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Important KPIs for Your HD Collision Business

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Important KPIs for Heavy Duty Collision
A full-on breakdown of the most common—and critical—HDT KPIs.

SHOP STATS: Bob Johnson's Body Shop  Location: Cayce, S.C.  Operator: Dean Hancock  Average Monthly Car Count: 125  Staff Size:  Shop Size: 41,499 sq ft; Annual Revenue;$2.8 million 

For Dean Hancock, his journey to owner of Bob Johnson’s Body Shop in Cayce, S.C., could be described as the “sucker who bought it” in 2006.

He was a paint rep for Sherwin-Williams—a post he’d held for nearly two decades—when Bob Johnson approached him to buy the shop in 2004. Hancock was a collision repair veteran, having started in body shops as a high schooler. That typical career progression from floor-sweeper to painter’s helper to full-on painter to vendor helped Hancock understand what made shops successful.

That’s why he felt like a sucker when he strongly considered Johnson’s offer.

Roughly 90 percent of the shop’s work mix came from heavy duty, a segment of the industry Hancock knew just enough about to be hesitant. He agreed to a two-year transition and trial period, working as the defacto operator of the business. And he came to an important conclusion: Just like a traditional collision repair business, success with heavy-duty truck operations come down to the numbers—KPIs that act as the lifeblood of the business.

The reason many HDT operations struggle, Hancock says, is because their operators struggle to understand those KPIs, their benchmarks and how they differ from traditional collision repair. Hancock, who coaches HDT businesses for Mike Anderson’s CollisionAdvice, helped FenderBender break down the six of the most important numbers you need to improve your business.

 

Sales Per Productive Employee/Non-Productive Employee

What it is: Very literal in name, this KPI analyzes sales per employee, broken up into productive (an employee who “creates sales,” like a technician) and non-productive (someone whose tasks cannot be billed to a customer, like admin personnel) categories.

Why it’s crucial: This number will determine whether your shop is staffed correctly. Hancock says that many shops have too many or too few people in the front office; some have issues lining up the proper number of technicians in the back. In order to calculate sales per productive employee, simply divide the total sales revenue for each month by the total number of productive employees on staff that month. The same goes for the non-productive employees. Most shops, Hancock says, do not track this number.

Benchmark: Profitability is key, Hancock says, and managing this number helps ensure that; your sales per productive/non-productive employee must align with your labor gross profit goals. Hancock gave an example of a shop that has these numbers in range: “With first-quarter gross sales of $958,643, that would be $79,887 per non-productive employee and $53,258 per productiployee.” By that math, a productive employee should produce just over $200,000 in sales each year, or nearly $17,000 per month; those numbers would be roughly $320,000 per year and nearly $27,000 per month for non-productive employees.

 

Gross Profit

What it is: Gross profit is the profit the company makes after deducting the costs associated with making and selling its products or offering its services.

Why it’s crucial: Aside from being a required number for income statements, gross profit is critical in determining whether your shop is adequately reimbursed for work and correctly compensates employees.

Benchmark: For flat-rate shops, Hancock says 65–70 percent gross profit on labor is the standard, and 25–30 percent on parts.

 

Overall Efficiency

What it is: The staff and the individual employee productivity on the job over long periods of times and short periods of times, such as a month.

Why it’s crucial: Defined as the time the technician worked in a day versus the hours in a day available to work, efficiency can prove if the technicians are motivated to produce more work.

Benchmark: Roughly 150 percent on flat rate. This number can be higher for shops that have technicians working time and a half.

 

Gross Profit per Stall

What it is: This KPI is useful to find if your shop uses its square footage effectively.

Why it’s crucial: Hancock says it is best to get jobs through stalls as quickly as possible. To calculate, take your overall gross profit (in dollars) per month and divide by the number of stalls in your shop.

From there, the goal is to decrease overhead and increase efficiency. To decrease overhead, his shop, for example, takes off truck hoods and paints multiple hoods at a time in one stall instead of wasting space by bringing the whole truck into the stall.

Benchmark: $13,000 gross profit per stall. This number can tell you if you’re utilizing your stalls and can vary from a high margin to a low margin, depending on how many truck-leasing companies with which the shop works. Hancock examines his overhead per stall in order to determine if the shop is bringing in enough vehicles to cover that number.

 

Estimator Conversion Rate

What it is: The percentage of work each estimator sold per month.

Why it’s crucial: This rate is determined by how many estimates were written versus how many estimates were actually closed as jobs. Hancock uses a software called ProfitNet to calculate estimator conversion rate. The software also enables him to change paint codes on the repair and keep the customer updated on where the vehicle is in the repair process.

Benchmark: 80–95 percent

 

Key-to-Key Cycle Time

What it is: The time from when a job begins to the time it ends.

Why it’s crucial: Most shops track cycle time from the time keys are exchanged hands with the technician to the time they’re given back to the customer. In the light-vehicle industry, Hancock experienced an average cycle time of roughly 3–5 days. In the heavy-duty truck industry, that cycle time is between 8–10 days because it might take roughly five days for parts to arrive at the truck shop. And the estimating software does not contain parts prices.

Benchmark: 8–10 days

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