Category Archives: Decision Optimization

The 39 Steps … err … The 39 Clues … err … The 39 Part Series to Help You Figure Out Where to Start with Source-to-Pay

Figuring out where to start is not easy, and often never where the majority of vendors or consultants say you should start. They’ll have great reasons for their recommendations, which will typically be true, but they will be the subset of reasons that most benefits them (as it will sell their solution), and not necessarily the subset of reasons that most benefits you now. While you will likely need every module there is in the long run, you can often only start with one or two, and you need to focus on what’s the greatest ROI now to prove the investment and help you acquire funds to get more capability later, when you are ready for it. But figuring out how much you can handle, what the greatest needs are, and the necessary starting points aren’t easy, and that’s why SI dove into this topic, with arguments and explanations and module overviews, both broader and deeper than any analyst firm or blogger has done before. Enjoy!

Introductory Posts:
Part 1: Where Do You Start?
Part 2: Where Should You Start?
Part 3: You Start with …
Part 4: e-Procurement, and Here’s Why.

Part 5: Defining an e-Procurement Baseline
Part 6: There are Barriers to Selecting an e-Procurement Solution (and they are not what you think)
Part 7: Over 70 e-Procurement Companies to Check Out

Interlude 1
Part 8: What Comes Next?

Spend Analysis
Part 9: Time for Spend Analysis
Part 10: What Do You Need for A Spend Analysis Baseline, I
Part 11: What Do You Need for A Spend Analysis Baseline, II
Part 12: Over 40 Spend Analysis Vendors to Check Out

Interlude 2
Part 13: But I Can’t Touch the Sacred Cows!
(including Over 20 SaaS, 10 Legal, and 5 Marketing Spend Management / Analysis Companies to Check Out)
Part 14: Do Not Stop At Spend Analysis!

Supplier Management
Part 15: Supplier Management is a CORNED QUIP Mash
Part 16: Supplier Management A-Side
Part 17: Supplier Management B-Side
Part 18: Supplier Management C-Side
Part 19: Supplier Management D-Side
Part 20: Over 90 Supplier Management Companies to Check Out

Contract Management
Part 21: Time for Contract Management
Part 22: Contract Management is a NAG: Let’s Start with Negotiation
Part 23: Contract Management is a NAG: Let’s Continue with [Contract]Analytics
Part 24: Contract Management is a NAG: Let’s End with [Contract] Governance
Part 25: Over 80 Contract Management Vendors to Check Out

Part 26: Time for e-Sourcing
Part 27: Breaking Down the ORA of Sourcing Starting With RFX
Part 28: Breaking Down the ORA of Sourcing Continuing with e-Auctions
Part 29: Breaking Down the ORA of Sourcing Ending with [Strategic Sourcing Decision] Optimization
Part 30: Over 75 e-Sourcing Vendors to Check Out!

Invoice-to-Pay (I2P):
Part 31: Time for Invoice-to-Pay
Part 32: Breaking Down the Invoice-to-Pay Core
Part 33: Over 75 Invoice-to-Pay Companies to Check Out

Part 34: How Do I Orchestrate Everything?
Part 35: Do I Intake, Manage, or Orchestrate?
Part 36: Over 20 Intake, [Procurement] [Project] Management, and/or Orchestration Companies to Check Out
Part 37: Investigating Intake By Diving In to the Details
Part 38: Prettying Up the Project with Procurement Project Management
Part 39: Deobfuscating the Orchestration and Fitting it All Together

Source-to-Pay+ is Extensive (P30) … And Sourcing IS Very Extensive … So Here Are 75 e-Sourcing Companies to Check Out!

And now the next post you’ve all been waiting for! A partial, starting, list of 75 e-Sourcing providers that may (or may not) meet some, or many, of the core baseline capabilities we outlined in the last three parts of this series (Part 27, Part 28, and Part 29) as we discussed the Optimization, RFX, and Auction sides of e-Sourcing today.

As with our lists of e-Procurement Companies (in Part 7), Spend Analysis Companies (in Part 12), Sacred Cow Companies that do, or support, customized “spend” analysis on Marketing, Legal, and SaaS (in Part 13), Supplier Management Companies (in Part 20), and Contract Management Companies (in Part 25), we must again give our disclaimer that this list is in no-way complete (as no analyst is aware of every company), is only valid as of the date of posting (as companies sometimes go out of business and acquisitions happen all of the time in our space), and does NOT include any e-Procurement vendors that support simple requisition or quick-quote capability to select vendors already in the system as that is not how we defined RFX capability.

