We continue to hear and read a great deal on the topic of parts procurement, thanks in large part to State Farm and PartsTrader. I want to raise a few points for your consideration regarding how we SHOULD be ordering our parts, particularly as MSOs; however, I am not interested in taking a position here on PartsTrader or any other particular system.
I continue to hear how complicated the situation is. Frankly, it shouldn’t be. Let’s consider our parts procurement in terms of the timeline of the average repair. After we perform a 100 percent teardown and blueprint the job, the next step is ordering parts. (Assuming in a typical DRP scenario we’ve already obtained customer and insurance authorization to proceed.) To maintain optimum workflow and cycle time, we have a great need to have the parts delivered ASAP. As part of the blueprint process, our estimator is to consider their parts options based on cost, condition and availability. An overriding key component here is PREDICTABILITY. Isn’t that a lot of what our goal of great cycle times is about? The more predictable the repair, the better we can be at scheduling, assigning tasks, maximizing shop flow and creating accurate completion dates. It serves not only the shop, but the insurer and consumer. Imagine if we could eliminate even most of our unexpected delays. Insurers could better predict rental costs, repair costs and customer satisfaction. Customers could plan for a predicted completion time and schedule accordingly, always reporting that the job was done on time.
OE parts offer predictability in cost and usually delivery time; however, our industry can do a better job through the repairer, dealer and manufacturer to refine the process. Some MSOs have negotiated guaranteed delivery times, such as a maximum of two days for each total order. Aftermarket parts can offer similar predictability. Our salvage parts industry needs work to offer predictability in terms of condition and sometimes delivery times. When we introduce price variables to the repair process equation, we diminish predictability. It is obviously understandable that we seek lower costs, but is the time to negotiate or shop for price when we have a job torn down occupying valuable shop space, a customer anxiously waiting for their repaired vehicle, a technician prepared to perform the repair, an insurer anxious to have the repair completed, and often someone paying for a rental car? You may argue that an automated exercise in shopping price may only take a few hours or less. However, keep in mind that the longer a shop has to wait for pricing and then ultimately parts arrival, the more likely it is that the vehicle must be pulled out of the process, usually going outside. Then it must not only wait for the parts, but also for the next available time slot for the actual repair. That can easily take days.
I propose that as an industry, we consider shopping for price with vendors in a broad sense so that we have a predictable model that does NOT offer ANY delays in the time we actually have the vehicle in our possession. Shopping on the spur of the moment may or may not be advantageous towards best pricing, depending upon market conditions. It can also diminish the value of a significant close working relationship with vendors that provide motivation for higher performance. I also suggest that we look at empowering regional salvage vendors to establish relationships with other salvage vendors outside of their geographic area, so that they can identify best performers and offer the most predictability in terms of price and part condition. A high performing salvage vendor will accumulate more data and typically possess greater ability to evaluate other salvage vendors. As a repairer, I do not want to be in the role of evaluating every potential salvage vendor out there. Frankly, it is not my core competence and does nothing to expedite the individual repair. It is not very practical to take the time to shop part availability, pricing and salvage vendor performance records for nearly every repair.
As we refine our current automated systems and develop new ones, I highly recommend some new thinking that prioritizes predictability.