JAGGAER brings AI and automation to streamline direct spend processes
- JAGGAER is bringing AI and automation to processes such as sourcing, contracts and supplier onboarding to help global businesses manage direct spend more efficiently.
The vendor names that spring to mind as source-to-pay pureplays — the likes of Coupa, Ariba and others — got started in the indirect spend category, which governs purchases of materials and services that are ancillary to the main operations of an enterprise — think stationery, furniture, cleaning, catering, consultancy and so on. But direct spend — purchases of components and services that go into the products and outcomes that make up the core business of an enterprise — is a much bigger and more mission-critical category of spend. While the aforementioned vendors are expanding their reach into direct spend, one vendor that is already an established player in this category is now known as JAGGAER. It's a sector that's getting a lot more attention in the wake of the supply chain disruptions of the past few years. Jim Bureau, CEO of JAGGAER, explains:
Direct materials has direct top-line revenue implications, if I can't source my goods. Yes, I can do it more efficiently. But if I can't get them at all, it's got massive implications on top-line revenue.
When we think about our business and our customers, particularly over the course of the last couple of years, that's where the orientation is really focused on. How do I make my supply chain more resilient and really have the ability to serve my customers?
The JAGGAER brand was adopted in 2017 as the new name for source-to-pay SaaS vendor SciQuest, along with acquisitions that year of direct materials procurement specialist Pool4Tool and spend management company BravoSolution. In 2019 the company launched its unified product suite JAGGAER ONE as its flagship offering, and global private equity firm Cinven took a majority stake. With over 1,700 customers and 1,100 employees globally, the company now operates in over 70 countries and manages over $500 billion in annual spend on its platform. Over a quarter of its business is in the manufacturing segment, while other significant industries in the customer base include business services, education, energy utiities and public sector.
Artificial intelligence and machine learning play a big role in the company's offering, with the aim being to automate as much routine process as possible. For example, customers such as engineering and technology giants Bosch and Siemens are able to perform 'touchless' sourcing events, where the system automatically follows established routines to complete the sourcing process. In the past year, Bosch has done 11,800 online negotiations using automated interactions between buyers and sellers, says Bureau. This frees up sourcing professionals to focus on more complex requirements. He explains:
Where the headcount gravitates to is more complex environments, and let the system do the volumes of less complex, less strategic sourcing events.
Attempts to diversify supply chains have only served to increase the burden on procurement teams, making such automation essential. He elaborates:
One of the things that we've seen over the course of the last two years is a pretty material shift in sourcing from Asia-Pacific regions to Latin America regions — a lot of it because of the COVID policies that China has put in place, as well as the shipping issues that they've had. But what's interesting is they're not severing those relationships, they're adding to those relationships, too. So your number of suppliers tends to go up.
Consequently, what the customer wants is smart matching of buyers and sellers, because they have more of a volume. What are the appropriate suppliers that match my need, at a very granular level? And so we put in all of our machine learning and artificial intelligence across that network in order to pinpoint matches.
Information is brought in from sources outside of JAGGAER's own dataset, such as EcoVadis, Riskmethods and Tealbook, to help inform recommendations.
Another area where customers are able to use AI-powered automation is in processing contracts. Enel, an Italian energy company, has put 30,000 contracts through JAGGAER's contracts management system, with the vast majority autonomously processed rather than needing a laywer to look at them. Bureau says:
We've been able to put in artificial intelligence, machine learning, to be able to go through and read through contracts and be able to pick out and automate clause libraries, so that an actual lawyer can skip the first one or two passes at a contract review and let the system do that on their behalf. It saves millions of dollars in attorneys' fees.
Easing the supplier experience
Easing the experience for suppliers has been a particular focus. When onboarding a new supplier, the system can pre-populate much of the required information, reducing the effort required to complete the process. He explains:
What we've been able to do is pull data from third party sources, et cetera, and pre-populate a lot of the supplier questions, and then put some logic behind it so that we can proactively go out and fetch from other sources where we may be missing data or what have you — and then proactively just give the supplier, 'Here are the things that are potential gaps.'
The company also took a decision several years ago to allow suppliers to use its platform to interact with any of their customers' procurement systems, irrespective whether this was JAGGAER One or a competitor. Bureau explains:
The problem that suppliers have today is, their buyers might use JAGGAER, they might use Ariba, they might use Coupa, what have you. The problem that they have is, every time they interact with somebody, they've got to go to a different portal in order to do whatever it is they do ...
[We said] if you would like to respond to this RFP with our tool, you can do so, but we're not going to limit it to just JAGGAER. You can take that tool and respond to anybody's RFP, whether it's Ariba, or Coupa, or iValua or whoever. That's what they're looking for — 'Give me a tool that I can use ubiquitously across all of my third parties.' Similar approach that we are taking with things like accounts payable and receivable ...
We've gone out of our way to make sure that that we can work across our competitive environments, and really differentiate based on ease of use.
Many customers are using the JAGGAER platform to bring together data from across multiple ERP systems in their business, with SAP very common and others including Oracle, Workday, Microsoft Dynamics 365 and, in the education sector, Ellucian. This enables better spend and supply chain management without the upheaval of an upgrade or rip-and-replace of the core systems. The SaaS architecture means that the system typically can be up and running in three to four months. That's important at a time when businesses are under pressure to improve supply chain performance while at the same time having to work within tight budget constraints. Bureau says:
Between geopolitical inflation, supply chain health, pandemic, there are so many different things influencing how people are managing their supply chain. Historically, there would have never been an instance where either a head of procurement or a head of supply chain had to report to the board of directors. We're getting that now. And as a result, what people are needing is more visibility and transparency to what's happening.
What we're actually seeing is a bit of a dichotomy, because you're seeing more of a need to be able to manage it, but then you've got economic conditions, which in some cases are making it difficult for people to act upon it ...
Nobody's got the appetite for a nine-month install anymore. Get time to value, low-hanging fruit and prioritize, so that these efforts that you're doing can pay for themselves right out of the gate.
Sourcing, spend management and supply chain planning and co-ordination are all areas where we're seeing a lot of practical applications of AI and machine learning coming to the fore. For many organizations, the first step is to pull together previously disparate datasets and functional silos of operation, but what makes the difference is how you then utilize that joined-up data and those end-to-end processes to deliver better results.