Furthermore, as we’ve said before, not all vendors are equal, and we’d venture to say NONE of the following are equal. The companies below are of all sizes (very small to very large, relative to vendor sizes in our space), cover the baseline differently (in terms of percentage of features offered, the various degrees of depth in the feature implementations, and differing levels of customization for a vertical), offer different additional features, have different types of service offerings (backed up by different expertise), focus on different company sizes, and focus on different technology ecosystems (such as plugging into other platforms/ecosystems, serving as the core platform for certain functions or data, offering a plug-and-play module for a larger ecosystem, focussing on the dominant technology ecosystem(s) in one or more verticals), etc.

Do your research, and reach out to an expert for help if you need it in compiling a starting short list of relevant, comparable, vendors for your organization and its specific needs. For many of these vendors, good starting points can again be found in the Sourcing Innovation archives, Spend Matters Pro, and Gartner Cool Vendor write-ups if any of these sources has a write-up on the vendor.

Company LinkedIn
HQ (State)
Optimization RFX Auction
Aestiva 17 California, USA R
Archlet 46 Switzerland O R
Aufait 114 India R
Bamboo Rose 205 Massachusetts, USA R
Bideg 3 Turkey A
Bonfire 87 Ontario, USA R
Claritum 8 United Kingdom R
Cloudia 40 Finland R
Cobblestone Software 131 New Jersey, USA R
Corcentric 588 New Jersey, USA R
cosmoONE 20 Greece R A
Coupa 3674 California, USA O R A
Deep Stream 25 United Kingdom R A
Delta eSourcing ?? United Kingdom R
ebidToPay ?? Bavaria R
Elcom 18 United Kingdom R A
eSupplier 6 Dubai R A
FairMarkit 161 Massachusetts, USA R
FullStep 128 Spain R
GEP 4650 New Jersey, USA O R A
Intenda 111 South Africa R
Ion Wave 22 Missouri, USA R A
ISPnext 59 Netherlands R
Ivalua 849 Ivalua O R A
Jaggaer 1266 North Carolina, USA O R A
K2 Sourcing 10 Wisconsin, USA R A
Keelvar 117 Cork, Ireland O R A
LevaData 58 California, USA O R
LGX Corporation ?? North Carolina, USA O R
LiveSource 7 Georgia, USA R
loopio 304 Ontario, Canada R
Market Dojo 34 United Kingdom R
MarketPlanet 72 Poland R A
Medius 562 Sweden R A
Merlin Sourcing 29 Germany R A
MySupply 15 Germany O R
NegoMetrix (Mercell) ?? Netherlands R A
Newtron 54 Germany R A
Oalia 22 France R
Oboloo 6 United Kingdom R
One Market (LogicSource) 307 Connecticut, USA R
One More Source ?? Bulgaria R
Onventis 129 Germany R A
Pantavanij 213 Thailand A
Penny Software 35 Saudi Arabia R
PostRFP ?? United Kingdom R
PratisPro (SabancıDx) ?? Turkey R A
Proactis 557 United Kingdom R
ProcurementFlow 5 Estonia R
ProcurePort 8 Indianapolis, USA R A
ProcureWare (Bentley Systems) 4830 Pennsylvania, USA R
Prokuria 8 Romania R A
Promena 20 Turkey R A
Prospeum 6 Germany R
Raindrop 27 Raindrop R
Ready Contracts 243 Australia R
RFP360 20 United States R
SafeSourcing 10 Arizona, USA R
SAP (Ariba) 2963 California, USA O R A
ScanMarket (Unit4) 60 Denmark R A
ScoutRFP 44 California, USA R A
Serex Procurement Solutions ?? Ontario, Canada R
Simfoni (EC Sourcing) 260 California, USA O R A
Sorcity ?? Texas, USA R A
SourceDogg 31 Ireland R
Sourcing Force 4 Ontario, Canada R A
SupplyFrame 310 California, USA R
SupplyOn 239 Germany R A
Synertrade 180 Germany R
TenderEasy (Alpega) 6 Sweden R
The Green RFP ?? Texas, USA R
Trade Interchange 27 United Kingdom R A
Vendorful 14 New York, USA R A
Vortal 188 Portugal R A
Zycus 1464 New Jersey, USA R A

And now, as you probably guessed, it’s on to Invoice-to-Pay in Part 31.

Source-to-Pay+ is Extensive (P29) … Breaking down the ORA of Sourcing, Concluding with Optimization

In our first post, Part 26, we noted that, after covering e-Procurement, Spend Analysis, Supplier Management, and Contract Management, it was finally time for Strategic Sourcing. When it comes to Sourcing, we have to deal with the ORA et labora. The work, and the prayer (that it gets the results we want). But at least when it comes to the prayer, we have three tools at our disposal:

  • Optimization
  • RFX
  • Auction

In Part 27 we started with the most classic sourcing tool, RFX, where RFX stands for Request for X, where X could be Bid, Information, Proposal, Quote, etc. depending on the depth of response required and the terminology used in the industry and geography the RFX is being issued in.

