Summary: Part 1 of this blog series described the conundrum that commodity-based manufacturers encounter when it comes to determining the best price, production mix, and volumes. It also introduced SignalDemand, Inc., which applies math and science to the problem of price and margin optimization software for large-scale manufacturers.

SignalDemand stands alone as the only provider of price management and optimization software that takes into account the key supply and production constraints impacting manufacturers. In other words, its application is using pricing as a demand and supply matching mechanism for manufacturers of consumer goods.

Namely, on the supply (upstream) side, commodity-focused hedge funds have long leveraged supply optimization software, while on the demand (downstream) side, wholesale distributors and retailers have for some time leveraged demand management and optimization software. Conversely, manufacturers have for too long been left in the middle shooting in the dark when it comes to concurrent pricing and demand management.

Pricing Science of Matching Supply and Demand

Other price optimization solutions really only consider the demand side of the pricing equation, and these results are insufficient for manufacturers to make decisions when they need information on capacity and production constraints as well.  SignalDemand's hand-picked team of scientists and mathematicians from prestigious universities have built a pricing science based on eight pending patents.

This sophisticated science drives the recommendations provided by the software application. When making decisions on margins, the idea here is to account for all major profit drivers, such as to

• align strategic business objectives with pricing decisions;
• understand demand drivers to forecast future sales;
• account for fluctuating costs;
• on the supply side, account for asset utilization, available capacity, and inventory situation; and
• determine the most profitable product mix for a given demand.

Accounting for all the above factors helps with much more complete, consistent, and actionable information to better anticipate future costs, forecast demand, identify poorly performing products or customers, and explore projections in the context of historical sales.

View article at Technology Evaluation