Category Archives: Vendor Review

Vendor Coverage on Sourcing Innovation

This page will be regularly maintained and link to all current vendor coverage on Sourcing Innovation, where “current” is defined to be within the last three years for the most recent vendor coverage.

Note that, as of December 3, 2025, all vendor coverage is sponsored. Any coverage included between December 1, 2022 and December 3, 2025* is because the vendor has sponsored new or upcoming coverage (and the older coverage is deemed to still be valid). All vendors covered adhere to Sourcing Innovation’s policies and review requirements as defined in the FAQ.

Company Primary Offering(s) Coverage
Spendata Analytics (2024 Mar 21)
The Power Tool for the Power Analyst
(2024 Jul 01)
A True Enterprise Analytics Solution
(2026)
New coverage coming soon!

* Any coverage that is currently private that occurred between December 1, 2022 and December 3, 2025 may be made public once a minimum of three years has passed.

Spendata: A True Enterprise Analytics Solution

As we indicated in our last article, while Spendata is the absolute best at spend analysis, it’s not just a spend analysis platform. It’s a general-purpose data analytics platform that can be used for much more than spend analysis.

The current end-state vision for business data analytics is a “data lake” database with a BI front end. The Big X consultancies (aided and abetted by your IT department, which is only too eager to implement another big system) will try to convince you of the data paradise you’ll have if you dump all of your business data into a data lake. Unfortunately, reality doesn’t support the vision, because organizational data is created only to the extent necessary, never verified, riddled with errors from day one, and left to decay over time as it’s never updated. The data lake is ultimately a data cesspool.

Pointing a BI tool at the (dirty) lake will spice up the data with bars, pies, waves, scatters, multi-coloured geometric shapes, and so on, but you won’t find much insight other than the realization that your data is, in fact, dirty. Worse, a published BI dashboard is like a spreadsheet you can’t modify. Try mapping new dimensions, creating new measures, adding new data, or performing even the simplest modification of an existing dimension or hierarchy, and you’ll understand why this author likes to point out that BI should actually stand for Bullsh!t Images, not Business Intelligence.

So how does a spend analysis platform like Spendata end up being a general-purpose data analytics tool? The answer is that the mechanisms and procedures associated with spend analysis and spend analysis databases, specifically data mapping and dimension derivation, can be taken to the next level — extended, generalized, and moved into real time. Once those key architectural steps are taken, the system can be further extended with view-based measures, shared cubes where custom modifications are retained across refreshes, and spreadsheet-like dependencies and recalculation at database scale.

The result is an analysis system that can be adapted not only to any of the common spend analysis problems, such as AP/PO analysis or commodity-specific cubes with item level price X quantity data, but also to savings tracking and sourcing and implementation plans. Extending the system to domains beyond spend analysis is simple: just load different data.
The bottom line is that to do real data analysis, no matter what the domain, you need:

  • the ability to extend the schema at any time
  • the ability to add new derived dimensions at any time
  • the ability to change mappings at any time
  • the ability to build derivations, data views, and mappings that are dependent on other derivations, mappings, views, inputs, linked datasets, and so on, with real-time “recalc”
  • the ability to create new views and reports relevant to the question you have … without dumping the data to Excel
  • … and preserve all of the above on cube data refreshes
  • … in your own copy of the cube so you don’t have to wait for anyone to agree
  • … and get an answer today, not on the next refresh next month when you’ve forgotten why you even had the question in the first place

You don’t get any of that from a spend analysis solution, or a BI solution, or a database pointing at a data lake. You only get that in a modern data analysis solution — which supports all of the above, and more, for any kind of data. A data analysis system works equally well across all types of numeric or set-valued data, including, but not limited to sales data, service data, warranty data, process data, and so on.

As Spendata is a real data analysis solution, it supports all of these analyses with a solution that’s easier and friendlier to use than the spreadsheet you use every day. Let’s walk through some examples so you can understand what a data analysis solution really can do.

SALES ANALYSIS

Spending data consists of numerical amounts that represent the price, tax, duty, shipping, etc. paid for items purchased. Sales data is numerical amounts that represent the price, tax, duty, shipping, etc. paid for items sold.

They are basically the inverse of each other. For every purchase, there is a sale. For every sale, there is a purchase. So, there’s absolutely no reason that you shouldn’t be able to apply the exact the same analysis (possibly in reverse) to sales data as you apply to spend data. That is, IF you have a proper data analysis tool. The latter part is the big IF because if you’re using a custom tool that needs to map all data to a schema with fixed semantics, it won’t understand the data and you’re SOL.

However, since Spendata is a general-purpose data analysis tool that builds and maintains its schema on the fly, it doesn’t care if the dataset is spend data or sales data; it’s still transactional data and it’s happy to analyze away. If you need the handholding of a workflow-oriented UI, that can also be configured out of the box using Spendata‘s new “app” capability.