Then, in our last post, Part 28, we continued with the primary alternative to RFX, e-Auction. In e-Auction, instead of asking for quotes which will be reviewed in a long, detailed, often weighted process, you’re asking for real-time quotes in an online auction where you can update your bids until you self-select to drop out.

The last tool at our disposal, which does require bids to be collected first (which does not need to be through RFX or e-Auction but can be done through every buyer’s favourite tool, Excel), is strategic sourcing decision optimization. It’s not used nearly enough considering that it will practically always identify a lower cost scenario, and even if you find the lowest cost scenario impractical, you understand exactly how much more a relationship is costing you and you are quantifying how much a better relationship, better quality, lower risk is worth to you and can make more informed, and better, decisions in the future.


Pillar #1: Solid Mathematical Foundations
The algorithms used must be sound (mathematically correct in all situations) and complete (capable of analyzing all possible solutions). An optimization engine based on Mixed Integer Linear Programming (MILP) would qualify as hybrid simplex approaches will provably converge on an optimal answer given sufficient time (and one can always compute a maximum distance from optimal based upon the calculations done to date since the longer the algorithm churns for, the more the lower bound on the optimal solution increases). In contrast, the application of many heuristic, simulation, or evolutionary approaches are likely not valid since the majority of these techniques do not guarantee full exploration of the potential solution space and, therefore, aren’t guaranteed to find the true optimal solution (although they may get close).

Pillar #2: True Cost Modelling
The model must allow you to define the full cost model, not just one (or two) fixed costs. For example, if a buyer is sourcing direct material, the platform must allow the buyer to include all indirect and incurred costs, such as freight, tariff, storage, processing, and marketing differential costs in the definition of the cost model.

Pillar #3: Sophisticated Constraint Analysis
The model must allow the buyer to build a model that capture a realistic approximation of real world constraints. If the business must select at least 2 suppliers, will not accept a product mix with an average quality or reliability of less than 8 (/ 10), if a supplier has a maximum capacity, or if a minimum allocation must be given to an incumbent because of a contract still in play, all this needs to be captured.

A strategic sourcing decision optimization platform must support four core constraint types. Capacity constraints that define a supplier (‘s location) capacity limit. An allocation constraint that defines a minimum or maximum allocation to a supplier (group) based upon existing contracts or business policies. Risk Mitigation constraints that ensure that business policies on supplier or geographic splits designed to reduce risk are captured. Qualitative constraints that allow for qualitative ratings such as reliability, quality, relative sustainability, etc. on a mathematical (e.g. 1 to 10) scale to be defined.

Pillar #4: What If Capability
The platform must support the creation of multiple what-if scenarios, each with different constraints. Buyers should be able to create them from scratch, or as modified copies of existing what-if scenarios.

Out-of-the-Box Scenarios
The solution should contain multiple out of the box scenario definitions, including unconstrained, x-supplier, incumbent, etc. that automatically generate these what-if scenarios for the bids being evaluated for optimization.

Scenario Comparison
The solution must contain a built-in capability for (side-by-side) scenario comparison that allows a buyer to easily see the cost differentials and get a feeling for what each scenario is costing them.


Integrated Analytics
Optimization models take exponential time to solve. While small models can solve in minutes, and even seconds, on a high powered multi-core machine, large models can take hours or days. The key to rapid model solution is minimizing model size. This can often be done by way of a preliminary analysis that determines that some supplier bids are just to high to ever be acceptable, some qualitative factors too low to ever be acceptable, and some supplier locations are in geographic regions that are just too risky. Eliminating award possibilities that will never be made can drastically decrease model size and solution time.

Constraint Relaxation
If a model is unsolvable, but could be solved by solved with lesser constraints, the platform should be able to identify which (near) minimal constraint set is preventing a solution and identify which (minimal) relaxations would allow a solution and present those to the user, who can accept them, or use that as input for defining an alternate relaxed model that may permit a solution. (Remember best practice is to prioritize constraints and add them incrementally until the model becomes unsolvable as that allows you to always choose the least important constraints to relax for solvability.)

Sensitivity Analysis
In optimization, a sensitivity analysis tells you how dependent a solution is on a certain constraint and what the impact of removing the constraint that is currently preventing a lower cost solution in terms of hard dollars. (For example, insisting an incumbent supplier get 50% of the award might be costing you $10 Million in a $100 Million category, while reducing the minimum to 25% might only cost you $2 Million [as it the supplier is more competitive on some products than others].)

