Category Archives: Decision Optimization

The Role of Optimization in Strategic Sourcing – Implementation 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.

The chapter starts out with a list of ten questions designed to help organizations evaluate the appropriateness of optimization for their sourcing event. And while I still contend that every event can benefit, the question list will help you determine how beneficial optimization could be. In short, the questions were:

  1. How complex is the buy?
    The more complex the buy, the more value decision optimization will offer but, unless you are an expert, the more likely you are to need provider support (at least in the beginning).
  2. What prior experience do you have?
    There is a learning curve associated with optimization.
  3. Do you need a suite or will a stand-alone solution suffice?
    If you can get by with a stand-alone solution, you can often get off to a faster start.
  4. Do you have accurate and clean data?
    You need clean data to create historical baselines and accurate models.
  5. What do you expect to get from using optimization?
    Does a more thorough and powerful analysis have a good chance of finding a significantly better solution?
  6. How powerful does the optimization software need to be?
    Will the software you have in mind cut it?
  7. To what extent is training provided?
    Implementing optimization requires trained buyers, trained customers, and trained suppliers.
  8. What is the sourcing strategy?
    Optimization does not establish sourcing strategies, it merely plays a role in them … and it plays a much stronger role in some strategies vs. others.
  9. Are there global suppliers who will require language translation?
    Does the software support the languages of your supplier base or are there resources available to do the necessary translations?
  10. How much creativity can your organization accommodate?
    Optimization allows you, and your suppliers, to get quite creative.

Next it goes on to discuss the resources required. While you will need each of the resources identified in your organization, you won’t necessarily need all of the resources on each team. For small projects, all you will need is a category expert with an intermediate level of optimization knowledge and a support person who can assist the suppliers in entering their bids. For reference, in addition to support personnel from your optimization solution provider who should be available as needed, the resources that need to be available to you in your organization if you are to make full use of sourcing optimization include:

  • team leaders
  • category experts
  • optimization champions
  • optimization power users
  • training and education resources
  • internal IS/IT resource

Then it goes on to discuss the different types of solution models you have to choose from, which basically fall into three categories:

  • Full Service
    The solution provider, working with your category manager, handles the event on your behalf and you never touch the tool.
  • Hybrid Service
    The buying organization uses the tool and runs the event and the solution provider is used for support as needed behind the scenes.
  • Self Service
    You do everything.

It concludes by discussing a number of awareness and training issues and process requirements. Some of the more critical awareness issues include:

  • the fact that optimization can improve sourcing decisions
  • change management is necessary
  • the support of an expert to facilitate implementation is necessary in the beginning
  • there will be a learning curve
  • training will be necessary for anything beyond simple models
  • every project should have a plan that includes the strategy and goals

Finally, the following implementation tips should be heeded:

  • the sourcing process must be established
  • specifications, the statements of work, and the RFX must be clear
  • supplier inquiries need to be responded to in a timely manner
  • any requests for bundled bids must be attractive to a sufficient number of suppliers
  • expectations must be reasonable in light of current market conditions

Next Part V: The Optimization Sourcing Cycle

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The Role of Optimization in Strategic Sourcing – Preparing for 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.

This chapter starts off by explaining the buyer and supplier data requirements for decision optimization and it does a good job. The five data requirements it lists for a buyer are spot on:

  • accurate historical data and projected volumes
    this allows you to not only create accurate baselines, but to perform a “sanity” check on the demand forecasts you are given
  • complete list of requirements
    these will form the foundations of your model constraints
  • minimum quantities and timeframes
    these not only specify minimum model awards, but help you determine what suppliers are qualified
  • complete specifications
    these are necessary for the suppliers to submit accurate bids
  • identification of locations and individual demands
    these define the minimum set of bids your suppliers need to submit

Next it goes on to discuss a number of optimization model issues including those of model size, complex sourcing events, small and standard buys, and the optimization sweet spot. The report did a good job on these issues, but three points need to be clarified.

While it is true that many moderately sized problems will still be challenging for a desktop or laptop, moderately sized problems can easily be solved in a matter of minutes (and sometimes even seconds) on average mid-end servers. Furthermore, today’s high-end servers can handle problems that are quite large indeed. And while it may still be the case that no single provider can handle all of your strategic sourcing decision optimization needs, the 80% solution is still a great one — license a solution that gives you 80% coverage and then utilize a second provider for large, custom, high-dollar events where the ROI will dwarf the additional cost.

