Category Archives: Sourcing Innovation

Is 2010 The Coming of Age for Sourcing and Supply Chain Optimization?

In the beginning, there was the reverse auction. Industry visionaries applied reverse auctions to their sourcing events for commodity and competitive categories (in the mid nineties) and saved a small fortune (which sometimes exceeded 30%, 50%, and even 70% of previous category costs). They were heroes and the world was good.

 

Then, a couple of years later when they circled back to the first categories and held another auction, something unexpected (to them) happened. The total savings shrunk considerably. The average savings, expressed in terms of percentages, dropped from the mid double digits to the (low) single digits. The savings often equalled what they would have expected from a traditional RFX / negotiation process. But the market was a seller’s market and the total event time, and thus the total event cost, was low, so with the right spin, they still looked quite successful. The world was still good.

 

Another couple of years passed, and they circled back to the first categories again. But this time, the market was a buyer’s market again and savings were bound to equal those seen in the initial category reverse auctions, right? Wrong! Instead, something really surprising (to them) happened — instead of saving money, total costs increased — sometimes in the double digits! The world was a dark and scary place. What happened? Could it have been avoided?

In short, as I explained in A Brief History of Optimization (published in By the Buy, the TradeExtensions Newsletter), reverse auctions are not the panacea that many auction platform providers still make them out to be and the identification of real savings through auctions can often be elusive at best. A new technology is needed, and as I have been saying (well, shouting from the rooftops) for years, that technology is optimization.

But, even though the technology is now a mature technology (as strategic sourcing decision optimization turns 10 this year, which makes it middle-aged in Internet years and a senior citizen in dog years), only the true market leaders (which generally account for 10% of the total market) have even tried it, and, in my estimates, less than half of those have truly adopted it on an organization level, even though the analysts have consistently found that strategic sourcing decision optimization consistently saves an average of 12% above and beyond what you’ll get from the best reverse auction.

Simply put, optimization is instant ROI. Guaranteed. In the absolute worst case, your allocation is already perfect and you won’t save any money. But I’ve NEVER seen this happen in practice. Even the most dismal events generally return 3% to 5% savings. Even if we’re only talking a 50M category, that’s still about 2M in savings. And now that you can run an event for (considerably less than) 100K, that’s still at least a 20X ROI!

And it doesn’t take a PhD to use it anymore. Now that most of the platforms offering true strategic sourcing decision optimization have easy to use GUIs, wizard-based constraint definition, and scenario and costing templates built right in — with full Excel integration for data collection, modification, and reporting, optimization is as easy to use as an auction platform. (And in Trade Extensions’ platform, it’s built into the auction.) And while it might still take a couple of days of training to master the advanced features, any of your senior analysts should have no problem picking it up quickly. And once they learn it, they can modify the templates for your organization and train your more junior staff, who will probably only need a couple of events to master most of what they’ll need to do on a daily basis for an average category.

And after reading this recent piece in Industry Week that says “Transformation is Out; Optimization is In” that pointed out that while organizations still want to ‘transform’ how they deliver back-office services, they typically want to move in pragmatic, incremental steps and focus on achieving best-in-class, standardized and optimized delivery models and said that while many organizations remain keen to avoid the costs of new capital and migrating to new suppliers, investment is being made in ensuring existing suppliers and internal processes are delivering optimum value, I’m starting to think that maybe optimization might finally begin to come of age. It appears that the term has finally entered the daily vocabulary of supply management professionals, who should now be more open to at least reviewing optimization solutions. And once they see the savings to be had, and the power that they can have at their fingertips, I can’t help but thinking that the followers are finally going to start to adopt this technology and become leaders in their own right. (The laggards will ignore it for years to come, but that’s okay. Most are still hunkered under their desk waiting for the recession to be over and will eventually go out of business anyway, so let’s not worry about them.)

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Legal Sourcing 101

A recent article in ISM on “crashing the legal department” had some great advice on what you need to do when helping your General Counsel source legal services.

  • You must take the lead in proposing alternate fee arrangements.
  • You must take the lead in finding out what “XYZ” task really costs.
  • You must figure out how waste can be driven out of the system.
  • You must insure that the firms you select can get your rates with the product and service providers they could also make use of.
  • You must help to quicken the death of the billable hour.