Here are three types of sales analysis that Spendata supports better than CRM/Sales Forecasting systems, and that can’t be done at all with a data lake and a BI tool.

Sales Discount Variation Analysis Over Time By Salesperson … and Client Type

You run a sales team. Are your different salespeople giving the same mix of discounts by product type to the same types of customers by customer size and average sales size?

Sounds easy right? Can’t you simply plot the product/price ratio by month by salesperson in a bubble chart (where volume size correlates to bubble size) against the average trend line and calculate which salespeople are off the most (in the wrong direction)? Sure, but how do you handle client type? You could add a “color” dimension, but when the bubbles overlap and the bubbles blur, can you see it visually? Not likely. And how do you remember a low sales volume customer which is a strategic partner, so has a special deal? Theoretically you could add another column to the table “Salesperson, Product/Price Ratio, Client Type, Over/Under Average”, and that would work as long as you could pre-compute the average discount by Product/Price Ratio and Client Type.

And then you realize that unless you group by category, you have entirely different products in the same product/price ratio and your multi-stage analysis is worthless, so you have to go back and start again, only to find out that the bubble chart is only pseudo-useful (as you can’t really figure it out visually because what is that shade of pink (from the multiple red and white bubbles overlapping) — Fuchsia, Bright, or Barbie — and what does it mean) and you will have to focus on the fixed table to extract any value at all from the analysis.

But then you’ll realize that you still need to see monthly variations in the chart, meaning you want the ability to drag a slider or change the month and have the bubble chart update. Uh-oh, you forgot to individually compute all the amounts by month or select the slider graph! Back to square one, doing it all over again by month. Then you notice some customers have long-term, fixed prices on some products, which messes up the average discount on these products as the prices for these customers are not changing over time. You redo the work for the third (or is it the fourth? time), and then you realize that your definitions of client type “large, medium, and small” are slightly off as a client that should be in large is in medium and two that should be in small were made medium. Aaarrrggghhh!!!

But with Spendata, you simply create or modify dimensions to the cube to segment the data (customer type, product groups, etc.) You leverage a dynamic view-based measure by customer type to set the average prices per time period (used to calculate the discount). You then use filters to define the time range of interest, another view with filters to click through the months over time, a derived view to see the performance by quarter, another by year. If you change the definition of client type (which customers belong to which client type), which products for customers are fixed prices, which SKU’s that are the same type, time range of interest, etc. you simply map them and the entire analysis auto-updates.

This flexibility and power (with no wasted effort) gives you a very deep analysis capability NOT available in any other data analysis platform. For example, you can find out with a few clicks that your “best” salesperson in terms of giving the lowest average discount is actually costing you the most. Turns out, he’s not serving any large customers (who get good discounts) and has several fixed price contracts (which mess up the average discounts). So, the discounts he’s giving the small clients, while less than what large customers get, are significantly more than what other salespeople provide to other small customers. This is something you’d never know if you didn’t have the power of Spendata as your data consultant would give up on the variance analysis at the global level because the salesman’s overall ratio looked good.

Post-Merger White-Space Analysis

White space sales analysis is looking for spaces in the market where you should be selling but are not. For example, if you sell to restaurants, you could look at your sales by geography, normalized by the number of establishments by type or the sales of the restaurants by type in that geography. In a merger, you could measure your penetration at each customer for each of the original companies. You can find white space by looking at each customer (or customer segment) and measuring revenue per customer employee across the two companies. Where is one more effective than the other?

You might think this is no big deal because this was theoretically done during the due diligence and the opportunity for overlap was deemed to be there, as well as the opportunity for whitespace, and whatever was done was good enough. The reality couldn’t be further from the truth.

If the whitespace analysis was done with a standard analytics tool, it has all the following problems:

  • matching vendors were missed due to different name entries and missing ids
  • vendors were not familied by parent (within industry, geography, etc.)
  • the improperly merged vendors were only compared against a target file built by the consultants and misses vendors
  • i.e. it’s poor, but no worse than you’d do with a traditional analytics tool

But with Spendata, these problems would be at least minimized, if not eliminated because:

  • Spendata comes with auto-matching capability
  • … that can be used to enrich the suppliers with NAICS categorization (for example)
  • Spendata comes with auto-familying capability so parent-child relationships aren’t missed
  • Spendata can load all of the companies from a firmographic database with their NAICS codes in a separate cube …
  • … and then federation can be used to match the suppliers in use with the suppliers in the appropriate NAICS category for the white space analysis

It’s thus trivial to

  1. load up a cube with organization A’s sales by supplier (which can be the output from a view on a transaction database), and run it through a view that embeds a normalization routine so that all records that actually correspond to the same supplier (or parent-child where only the parent is relevant) are grouped into one line
  2. load up a cube with organization B’s sales by supplier and do the same … and now you know you have exact matches between supplier names
  3. load up the NAICS code database – which is a list of possible customers
  4. build a view that pulls in, for each supplier in the NAICS category of interest, Org A spend, Org B Spend, and Total Spend
  5. create a filter to only show zero spend suppliers — and there’s the whitespace … 100% complete. Now send your sales teams after these.
  6. Create a filter to show where your sales are less than expected (eg. from comparable other customers or Org A or Org B). This is additional whitespace where upselling or further customer penetration is appropriate.