The report correctly states that while it is quite possible that the software will not find ‘provable’ optimal solutions for the model, the software can nearly always find good solutions that will be ‘near optimal’. However, it does not state that those ‘near optimal’ solutions will be ‘provably’ near optimal, which is always the case with MILP optimization (that is the foundation of the majority of strategic sourcing decision optimization products on the market today). Since MILP solvers start by finding the optimal solution to the relaxed linear model, the distance of each successive solution from the absolute lower bound (which can be increased every time a solution sub-space is fully explored) is always known. So even though there may not be enough time to fully explore the potential solution space and find the provably optimal solution, the solution returned is provably near optimal within a certain tolerance.

Finally, the statement that most e-purchasing suites have an optimization module that can address the large number of bidding opportunities in this area is laughable. There are dozens (and dozens) of providers who offer e-sourcing and / or e-procurement suites (just check the resource site), but only a handful that offer (true) strategic sourcing decision optimization. When it comes to strategic sourcing decision optimization, you’re pretty much limited to Algorhythm, Bravo Solution (Vertical Net), CombineNet, Emptoris, Iasta, or Trade Extensions … only four of these can be considered suites … and only four are self-service. While I expect that we will see more providers with true optimization offerings as part of their suites in the future, until the utilization of strategic sourcing decision optimization becomes mainstream, I don’t expect any new providers will emerge.

Next Part IV: Implementation Issues

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The Role of Optimization in Strategic Sourcing – The Benefits 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.

The second chapter did a great job of highlighting the many benefits of optimization from a productivity, cost/price, and decision visibility perspective. In brief, they are:

Productivity

  • Faster Sourcing Cycles
    No more fiddling with error-prone spreadsheets. (Remember that 90% of spreadsheets contain errors!)
  • More Thorough Analysis
    A broader, deeper analysis that looks at more alternatives.
  • Higher Quality
    Data integrity is much higher.
  • Better Planning
    Better up-front planning is done before the event.

Cost/Price

  • Significant Savings
    Especially on the first event in a category.
  • Cost/Value Trade-offs
    You can analyze whether the additional cost associated with a service is worth it.
  • New Savings Opportunity
    The expressiveness allows suppliers to get creative and find ways of providing you their lowest total cost.
  • True Market Baselines
    An unconstrained scenario will give you the absolute lowest cost.

Decision Visibility

  • Centralized Knowledge-Base
    Your sourcing team can learn from each other and management gets better visibility into cost trade-offs.
  • Cost Premiums
    You can run historical events through the model and determine the cost premiums paid for preferred awards.
  • Cost Drivers
    You can analyze multiple events and zero in on cost drivers such as particular locations or raw commodity categories.
  • Competitive Feedback
    You can let your suppliers know where they are, and aren’t competitive, and why they won or lost a bid.

It also did a good job pointing out that good strategic sourcing decision optimization models also allow qualitative criteria to be analyzed. For example, you can exclude all suppliers with a service level of less than 95% or a product quality less than 8 (on a scale of 1 to 10). The ability to consider non-price decision criteria, used creatively, allows you to model and calculate a wide range of cost vs. value trade-offs and make better overall sourcing decisions. A great example of the power is the user who ran two scenarios where one scenario forced all rubber-based parts against a baseline that allowed the user to gain insight into how the cost of rubber was impacting her costs.

Next Part III: Preparing for Optimization

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Dell Optimizes Its Way To Success

I was thrilled to see this article in Logistics Management about Dell’s inventory optimization pilot which saved them 55% in its initial application. In a mere 90 days, a pilot, rolled out to two suppliers, that was primarily focused on managing suppliers and replenishment processes via a consistent inventory policy reduced inventory, and associated costs, by 55% (from $6M to $2.7M). Imagine the savings Dell is going to realize when it rolls the new system out to the majority of its supplier base (that constitutes the top 80% of its spend), cleans, and analyzes its historical data. It’s savings will easily be in the hundreds of millions.

Compare this to Intel’s recent success through an optimized make-to-order cycle which significantly reduced inventory builds and associated costs. Do you see a pattern? I hope so!

The simple truth of the matter is that a good inventory optimization system that either contains demand planning and production planning optimization, or that integrates with demand planning and production planning systems, will save you a small fortune. So if you regularly carry tens of millions of dollars of inventory and don’t have an inventory optimization system, get one … NOW!.

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The Role of Optimization in Strategic Sourcing – Optimization in the Strategic Sourcing Process

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.

The first chapter in the report provides a brief history of optimization, which dates back to 1947 when G. Dantzig developed the simplex algorithm, defines sourcing optimization, describes some inherent complexity of sourcing models that makes them well suited for decision optimization, and places optimization in the sourcing process. It also defines “expressiveness“, which is just a fancy term used by a couple of providers to say that the models can handle sophisticated bidding with tiered and volume-based discounts and bundles.