For more advice on how you can help decrease legal costs, see:

… here on Sourcing Innovation and

  • e-Discovery + e-Sourcing = e-Normous Savings
  • e-Discovery Stage 1: Ending the Cold War
  • e-Discovery Stage 2: Understand the Risk Profile
  • e-Discovery Stage 3: Execute with Efficiency
  • Sourcing Legal Services Gaining Momentum

… over on e-Sourcing Forum.

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The Role of Optimization in Strategic Sourcing – The Future of Optimization

This series discusses the recent report from CAPS Research on “the role of optimization in strategic sourcing”. The primary goal is to highlight, clarify, and, in some cases, correct parts of the report that are important, confusing, or incorrect to insure that you have the best introduction to strategic sourcing decision optimization that one can have.

What will the future hold? The authors predict development in the following two directions:

  1. more self-serve applications that require no third-party involvement
  2. more powerful services that handle even larger, more complex problems

In other words, the same-old same-old for the foreseeable future. I’m sad to say I have to agree. Until strategic sourcing decision optimization catches on, most of the current providers are not going to make significant investments exploring new vistas for a solution that the majority of their customers aren’t even coming close to stressing out today. You see, current applications are just “scratching the surface” of potential uses. Optimization is very powerful and could ultimately be used to optimize the entire supply chain.

However, the applications will continue to get more user-friendly and easier on the self-service front as providers get exposed to even wider ranges of models and uses and refine their interfaces to support even more possibilities (while simplifying the definition of the average model). So this is something to look forward to.

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The Role of Optimization in Strategic Sourcing – Challenges / Issues

This series discusses the recent report from CAPS Research on “the role of optimization in strategic sourcing”. The primary goal is to highlight, clarify, and, in some cases, correct parts of the report that are important, confusing, or incorrect to insure that you have the best introduction to strategic sourcing decision optimization that one can have.

This chapter addresses the challenges associated with optimization from a user perspective, which, unfortunately, are numerous. The good news is that they are all easily overcome if you are aware of the issues and address them early on.

User challenges fall into three categories:

  • Increased Knowledge Requirement
    • some people will be afraid of the unknown
      easily addressed with a little education
    • others think optimization is a threat to their job
      easily addressed by explaining optimization is not intelligent, users still need to formulate the models
    • many do not understand the power associated with optimization
      easily addressed by a well-defined pilot
    • utilization can require a significant cultural change
      easily addressed by helping an organization define a change management plan
    • there is a learning curve
      easily addressed through repeated use over time
    • the proper constraints need to be utilized for maximum results
      easily addressed with provider support
  • Measurement of Results
    • it can be difficult to tie cost/benefit to the tool
      but a baseline scenario always provides a good measure
    • market timing can affect the event
      but what-if scenarios can illustrate how much more could be saved/lost with price changes
    • good historical data may not be available
      which means you form a baseline by comparing the unconstrained scenario to the cost of a human-generated solution
    • overuse can lead to “analysis paralysis”
      which means you define the relevant scenarios before you start using the tool, and stop when you have analyzed all of the relevant ones
  • Software Provider Issues
    • tools are still evolving
      the provider needs to explain how current functionality will not be affected, only additional capabilities will be added
    • it’s hard for a novice buyer to select the right solution(s)
      a good provider could recommend that the buying organization engage an external, unbiased, expert for educational purposes before evaluating vendor solutions
    • internationalization — are the right languages supported
      this is tough since no tools allow users to add new languages, but there’s no reason a buying organization could not work with a solution provider to create a new translation if it was needed
    • installed vs. on-demand; supplier-driven vs. buyer driven
      this requires a good understanding of your needs
    • some providers recommend minimum event sizes
      well, this is their problem … if they don’t want your business, what can you do?
    • the provider might not understand the buyer’s business
      the provider simply has to spend some time getting to know the customer

Other points to note are the following statements which are somewhat misleading:

  • the first time you save a lot of money, then returns diminish
    while it is true that the first application on a category will see more substantial returns than subsequent applications, creativity can be used to repeatedly find savings that you might not expect through product substitutions, supplier-defined bundles, and alternative delivery requirements
  • demand collaboration, sales forecasts, and performance management … may be perceived as conflicting with optimization
    while some providers who have these solutions and do not have optimization may position these as “alternatives”, they are not the same thing and they do not conflict with optimization — in fact, they enhance optimization because better forecasts and improved performance give you more reliable data which gives you more reliable models which give you better results

Finally, the chapter summarizes some of the suppliers’ perception of adoption barriers which are worth noting.

