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You are here: / / Risk Management / Managing Risk with Procurement, Featuring Linda Tuck Chapman

With the increasing complexity of global supply chains, one thing is on the mind of every procurement organization: third party risk. What types of risk? How can we mitigate it? Many firms implement internal and external controls for risk, but few truly strategize third party risk management in their procurement operations.

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, president of Ontala Performance Solutions and a third party risk management subject matter expert. Author of Linda believes the procurement function is vital in managing organizational risk. She advocates for transparency and consistency in the approach.

Procurement was traditionally a cost reduction function. Today, Linda believes procurement has the opportunity to do more strategic work and add value to the organization. “Companies that embrace third party risk management as an exercise in regulatory or legal compliance miss the point,” she said. “Third party risk management, done well, drives better outcomes for the business and the company. With today’s threat landscape changing so quickly, procurement has an opportunity to become a vital part of the company’s risk management team.” Linda believes that the mantra for procurement success should be to 1) drive the best value for money, 2) protect the organization from harm, and 3) improve the customer experience.

“Risk management isn’t a separate or discrete activity from procurement processes,” Linda stated. “Third party risk management is a team sport. We must rely on specific expertise provided by risk domain leaders in order to understand certain risks, to ask the right questions, and implement the right controls.” For organizations that are just beginning to explore risk management, Linda suggests careful analysis and collaboration with the business and risk experts from other functions, such as information security or fraud.

Innovation offers new opportunities, but often carries new risks. “Spend more time on the things you don’t understand, and identify risk drivers and controls that positively impact the community your organization serves.”

Below is an excerpt from her book, , published by Merrell Womens Grassbow Air Trail Running Shoe Granite/Mint 8Mh4N
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“Procurement is a Risk Control Function”

© 2018 RMA

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Managing Risk with Procurement, Featuring Linda Tuck Chapman

“As a CPO, I always felt something was missing from our processes,” she said. “For example, once a contract was signed, we no longer had any visibility. The organization does all this hard work getting the best possible contract, with tight controls, and optimal service levels in place, and then you hand it over to the business. You didn’t see the [risks] and outcomes after that.”

MathML
(5)
Figure 3

Illustration of the structure of the synthetic data with parameters: p = 3, n = 2, D = 2, N = 3, and Δ = 3.0. The differentially expressed genes are gene1 and gene3. The two different colors of points indicate the two classes of samples: “control” and “perturbed”.

The Characteristic Direction method is represented by a vector in expression space, each component of which corresponds to a gene. We interpret this vector by taking the square of each component to be a measure of the importance of the corresponding gene in the differential expression; the larger the squared component the more significant the gene. In order to determine the appropriate threshold above which to accept genes as differentially expressed we derive a null distribution for the ranks of the squared components as follows:

Generate two random sample means by drawing from the multivariate student t distribution with - 1 degrees of freedom and find their difference.

Calculate the null characteristic direction = Σ Δ

Calculate MathML and rank the components into descending order of magnitude

MathML

To compare the real distribution the null we take the ratio: MathML . The simplest and most conservative approach would be to accept into the set of differentially expressed genes all those genes for which the ratio: MathML . A less conservative method to derive the threshold from the data is to consider the inflection in the curves which can be isolated with the cumulative distributions.

Figure 4

Illustration of gene set enrichment with the characteristic direction concept. a) Similarity between two perturbations can be interpreted as the angle subtended between two characteristic directions. b) Gene set enrichment analysis can be formulated as the principal angle between the characteristic direction and the subspace spanned by the genes in a gene set.

We collected 73 experiments from GEO (Additional file 1 : Table S1) which contain expression data for control verses TF perturbation with at least three biological replicates in each of these classes. The TF perturbations consisted of knockdowns (32), knockouts (29), over-expressions (5), and other types of perturbation (7) such as partial mutations for example. A complete list with the details about these experiments can be found in the Additional file 1 . We extracted processed expression values from the SOFT files downloaded from the GEO database. For each experiment, we compared control and perturbed classes with four different methods: the fold change, Welsh’s t test, SAM, and the geometrical approach described above which we shall refer to as the “characteristic direction” approach. Each experiment and method pair resulted in a ranked list of all genes on the particular array chip in order of their estimated significance in the differential expression.

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