Bill Rate Analysis

A smart company doesn’t just analyze their (total) spend by service provider, they analyze by service role and against the service role average when different divisions/locations are contracting for the same service that should be fulfilled by a professional with roughly the same skills and same experience level. Why? Because if you’re paying, on average, 150/hr for an intermediate DBA across 80% of locations and 250/hr across the remaining 20%, you’re paying as much as 66% too much at those remaining locations, with the exception being San Francisco or New York where your service provider has to pay their locals a cost-of-living top-up just so they can afford to live there.

By the same token, a smart service company is analyzing what they are getting by role, location, and customer and trying to identify the customers that are (the most) profitable and those that are the least (or unprofitable when you take contract size or support requirements into account), so they can focus on those customers that are profitable, and, hopefully, keep them happy with their better talent (and not just the newest turkey on the rafter).

However, just like sales discount variation analysis over time by client type, this is tough as it’s essentially a variation of that analysis, except you are looking at services instead of products, roles instead of client types, and customer instead of sales rep … and then, for your problem clients, looking at which service reps are responsible … so after you do the base analysis (using dynamic view based measures), you’re creating new views with new measures and filters to group by service rep and filter to those too far beyond a threshold. In any other tool, it would be nigh impossible for even an expert analyst. In Spendata, it’s a matter of minutes. Literally.

And this is just the tip of the iceberg in terms of what Spendata can do. In a future article, we’ll dive into a few more areas of analysis that require very specialized tools in different domains, but which can be done with ease in Spendata. Stay tuned!

Spendata: The Power Tool for the Power Spend Analyst — Now Usable By Apprentices as Well!

We haven’t covered Spendata much on Sourcing Innovation (SI), as it was only founded in 2015 and the doctor did a deep dive review on Spend Matters in 2018 when it launched (Part I and Part II, ContentHub subscription required), as well as a brief update here on SI where we said Don’t Throw Away that Old Spend Cube, Spendata Will Recover It For You!. the doctor did pen a 2020 follow up on Spend Matters on how Spendata was Rewriting Spend Analysis from the Ground Up, and that was the last major coverage. And even though the media has been a bit quiet, Spendata has been diligently working as hard on platform improvement over the last four years as they were the first four years and just released Version 2.2 (with a few new enhancements in the queue that they will roll out later this year). (Unlike some players which like to tack on a whole new version number after each minor update, or mini-module inclusion, Spendata only does a major version update when they do considerable revamping and expansion, recognizing that the reality is that most vendors only rewrite their solution from the ground up to be better, faster, and more powerful once a decade, and every other release is just an iteration, and incremental improvement of, the last one.)

So what’s new in Spendata V 2.2? A fair amount, but before we get to that, let’s quickly catch you up (and refer you to the linked articles above for a deep dive).

Spendata was built upon a post-modern view of spend analysis where a practitioner should be able to take immediate action on any data she can get her hands on whenever she can get her hands on it and derive whatever insights she can get for process (or spend) improvement. You never have perfect data, and waiting until Duey, Clutterbuck, and Howell1 get all your records in order to even run your first report when you have a dozen different systems to integrate data from, multiple data formats to map, millions of records to classify, cleanse and enrich, and third party data feeds to integrate will take many months, if not a year, and during that year where you quest for the mythical perfect cube you will continue to lose 5% due to process waste, abuse, and fraud, and 3% to 15% (or more) across spend categories where you don’t have good management but could stem the flow simply by identifying them and putting in place a few simple rules or processes. And you can identify some of these opportunities simply by analyzing one system, one category, and one set of suppliers. And then moving on to the next one. And, in the process, Spendata automatically creates and maintains the underlying schema as you slowly build up the dimensions, the mapping, cleansing, and categorization rules, and the basic reports and metrics you need to monitor spend and processes. And maybe you can only do 60% to 80% piecemeal, but during that “piecemeal year”, you can identify over half of your process and cost savings opportunities and start saving now, versus waiting a year to even start the effort. When it comes to spend (related) data analysis, no adage is more true than “don’t put off until tomorrow what you can do today” with Spendata, because, and especially when you start, you don’t need complete or perfect data … you’d be amazed how much insight you can get with 90% in a system or category, and then if the data is inconclusive, keeping drilling and mapping until you get into the 95% to 98% accuracy range.

