They might decide to evaluate only the suppliers that represent a significant risk to the business, or they might decide that they actually want to review all suppliers of a certain scale that represent a certain amount of spend in their business.
What EcoVadis provides is a 10-year-old methodology for assessing businesses based on evidence-backed criteria. We put out a questionnaire to the supplier, what we call a right-sized questionnaire, the supplier responds to material questions based on what kind of goods or services they provide, what geography they are in, and what size of business they are in.
Of course, very small suppliers are not expected to have very mature and sophisticated capabilities around sustainability systems, but larger suppliers are. So, we evaluate them based on those criteria, and then we collect all kinds of evidence from the suppliers in terms of their policies, their actions, and their results against those policies, and we give them ultimately a 0 to 100 score.
And that 0 to 100 score is a pretty good indicator to the buying companies of how well that company is doing in their sustainability systems, and that includes such criteria as environmental, labor and human rights, their business practices, and sustainable procurement practices.
Gardner: More data and information are being gathered on these risks on a global scale. But in order to make that information actionable, there’s an aggregation process under way. You’re aggregating on your own -- and SAP Ariba is now aggregating the aggregators.
How then do we make this actionable? What are the challenges, Tony, for making the great work being done by your partners into something that companies can really use and benefit from?
Timely insights, best business decisions
Harris: Other than some of the technological challenges of aggregating this data across different providers is the need for linking it to the aspects of the procurement process in support of what our customers are trying to achieve. We must make sure that we can surface those insights at the right point in their process to help them make better decisions.
The other aspect to this is how we’re looking at not just trying to support risk through that source-to-settlement process -- trying to surface those risk insights -- but also understanding that where there’s risk, there is opportunity.
So what we are looking at here is how can we help organizations to determine what value they can derive from turning a risk into an opportunity, and how they can then measure the value they’ve delivered in pursuit of that particular goal. These are a couple of the top challenges we’re working on right now.
We're looking at not just trying to support risk through that source-to-settlement process -- trying to surface those risk insights -- but also understanding that where there is risk there is opportunity.
Gardner: And what about the opportunity for compression of time? Not all challenges are something that are foreseeable. Is there something about this that allows companies to react very quickly? And how do you bring that into a procurement process?
Harris: If we look at some risk aspects such as natural disasters, you can’t react timelier than to a natural disaster. So, the way we can alert from our data sources on earthquakes, for example, we’re able to very quickly ascertain whom the suppliers are, where their distribution centers are, and where that supplier’s distribution centers and factories are.
When you can understand what the impacts are going to be very quickly, and how to respond to that, your mitigation plan is going to prevent the supply chain from coming to a complete halt.
Gardner: We have to ask the obligatory question these days about AI and ML. What are the business implications for tapping into what’s now possible technically for better analyzing risks and even forecasting them?
AI risk assessment reaps rewards
Harris: If you look at AI, this is a great technology, and what we trying to do is really simplify that process for our customers to figure out how they can take action on the information we’re providing. So rather them having to be experts in risk analysis and doing all this analysis themselves, AI allows us to surface those risks through the technology -- through our procurement suite, for example -- to impact the decisions they’re making.
For example, if I’m in the process of awarding a piece of sourcing business off of a request for proposal (RFP), the technology can surface the risk insights against the supplier I’m about to award business to right at that point in time.
A determination can be made based upon the goods or the services I’m looking to award to the supplier or based on the part of the world they operate in, or where I’m looking to distribute these goods or services. If a particular supplier has a risk issue that we feel is too high, we can act upon that. Now that might mean we postpone the award decision before we do some further investigation, or it may mean we choose not to award that business. So, AI can really help in those kinds of areas.
Gardner: Emily, when we think about the pressing need for insight, we think about both data and analysis capabilities. This isn’t something necessarily that the buyer or an individual company can do alone if they don’t have access to the data. Why is your approach better and how does AI assist that?
Rakowski: In our case, it’s all about allowing for scale. The way that we’re applying AI and ML at EcoVadis is we’re using it to do an evidence-based evaluation.
We collect a great amount of documentation from the suppliers we’re evaluating, and actually that AI is helping us scan through the documentation more quickly. That way we can find the relevant information that our analysts are looking for, compress the evaluation time from what used to be about a six or seven-hour evaluation time for each supplier down to three or four hours. So that’s essentially allowing us to double our workforce of analysts in a heartbeat.
