Don’t Automate Your Shop

Until You’ve Got This Foundation In Place

Jordan Easterling

A lot of automation platforms promise a turnkey experience for modular construction shops. All you have to do is plug in the platform and—boom—efficiency.

As much as we all want this to be true, it’s simply not.


The reality is automation can only smooth out things that already work rather than make something work from nothing. What modular construction shops really need is proper measurement, leading to the identification of constraints. Only then should you think about automation.

Understand the data hierarchy

Automation is fun to think about because it means less work for humans, which is wonderful in many cases. However, automation is actually a data problem—you can only automate if you’ve got the right data, in the right places, at the right time for the computer to do its thing.


Data needs to be presented in very logical ways in order for a computer to understand it. Automated equipment—CNC mills, robotics arms, and other digital fab tools—need accessible data that’s formatted a specific way to automatically build things in a shop. And artificial intelligence can do much if it can’t even access the data.

That logical presentation is called a data hierarchy. There are foundational pieces of information that are either single-variable or can be produced by observing work. All other data is built from this information, meaning you have to collect things in the right order to correctly build the data machine that eventually empowers automation.

The data science hierarchy of needs. Source: HackerNoon

Start with constraints, not technology

Generally speaking, all modular or prefab production shops have a similar goal in mind: increase high quality throughput to deliver more builds at a lower cost (and in less time) for the business.


But businesses have to contend with the theory of constraints approach, meaning find the bottleneck and figure out how to alleviate it in order to increase overall production. The theory of constraints is based on the premise that you don’t have to improve everything in order to get positive change. Instead, you only need to improve the most painful, time consuming, or expensive constraint to see rapid improvement.


If you don’t already know the constraints in your production process, document it out with time, resources required, and costs of each step. With these three data points across your production map, your key constraint will become clear.


This foundational information—your process and steps you follow / inputs you need for each step—is what feeds all later analysis and automation.

Figure out your key considerations

Considerations are additional pieces of information that are specific to your company and use case.


Broadly speaking, they fall into three categories:


1. Cadence: What data points do you measure once per project (for instance, cost) and what do you measure regularly (for instance, hours worked per build or module throughout the project)?


2. People: Where are your people getting slowed down or stuck in their work? What information could you collect to better understand what’s blocking your team—and what tool is best to collect that data?


3. Tech that fits the work: Ensure all technology makes sense for its use case. For example, tablets versus laptops for employees who move around a lot during their regular work. The right device or technology will help increase data collection, whether manually or automated, because the technology moves with your team and your work.

Get ready for automation

By design, automation can only work with what exists. That’s why the previous steps are so important. Once you know what information you need to collect, you can start thinking about automation in two ways:


1. Automating data collection: If information is documented in one system then needs to be manually copied into another, see if there’s a way to automate the data connection. That might mean connecting the two platforms with an API or potentially choosing a new centralized, use-case specific platform for modular construction that can serve both functions.


2. Automating data analysis: If you need to calculate certain metrics or begin forecasting with known triggers (for instance, if supply goes below X level, reorder), you can automate the calculations and reminders with the right technology.


The key to finding the right technology is knowing what information you have coming in and what calculations you need—this will help identify which platform can do what you need.

Focus on a strong foundation

Automation in modular construction is possible, but you have to start with the foundational elements of knowing what information you need. Only then can you think about automating data collection then eventually automating calculations, analysis, and your production lines.


But overall, it’s worth closing on this note: it’s not about automation itself. Automation is a tool to help you achieve your goals. What really matters is collecting information that can help you learn and improve so you can continually deliver for customers, employees, and the bottom line.

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