Since I have already provided you with a good introduction to decision optimization in the wiki-paper, which includes the basic requirements for a true strategic sourcing decision optimization solution, I am instead going to focus on those aspects of the chapter that I found to be misleading or, in a couple of cases, incorrect.

First up, the statement that optimization applications for strategic sourcing are rather new is just plain wrong. These solutions, which turn 10 next year, have been around since 2000! To put things in perspective, the first generally available e-auction platforms did not start appearing until 1996, the year after FreeMarkets was formed. I’ll admit that there were only a few solutions at first, and that the first instantiations were primitive by today’s standards, but by 2003, a few of these solutions could handle models that were very sophisticated. It’s true that solution times were in the hours, and sometimes days, for some of these models, but that was still better than sending a scenario over to an operations research group and waiting 2-3 weeks for them to do a custom analysis. And today, with increased computing power and better solvers, those models generally solve in a matter of minutes, and sometimes seconds.

Next up, Figure 1 on the Strategic Sourcing Process. While this figure does capture all of the options and steps, it’s a little misleading because optimization can precede, follow, or be used simultaneously with sealed bids, negotiations, and reverse auctions, and the sub-cycle can be repeated as many times as you like. You can bid to get potential suppliers, optimize to find those suppliers who qualify and fall close to the required bid range, do an e-auction to get initial bids, optimize, negotiate with the top 5 suppliers, optimize again, and then make awards to the top 3 in a 50/30/20 split, for example. (Provided that you explain to the suppliers up front it will be a multi-round sourcing event so that you can’t be accused of unethical conduct.)

Continuing on, while the statement that any category with a medium to high level of spend and complexity is a candidate for optimization is true, it is a bit misleading because it doesn’t define “medium to high level” and conjures up images of global sourcing models with dozens of suppliers supplying hundreds of items across thousands of lanes in the minds of some potential users. The reality is that if you have a model where only three suppliers are bidding on only three items for only three locations and where shipping costs are dependent on the total volume on a lane, you already have a complex model that most professionals will solve sub-optimally even though it can be solved, with effort, in a spreadsheet. Consider the simple example discussed in the NLP podcast (transcript). Three bidders, one item, three locations, volume discounts on the bids, and a supply constraint. An “obvious” solution could easily cost you 2.5% more than you need to pay. An even worse solution could cost you 4.5% more than you need to pay … on a model that you would probably label “child’s play”. So just imagine how much you could be overspending on even your average sourcing event!

This says that any company who followed the lead of the company that put a lower limit of 5M on a sourcing event before optimization could be used would probably be foolish. Considering that repeated studies have found that strategic sourcing decision optimization returns an average of 12% beyond what reverse auctions and other standard negotiation methodologies can deliver, this company is likely leaving hundreds of thousands on the table in an average sourcing event over 1M, if not more! A number of sourcing providers have delivered returns of 20%, 30%, and 40% on categories under 5M using strategic sourcing decision optimization. If you acquire a blanket license, and appropriately train your team, the cost per event becomes ludicrously cheap in comparison to the potential savings and it becomes stupid not to at least run an unconstrained scenario to understand your base cost.

Next we have the statement that if it is expected that a supplier’s pricing will depend on the total amount of business they are awarded, then they must be asked to bid on larger bundles as well as on discrete parts, which is just plain wrong. Maybe a few of the older products have this limitation, but the new products don’t. This is fairly easy to encode on a true strategic sourcing decision optimization platform that uses a powerful Mixed-Integer Linear Programming (MILP) solver at its core. Good products support tiered and volume-based quotes on individual products and arbitrary product groups, negating the need for a supplier to provide multiple quotes at pre-defined price points and for pre-defined lots. They only have to define the discounts they offer. No more. No less.

Finally, while the statement that there are limits to the ‘expressiveness’ that optimization can accommodate is true, the example provided is not. The report states that “one bidder submitted a bid that had the potential to save the buying company 10M, but would require the buying company to hire 18 additional people and place them on site at the supplier’s location … because it was different, this bid could not be included in the optimization analysis. Again, while this may have been a limitation of the particular tool being used, it is not a limitation of strategic sourcing decision optimization models in general. There are a number of ways this could have been handled. The easiest way is to treat it as a fixed cost. Since the optimization model is for an expected demand over a fixed time frame, we have 18 bodies at an average overhead (salaries and benefits) of $Y/month for X months. This says that utilizing this supplier’s bid comes with an overhead of 18 * Y * X. Fixed costs are very simple binary constraints in MILP models and a number of optimization products on the market today can handle fixed costs.

Other than that, this chapter served as a good introduction to the report and strategic sourcing decision optimization in general.

Next Part II: The Benefits of Optimization

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