Hard and Soft Constraints
The platform should allow you to define constraints as hard and soft. When a model is unsolvable and needs to be relaxed, the solution will only allow soft constraints to be relaxed. Furthermore, it should also allow for an indication of when a soft constraint can be relaxed. For example, average quality can only be reduced from 9 to 8 if the savings increases by at least 3%.

Integrated Freight Model Support
In addition to supporting true cost modelling, the platform should also have built in freight models that understands transport types and modes (truck vs rail, refrigerated vs dry, etc.) and allow for the easy definition of complex freight models when those models might allow for overall lower costs of ownership when carrier bids are also included in the model.

Of course, this is not a complete list of what a strategic sourcing decision optimization platform might have, or necessarily should have, as systems continue to improve, but a baseline of what they must have to be considered a modern solution.

Next up: the vendor list in Part 30.

Algorhythm: Twenty Years Later and the Optimization Rhythm Has Not Missed a Beat

It’s been almost a decade since we covered Algorhythm (Part I and Part II), and that’s because the last time the doctor caught up with them mid-decade, they were deep into creating their new accelerated cloud-native rapid application development platform, called AppliFire, with native mobile-first development support capabilities. And while it was very interesting, it was not Supply Chain focussed at the time, and not the core of what SI covers.

But fast forward about five years later, and Algorhythm has re-built their entire Supply Chain Planning, Optimization and Execution Management platform on top of this new development platform and now has one of the most modern cloud-native suites on the market — which not only has the capabilities of big name peers like Kinaxis, E2 Open and Infor, but also the ability to run on any mobile platform with seamless integration across modules and platforms.

And their optimization capabilities are still among the best on the market, and possibly only rivaled by Coupa Sourcing Optimization (powered by their Trade Extensions acquisition) — demonstrated by the fact that whether you are dealing with a demand plan, manufacturing plan, production plan, supply plan, logistics plan, route plan, or any other plan supported by the system, their system can find the optimal solution no matter how many demand locations, plans, sites, suppliers, products, lanes, etc. — and can do so rapidly if the user doesn’t overload the scenario with unnecessary constraints. (Even without constraints, these models can get huge, as the doctor knows all too well, but yet they solve rather rapidly in the Algorhythm platform.)

The Algorhythm suite of twelve (12) integrated Supply Chain Planning, Optimization, and Execution Management Modules is not only one of the most complete end-to-end suites on the market, but one of the most seamlessly integrated as well. It’s very easy to take the output of the “Demand Planner” (which allows the entire organization to collaborate on forecasts) and pump it into the “Manufacturing Network” (which integrates with the “Distribution Network” and “Inventory Planner”) to create a manufacturing (site) plan and then pump that into the “Production Planner” to create a manufacturing schedule by site and then push that into the “Logistics Planner” to determine the best logistics plan and then push that output into the “Route Planner” to optimize lanes and so on. (The suite also includes a “Supply Planner” to optimize individual shipments for JIT manufacturing; a S&OP planner to help sales and operations balance demand vs. supply; a “Manufacturing Execution System” to break PDI (Production Parameters) down, fetch actual production data, and validate results; a “Distributor Ordering” Management module to automatically create distributor orders across thousands of distributors; and a “Beat Planner” to optimize last mile delivery for outbound supply chain for distributors or CPG companies in geographies — like Asia — where last mile is difficult (due to inability to send large trucks, need to restock daily, etc.) With the exception of strategic sourcing and initial supplier selection, they basically have inbound demand to outbound supply covered in terms of supply chain optimization and management once you know the suppliers you are going to buy from and the products that are acceptable to you.

The UI is homogenous across the suite, and the modern web-based components such as drill-down menus, buttons, pop-ups, and so on make the suite easy to use — especially when it comes to tables and reports. The application supports built-in dynamic Excel like grids and tables which can be altered dynamically on the fly with built-in pagination to make navigation and view-control navigable, especially on tablets (for users on the go). It also supports standard (Excel-like) charts and graphs with drill-down, as well as modern calendar and interactive Google Map components. Navigation is easy, with bread-crumb trails so a user doesn’t get lost, and response time is great. It’s powerful and useable, which is exactly what you need to manage your supply chain on-the-go.

There’s a reason they have some of the biggest names in the F500 as clients, and that reason is their unique combination of

  1. power,
  2. ease of use, and
  3. and understanding of the Asian supply chain needs (especially around last-mile delivery).

