Category Archives: Decision Optimization

The Power of Optimization-Backed Sourcing is in the Right Sourcing Mix Across Scales of Size and Service

the doctor has been pushing optimization-backed sourcing since Sourcing Innovation started in 2006. There’s a number of reasons for this:

  • there is only one other technology that has repeatedly demonstrated savings of 10% or more
  • it’s the only technology that can accurately model total cost of ownership with complex cost discounts and structures
  • it’s the only technology that can minimize costs while adhering to carbon, risk, or other requirements
  • it’s one of only two technologies that can analyze cost / risk, cost / carbon, or other cost / x tradeoffs accurately

However, the real power of optimization-backed sourcing is how it can not only give you the right product mix, but the right mix across scales. This is especially prevalent when doing sourcing events for national or international distribution or utilization. Without optimization, most companies can only deal with suppliers who can handle international distribution or utilization. This generally rules out regional suppliers and always rules out local suppliers, some of whom might be the best suppliers of goods or services to the region or locality. While one may be tempted to think local suppliers are irrelevant because they will struggle to deliver the economy of scale of a regional supplier and will definitely never reach the economy of scale of a national (or international) supplier, unit cost is just one component of the total lifecycle cost of a product or service. There’s transportation cost, tariffs, taxes, intermediate storage, and final storage (of which more will be required since you will need to make larger orders to account for longer distribution timelines) among other costs. So, in some instances, local and regional will be the overall lowest cost and keeping them out of the mix increases costs (and sometimes increases carbon and risk as well).

When it comes to services, the right multi-level mix can lead to savings of 30% or more in an initial event. the doctor has seen this many times over his career (consulting for many of of the strategic sourcing decision optimization startups) because while the big international players can get competitive on hourly rates where they have a lot of resources with a skill set, when it comes to services, there are all in-costs to consider, which include travel to the client site and local accommodations. The thing with national and international services providers is that they tend to cluster all of their resources with a certain skill set in a handful of major locations. So their core IT resources (developers, architects, DBAs, etc.) will be in San Francisco and New York, their core Management consultants will be in Chicago and Atlanta, their core Finance Pros in Salt Lake City and Denver, etc. So if you need IT in Jefferson City, Missouri, Management in Winner, South Dakota, or accounting in Des Moines, Iowa, you’re flying someone in, putting them up at the highest star hotel you have, and possibly doubling the cost compared to a standard day rate.

However, simple product mix and services scenarios are not the only scenarios optimization-backed sourcing can handle. As per this article over on IndianRetailer.com, retailers need to back away from global sourcing and embrace regional (and even local) strategies for cost management, supply stability, and resilience. They are only going to be able to figure that out with optimization that can help them identify the right mix to balance cost and supply assurance, and when you need to do that across hundreds, if not thousands, of products, you can’t do that with an RFX solution and Microsoft Excel.

Furthermore, when you need to minimize costs when a price is fixed, like the price of oil or airline fuel, you need to maximize every related decision like where to refuel, what service providers to contract with, how to transport it, etc. When it can cost up to $40,000 to fuel a 737 for a single flight (when prices are high), and you operate almost 7,000 flights per day with planes ranging from a gulf stream that costs about $10,000 to refuel to a Boeing 747 that, in hard times, can cost almost $400,000 to refuel, you can be spending $60 Million a day on fuel as your fleet burns 10 Million gallons. Storing those 10 Million gallons, transporting those 10 Million gallons, and using that fuel to fuel 7,000 planes takes a lot of manpower and equipment, all of which has an associated cost. Hundreds of thousands of associated costs per day (on the low end), and tens of millions per year. Shaving off just 3% would save over a million dollars easy. (Maybe two million.) However, the complexity of this logistics and distribution model is beyond what any sourcing professional can handle with traditional tools, but easy with an optimization backed platform that can model an entire flight schedule, all of the local costs for storage and fueling, all of the distribution costs from the fuel depots, and so on. (This is something that Coupa is currently supporting with its CSO solution, which has saved at least one airline millions of dollars. Reach out to Ian Milligan for more information if this intrigues you or how this model could be generalized to support global fleet management operations of any kind.)

In other words, Optimization-Backed Sourcing is going to become critical in your highly strategic / high spend categories as costs continue to rise, supply continues to be uncertain, carbon needs to be accounted for, and risks need to be managed.

Don’t Overlook the Network (that Corresponds to the Award)

According to a recent Forbes article on Supply Chain Software’s Best Return on Investment, per $1 Billion in company revenues, no supply chain application has a better return on investment (ROI) than network design! And the doctor couldn’t agree more.

Just like strategic sourcing decision optimization is the best bang for the buck in Source to Pay, with documented, average returns of up to 12% year-over-year (by multiple analyst firms) as it can minimize total landed cost, and even total cost of ownership in some cases (including internal inventory costs, waste costs, etc.) and not just bids, while ensuring all business constraints are adhered to, an optimization-backed network design application can help minimize overall organizational supply chain costs. This is because a supply chain network optimization platform can minimize transportation costs, intermediate warehousing costs, tariffs, waste, emergency replenishment in the case of an unexpected stock-out, carbon/GHG, etc.

Plus, as the article notes:

  • network design solutions are absolutely necessary to uncover business value when the production-distribution infrastructure is large (and not just because you just can’t model that infrastructure in a spreadsheet)
  • network design solutions can look at Total Cost to Serve (TCTS) across a wide-range of fixed and marginal costs (and identify unintended circumstances of network design changes that could cause marginal costs to skyrocket)
  • network solutions can allow for multiple scenarios to be defined and multiple models to be run and cross-model and cross-scenario Pareto analysis to be run, trade-offs to be analyzed, and the best decisions to be made

One point that should not be overlooked is that projects will take some time, and it’s not because of the complexity of the network modelling or the time it takes to run the scenarios (as modern computing architectures are super powerful and modern algorithms highly optimized to be efficient and take advantage of massively parallel processing), it’s because you need a lot of good, clean, data. It can take months (and months) just to identify, collect, clean, and enrich the data required for global supply network optimization. But once you do that, the ROI will be beyond the expectations you have for every other supply chain solution.

The article, which describes a project to redesign the spare parts supply chain for a global automotive manufacturer, resulted in a redesign that immediately reduced network costs by 4% and identified transportation cost reduction opportunities through consolidation and re-allocating of routes to a smaller set of 3PLs that will save another 2.5% at contract renewal time. In today’s climate, especially in direct supply chains, a savings of 6%+ across the entire supply chain, and not just one category, is phenomenal!

Plus, as the article notes, in the age of sustainability, reduced transportation mileage and fuller trucks also equate to significant reductions in carbon emissions. WHAT A BONUS!

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.

e-Procurement
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

e-Sourcing
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

Orchestration:
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 some 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.

Finally, a second reminder that inclusion on this list DOES NOT imply Sourcing Innovation is recommending the vendor.

Company LinkedIn
Employees
HQ (State)
Country
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.

BASIC

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.

ADVANCED

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.