution across each workstream.
How do you define and document ownership across teams for system performance, data integrity, and ongoing support once an automation solution is live?
Data integrity is foundational. In my view, it’s a prerequisite for most automation solutions. The approach needs to start with tools that can identify data quality issues at the front end - essentially an integrity check.
Many models are built to ensure that only high-quality data enters the system, and that poor data is automatically filtered out and quarantined before it can pollute the model. To do that effectively, you first need a standards framework that defines what “good” data is, and what level of deviation is acceptable versus unacceptable.
Then you need automated tools to filter and quarantine data that doesn’t meet the standard. The people who own that data - stewards and custodians - must be part of the feedback loop so they know when and why data has been quarantined. That builds trust in the model, because people know the data powering it is reliable - a “gold table.”
From there, you can place an AI agent on top of that trusted data foundation and use it as a first line of support. This idea of self-cleansing data becomes increasingly important as we move further into the information age.
What are you most looking forward to at the American Manufacturing Summit this year?
I’m looking forward to exchanging practical insights with leaders who are driving innovation, modernization, and automation in their organizations. I’m especially interested in discussions around predictive AI, robotics, sustainability, and skills development.
I’m also looking forward to cross-pollination—sharing ideas and learning what others have proven out. What can we go back and pilot quickly? What have others demonstrated that we can leverage? What are the lessons learned?
And, on a personal note, I’m looking forward to trying Chicago deep dish pizza for the first time!
In a world where operational efficiency and automation are paramount, Mike’s insights into advancing manufacturing to lights-out operations offer a glimpse into the future of fully autonomous, high-efficiency production.
Don’t miss the opportunity to hear his full session and connect with industry leaders as they explore the next frontier of manufacturing innovation on March 17-18 in Chicago.
Final passes are available at manusummit.com.