One Strategy to Describe GM History in more than 11 Chapters

Leading up to a likely bankruptcy, GM Executives have had to make numerous big decisions recently about restructuring to maintain its future viability. Closing a large number of dealerships was one of the chosen strategies. In order to make smart decisions about which dealerships to close, the leaders would need two things: selection criteria aligned with the scope of the goal and a lot of data that links key performance indicators to dealership locations.

Despite the media reports of “successful” dealerships tagged for closure, the selection process is not likely as simple as just picking the bottom 40% of dealerships from a list of annual sales. Some accounts have described the targeted dealerships as “under-performing”. Under-performing is relative condition, especially in a huge organizational network implementing other significant changes that will impact dealership performance in the future, such as eliminating entire Makes (Pontiac) with multiple product lines.

Strategies like this one require careful consideration of a number of complex scenarios to minimize risk while maximizing the predictability and effectiveness. With over 6000 dealerships, it is unlikely that a comprehensive comparative review of each individual dealership can be conducted. More likely a review will be made of multiple combinations of aggregated key performance metrics like:

• sales history by make, model, and region
• forecast by make, model, region
• volume vs. mix
• up-sell/cross-sell statistics
• profitability by new/used car sales
• service labor vs. parts sales
• warrantee reimbursement
• dealership proximity concentration
• years in business - short term vs. long term performance
• customer loyalty
• average vehicle inventory
• regional demographics affecting future sales forecasts
• seasonal sales variation

*I’m not connected to the details of the actual selection process; I am supposing.

The aggregated data will be sliced and diced, repeatedly applying a great deal of analysis, criteria refinement, and simulations before lines are drawn and lists of targeted dealerships are made. The actual data specific to the selected locations are likely plugged back into the equation to solidify the estimate.

Executing a strategy like this without the right process would be a monumental task carrying high risk of missing expectations. It is necessary to have:

• strategy - clear understanding of the assumptions/expectations behind the selected strategy
• process - a process to identify the combination of complex criteria that will yield the optimum result in the face of many significant, changing, and sometimes unpredictable factors
• technology - a fast and flexible process to filter, sort, and aggregate massive amounts of demographic and performance data in any number of complex combinations and extrapolate that into specific dealership locations