Characterizing Risk in Your Supply Chain

March 2014 Open Analytics Summit

Chris & Tom- Can you scale/make this fit within the box or customize the blue to

cover the white above and below?

Disruption Factors

Case Study

General background of HT in Agriculture

In the US, Agriculture is the 7th most common labor

trafficking industry according to the Polaris Project.

Key Challenges

1.  Supplier non-responses

2.  High over-turn of small suppliers

3.  Understanding Tier 1 Supplier landscape does not dive deep enough to capture overall risk.

4.  Lack of information on Tier 2, Tier 3 suppliers, etc.

5.  Initial supplier survey did not solicit enough quantitative data

Understanding Global Risk at a Country, State and

individual item level.

Risk Characterization of All Responses

● Identification of potential high risk vendors and areas to focus on for risk mitigation and further analysis

● Establishment of a methodology which can be used to process all future Vendor surveys

● Identification of enhancements to survey questions

Risk Characterization of All Responses

Statistical Distribution of Risk

Enhanced Risk Characterization

● Enhanced risk characterization going beyond Tier 1 resulting in more reliable information

● Establishment of a methodology which can be used to process all future & non-responsive Vendor surveys requiring less data from vendors

● Interactive map which could in the future be used by analysts and on web

Opportunities

1.  Wealth of publicly available data to draw from (NGOs, Governmental and inter-governmental data)

2.  Paid data subscription services can greatly enhance scope of data

3.  Best practice in remediation available

4.  Industry news subscriptions for sector

5.  Social media monitoring and alerting