The document summarizes a panel discussion on supplier risk management. Kurt Albertson of The Hackett Group, Julius Rusnak of Semantic Visions, and Rob Conti of First Niagara Bank discussed their organizations' approaches to identifying, monitoring, and mitigating supplier risks. Semantic Visions uses semantic analysis of big data to detect various supplier risk types from multiple global sources. First Niagara Bank implemented Ariba solutions to improve supplier performance monitoring, contracting, and spend analysis capabilities for managing regulatory risk. The panelists encouraged organizations to take a proactive, integrated approach to supplier risk management.
8. #AribaLIVE @ariba
Protecting supply continuity, ensuring
compliance and supplier financial stability are
the top drivers of supply risk mgmt programs
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Note the Low ranking of price fluctuations but the relative high exposure we identify later on. Define what we mean by continuity of supply.
Fiscal crises in key economies – 2.59
Unexpected regulatory changes, non-compliance – 2.35
Large-scale cyber-attacks – 2.26
Environmental disasters (e.g., storms, floods, fires, power outages) – 2.13
Supplier competitive events (acquisitions, mergers) – 2.04
Purchase price escalation (including currency/inflation) effects – 1.93
Price and supply volatility (sudden price and capacity changes) – 1.91
Unfavorable currency movements – 1.87
Loss/theft of sensitive/private data – 1.85
Data loss/theft (i.e., the unauthorized modification, deletion, or disclosure of confidential and/or personal information) – 1.75
Note the relatively low ranking of supplier stakeholder survey results. That’s a red flag. Also note how despite the hype around being able to decipher new feeds to sort through the chatter and see what events might impact a company, this is clearly not being done effectively yet.
Top used today: Office, financial risk providers
Top used future: Same
Biggest delta: show on the left.
Talk about why Excel is not the right tool for the job.
Recently, many companies started to work in the field of semantic analysis.
We’ve been working on it for over 10 years.
When two do the same, it is never the same.
The triangle synergy results into an unmatched scope, speed and precision.
Not easy to recognize the knowledge from the noiseNot everyone realizes that issues are discussed a lot at conferences. Or that there could be a report that nothing new happened during the last 30 days. Or they can reference an old case. It is always a real thing but not a new alert not a new threat.
It's easy to say "everyone knows about it". It's difficult to be the first one to know a to report relevantly.
Thinking back, we all know what happened. But to detect relevant events from the big data of events is a challenge.
Especially because one cannot just search. To search, you have to know what you're looking for. We detect.