Spendata was also designed from the ground up to run locally and entirely in the browser, because no one wants to wait for an overburdened server across a slow internet connection, and do so in real time … and by that we mean do real analysis in real time. Spendata can process millions of records a minute in the browser, which allows for real time data loads, cube definitions, category re-mappings, dynamically derived dimensions, roll-ups, and drill downs in real-time on any well-defined data set of interest. (Since most analysis should be department level, category level, regional, etc., and over a relevant time span, that should not include every transaction for the last 10 years because beyond a few years, it’s only the quarter over quarter or year over year totals that become relevant, most relevant data sets for meaningful analysis even for large companies are under a few million transactions.) The goal was to overcome the limitations of the first two generations of spend analysis solutions where the user was limited to drilling around in, and deriving summaries of, fixed (R)OLAP cubes and instead allow a user to define the segmentations they wanted, the way they wanted, on existing or newly loaded (or enriched federated data) in real time. Analysis is NOT a fixed report, it is the ability to look at data in various ways until you uncover an inefficiency or an opportunity. (Nor is it simply throwing a suite of AI tools against a data set — these tools can discover patterns and outliers, but still require a human to judge whether a process improvement can be made or a better contract secured.)

Spendata was built as a third generation spend analysis solution where

  • data can be loaded and processed at any point of the analysis
  • the schema is developed and modified on the fly
  • derived dimensions can be created instantly based on any combination of raw and previously defined derived dimensions
  • additional datasets from internal or external sources can be loaded as their own cubes, which can then be federated and (jointly) drilled for additional insight
  • new dimensions can be built and mapped across these federations that allow for meaningful linkages (such as commodities to cost drivers, savings results to contracts and purchasing projects, opportunities by size, complexity, or ABS analysis, etc.)
  • all existing objects — dimensions, dashboards, views (think dynamic reports that update with the data), and even workspaces can be cloned for easy experimentation
  • filters, which can define views, are their own objects, can be managed as their own objects, and can be, through Spendata‘s novel filter coin implementation, dragged between objects (and even used for easy multi-dimensional mapping)
  • all derivations are defined by rules and formula, and are automatically rederived when any of the underlying data changes
  • cubes can be defined as instances of other cubes, and automatically update when the source cube updates
  • infinite scrolling crosstabs with easy Excel workbook generation on any view and data subset for those who insist on looking at the data old school (as well as “walk downs” from a high-level “view” to a low-level drill-down that demonstrates precisely how an insight was found
  • functional widgets which are not just static or semi-dynamic reporting views, but programmable containers that can dynamically inject data into pre-defined analysis and dimension derivations that a user can use to generate what-if scenarios and custom views with a few quick clicks of the mouse
  • offline spend analysis is also available, in the browser (cached) or on Electron.js (where the later is preferred for Enterprise data analysis clients)

Furthermore, with reference to all of the above, analyst changes to the workspace, including new datasets, new dashboards and views, new dimensions, and so on are preserved across refresh, which is Spendata’s “inheritance” capability that allows individual analysts to create their own analyses and have them automatically updated with new data, without losing their work …

… and this was all in the initial release. (Which, FYI, no other vendor has yet caught up to. NONE of them have full inheritance or Spendata‘s security model. And this was the foundation for all of the advanced features Spendata has been building since its release six years ago.)

After that, as per our updates in 2018 and 2020, Spendata extended their platform with:

  • Unparalleled Security — as the Spendata server is designed to download ONLY the application to the browser, or Spendata‘s demo cubes and knowledge bases, it has no access to your enterprise data;
  • Cube subclassing & auto-rationalization — power users can securely setup derived cubes and sub-cubes off of the organizational master data cubes for the different types of organizational analysis that are required, and each of these sub-cubes can make changes to the default schema/taxonomy, mappings, and (derived) dimensions, and all auto-update when the master cube, or any parent cube in the hierarchy, is updated
  • AI-Based Mapping Rule Identification from Cube Reverse Engineering — Spendata can analyze your current cube (or even a report of vendor by commodity from your old consultant) and derive the rules that were used for mapping, which you can accept, edit, or reject — we all know black box mapping doesn’t work (no matter how much retraining you do, as every “fix” all of a sudden causes an older transaction to be misclassified); but generating the right rules that can be human understood and human maintained guarantees 100% correct classification 100% of the time
  • API access to all functions, including creating and building workspaces, adding datasets, building dimensions, filtering, and data export. All Spendata functions are scriptable and automatable (as opposed to BI tools with limited or nonexistent API support for key functions around building, distributing, and maintaining cubes).

