Much of the innovation in our industry is on the manufacturing side, such as automated welding, scanning equipment, or weld inspection. We are unique because we focus on making every part of the customer interaction excellent, whether in the shop or the office. This sets us up for success.
We want to make doing business with Enerfab easy. One way we achieve this is through our budgetary process, long before anything happens on the shop floor. If our sales team can produce a budgetary proposal in minutes, while others may need weeks, business is easier with Enerfab. How do we achieve this speed?
Estimating Presents an Opportunity to Automate & Optimize
Much of our work requires close collaboration with our estimating team. As an engineer, I might assume a change will save the customer money, but I need to confirm with estimating: “How many hours did we figure to weld this?” or “How many hours would this new design actually take?”
Estimating work is repetitive. We discuss material takeoffs, thicknesses, weld passes, and welding efficiencies. Customers often scale pilot plants to full production and get budget pricing from tank vendors to determine feasibility. Estimators must complete a full estimate for every project and scenario, which is exhausting.
We receive two types of inquiries: budget or firm. A firm inquiry means the customer wants a price they can order against, so we use our traditional estimating process. A budget inquiry is where machine learning can help. The customer is in the funding stage and only needs an approximate price.
Over the past 10 years, we have completed thousands of projects. This allowed us to collect data and format it for quick price calculations. We identified key characteristics that drive price and created an algorithm to generate accurate estimates.
Enter ALIS, our proprietary AI estimating tool developed by our internal engineering and estimating team. ALIS stands for “Automated Learning for Industrial Solutions” and enables us to provide a streamlined estimating process and quickly get quotes in the hands of our customers.
Data Can Be an Obstacle to Innovating with Machine Learning
One of the biggest challenges in creating this tool was the data. We thought we had good data, but it was messy—spread across many spreadsheets and folders from the past 10 years. Extracting and consolidating it was a manual, labor-intensive process.
It was not as easy as using a search function. Instead, it took months of opening Excel documents, finding the right data, and entering it into a new sheet. We processed thousands of files, involving many team members.
We thought it would be easy. We considered using character recognition tools to extract data from old drawings, but the data was not always in the same place, so the tool struggled. The process is worth it, but you need to be prepared for the effort involved.
If you are considering a project like this, set yourself up for the future. Make sure your ERP data and estimates are in an accessible format. It’s easier to change your process now than to go back later. We needed 10 years of data, so we had to extract it manually. The effort was worth it, as it saved significant estimating time on budgets. Data cleanup was the biggest challenge and can be a deterrent if you are not prepared.
If you are considering a project like this, set yourself up for the future. Make sure your ERP data and estimates are in an accessible format. It’s easier to change your process now than to go back later.
Machine Learning Requires a Major Investment in Labor, Not Dollars
Most of the expense was the labor needed to update and prepare the data. The only other cost was hiring a third party to develop the tool. Investing in a machine learning tool is much less than investing in shop floor innovations like robotics.
Leveraging Machine Learning Can Make Jobs More Sustainable and Rewarding
Estimating is a valuable role, but it can be exhausting if it’s all you do. The cycle of preparing proposals, with only a fraction resulting in projects, can feel repetitive.
When we win a project, the estimator is the most knowledgeable team member about the customer’s needs and execution plan. Over time, we’ve learned that estimators often want to become project managers. They want to manage the project through engineering, procurement, subcontractors, and customer relationships, gaining a more holistic experience.
This machine learning tool removes budget estimates from estimators’ workload. It gave them some time back to focus on other tasks, such as project management. The organization welcomed the change, and both estimators and sales teams recognized the benefits.
It’s All About Maximizing Value to Our Customers
There may be a bias toward shop floor innovations because their advantages are highly visible. Administrative innovations are less tangible. The key question is, “Does the innovation make it easier for us to do business with our customers?” Automating pricing may allow us to estimate weeks ahead of competitors, making it easier for customers to work with Enerfab. The value of machine learning goes deeper than just speed.
There may be a bias toward shop floor innovations because their advantages are highly visible. Administrative innovations are less tangible. The key question is, “Does the innovation make it easier for us to do business with our customers?”
Whether on the shop floor or in the office, the most obvious benefits of innovation are speed, accuracy, and precision for our customers. Less tangible wins also add value. Our machine learning tool shifted budgeting to the sales team, freeing estimators to focus on project analysis and cost-effective solutions. This extra time allows estimators to be more creative and maximize project value.
We can produce optimal plans for full-scale projects in the time it takes competitors to create basic estimates. Automation also builds project management into the estimating role, enriching the job, reducing burnout, and allowing estimators to see projects through to completion.
We now understand the process, making it easier to apply in other areas and unlock new advantages for our customers. Even if the benefits are not immediately obvious or the investment is significant, these innovations are worth pursuing.
