clive boulton's net-net

Hadoop’s the new MRP: Where’s my Golden Pony?

  • MRP = supply/demand (push & promote)
  • Hadoop = demand/supply (position & pull)
  • Respect = personal vendor relationships?    

Many of us expert in industrial recommender systems matching “S’pply n D’mand” missed the change from shortages of supply to shortages of demand. VCs didn’t miss this backing anything mining demand inferences, particularly Hadoop based leaving new sources of global supply from global suppliers by companies like Apple. In doing so the open source Web 2.0 hadoop business architecture was created to mine in closed "big data" sources for your demand: Results soldto advertisers.  

Private Equity funds didn’t miss this shift from supply to demand, taking nearly every public ERP company operating at scale private (virtually all bar SAP and Oracle). At the core of ERP is MRP (material requirements planning) which business use to match supply/demand. With shortage of supply behaves as recommender system for exceptions. With abundant supply, quality demand becomes the imperative. 

The VC’s backing quality demand brought us CRM, Search, Social and Ecommerce. Greg Gottlesman, VC at Madrona believes that Amazon out of all is in the process of becoming the world’s most valuable company.

Amazon’s businesses create the best quality demand, a credit card cleared order, not social engagement inferences mined from Facebook via Hadoop et al.

What can upset this Golden Pony, shortage of supply?

Shortage of water, oil and other precious raw materials can and will disrupt supply and demand. For example McDonalds and Nike are already investing in smaller suppliers and alternative sources to add diversity and reduce risks in regions of the world with sustainability issues like Indonesia and drought regions.

Nike even went as far as hiring Ward Cunningham to help with prototypes for working in the emerging era of sustainability. Alas, currently over investment in demand systems feels inversely proportional to under investment in supply systems technologies. 

For example the core algorithm for MRP, around since 1970s has been revised for the demand driven era by Carol Patk & Chad Smith in Orlicky’s third ‘DDMRP’ edition — but not web 2.0 social technology updated. Internally we can surmise Amazon has built Hadoop MRP.

The DDMRP authors are superb APICs, Goldratt, TOC, Lean, operations experts not computer scientists — Carlos Guestrin, Ed Chi — versed in machine learning, or social computing technologies.

What can upset this Golden Pony, shortage of demand?

Another possibility, the search for quality demand turns away from big data to personal data. Ward’s - Smallest Federated Wiki, Brad’s - Camlistore, and Doc Searls - project VRM all point in this direction.

The Respect Network is helping bring personal quality demand via emerging personal and enterprise datastores. These private by default, shared in a granular fashion, personal clouds were part of Doc’s keynote for the respectnetwork.com brings trust into perspective. 

Other technologist’s such as Adrian Hall have startups such as deconstructed.io focused on user data flows across devices without mining for targeting.

Net-net here, I need to find an enterprise startup crazy enough to explore updating MRP here, matching demand/supply via VRM/Respect.

Jordan Ritter of Napster fame has set up Ivy Softworks for Data Psychics to incubate joining up the dots in this space. Till then, inspired by a16z’ Ben Horowitz, listening to the Egosouls-Nirvana: Produced by S’pply ‘N’ D’mand: Think I’m going crazy!

Haiku:

Respect Networks…

Why aren’t they famous yet?

Only time will tell.

Soundcloud does terrific experience design: You’ve signed out. Now go mobile!

Soundcloud does terrific experience design: You’ve signed out. Now go mobile!

Personalization appeals to a Western, egocentric belief in individualism. Yet it is based on the generalizing statistical distributions and normalized curves methods used to classify and categorize large populations. Personalization purports to be uniquely meaningful, yet it alienates us in its mass application.