However, as we noted in our introduction, even though this put Spendata leagues beyond the competition (as we still haven’t seen another solution with this level of security; cube subclassing with full inheritance; dynamic workspace, cube, and view creation; etc.), they didn’t stop there. In the rest of this article, we’ll discuss what’s new from the viewpoint of Spendata Competitors:

Spendata Competitors: 7 Things I Hate About You

Cue the Miley Cyrus, because if competitors weren’t scared of Spendata before, if they understand ANY of this, they’ll be scared now (as Spendata is a literal wrecking ball in analytic power). Spendata is now incredibly close to negating entire product lines of not just its competitors, but some of the biggest software enterprises on the planet, and 3.0 may trigger a seismic shift on how people define entire classes of applications. But that’s a post for a later day (but should cue you up for the post that will follow this on on just precisely what Spendata 2.2 really is and can do for you). For now, we’re just going to discuss seven (7) of the most significant enhancements since our last coverage of Spendata.

Dynamic Mapping

Filters can now be used for mapping — and as these filters update, the mapping updates dynamically. Real-time reclassify on the fly in a derived cube using any filter coin, including one dragged out of a drill down in a view. Analysis is now a truly continuous process as you never have to go back and change a rule, reload data, and rebuild a cube to make a correction or see what happens under a reclassification.

View-Based Measures

Integrate any rolled up result back into the base cube on the base transactions as a derived dimension. While this could be done using scripts in earlier versions, it required sophisticated coding skills. Now, it’s almost as easy as a drag-and-drop of a filter coin.

Hierarchical Dashboard Menus

Not only can you organize your dashboards in menus and submenus and sub-sub menus as needed, but you can easily bookmark drill downs and add them under a hierarchical menu — makes it super easy to create point-based walkthroughs that tell a story — and then output them all into a workbook using Spendata‘s capability to output any view, dashboard, or entire workspace as desired.

Search via Excel

While Spendata eliminates the need for Excel for Data Analysis, the reality is that is where most organizational data is (unfortunately) stored, how most data is submitted by vendors to Procurement, and where most Procurement Professionals are the most comfortable. Thus, in the latest version of Spendata, you can drag and drop groups of cells from Excel into Spendata and if you drag and drop them into the search field, it auto-creates a RegEx “OR” that maintains the inputs exactly and finds all matches in the cube you are searching against.

Perfect Star Schema Output

Even though Spendata can do everything any BI tool on the market can do, the reality is that many executives are used to their pretty PowerBI graphs and charts and want to see their (mostly static) reports in PowerBI. So, in order to appease the consultancies that had to support these executives that are (at least) a generation behind on analytics, they encoded the ability to output an entire workspace to a perfect star schema (where all keys are unique and numeric) that is so good that many users see a PowerBI speed up by a factor of almost 10. (As any analyst forced to use PowerBI will tell you, when you give PowerBI any data that is NOT in a perfect star schema, it may not even be able to load the data, and that it’s ability to work with non-numeric keys at a speed faster than you remember on an 8088 is nonexistent.)

Power Tags

You might be thinking “tags, so what“. And if you are equating tags with a hashtag or a dynamically defined user attribute, then we understand. However, Spendata has completely redefined what a tag is and what you can do with it. The best way to understand it is a Microsoft Excel Cell on Steroids. It can be a label. It can be a replica of a value in any view (that dynamically updates if the field in the view updates). It can be a button that links to another dashboard (or a bookmark to any drill-down filtered view in that dashboard). Or all of this. Or, in the next Spendata release, a value that forms the foundation for new derivations and measures in the workspace just like you can reference a random cell in an Excel function. In fact, using tags, you can already build very sophisticated what-if analysis on-the-fly that many providers have to custom build in their core solutions (and take weeks, if not months, to do so) using the seventh new capability of Spendata, and usually do it in hours (at most).

Embedded Applications

In the latest version of Spendata, you can embed custom applications into your workspace. These applications can contain custom scripts, functions, views, dashboards, and even entire datasets that can be used to instantly augment the workspace with new analytic capability, and if the appropriate core columns exist, even automatically federate data across the application datasets and the native workspace.

Need a custom set of preconfigured views and segments for that ABC Analysis? No sweat, just import the ABC Analysis application. Need to do a price variance analysis across products and geographies, along with category summaries? No problem. Just import the Price Variance and Category Analysis application. Need to identify opportunities for renegotiation post M&A, cost reduction through supply base consolidation, and new potential tail spend suppliers? No problem, just import the M&A Analysis app into the workspace for the company under consideration and let it do a company A vs B comparison by supplier, category, and product; generate the views where consolidation would more than double supplier spend, save more than 100K on switching a product from a current supplier to a lower cost supplier; and opportunities for bringing on new tail spend suppliers based upon potential cost reductions. All with one click. Not sure just what the applications can do? Start with the demo workspaces and apps, define your needs, and if the apps don’t exist in the Spendata library, a partner can quickly configure a custom app for you.

