Case study 1 - Goldcorp

Seeker: GoldCorp (http://www.goldcorp.com)

Solvers: 1,400 individuals and groups from approx 50 countries

Challenge & Solution communication mode: Internet

Primary play: Wisdom of crowds

Industry: Extraction, Mining, Metals

Year: 2002

Country: Canada (Red Lake, Ontario)

Challenge: Goldcorp was in financial trouble, the cost of searching for gold sources within the mine was going UP, while the success of find DOWN. CEO Rob McEwen knew that they have more gold in their mines, but he and his men did not know where. Rob had a challenge in front of him to bring Goldcorp out of this crisis. (Though mines own huge amount of land, they may not know each and every place where gold resides)

Trigger: McEwen in a technology forum heard how Linux is developed and maintained, and he started thinking how he can leverage similar phenomenon for his ailing company.

Solution: Mr. McEwen decided to run a contest that provide access to the geological data of the company's Moribund Red Lake Mine in Northern Ontario to the solvers to have a crack at finding its richest gold deposits. The process, made possible by the Internet and advanced computer software, was novel in scientific circles not to mention unheard of in the tight-lipped mining industry.

The Goldcorp challenge required his 14 geologists to package and disseminate 45 years of proprietary data on the mine and its surroundings and throw away their long-held preconceptions. With prizes totaling $500,000 (U.S.), contestants were asked to suggest the best plan for finding gold deposits on the 20,200-hectare property.

The challenge received phenomenal response, there were groups which collaborated offline and submitted the group entry, many went solo. These groups and individuals used the data made available to them and applied their knowledge to pin point where the gold deposits could be. Responses came from 1,400 interested parties from 50 countries. A total of 25 semi-finalists and then three finalists submitted sophisticated proposals that identified 110 exploration targets 50 per cent of which were unknown to the mine's geologists and 80 per cent of which have since netted significant deposits.

Rob’s idea paid off, making what is today the world's richest gold mine.

Outcome / Benefits:

1) 110 sites identified by the solvers, of which 50% were new, 80% of which produced gold.

2) 8 million ounces found.

3) Company value up from $100 million to $9 billion. (Pre contest and post contest)

Extent of openness: High (The challenge was open to the entire world, knowledge of English language and access to internet were the only prerequisites. Though not everybody who could have contributed, contributed, but the reach was fairly good)

Diversity of solvers: Medium (The inputs came from solvers with various background, mostly from the people who understood geological or location based data interpretations and calculations)

Learning’s:

1) Secrecy could be the rule number one in your books, but a systematic disclosure of your secret data can benefit you immensely.

2) ‘Reward’ may not be a number one motivational factor, but it holds itself at par with the motivation to be creative.

3) Not all the intelligence which has answers for your challenges walk into your office everyday morning.

Quote(s):

Gold mining is an old industry, a tired industry. The pace of change is glacial. Traditionally, mining companies have worried about how strong your back is, not how big your brain is. We wanted to do something that no one in the industry had done, to tap into the intellectual capital of the whole world (Taylor and LaBarre2006:63)

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Compiler(s), Contributor(s):

Jayesh Badani (http://blog.ideaken.com/)

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References / Information sources

1) http://www.prnewswire.co.uk/cgi/news/release?id=36171

2) http://www.theglobeandmail.com/servlet/story/RTGAM.20070424.wgtrinnoprizeside24/BNStory/GlobeTQ/home

3) http://www.microengagement.com/index.php?option=com_content&view=article&id=53:goldcorp-challenge&catid=25:examples&Itemid=34

4) Measure What Matters to Customers: Using Key Predictive Indicators by Ronald J. Baker

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