Category Archives: Decision Optimization

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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The Role of Optimization in Strategic Sourcing – A Brief Introduction

As hinted at in Supercalifragilisticexpialidocious this summer, in this series that I will be starting next week, I will be digging into the recent report on “the role of optimization in strategic sourcing” from CAPS Research. This report, which is the most extensive effort I’ve seen by anyone [other than myself and my efforts here on this blog, in the wiki-paper, in the e-Sourcing Handbook, and the NLP sponsored podcast (part I, part II, and transcript)] to define the role of strategic sourcing decision optimization, provides a great introduction to someone just getting started with this very valuable, but still under-utilized, technology.

That being said, there are some statements in the report that need to be highlighted, some important points that were missing, some statements that were misleading (at least in my view), and some statements that were, frankly, just plain wrong. In this series, which will focus on some of the finer points of this report, I, as an expert in strategic sourcing decision optimization and a practitioner who has (single-handledly, in the first case) designed two of the leading systems on the market this decade, will focus primarily on those statements that need to be stressed, added, clarified, or corrected. The hope is that upon reading the report and this “editorial”, those of you who have not yet tried strategic sourcing decision optimization will understand, at least at a basic level, what decision optimization is, what it does, the value it can bring, and why you should be using it as part of your sourcing process to save an average of 12% above and beyond what you’ll save if you are still relying on e-RFX and (reverse) auctions alone.

Finally, before we begin, while the report thanked Ariba, CombineNet, Emptoris, and Iasta, you should be aware that Trade Extensions and Algorhythm are major players and that, as far as the doctor is concerned, Ariba does not have a true strategic sourcing decision optimization solution that meets the basic requirements outlined in the wiki-paper.

Next, Part I: Optimization in the Strategic Sourcing Process.

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Trade Extensions Trades Up Its UI and e-Negotiation Management Capabilities

About a year ago, I introduced you to Trade Extensions (TE) on the eleventh day of X-Mas. A provider of an extensive on-demand e-Negotiation platform, Trade Extensions is an emerging player in the global e-Sourcing marketplace — one that offers negotiation management, extensive RFX, and (reverse) auctions with embedded real-time decision optimization.

Since my initial coverage, Trade Extensions has made the following significant updates to their platform:

  • a brand new UI across their end-to-end system
    The new UI is crisp, clean, and click-minimal. It’s quick and easy to use and very self evident. Plus, their online help pages are very extensive and updated regularly by the entire TE development and consulting teams.
  • integrated data cleansing & classification capabilities
    Have to fix a lot of data? Just create a rule and map it, just like you’d do in a spend cleansing and classification system.
  • OLAP Reporting for Scenarios
    Users now have access to full OLAP capabilities when viewing scenario results and reports.

In addition, the following features, which I have not covered before, have been improved:

  • extensive modifications to their bid supplement functionality
    Data — pricing, discount(s), rebate(s), etc. — can be captured at any level (supplier, business unit, plant location, etc.) and used for mark-ups, discounts, qualitative scores, or as the basis for any formula(s) the user wishes to define.
  • flexible bid forms
    Not only does TE support full Excel integration, but bid forms can be designed by the user to fit their business needs. There’s no need to force your information into a single system format. A user can create additional worksheets, add columns and rows to existing worksheets as required and add macros and formulas without interfering with the platform’s ability to read completed bid forms.
  • outlier analysis and statistical reporting
    The platform can automatically detect bids that might be too high or too low and flag them for your review (after you define your outlier rules, such as specific bid field values x% away from average / historic / custom calculation). The platform also includes a number of statistics reports, including a parameter statistics report that contains a detailed analysis at the lot and bid level.
  • composed filters
    Filters, which allow you to define constraints on any set of suppliers, ship from locations, ship to locations, products, etc., can now be defined on other filters to allow for very easy, and very powerful, constraint creation.
  • selection sheets
    Excel spreadsheets can be used to define allocation constraints, discounts, penalties, and multipliers … greatly simplifying discount and constraint creation in many cases.
  • project management functionality
    100% integrated into the cohesive e-Negotiation platform, the project management functionality allows for the creation of phases and tasks, the allocation of resources to phases and tasks, and the creation of scopes (by supplier, geography, etc.) as appropriate.

They’ve also continued to increase its power. Consider a recent project run by a financial services firm that tendered all of the components of a direct mail project that would result in the mailing of 1.8 Billion documents. The project, which consisted of 65,000 items, 60,000 transport destinations, and 400,000 bids from over 100 suppliers was valued at $1 Billion with a “B”.

The project was to ultimately deliver documents to the firm’s customers, but to get to that stage each part of the supply chain needed to be tendered. This included design, paper supply, printing, assembly, and transport. The project was completed as a single tender with offers collected on-line and all components tendered concurrently. In addition, suppliers could make conditional offers that reflected their own efficiencies that could present the firm with further savings. This was a project that could not even be attempted by hand as it would take someone close to two weeks just to scan each bid. There’s no way someone could even fathom attempting to optimize this scenario if all they had was a spreadsheet solution that couldn’t handle more than 65,536 rows.

Finally, TE is one of the few players in the market to make their pricing scheme public.

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