And this is just the beginning of what you can do with Spendata. Because Spedata is NOT a Spend Analysis tool. That’s just something it happens to do better than any other analysis tool on the market (in the hands of an analyst willing to truly understand what it does and how to use it — although with apps, drag-and-drop, and easy formula definition through wizardly pop-ups, it’s really not hard to learn how to do more with Spendata than any other analysis tool).

But more on this in our next article. For The Times They Are a-Changin’.

1 Duey, Clutterbuck, and Howell keeps Dewey, Cheatem, and Howe on retainer … it’s the only way they can make sure you pay the inflated invoices if you ever wake up and realize how much you’ve been fleeced for …

TenderEasy: Easy Breezy Beautiful Freight Quotes

First things first: if you are shipping globally, you need a(n) RFQ / Spot Bid solution built for freight. You may believe that just because you have a generic RFQ / e-Auction solution that can be used to collect freight quotes that you don’t need a custom freight tendering solution, but nothing could be further from the truth. When it comes to freight, at a minimum you have to consider:

  • five modes: road, rail, ocean, air, and small parcel,
  • multiple cargo types: dry, cold, frozen, and liquid,
  • palletized vs. non-palletized,
  • LTL vs FTL,
  • regular vs flammable vs hazardous, and
  • multiple cost tiers

and that’s quite a few data elements that most RFX tools are not setup to collect out of the box. Furthermore, even if the solution is highly configurable and can allow the creation of bid collection matrices that will collect all of the associated bid and lane data, chances are the platform isn’t setup with the rules to enforce the right bidding, the analytics for the right comparison, or enough sophistication in auto award scenario creation even for a baseline low-cost cherrypick scenario.

Furthermore, when you are shipping globally, you need to

  • understand approximate current lane costs / benchmarks,
  • know who is shipping in a region AND their typical capacity, and
  • be able to quickly access current rate agreements or spot-market bid rates

and your typical out-of-the-box RFX tool for indirect or direct sourcing is not going to do that.

However, a tool built by freight sourcing / logistics professionals for freight sourcing is going to do that and more. That’s what TenderEasy is. Founded almost two decades ago in 2004 to help organizations optimize their freight sourcing, they launched the first version of their fully SaaS-enabled freight tendering solution eleven years ago. Their freight tendering solution was among the first solutions that were custom built to help global companies manage their global fright RFQs across air, land, and sea. Since then, they have added spot quote capability, rate (contract) management, an integration API for custom data push to any TMS, ERP, or S2P system you want to transfer the awards to, out-of-the-box integrations with multiple TMS systems (e.g., Alpega, SAP4Hana), out-of-the-box APIs with public freight rate benchmark and analytics platforms (including Xeneta, Freightos, Upply, and Alpega FX), out-of-the-box integrations with container management platforms (including BuyCo), and out-of-the-box integrations with freight/lane-based emission calculators (including EcoTransIT World).

There are three main parts to the TenderEasy platform:

  • Administration
  • Buyer Interface
  • Supplier Interface

Administration

There are six main parts to the administration interface:

  • User Management: where you can manage your internal users with easy profile settings controlling visibility, accessibility and inter-activity with bidders
  • Supplier Management: where you can import, add and manage suppliers, including the ability to #tag supplier groups, and this management includes the management of (supplier) modes, cargo types, pallet capability, whether or not they do LTL or FTL, any certifications for flammable and hazardous materials, countries they can operate in, etc.
  • Currency Rates: where you can define, on project level, the currencies you support and the rates you wish to use for base conversion
  • Keyword Lists: where you can define as many arbitrary value lists as you want for bid and data collection during a tender (to make sure responses are with the right naming convention for rule creation and future data integration with your TMS, ERP, and/or S2P system)
  • Integrations: where you manage your export connectivity to whatever systems you want to push data to
  • Partners: where you select the data partners you wish to connect with for data enrichment of your analysis data (freight benchmarks, emissions, service KPIs, etc.). With some Partners you can “pay-as-you-go” via TenderEasy. Other partners will require a subscription and your partner license key credentials to access the data.

Supplier Interface

The supplier interface has four main parts and is designed to be as simple as possible for the suppliers:

  • Tender List which lists the tenders they are currently invited to and the status of those tenders
  • Tender Details where they enter their bids by lane
  • Import/Export where they can export the tender to Excel, fill it out in their favourite tool, and then import it
  • On-line bidding where Suppliers can fine-tune bids on-the-fly

Buyer Interface

There are four main parts to the buyer interface:

  • RFQ/Tendering which is where the multi-round magic happens (which we will dive into shortly)
  • Spot Quote Request where a buyer can empower their organization to execute quick spot requests for a single load in a transparent and compliant way
  • Rate Management where the buyer can store and manage their contracted rates in an auditable and sustainable way
  • Rate Search where the buyer’s stakeholders can search for contracted services and rates (that are stored in the system) in real-time, including historical rate records

The core is the tendering component where the buyers spend most of their time.

