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SignalDemand: Dealing with Supply- and Demand-side Pricing Matters - ...
SignalDemand: Dealing with Supply- and Demand-side Pricing Matters - Part 1
January 5 2009
's previous articles and blog posts about
pricing management and optimization vendors like
, the main focus was on finished goods (including spare parts). Whether these final products are sold at retail shelves to consumers or dealt directly between trading partners, their proper pricing is meant to create demand and profitability for the seller. In other words, the idea is to harness science to understand products’ baseline demand,
, and the impact of pricing actions based on demand sensing insights.
Recently, however, I had a chance to meet with an interesting pricing optimization startup vendor whose aim is to help upstream manufacturers and suppliers understand how to better translate commodity (e.g., corn, soy, oil, gas, electricity, metals, polypropylene) prices into viable final product mixes. For example, how can a meat packer make better downstream supply chain decisions on its choice of cuts (e.g., a beef carcass as a source material can yield more than one thousand various meat cuts as finished products) and ensure that they are priced best on the retail shelf at the end of a highly perishable supply chain?
This technology has been particularly instrumental for major food cooperations and processes with reverse
requirements. Such exemplar places where a source ingredient gets broken down into many pieces would be slaughterhouses. Reverse BOMs are also known as
elsewhere in those industries that have to manage co-products and by-products. For instance,
printed circuit board (PCB) makers
can have one breeder item (a circuit board) that produces multiple co-products (individual chips), while
a metal center
will start with a standard steel sheet and produce a number of cuts to certain sizes (and off-cuts or remainders).
This brings me to
which was founded by Mike Neal and Dr. Hau Lee of
in 2004. The idea behind the San Francisco, California (US)-based company is to apply maths and science to the problem of price and margin optimization software for large-scale manufacturers.
In 1999, Mike Neal and Dr. Hau Lee co-founded
(NASDAQ: DMAN), a renowned public provider of demand and pricing management software to the retail sector. Soon after, they saw an opportunity to apply optimization science and math to help companies further upstream the supply chain (i.e., consumer goods manufacturers) make better pricing, product mix, sourcing, and production decisions. SignalDemand’s CEO Mike Neal was previously an executive at
(a former supply chain optimization software that is now part of
SignalDemand is privately held and raised a US $20 million round of funding in April 2008, led by new investor
. Existing investors
Hummer Winblad Venture Partners
General Catalyst Partners
also participated in that round. Despite the tough investment climate, SignalDemand raised the round to spearhead its expansion into new vertical and geographic markets and further enhance its
on-demand software as a service (SaaS)
technology offering. To that end, SignalDemand opened its European headquarters in London (UK) in July 2008.
The company’s mission statement of sorts is to be the leading optimization software for manufacturers worldwide. The
solution is supposed to determine the best price points for manufacturers on a daily basis, identify production quantities that achieve their margin goals, and define the most profitable product mix. And all of these recommendations should take place across products, channels, and customers.
SignalDemand’s Target Market
SignalDemand's customers are some of the world's largest process manufacturers (especially the
) who typically use its application to make price and product mix decisions on billions of dollars of revenue. Referenced customers are showcased on the vendor’s website and include
Cargill Meat Solutions
(a division of
SignalDemand's solution is well suited to address the needs of commodities-based manufacturers, whether they leverage reverse-BOMs or
more common, A-shaped BOMs (where many entry components make one final product
). These companies have to deal with billions of
stock-keeping units (SKUs)
and make even more billions of pricing decisions annually. While the individual data are quite confidential, SignaDemand claims to have added about US$100 million in profits to its about 20 large customers’ bottom lines, with margin increases between 20 and 40 percent and revenue growth between two and five percent.
When it comes to sourcing commodities for manufacturing, price volatility is a given, and financial risk increases the further out in time one goes. A lot of external factors influence supply, such as the price of feed, oil, gas, weather conditions/natural disasters, diseases, geopolitical landscape, and required quality of end product. Many of these are already factored into futures prices on global
But even if the commodity price can somehow be determined or projected, such as for beef or pork carcasses or
, it is much more difficult for manufacturers to properly price and determine ideal volumes and product mixes of the subsequent finished goods. That has traditionally been an art and intuition rather than an exact science. Additionally, consumers demand product variety that will meet their widespread preferences. For beef, as an example, that means an assortment of meat cuts that will eventually fill store shelves and be purchased by consumers of different buying powers and tastes.
Given the volatility present in the many inputs for food manufacturers, SignalDemand has been particularly successful in developing a marquis customer list for this space. Other notable customers include
National Frozen Foods Corporation
Rich Products Corporation
SignalDemand's Value Proposition
As it is still the case with many pricing vendors (in an emerging software category), to a large degree, the biggest competitor SignalDemand encounters is
. Many of even the most sophisticated manufacturers still use pesky (if not even dangerous) spreadsheets to make critical decisions on price, product mix, and production runs.
SignalDemand has encountered competitors apart from
, but the majority of these opportunists are not truly competitive. Some companies will perhaps use in-house PhD’s or math wizards with special algorithmic models. Still, this traditional approach relies on non-distilled information and intuition (a voodoo of sort, rather than relying on science-based software) and focuses on raw data instead of on the pricing strategy.
Humans (however talented they might be) can also effectively consider only 10 or so variables, and conduct roughly thousand of scenarios within cycle times measured in days. In contrast SignalDemand's is capable of crunching over 10,000 variables, billions of scenarios, and cycle times measured in seconds.
Part 2 of this blog series will further explain SignalDemand’s pricing science. Your views, comments, opinions, etc. about the above-mentioned pricing conundrum for manufacturers and about the software category per se are welcome in the meantime.
We would also be interested in hearing about your experiences with this nascent software category (if you are an existing user) or your general interest in evaluating pricing solutions as prospective customers.
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