The last point is especially relevant as many of the big name American (and even German) supply chain companies don’t really understand the unique complexities of (last-mile) supply chains in India and Asia. However, Algorhythm’s unique capability combined with their understanding has made their platform a force to be reckoned with in a market that is one of the hardest in the world. And as a result, they have built a platform that is more than sufficient for every other market as well. the doctor is looking forward to seeing more of Algorhythm outside of the Asian market as, at least in his view, the supply chain market in general needs a good kick in the pants as innovation there-in has considerably lagged the Source-to-Pay market that we primarily cover here on SI.

So if you need a good Supply Chain Orchestration solution, the doctor strongly encourages you to check out Algorhythm … you won’t be disappointed.

Optimization: Is it at least time to move beyond logistics and indirect sourcing?

A big focus of this blog is, of course, Strategic Sourcing Decision Optimization (SSDO), one of the few advanced sourcing methodologies guaranteed to save your organization, on average, 12% if correctly applied (as demonstrated in two back-to-back studies by Aberdeen last decade and discovered over and over again by organizations applying it properly) and the doctor‘s specialty.

But it’s not the only place you can apply optimization in Supply Management to save money. Another area, as covered a number of times on SI, is Supply Chain Network Optimization (SCNO). And, of course, some companies just focus on the intersection and do Logistics optimization. But this is not everything that can be done, or should be done, especially in an age where many industries now see The End of Competitive Advantage and don’t actually own physical assets, leasing them as need be to create the products and services desired by their prospective customers.

In this situation, what matters is Asset Optimization, where you optimize a one-time dynamic network to minimize sourcing, network, and logistics costs to minimize the total supply chain costs associated with the product you wish to produce. This is easier said than done. In sourcing, you are mainly considering bids, lanes, and associated costs to compute the optimal TCO (Total Cost of Ownership), and if lifetime costs and metrics are available, or TVG (Total Value Generated) with respect to a fixed situation. In network optimization, you are optimizing the location of owned factories, supplier production centers, warehouses, and retailers to optimize the distribution costs. But in asset network optimization, you have to simultaneously consider the network and associated distribution costs, the sourcing requirements and associated production costs, and the costs of using, or not using, the resources you already have available and contracts you have already negotiated. In addition, you have to consider the risks associated with each potential supplier and location, the sensitivity of the overall asset network to each supplier and location (and is there a single point of failure), and the ability to dynamically alter the network should a failure occur or customer demands change.  And track all of that information.

Plus you have all of the difficulties associated with each type of optimization. With respect to the network, there will be many alternatives for production site, each site will have multiple, and different, asset lines, and each asset will be qualified for a certain operation with respect to a certain product. In addition, some assets will be more efficient and cost effective, and unqualified assets will have a qualification/certification step, which will require limited manpower – a variable that does not need to be modelled in traditional sourcing or SCNO models. It’s a very difficult problem that requires modelling of multiple types of variables and constraints at multiple levels at multiple times. And this last requirement makes the model even more complex.

Plus, in a traditional sourcing model, you don’t really need to consider “time”, as it doesn’t matter how often the trucks deliver your product, just how many trucks are needed to deliver your product as you are billed FTL or LTL by the delivery. And it doesn’t matter what production schedule the supplier(s) use(s) as long as your products are ready on time, so only the total volume need be considered. But when you are dealing with production models, especially when trying to dynamically construct and optimize an asset network, production schedules are significant. If a certain location only has 30% of capacity left available and can only schedule it during a given time-frame, that has to be taken into account. If some of the products have to be delivered before they can complete the first production run, then there has to be a location that is able to do so. And if a continual supply is needed over nine months, the production cycles should more or less line up with minimal overlap as, otherwise, inventory costs would soar.

It’s a complicated problem, but one that is becoming more and more important in fast moving industries such as fashion and consumer electronics — and one that most SSDO providers can’t address. Why?

First of all, they don’t track the necessary data.

Secondly, they don’t have the right underlying optimization platform.

Third, they don’t have the skills to build the right model.

But recently, a few of the bigger players with optimization have started not only tracking all of the direct (material) sourcing requirements, but assets as well.  So the data is there.

Secondly, a few of the optimization platforms have become significantly more powerful and flexible (and now have the necessary computing power under the hood to support them) and could, at the very least, run a series of optimization models (according to different time-spans, which minimizes the need to consider complex timing constraints in a single model) to tackle problems such as this.

Thirdly, there are independent experts with decades of experience who can help design the right model.

So why are none of the big players doing it?  It seems logical, and soon necessary, if an organization wants to continue to identify, and capture, new sources of value year-over-year.