AI is helping us scan through the documentation more quickly. That way we can find the relevant information that our analysts are looking for, allowing us to double our workforce of analysts.
The other thing it’s doing is helping scan through material news feeds, so we’re collecting more than 2,500 news sources from around all kinds of reports, from China Labor Watch or OSHA. These technologies help us scan through those reports from material information, and then puts that in front of our analysts. It helps them then to surface that real-time news that we’re for sure at that point is material.
And that way we we’re combining AI with real human analysis and validation to make sure that what we we’re serving is accurate and relevant.
Harris: And that’s a great point, Emily. On the SAP Ariba side, we also use ML in analyzing similarly vast amounts of content from across the Internet. We’re scanning more than 600,000 data sources on a daily basis for information on any number of risk types. We’re scanning that content for more than 200 different risk types.
We use ML in that context to find an issue, or an article, for example, or a piece of bad news, bad media. The software effectively reads that article electronically. It understands that this is actually the supplier we think it is, the supplier that we’ve tracked, and it understands the context of that article.
By effectively reading that text electronically, a machine has concluded, “Hey, this is about a contracts reduction, it may be the company just lost a piece of business and they had to downsize, and so that presents a potential risk to our business because maybe this supplier is on their way out of business.”
And the software using ML figures all that stuff out by itself. It defines a risk rating, a score, and brings that information to the attention of the appropriate category manager and various users. So, it is very powerful technology that can number crunch and read all this content very quickly.
Gardner: Erin, at Maplecroft, how are such technologies as AI and ML being brought to bear, and what are the business benefits to your clients and your ecosystem?
The AI-aggregation advantage
McVeigh: As an aggregator of data, it’s basically the bread and butter of what we do. We bring all of this information together and ML and AI allow us to do it faster, and more reliably
We look at many indices. We actually just revamped our social indices a couple of years ago.
Before that you had a human who was sitting there, maybe they were having a bad day and they just sort of checked the box. But now we have the capabilities to validate that data against true sources.
Just as Emily mentioned, we were able to reduce our human-rights analyst team significantly and the number of individuals that it took to create an index and allow them to go out and begin to work on additional types of projects for our customers. This helped our customers to be able to utilize the data that’s being automated and generated for them.
We also talked about what customers are expecting when they think about data these days. They’re thinking about the price of data coming down. They’re expecting it to be more dynamic, they’re expecting it to be more granular. And to be able to provide data at that level, it’s really the combination of technology with the intelligent data scientists, experts, and data engineers that bring that power together and allow companies to harness it.
Gardner: Let’s get more concrete about how this goes to market. Tony, at the recent SAP Ariba Live conference, you announced the Ariba Supplier Risk improvements. Tell us about the productization of this, how people intercept with it. It sounds great in theory, but how does this actually work in practice?
Harris: What we announced at Ariba Live in March is the partnership between SAP Ariba, EcoVadis and Verisk Maplecroft to bring this combined set of ESG and CSR insights into SAP Ariba’s solution.
We do not yet have the solution generally available, so we are currently working on building out integration with our partners. We have a number of common customers that are working with us on what we call our design partners. There’s no better customer ultimately then a customer already using these solutions from our companies. We anticipate making this available in the Q3 2018 time frame.
And with that, customers that have an active subscription to our combined solutions are then able to benefit from the integration, whereby we pull this data from Verisk Maplecroft, and we pull the CSR score cards, for example, from EcoVadis, and then we are able to present that within SAP Ariba’s supplier risk solution directly.
What it means is that users can get that aggregated view, that high-level view across all of these different risk types and these metrics in one place. However, if, ultimately they are going to get to the nth degree of detail, they will have the ability to click through and naturally go into the solutions from our partners here as well, to drill right down to that level of detail. The aim here is to get them that high-level view to help them with their overall assessments of these suppliers.
Gardner: Over time, is this something that organizations will be able to customize? They will have dials to tune in or out certain risks in order to make it more applicable to their particular situation?
Customers that have an active subscription to our combined solutions are then able to benefit from the integration and see all that data within SAP Ariba's supplier risk solutions directly.
Harris: Yes, and that’s a great question. We already addressed that in our solutions today. We cover risk across more than 200 types, and we categorized those into four primary risk categories. The way the risk exposure score works is that any of the feeding attributes that go into that calculation the customer gets to decide on how they want to weigh those.
If I have more bias toward that kind of financial risk aspects, or if I have more of the bias toward ESG metrics, for example, then I can weigh that part of the score, the algorithm, appropriately.
