Optimizing the Automotive Special Order Parts (SOP) supply chain using Conversational AI

More Parts, More Problems

The Auto Dealership’s Approach to SOP

With a 72% gross revenue margin, special order parts (SOP) is the most profitable department in a dealership. But on the flip side of the equation are the parts managers who are tasked with keeping special parts inventory to a minimum. Their goal is to reduce parts obsolescence so that it does not affect gross margins. As a result, many dealerships scramble to order special parts at the last moment to satisfy customers requests.

Versus the Auto Manufacturer’s Approach

As automakers offer more options and technology to customers, the number of part numbers has grown. Case in point: in 2011, Ford offered three choices of batteries for its F-series trucks. Today, that number has grown to six choices. The wide array of options makes special parts ordering more complex. SOP that generally are not needed in large volumes fall outside recommended stock. Some automakers permit returns of these parts only through prescribed allowances or reimbursements for scrappage.

A Cumbersome Process

Parts managers were required to call the customer service desk and wait in a queue before being connected to an agent. The parts manager would then provide identifiable information such as the name, dealer number, part number, and VIN to check parts availability. Average handle time hovered around 10 minutes on normal days and longer on busy days. A dealer had to understand the automaker’s ordering protocols to navigate parts availability and finalize the ordering process online. The frustration with long wait times during recalls, error in part number entry, and antiquated process that combined a hybrid of call center and online made it ripe for self-service automation.

The Challenge

Managing the SOP process is incredibly difficult for dealerships. Dealer frustration increases during auto recalls and insurance-driven replenishments, when timely order management and tracking causes tension between automakers and their dealers. Changing the existing way of doing business is paramount, with both parties expressing the need to:

Make the SOP ordering process easier and more personable

• Improve order accuracy and turnaround.
• Reduce the influx of angry calls and emails.
• Transforming the SOP Process Using Conversational AI
• Streamlining the SOP process with Avaamo

A conversational AI-driven virtual assistant was developed to provide the dealer with a seamless experience for ordering parts and checking availability, with intuitive conversational navigation. With Avaamo, the dealer uses a single natural language interface that handles parts order creation and availability. Since there is no need to navigate through voice and online separately, the SOP process is simple and straightforward.

Personalizing the User Experience

The Avaamo virtual assistant provides “persistence,” by immediately identifying a dealer’s previous interactions and uses disambiguation queries to ensure accuracy and order context. In addition, the virtual assistant has pre-trained language models to understand industry acronyms, accents, and even parts-order process domain syntax, to understand and handle orders like a human with extensive experience in the auto industry.

Backend Integration with SAP

Avaamo’s backend integration with SAP enables straight through processing of initiating, verifying, and confirming a special order part in a single conversational session — no need for data entry, forms, or 32-number part entries. This reduced the average handle time from 10 minutes to less than 1 minute. As a secondary use case, the virtual assistant proactively informs the user about the SAP system’s scheduled downtime, whenever applicable.

Results

Benefits with Avaamo

In the few short months since going live, the system has successfully executed 8000+ orders a month and handled over 2 million user queries. Avaamo’s machine learning and pre-built automotive domain models continue to ensure 99% accuracy in order execution and query management, exceeding human accuracy.

While conversational AI is generally perceived to be an effective tool for customer service, this forward-thinking automaker used the same conceptual framework to create a better experience for their “customer” — in this case, automotive dealerships. The Avaamo solution was successful in creating a buying experiences that meet the needs of more mature internal buyers, underpinned by a seamless, straight through transactional processing.