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Suplari: Vendor Analysis (Part 3) — SWOT, Competitors, Selection Guide, Analysis

Suplari faces significant competition from multiple market segments: S2P/P2P/S2C suites, best-of-breed spend analysis vendors, and generic best-in-class analytic and business intelligence (BI) solutions that are adding in a few spend/supplier-centric reports and touting themselves as solutions for back-office sourcing/procurement professionals. It's a very noisy, messy space, and it can be very confusing for an analytic novice to figure out which solutions are real and which are just the result of marketing mis-information.

And the trend is likely to continue. Competition will continue to increase as everyone jumps on the analytics bandwagon and peddles a platform that just might be free of any modern analytics capability whatsoever. And the relative lack of knowledge about Suplari, even compared to larger best-of-breed peers that have been around longer, as well as it's current lack of globalization puts it at a disadvantage, despite its focus on building a platform backed by machine learning and more advanced analytics than many second generation platforms out there.

In this final installment of our three-part Spend Matters Pro Vendor Snapshot on Suplari, we offer a competitive analysis and comparison with other providers of spend analytic solutions, like AnyData, Coupa, GEP, Jaggaer, Orpheus, Sievo, Simfoni and Xeeva. Part 3 also provides a SWOT analysis, selection considerations and final commentary. For an overview of the Suplari solution, see Part 1. For a deep dive into the platform's strengths and weaknesses, see Part 2.

Suplari: Vendor Analysis (Part 2) — Strengths and Weaknesses

As indicated in Part 1, Suplari was formed to get the relevant purchasing data out of siloed enterprise systems and into the hands of procurement professionals who needed it to make decisions. Billing itself as “AI-Driven Analytics for Modern Procurement Teams,” Suplari was formed with the goal to use all of the available, disparate enterprise data, machine learning and a modern user experience to put the enterprise — and the employee — back in charge when dealing with their suppliers in negotiations.

The co-founders all had over two decades of experience in enterprise software, SaaS/Cloud, and data, so they realized this is no easy feat. Not only did they know that the data was usually dirty, and disparate, but that simply providing one view would result in a deluge that would be more than the average procurement professional could process, and that the professional would be no better off with too much data to try to make sense of in a limited time as they are when they have too little. To solve this problem, they decided they would apply machine learning and AI to identify patterns and simplify the processes of cleansing, classification and connection — the third being the more untapped need — and opportunity — in the procurement space today.

This Spend Matters PRO Vendor Snapshot will explore Suplari's strengths and weaknesses, providing facts and expert analysis to help organizations decide if the vendor is the right one for their shortlist. For an overview of the provider and its platform, see Part 1. In Part 3, we will conclude with an analysis of Suplari’s competitors and offer a final summary.

Suplari: Vendor Analysis (Part 1) — Background, Solution Overview, Selection Checklist

This three-part Spend Matters Vendor Snapshot series will give an overview of the spend analytics vendor Suplari, examine its strengths and weakness, and provide a comparison with its competitors in the procurement technology market.

Billing itself as “AI-Driven Analytics for Modern Procurement Teams,” Suplari was formed to get the relevant purchasing data out of siloed enterprise systems and into the hands of procurement professionals who needed it to make decisions. However, realizing that the data is usually dirty, disparate and deluging for the average procurement professional, they also aimed to apply machine learning and AI to identify patterns and simplify the processing of cleansing, classification and connection.

In the early days of spend analysis, most of the best-of-breed vendors hitting the market focused on classification and categorization — because that was supposed to be the hard problem and everything else would be easy if you had clean, classified data. But that was just the first obstacle to good spend analysis. The next obstacle was connecting the dots to find the opportunities.

Early vendors purported to solve this problem with some canned top N reports — top N categories, top N suppliers, top N geographies, top N departments, top N off-contract categories, top N off-contract suppliers, etc. This worked well in the early days. A scrupulous sourcing professional would work their way through each and every report until they had evaluated the top 20 or so suppliers, geographies, departments and so on (or until they analyzed the top 80% of spend) and put contracts or procedures in place to capture the bulk of the savings. Six months later they'd run the reports again and then find ... nothing. They'd still be bleeding into the red, but wouldn't be able to do anything about it because most of the bleeding would not be with the top N suppliers, geographies, departments and so on.

Next-generation vendors reported to solve this problem with do-it-yourself reporting where buyers could run reports to target the suppliers, categories, geographies, departments, etc. where they believed problems lied. This was one step up, but the amount of time and effort it typically took to run a report, analyze it for a potential opportunity, determine the opportunity was not worth the effort it would take to capture it, and run another report made it too costly to find and capture all but a few opportunities. As a result, many second-generation solutions ended up being valueless and abandoned not long after their first-generation counterparts.

What was needed was a system that could iterate through all the categories, suppliers, geographies, etc. and find the largest opportunities in each, rank them in order of opportunity size, and present them for easy review by a procurement professional.

And what is really needed is a system that can look at the opportunity size, look at the contracts in place, look at the market pricing, look at historical and community results, and identify not only the opportunities that appear to have the largest size, but the largest opportunities that can be captured now. And while there isn't a system that's here yet, this is where a modern system should be going — and it's where Suplari wants to go.