A tender can be created from an existing tender (as a copy) or from scratch. Creating a tender from scratch is quite easy:

  1. name it
  2. select a currency group and a default currency
  3. define the transport mode
  4. define the end time of the current round (with start [auto-]populated when you publish it)
  5. define the range for which supplier bids must be valid
  6. optionally upload any attachments with requirements
  7. optionally provide a detailed event description
  8. optionally define any terms and conditions (separate from the file uploads)
  9. create the bid / rate matrix by either
    • copying a matrix from a previous event
    • instantiating one from a best-practice template defined on system implementation
  10. add the suppliers (and you can easily upload their details via Excel)
  11. select/customize notifications
  12. publish

That’s it. Complex freight events can be instantiated in a matter of minutes. Why?

  • pre-defined best practise rate cards can be utilized, or you can copy a previous RFQ
  • pre-defined currency groups make currency definition one-click
  • the platform can store attachments in the platform, creating libraries for your standard specialized requirements, Ts&Cs, etc.
  • the buying organization can define matrices for every mode – region – good type / transport requirement they have on system implementation, including all of the validations and rules that are 100% compatible with Excel, with all of the appropriate lanes
  • the system will automatically select the suppliers associated with the mode and region with the necessary characteristics (hazardous certification, etc.) and all the buyer has to do is check the suppliers it wants to invite
  • there are ready-made automatic notifications in the system for every event you want to action

A key point to note is that TenderEasy supports full Excel capability within the platform, and easy wizard base definition of column and cell settings and properties. For example, each column can have a type, an associated validation rule, display/coloring properties, a visibility definition (buyer or supplier, read or write), etc. and each cell can have a more specific validation based

Another key point is that it’s stupid simple to import benchmark data into (private) columns in the matrix that you can use to evaluate bids (and, automatically, flag any that are too high or too low, possibly with colour coding in the column, or a separate column if you are using colour coding to show the percentage change in a bid from round to round. You simply select “import benchmark” and select the benchmark provider you want to use (which is typically the one you have a subscription with) and the quotes get sucked in automagically.

Bid analysis is also very easy. It’s simple to define a scenario that auto-selects the appropriate carrier and bid for each lane. There’s an integrated scenario builder where you simply define the grouping columns, the supplier group to consider, the tariffs to use, the (optional) adjustments to apply (where you can favour incumbents or innovative carriers and disfavour new carriers or eco-unfriendly carriers or low reliability carriers using a financial cost percentage adjustment or fixed cost modifier), and whether or not you want to use breakpoint optimization (where it will select the FTL amount when that is cheaper than the LTL amount at the current weight / space utilization).

Supplier feedback can also be customized and color coded in a multi-round tender to tell a supplier approximately how far off they are from being selected (e.g. < 10%, 10% to 20%, > 20%). You can generate feedback on any numerical value in the rate card, including service data, emissions, quality etc.

You can create as many (partial) bid analysis as you want, including baselines, using whatever rules you want, and then visualize them graphically in the dashboard, where you can also define thresholds to alert you if any carrier would get too little or too much business. You can also compare them side by side to help you identify the awards you want for each lane. When you figure out what you want, you can incrementally build (by combining partial awards from existing scenarios) the award scenario you want, push it into your external system for contracting, and lock it down as a set of rates to be included in the rate management part of the platform.

If you do need help (which won’t happen often as the platform is very usable, it is usually quite obvious what to do next, and all of the up-front setup on implementation jump-starts pretty much everything you will ever do), there is extensive help built into the platform, training material and self-testing, and a webinar archive.

There is pretty much everything you need out of the box to get going, with the only obvious exceptions being

  • combinatorial carrier optimization (once you have selected the preferred carriers) to balance cost, emissions, and/or delivery time (which they are currently investigating)
  • market-based alerts if a supplier you select is not likely to have current capacity (based on the spot market), if prices are going up quickly (and you should make lock in an award sooner rather than later), or if KPIs are dropping for current carriers (which are currently under investigation, with KPIs and improved benchmarks, which are needed at the foundation level, being investigated with Partners on how to best share this information pro-actively)

In other words, if you do global freight, and you don’t have a custom solution for freight RFQs and spot buys, you should not only have one but include TenderEasy on your shortlist. Once you see the capability a platform like TenderEasy can provide and how much more efficient and effective it can make your freight buyers, you’ll wonder how you ever lived without it. (Like any good e-Sourcing tool, it will quickly pay for itself many times over.)

Serex Procurement: Easy e-Auctions for the Small Enterprise and Lower Mid-Market

Serex Procurement is a point-based solution with one purpose: to replace the spreadsheet that most e-Procurement departments in smaller companies still use to manage their procurement (as well as eliminate the thousands of emails needed to collect prices and update that spreadsheet).

