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Apr 21 2014

Using Big Data Analytics to Minimize Your Supply Chain Risk

Today’s global supply chains face a tremendous amount of risk. From unreliable transportation, volatile costs in supplies, manufacturing/quality failures, to unpredictable events like natural disasters, it’s incredibly difficult to gain the insights needed to reduce your risk.

According to the MIT Forum for Supply Chain and PwC Global Supply Chain and Risk Management Survey more than 60% of the companies surveyed said their performance indicators had dropped by 3% or more because of supply chain disruptions.

Even with access to the appropriate data how do you determine; risky investments;  unsafe working conditions;  job dissatisfaction;  counterfeit products; or a catastrophic weather phenomena? These are all risks that can have a ripple effect and can impact resiliency, sustainability, and performance of vendors throughout the world. However, it is becoming increasingly difficult to determine which vendors have these risks, and which vendors are more proactive in managing their global supply chains.

As the supply chain becomes more complicated and less visible, we have to ask, how do I, as a procurement officer, analyst, or decision maker, determine the riskiest vendors?

Big data analytics for supply chains

Ikanow partners with organizations full of brilliant people that are taking these problems head on. When it comes to supply chain risk (among other things), nContext™ is one of the best.

Combining Ikanow’s platform with nContext’s supply chain expertise and global reach, we can provide clients with deeper insights allowing them to make more informed decisions, reducing their risk.

Technically speaking, systems that exist today can achieve analytical results at a speed far greater than manual “hunting and gathering”.  With the nContext™ and Ikanow solution, more time is put on the actual analysis, and more insights are found due to our coordinate, holistic approach to supply chain risk characterization. This specific solution pulls in numerous data sets (open source and paid, structured or unstructured) into the analytical engine, displaying significance-rated data points in our Analyst Graphical User Interface (GUI).  This enables quick discovery of vulnerabilities, trends, keywords, and deep-dive analysis.  Such information has enabled us to find competitors, new investments, cyber breach activity, questionable leaders, import/export concerns, compliance issues, and recently awarded contracts.

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Supply Chain Risk Characterization Use Cases

nContext’s initial work in this sector focused on consumer-based companies, such as major grocery chains, in which they assessed the potential influence of Forced Labor in their entire supply network (2,000+ suppliers).  Due to a California mandate, companies that had a certain revenue in California each year, had to show due diligence in mitigating the potential risk of Forced Labor in their supply networks.  To do this, nContext™ identified open source data sets to plot locations of factories and distribution sites, understand related legal proceedings, determine employee satisfaction, and identify industry awards and accolades (just to name a few data inputs) for the client’s Tier 1 through Tier 7 suppliers.  They then modified their risk model to meet their requirements, ran the data, and produced a numerical score for each supplier.  With this information, the grocery chain was able to determine its riskiest suppliers, taking actions such as audits and educational trainings.

More recently, nContext’s supply chain risk characterization efforts have focused on the analysis and assessment of risk for potential vendors who would like to do business with the US Government. The objective is to assist procurement departments in acquiring software for their companies that is low in counterfeiting risk and high in sustainability. Key to this analysis is understanding the complexity and any potential vulnerabilities in the decentralized supply networks.

The impact of big data analytics solutions on supply chains

Our results are driven by the customer’s objectives.  Whether the customer’s problem is a lack of being able to make a decision about a new vendor or difficulty in determining which vendors lack internal supply chain management, the nContext™ and Ikanow solution, provides actionable analysis for making those decisions.

For the grocery chain, nContext™ took a sample set of 2,000 suppliers, and determined the chain’s 150 riskiest suppliers. Previously, the company had no mechanism or process for evaluating their suppliers, so with this solution the chain was not only able to be in compliance with the California law, but to also gain visibility into their supply network.

For the US Government, they compressed the analytical timeline for characterizing vendor risk from 30 days to 5 days. Today, as we have developed our analytical engine and data output process, we can do this in 2-3 days (variance is due to the size of the vendor, complexity of the supply chain, and type of product).  With this solution, the backlog of potential vendors can be eliminated for the US Government.

The demand for vendor risk characterization will continue to grow, especially with new legislation regarding counterfeit prevention being proposed in the US Government.  The nContext™ and Ikanow team looks forward to being a part of it.

If you would like to learn more check out Learning Library or contact us to Schedule a Demo:

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