Gardner: Before we close out, let’s examine the paybacks or penalties when you either do this well -- or not so well.
Erin, when an organization can fully avail themselves of the data, the insight, the analysis, make it actionable, make it low-latency -- how can that materially impact the company? Is this a nice-to-have, or how does it affect the bottom line? How do we make business value from this?
Rakowski: One of the things that we’re still working on is quantifying the return on investment (ROI) for companies that are able to mitigate risk, because the event didn’t happen.
How do you put a tangible dollar value to something that didn’t occur? What we can look at is taking data that was acquired over the past few years and understand that as we begin to see our risk reduction over time, we begin to source for more suppliers, add diversity to our supply chain, or even minimize our supply chain depending on the way you want to move forward in your risk landscape and your supply diversification program. It’s giving them that power to really make those decisions faster and more actionable.
And so, while many companies still think about data and tools around ethical sourcing or sustainable procurement as a nice-to-have, those leaders in the industry today are saying, “It’s no longer a nice-to-have, we’re actually changing the way we have done business for generations.”
And, it’s how other companies are beginning to see that it’s not being pushed down on them anymore from these large retailers, these large organizations. It’s a choice they have to make to do better business. They are also realizing that there’s a big ROI from putting in that upfront infrastructure and having dedicated resources that understand and utilize the data. They still need to internally create a strategy and make decisions about business process.
We can automate through technology, we can provide data, and we can help to create technology that embeds their business process into it -- but ultimately it requires a company to embrace a culture, and a cultural shift to where they really believe that data is the foundation, and that technology will help them move in this direction.
Gardner: Emily, for companies that don’t have that culture, that don’t think seriously about what’s going on with their suppliers, what are some of the pitfalls? When you don’t take this seriously, are bad things going to happen?
Pay attention, be prepared
Rakowski: There are dozens and dozens of stories out there about companies that have not paid attention to critical ESG aspects and suffered the consequences of a horrible brand hit or a fine from a regulatory situation. And any of those things easily cost that company on the order of a hundred times what it would cost to actually put in place a program and some supporting services and technologies to try to avoid that.
From an ROI standpoint, there’s a lot of evidence out there in terms of these stories. For companies that are not really as sophisticated or ready to embrace sustainable procurement, it is a challenge. Hopefully there are some positive mavericks out there in the businesses that are willing to stake their reputation on trying to move in this direction, understanding that the power they have in the procurement function is great.
They can use their company’s resources to bet on supply-chain actors that are doing the right thing, that are paying living wages, that are not overworking their employees, that are not dumping toxic chemicals in our rivers and these are all things that, I think, everybody is coming to realize are really a must, regardless of regulations.
Hopefully there are some positive mavericks out there who are willing to stake their reputations on moving in this direction. The power they have in the procurement function is great.
And so, it’s really those individuals that are willing to stand up, take a stand and think about how they are going to put in place a program that will really drive this culture into the business, and educate the business. Even if you’re starting from a very little group that’s dedicated to it, you can find a way to make it grow within a culture. I think it’s critical.
Gardner: Tony, for organizations interested in taking advantage of these technologies and capabilities, what should they be doing to prepare to best use them? What should companies be thinking about as they get ready for such great tools that are coming their way?
Synergistic risk management
Harris: Organizationally, there tend to be a couple of different teams inside of business that manage risks. So, on the one hand there can be the kind of governance risk and compliance team. On the other hand, they can be the corporate social responsibility team.
I think first of all, bringing those two teams together in some capacity makes complete sense because there are synergies across those teams. They are both ultimately trying to achieve the same outcome for the business, right? Safeguard the business against unforeseen risks, but also ensure that the business is doing the right thing in the first place, which can help safeguard the business from unforeseen risks.
I think getting the organizational model right, and also thinking about how they can best begin to map out their supply chains are key. One of the big challenges here, which we haven’t quite solved yet, is figuring out who are the players or supply-chain actors in that supply chain? It’s pretty easy to determine now who are the tier-one suppliers, but who are the suppliers to the suppliers -- and who are the suppliers to the suppliers to the suppliers?
We’ve yet to actually build a better technology that can figure that out easily. We’re working on it; stay posted. But I think trying to compile that information upfront is great because once you can get that mapping done, our software and our partner software with EcoVadis and Verisk Maplecroft is here to surfaces those kinds of risks inside and across that entire supply chain.
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