  • organizational issues
    • leadership conviction is required for a sale
    • a long term focus is required
    • it’s a hard sell to any organization not already using other e-sourcing tools
    • organizations using other e-tools, including auctions, advanced forecasting, and performance management often see optimization as unnecessary
  • inertia
    • too many organizations are content with costs that are “good enough”
    • others “fear the unknown”
    • optimization is not a well understood term in sourcing
    • there is the fear that optimization can eliminate jobs
  • implementation issues
    • significant resources are often spent on supporting today’s customers, leaving few resources for developing better solutions
    • users rarely have clean data
    • the buyers are not always sophisticated enough to properly structure the problem

In other words, your success is in your hands — you have to see the value, acquire a solution, and get trained if you really want to see optimal results.

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The Role of Optimization in Strategic Sourcing – Optimization and Reverse Auctions

This series discusses the recent report from CAPS Research on “the role of optimization in strategic sourcing”. The primary goal is to highlight, clarify, and, in some cases, correct parts of the report that are important, confusing, or incorrect to insure that you have the best introduction to strategic sourcing decision optimization that one can have.

In this chapter, the benefits of using optimization with reverse auctions are discussed and a number of case studies are presented. Specifically:

  • Fasteners #1
    Before the event, which was conducted as a reverse auction followed by an optimization-based analysis, the suppliers were projecting a 20%+ price increase. After the two-stage event, the end result was an increase of 11%, which was split among one new supplier and two incumbents, while two incumbents lost business.
  • Fasteners #2
    A company decided to centralize its buy across eight business units. A reverse auction followed by optimization-based analysis identified savings of over $80,000.
  • Shelving
    A shelving buy for 35 stores covering 150 items from 10 different sources realized a total savings of 10% when optimization was applied after a reverse auction.

Next, the chapter discussed the challenge of tiered and bundled bids. They are challenging in a number of respects — they are a challenge to define, they can be a challenge to explain, they are often a challenge to “normalize”, and they can be a big challenge to implement for even sophisticated developers — but not as challenging as the report would have you believe. After all, a few providers support both of these bid-types, and at least two do so in their self-service tools.

The statement that only after the model is solved can it be discovered if the business allocated to a supplier would have been sufficient to earn a discount is false! While the specific solution being used, by the company in the example, may not have supported discounts, a number of solutions on the market fully support tiered and volume discounts, which include the type described within the example. These solutions support models which dynamically update the total cost when a threshold is reached. (I have personally designed and implemented two solutions with this capability, one of which is still on the market.)

The one thing that should have been noted, but wasn’t, is that implementing these discounts usually requires a sophisticated set of binary equations. If discounts are required in bulk, the size and complexity of the model will increase significantly and this can negatively impact solve time in a big way.

In addition, not only are tiered and bundled bids the most common form of creative bidding supported by many optimization applications, but they are also the most powerful when combined with discounts and used appropriately.

Finally, there’s no reason that the optimization cannot be applied on-line, in real-time, during the auction. If you’re buying a commodity, or if you can completely specify your business rules and constraints up-front, you can run an optimization-enhanced auction and make (automated) contract offers immediately after the optimization completes. While most providers don’t yet have this capability, Trade Extensions, for example, does. Now, the model has to be of a size and complexity that can be solved in real-time during the auction, but thanks to the advances in processing power and solution algorithms that have materialized over the past five years, you’d be surprised just how big the model can get and still solve in the 15 to 30 minutes typically allocated for a mid-size real-time auction.

Next Part VIII: Challenges / Issues

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