Calculating pricing optimization
Texas A&M has pulled together a consortium of distributors, manufacturers and service providers to create pricing solutions
By F. Barry Lawrence, Ph.D., Texas A&M University -- Industrial Distribution, 8/1/2006 6:00:00 AM
Pricing is, without question, the hottest topic in the distribution community lately. 
Distributors have seen the demand for services explode with customers requiring local inventory, vendor-managed inventory (VMI), integrated supply (I/S), more technical support, advanced sourcing solutions, repair services, purchasing management, e-commerce solutions, etc.; the list seems endless. Over the years, profit margin has become a hot topic, and distributors are being pressured to reduce it for customers, but try to grow it for their own businesses.
(The distributors' first reaction was to charge for services but that fell through quickly, in most cases. Customers were refusing to pay for existing services, and, when distributors create new services, they only get to charge until some distributor breaks ranks and offers the service for free. On the product side, the customers expected products to be cheaper, delivered faster and better quality. The net result has been a compression of distributor margins for years.)
Distributors have had to address the problem by either lowering cost to serve, the lower side of the gross profit, or increasing prices, the higher side. The cost-reduction process has been pursued aggressively, but the process has a limit. Therefore, the distribution community has been trying to find ways to address the pricing issue.
Researchers at Texas A&M's Supply Chain System Laboratory, a research institution for industrial distribution, felt it was critical to solve this problem. The lab is supported through funded research projects conducted with many distributors and through the generosity of donors such as Intuit Eclipse, IBM, and Oracle. It has created a consortium of distributors, manufacturers, and technology providers like Intuit Eclipse and Oracle to fund a massive study to create pricing solutions.
The lab's solution will be spreadsheet tools for consortium distributor and manufacturer members, that enable pricing within the technology providers on their systems, and a program offered by Texas A&M to educate the distribution community.
Early results were focused on first creating a mathematical representation of the pricing environment. If math isn't applied, no system can be built, since math is the language of computers (not to mention profitability). The consortium members worked in focus sessions and provided data to build the model. The key issues of customer types, product status, and service were addressed in the development of parameters. A model was first constructed to stratify customers.
That model segments customers as: those that do little business for only a short time and, either through price pressure or high service requirements, offer no profitability (most salespeople have no problem identifying these customers and getting rid of them); those that come in for a quick buy (usually when a competitor is out of stock), and have a negative impact on core customers, as inventory is unavailable when it is needed to sustain that business; those that buy in good volume, on a regular basis, for a long time, and either through decreased pricing pressure or through using our standard delivery processes create high profitability (without these customers, no business can survive, and they should be protected at all times); and those that access services tremendously and/or beat distributors up on pricing regularly (these service-drain customers are constantly on the sales force's problem list).
Each of the four dimensions described is captured by the system so the first piece of the puzzle can be assembled. The next is the history of how products have been sold to this customer type and where the best prices have been achieved. The key dimensions for this portion of the puzzle include: geography (branch/region, market level, level of competition); time (seasonality, trend-lead time); customer (rank, group/type); and item (rank, product group, substitution effects, complementary effects, kits/assemblies). Combined with the cost of goods sold (COGS), the customer stratification, and the cost to serve, the optimal price can be created.
The service variable is being built and will offer many challenges. Even if all the challenges could be overcome, however, it is unreasonable to assume that a perfect model can be assembled. First, the data needs would overwhelm many systems. Second, many firms do not collect all the necessary data. Finally, no matter how much information one collects and calculates, the sales force will still routinely face service and customer combinations that have not occurred before.
Therefore, the intent of the consortium is to first create a complex model, then simplify it to one that can be used on a desktop by the average salesperson. We will seek to identify what matters most and can be collected in the average distributor's system. In the end, the salesperson's judgment will always be necessary. The tools and training will seek to give the sales force a benchmark from which to start.
The consortium will complete its research in January 2007, and the pricing optimization programs will begin in early spring 2007.
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