You might ask why, with so many auction-centric mini-suites available on the market, and over 70 e-Sourcing solutions available, we would focus on a niche solution centred around e-Auctions, request-for-price, and centralized buyer-supplier communications. The answer is simple — not every Procurement department is supporting a large enterprise and not every Procurement department needs advanced functionality, a mini-suite, or a pricey solution with bells and whistles they aren’t going to (be ready to) use at the current state of their Procurement evolution.

It may be 2023, but there are a still a large number of Procurement departments still running Procurement events from a spreadsheet, still stuck with an archaic ERP, and, especially in the small enterprise / mid-market, still burdened with a very limited budget for Sourcing and Procurement software. Furthermore, for these departments, this single step up is sometimes everything they need from an e-Sourcing perspective at the current step of their e-Procurement journey which gives them incredible value today. (And, as per our stance that what Procurement needs is a platform that allows them to add one module at a time on their digitization journey, it’s the puzzle piece they need right now.) Also, there are organizations that bought into super suites that have an end-to-end Source-to-Pay process but no auctions, an archaic process that doesn’t support quick and easy auctions, or a user-based licensing model that prevents roll-out to the entire organization. For these organizations, a point-based auction solution with an API that supports data pull and award push through the API (or fixed-format spreadsheets where the current solutions don’t support a modern API) is exactly what they need.

Plus, the blended pricing model Serex Procurement offers makes it very affordable for companies with no budget to start on a gain share and, once the value is proven, move to a fixed price model with no gain share. More specifically, an organization can sign up for as little as $300/month by giving up 50% of their savings, or get unlimited events with 0% gain-share for $5,000/month. (Or pick a tier somewhere in between — Sourcing Innovation recommends doing the math based on the conservative end of the estimated savings on the events you will be running and picking the tier appropriately.) Furthermore, while there is a minimum contract term, an organization can upgrade their tier at any time during the contract term (once they are confident they’ll save more by upping the tier.)

The fully SaaS e-Auction solution has all of the core capability you’d expect from a modern SaaS e-Auction platform, and then some. One of the key differentiators is that the platform is SKU-based and supports the definition of SKU groups in a manner that makes it just as easy to source a direct Bill of Materials as it is to source an indirect lot of office supplies. Not many sourcing/auction platforms make it easy to do both, but Serex Procurement does.

In addition to product groups, the platform also allows the definition of bidder groups which allows an organization to group suppliers that it typically invites for a certain category (indirect) or Bill of Materials (direct). Combine this with the fact that an auction can be defined on a set of product groups and bidder groups, and this makes it extremely quick to define a new auction from scratch, and even quicker to define a new auction as a copy of a historical auction, as this only requires changing the start and end dates and times to make the copy. (Once the copy is made, a buyer can edit whatever they wish.)

As we indicated above, the auction platform supports the standard parameters you would expect, including:

  • start and end time
  • auto-extension of y minutes with a bid in the last x minutes
  • show/hide bidder position
    by setting to “hide”, the platform can be used as a simple request for price
  • tie bids accepted/rejected
  • bid increment
  • max bidder position
  • bid validation window
  • landing factors (% of savings required to leave an incumbent to acknowledge switching costs;
    basically it is a penalty factor on new suppliers)
  • shipping factor (to acknowledge additional transportation costs / currency exchange costs)
  • product yields (to account for variable wastage due to packaging/sizing)

It also supports new bidder definition, new product definition, import from spreadsheet or API for bidder or product definition, and Excel-based bidding for suppliers. Product sheets can be uploaded and attached with as little or as much information as needed, and in-platform messaging allows for easy direct or group-based communication between the buyer and suppliers.

The platform allows the buyer to setup as many email / message templates as they like, to make supplier communications quick and easy as events are created / modified. It’s also very easy to search products, bidders, and past events (to find the perfect instance of the event to copy for quick setup).

When you think about its intended market, about the only thing missing is better quick-hit RFP functionality, as the way you do it now is to set up a private price-based auction. Since they already have document management, bid sheet support, supplier and messaging support, and history management, it would be quite simple to add easy three-bids-and-a-buy RFPs where suppliers can get requests, upload their own options to fulfill a request as well as corresponding prices (at different volume breaks), and provide more detailed information and the buyer can then select one or more to either direct award to or invite to an auction with pre-certified options. Serex has acknowledged that this would be useful, and is in their queue for future development, but they are very customer driven and the queue gets prioritized based upon what current customers are asking for. However, should multiple customers converge on this, it would not take them very long to build it as they have three decades of software development and implementation experience (with the first 20 years in CRM system selection, implementation, and custom integration add/on development — an area their other division are still leading experts in).

If you are in the market niche, we strongly encourage you to check Serex Procurement out as you can get a great, unlimited use, e-Auction solution for a few thousand a month (plus modest gain share, or unlimited use platform for only 5K/month) without the need to bite off more than you can chew solution capability (and cost) wise.