Better communication company-wide is one of many valuable benefits of becoming a data analytics-driven sheet metal works company.

He says the ductwork needs to be cleaned before the system can be started. Your first thoughts are, “It was clean when it left the shop. Why is it my fault that it’s full of construction debris?” 

With construction schedules constantly tightening, the capabilities offered to firms that use data for monitoring and communicating project status ensures greater success and financial returns. If your company isn’t using data analytics for sharing and communicating, you’re missing a great opportunity for improving your practices. The data already exists. What is required is establishing a repeatable process for collecting relevant information, storing it and reusing it for reporting, analysis and review. 

This article sheds light on the basic principle of data analytics and how it can be easily integrated with minimal computer skills inside operations of a sheet metal products or HVAC construction company. 

Data analytics is the practice of continual review of a firm’s project data to optimize its construction practices. Sheet metal works contractors engaged in this practice treat their data as a corporate asset and leverage it for competitive advantage. Data analytics also refers to the practice of using past process performance to gain insight and drive business planning. It’s not new in so much that job and cost codes have been around the HVAC sales industry for years. But only a few recognize the competitive advantage of fully engaging in the process that comprehensive data analytics offers sheet metal contractors. 

By engaging and integrating data and business analytics, communication is significantly improved across a company’s project teams, project managers and senior management. It provides more control of the construction process and plays a major role in a contractor’s ability to consistently deliver high-quality service to their customers at the best price. 

Let’s start when project data first enters your firm. It arrives in a variety of forms and formats. For example, it could show up via contract documentation or a fully coordinated mechanical drawing. 

More than likely, however, the first instance of rich project data that can be collected and reused is data that was created from within your estimating department and exists in your estimating software application. This estimate and its associated data is often based on 2-D schematic design drawings, specification documentation, industry-standard metrics, historical labor data and cost metrics. It also represents your estimator’s skill and knowledge set reflected in how this estimator interpreted and organized the project from a work breakdown structure perspective. 

Here are three steps in this process: 

1. Establishing your project’s construction dataset baseline in Excel

The practice of organizing and categorizing a project around a work breakdown structure is not only of great value for increasing the chances of winning the project, but it’s also what hopefully ensures it’s a financially successful project. Once awarded, the estimate and its data are more often than not filed away and rarely, if ever, repurposed or reviewed. The practice of data analytics recommends the opposite approach, which is to collect the estimate data by exporting the estimate into a spreadsheet format that can then be easily managed, reconfigured and shared via reports with members of the project team and management. This first instance of the project’s data set allows your firm to establish a baseline from which all other data will be compared against for the sole purpose of monitoring processes, costs, productivity and schedules through the entire lifecycle of the project.

2. Create a company standard Excel project template

One of the major goals in establishing data analytics should be to make the process of collecting and managing data as automated as possible. Exporting the data as raw Excel data and building it into a standard company project template is the best approach. The template contains a number of shared cells within multiple worksheets that when one cell is populated, it automatically is carried forward into other references contained within other reports and worksheets. In Figure 1, the screenshot highlights a standard template containing a variety of reports, graphs and tables that reference and move data between them to represent at any one point in time the current status, including what has been completed and what portions of the project remain. Using the simple Excel cell equation, “=+Sheet1!A1” provides a user a mechanism to, in this case, select data in cell “A1” from worksheet “sheet 1” and pass it to worksheet “sheet 2” in whatever cell the user wants to place that specific data. It makes for an easy and efficient way to create reports and graphs throughout the project template.

3. Sharing the project data from estimating to project management, and creating the project manager estimate

The practice of transitioning the project from the firm’s estimating department to project management typically includes a formal handover or turnover meeting, where the estimator passes and shares the estimate data with the project manager, site supervisor and other department professionals. From a data analytics perspective, this is an excellent opportunity to establish the baseline by saving the estimate in a project data file and establish a second estimate in coordination with the other critical project team members, such as the field supervisor.

This is called the project manager estimate. If it’s not close to the original, a group meets to pull the project apart and understand exactly where the discrepancies exist. The team and supervisor continue to review the data until they’re comfortable with the numbers. This activity is an excellent exercise because it provides invaluable insight over the upcoming project, allowing the project manager to fully understand critical aspects of the job. As part of this process, a number of critical decisions are made, such as which parts of the job are to be prefabricated or whether larger ductwork fabrication in the shop will increase potential savings. The most important objective to the process, however, is to identify opportunities for where and how to save field labor.

The codes used in tables No. 1 and No. 2 are meant to communicate and to connect the estimate to manufacturing and through to site installation. The focus throughout the data collection and management process is on effectively managing labor productivity and the percentage of the project that is complete compared with the estimate projections. The estimate divides the project into small, quantifiable collections of work with appropriately assigned codes. The process is successful when everything is planned out in detail, and every item is assigned a job and cost code. When site work begins, the benefits of job and cost codes are realized as a relevant way to measure field productivity, percent complete and so on. Being able to see well in advance that some aspect of the job is not on schedule provides teams a variety of options for countering its impact on the overall financial success of the project. 

Using a data-driven, analytical approach, weekly project reports are easy to create and identify each cost code on the job — including estimated hours and estimated dollars — highlighting used hours and dollars and percent complete. The percent-complete number helps firms determine whether the project is on the right or wrong track for a particular portion of a project. The goal is to identify early if a situation isn’t going in the right direction. When a project is heading toward trouble, firms who have learned to gather and use project data, and data analytics can intervene right away, sending the superintendent to the job site to determine what steps should be taken. The reports include estimates against real hours, as well as feet per day, pounds per day and pounds per hour by type of installation or fabrication.

Sort codes for measuring fabrication efficiency 

Another data analytical tool used by some of the top-tier fabricators is one that incorporates “sort codes.” Sort codes, which refer to specific manufactured parts, are a measurement technique to monitor the shop’s labor and production efficiency. Individual parts are flagged randomly at the beginning of their run. As the part moves through fabrication, timed data is collected and stored. The operations manager and the shop manager meet weekly to review the sort code report and gauge how they are measuring up in the current week compared to past weeks, months and years. 

The sheet metal works shop environment is constantly in flux. The data has to be measured against assumptions of what’s going on in the shop that week. Using sort codes as a data analytical measurement tool, management can see issues arising fairly quickly and can address them immediately. 

By actively managing its data, firms can be in a better position to stay in front of installation variables, such as slipping schedules or site-availability issues because the issues — available via data — can identify them much earlier in the process. For example, if the on-site installation crew were finishing up the first floor of a 20-story building and had only grilles to finish when the project hit the hours projected, the company would have confidence moving to the next floor. On the other hand, if more work was still needed and the project is already 10 percent over budget, all of the options would need to be explored to minimize any long-term project impact as soon as possible. 

Questions should be asked. Why? What happened? Was there something different and unexpected on the first floor that won’t occur on the second? How do we make sure we don’t slip each floor? 

Having constant access to past and current data and using data analytics helps answer questions like these and provides insights for making better business decisions.