SciQuest is arguably the most challenging procurement technology suite vendor to compare directly to the competition. This perhaps explains why it has tended to surface less in competitive procure-to-pay (P2P) or source-to-pay (S2P) suite opportunities outside of its traditional key vertical sectors for transactional procurement (e.g., higher education, laboratory/research, life sciences, public sector) in the past. While it is possible to label SciQuest as a procurement technology suite vendor (source-to-pay, source-to-contract and procure-to-pay), the reality is that to date, the provider has competed in multiple, infrequently overlapping segments of the procurement technology market.
Historically, SciQuest has generally had different sets of customers, prospects and competitors for its core P2P product and its sourcing optimization, spend/supply analytics and contract lifecycle management solutions. This stands in contrast to many of its peers, which have generally chosen to focus on fewer market segments (e.g, eProcurement, invoice-to-pay, etc.) rather than more as primary entry points to customers.
Yet after being acquired by Accel-KKR last year, SciQuest has started an accelerated strategic and marketing transformation process that is uniting its disparate suite elements. Today’s Spend Matters Plus analysis provides an introduction to SciQuest for procurement organizations looking to understand whether they should consider adding the provider to their shortlists for consideration, as well as competitive alternatives.
Contract Lifecycle Management (CLM) may seem like an application area that should be hardwired to an ERP system. Contracts are the ultimate commercial system of record, so they should be housed in an enterprise-wide software suite, available to everyone, right?
Not necessarily. Far from the elegant, centralized solution advertised, ERP suites have often fallen short. Their generic capabilities are often housed in functionally stovepiped modules that fail to meet the unique needs of various stakeholders. They can describe contract documents and have contract attachments, but they don’t understand the data and meaning of the contract clauses and language itself. This shortfall can lead to workarounds, customizations and frustration all around.
Coupa’s acquisition of Spend360 brings an immediate set of capabilities to Coupa customers — and the basis of a broader, disruptive and embedded offering integrally linked to Coupa’s cloud solution. But what is spend analytics (and spend classification, specifically) and why should customers care? What types of reports should you be able to run? And more narrowly, what specific capabilities does Spend360 bring and how are these different than alternative approaches (e.g., AI/machine learning vs. rules-based classification).
There’s also the question of what Spend360 will ultimately enable Coupa to achieve by applying an AI-driven approach to the firm’s broader source-to-pay platform, including guiding users to better decisions based on insights their own data can provide, a broader topic Coupa’s CEO, Rob Bernshteyn, hinted at during a talk earlier this year. But we’ll leave this specific topic to a longer exploration after we’ve had the chance to delve into the “so what” for customers from the acquisition.
This Spend Matters PRO research brief provides a product-centric analysis that will help Coupa customers and prospects get beyond the headlines of the announcement and understand, on a comparative basis, what spend analytics really is, how spend classification works and how Spend360 stacks up to others.
Earlier today, WNS announced it had acquired Denali Sourcing Services, a procurement solutions provider. Denali was one of the pioneers in procurement managed services. But more recently, the firm has expanded its broader practice in such areas as spend analytics, market intelligence and training. Not all of these capabilities will transition over to WNS — some will remain with the original Denali group, which will be rebranded in the coming months.
Denali brings a number of capabilities to WNS, including a center of excellence (COE)-driven managed services capability for spend analytics, strategic sourcing, category management and supplier management. In short: It adds many “baited hooks” to chum the waters for the broader business process outsourcing (BPO) and outsourcing nets WNS is casting in the market.
But perhaps most important from a market perspective, the transaction will put pressure on other offshore BPO firms, including Genpact, Infosys, HCL and Tech Mahindra, looking to accelerate penetration in North America and Europe through the addition of onshore expertise and capability. For this reason — among others — Spend Matters believes this deal is likely a harbinger of more acquisition activity to come in 2017. In the near term, however, Spend Matters believes the acquisition will do little to blunt the leadership positions of Accenture and GEP for procurement BPO and managed services.
This Spend Matters PRO First Take Analysis provides an overview of the capabilities Denali brings to WNS, key takeaways, an overview of procurement managed services and insight into select additional acquisition candidates that could contribute to further consolidation in this market.
Artificial Intelligence in Contract Management (Part 4: Natural Language Processing and Machine Learning)
In this final installment of our series on artificial intelligence in contract management, we turn our attention to natural language processing (NLP). In earlier posts in this series, I mentioned chatbots and the Turing test, both of which require NLP. I also mentioned machine learning and the process of classifying text to the domain-specific ontologies that model commercial knowledge. At this point, you may have wondered, “Yeah, but how the heck do I actually model and extract all of this knowledge out of our existing contract documents in the first place?”
In the last two installments of this AI in CLM — Artificial Intelligence in Contract Lifecycle Management — series of considerations for procurement practitioners, I introduced the topic and then dove into the concept of knowledge representation which discussed the importance of building a contract domain knowledge model in the form of a rich repository of contract clauses and related data (e.g., risks) and metadata – not just contract document artifacts. In fact, more broadly, you can think of concepts such as contracts, clauses, obligations, risks, remedies, milestones, suppliers, etc. as classes of “objects” that represent the knowledge of those physical/logical entities. These objects are increasingly richly classified, attributed, and interconnected (beyond traditional relational database models used in ERP-type systems), but making sense of them is where reasoning comes in.
In the first part of this series, I introduced the topic of artificial intelligence (AI) in contract management. Today, I’ll touch on AI itself and the first of its areas that will be highlighted in this series: knowledge representation. AI systems are computer systems that emulate (and even surpass) human intelligence needed to perform valued tasks. The Turing test for an AI expert system is whether it is indistinguishable from a human expert that is communicating via, say, a “chatbot” interface. An AI-based CLM system would therefore be able to embody the collective intelligence of contracting professionals (internal and/or outsourced) into computer systems used during ongoing contracting strategy, negotiations and monitoring.
In case you haven't noticed, artificial intelligence (AI) is becoming a very big deal in business. And there is perhaps no area where the impact of AI systems (i.e., systems that exhibit intelligent behavior) will be felt more than in legal departments and, more broadly, in the area of managing contracts. Consider the enormity of collective knowledge that makes up commercial law and amount of money spent translating the semi-structured “legalese” that enshrouds what should be logical business constructs that sit at the core.
The Amazon Go story isn’t really about retail, but rather about the broader supply chain. Amazon finally topped the leaderboard on the Gartner supply chain rankings last year — and not just because of its role in retail, but because of its broader supply chain and digital capabilities. These capabilities have been built methodically and also incrementally. The capabilities then help “unlock” value down the road.
This post is part of our 2016 Year-End Procurement Tech Review, in which we offer procurement practitioners a bird’s-eye view of some key vendors and their solutions in select categories. This is the last week of this series, and today we’re highlighting a company in the procurement technology space.
While it is possible to label SciQuest as a procurement technology suite vendor (source-to-pay, source-to-contract and procure-to-pay), the reality is that SciQuest competes in multiple, infrequently overlapping segments of the procurement technology market. To date, SciQuest has generally had different sets of customers, prospects and different competitors for its core P2P product. This stands in contrast to many of its peers, which have generally chosen to focus on fewer market segments rather than more.
There’s been a lot of hoopla on Amazon Go, Amazon’s foray into brick-and-mortar retailing, especially due to its recent video commercial. The online giant is experimenting with some highly automated small format grocery stores that add up to a promised near-frictionless experience for consumers to just swipe into the store, grab what they want and walk out with waiting in line or self-scanning items at a checkout counter. What could possibly go wrong? Well, lots of things, actually