Category Archives: Decision Optimization

Information … Information … Information

Yesterday’s post discussed the lack of realistic starting points for an average organization that wants to merge onto the value focussed path and the need for information. Then the post discussed e-RFX applications and how they are not always the answer as most are not configured for collecting more than a moderate amount of data, and the information required to make the right decision might require a large amount of data to be collected.

For example, consider the information required to make the right decision in a global freight bid where the company has over 5,000 lanes across five continents that are currently being serviced, in part, by almost 500 carriers. Not only will there be a need to collect up to 1,000,000 LTL and TL bids to know what the lowest rates are, but there will be a need to collect data on capabilities (refrigerated, freezer, hazardous martial, etc.), capacities, and serviced lanes. And then, once all of the information has been collected, past performance, guaranteed service levels, (commitments to) sustainability (such as biofuels and hybrid vehicles) will have to be considered in addition to costs and on-time-delivery capabilities. And if multiple carriers are almost equal, long term viability, strategic partnerships, and/or commitment to social responsibility might also need to be considered.

All-in-all, this represents a significant amount of data that needs to be collected, analyzed, and distilled into useful information — data that is not even going to be collected if a firm is still using a first-generation e-Sourcing platform. This is because:

  1. Traditional RFX tools, which are now a commodity (as every provider and their dog has one — trust me), are not built to collect that much information.
  2. Most of the RFX tools that can handle that much information, typically by way of Excel import and export, are not designed with supplier usability in mind. No supplier is going to quote 5,000 lanes at multiple LTL and FTL levels if they only service 3,000 and 2,000 can be broken into 20 cross-regional groups where each lane in the group is priced the same by mile.
  3. Of the few tools that allow for generic pricing and (typically) single-dimensional overrides, most won’t designed with the ability to easily design multiple levels of overrides and the OLAP-like navigation that’s really need to quickly zoom in on the relevant data items (which need to be viewed or altered).
  4. And while most of the better RFX tools allow a user to define as many RFIs, RFPs, and RFQs as the user desires, these generally have to be crammed into rigid workflows that may or may not fit the scenario at hand.
  5. Plus, while most of the tools can push data out into an auction or a SIM tool (that is the foundation for SPM and/or SRM), most don’t allow data to be pulled back in, since the first generation e-Sourcing model was a linear RFX -> Auction -> Decision Optimization -> Award -> Contract Management -> SPM flow.

And then, once you get past all that, you still have to analyze the data to distill the information required to make a good award decision. Because even the best strategic sourcing decision optimization on the market will fail if it’s not provided with the right data AND the right constraints (or, depending on your choice of terminology, rules). The right constraints can only derived by a knowledge individual that has the right information at her disposal.

So how do get the right information? You take your sourcing to the next level. So what does this Next Generation Sourcing look like? Stay Tuned.

VFS Enablers: Competitive Enablers in a New Wrapper

Generally speaking, I’m not hard on CAPS Research because they tend to produce some of the best research and papers in the space, but I had to take a crack at VFS in yesterday’s post because I don’t think we need another acronym. And while it may look like I’m taking another crack at their recent “Value Focused Supply” publication in this post, I’m trying to point out that the next level of strategic supply management in your organization, regardless of what you call it, isn’t that hard to obtain. It’s just the next rung on the ladder, and only one small addition to the capability repertoire will get an organization there.

According to the white paper, the critical enablers of VFS are:

  • executive engagement
    No initiative will succeed over the long term without executive engagement, which is also a critical enabler of classic competitive supply strategies.
  • value chain goal alignment and measurement
    This is a fundamental requirement of any supply strategy designed to enhance an organization’s overall competitive position — and a core requirement for any enhanced competitive supply strategy, such as DDSN and TVM.
  • supply market understanding
    Without supply market understanding, even a simple e-Auction will fail miserably.
  • collaboration approaches
    The best results always materialize from collaboration.
  • supplier relationships
    Without a good supplier relationship, quality, on-time delivery, and emergency orders are at risk.
  • organization and human resources
    The right people will always be required to pull the strategy off.
  • information/analytic capabilities
    This is essentially the only enabler that’s new, sort-of. While information/analytic capabilities are a requirement of competitive sourcing strategies, as good information is necessary to select the right strategy and analyze the bids, classic competitive sourcing did not require decision optimization, modern (POS-based) forecasting techniques, inventory optimization strategies, or (true) spend analysis.

Thus, any organization that has mastered standard competitive sourcing can easily move on to next generation sourcing strategies simply by adding a new tool or two to their toolkit — complete overhauls not required.

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You Cannot Overlook SSDO And Optimize Your Supply Chain

I was taken aback at this recent article in SupplyChainBrain on Supply Chain Optimization in the New Analytics Economy which outlined five analytics-enabled objectives which did not include strategic sourcing decision optimization, which is the next logical step in the sequence. Consider the objectives:

  • Supply Chain Visibility
    Step one is to understand how much the supply chain is costing you.
  • Demand Forecasting and Inventory Optimization
    Step two is to segment the supply chain, forecast demand, and then optimize inventory for each segment.
  • Network Optimization
    Step three is to periodically perform TCO assessments on the different segments of the existing supply chain network to identify the optimal performance configuration.
  • Predictive Asset Maintenance
    Step four is to perform preventative maintenance to minimize downtime and maximize uptime.
  • Spend Analytics
    Step five is to understand how much is being spent on each procurement category and identify those with the most savings opportunities.

The next natural step is:

  • Strategic Sourcing Decision Optimization
    Once the categories with the biggest savings opportunities are identified, it’s time to optimally source them so the overall TCO is minimized and the utilization of the current networks, optimized in step three, is maximized.

How could you possibly stop at step five?

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Analytics I: Optimization Comes of Age

Today’s post is by Eric Strovink of BIQ.

I remember my first experience with optimization. I was taken to a guidance counsellor’s office at my local high school, where a special terminal was set up. This terminal was connected to a system that would allegedly try to find the “best” college for me. It asked many questions. Questions like, “Would you prefer a warm climate?” and “Would you prefer an academic setting with equal numbers of men and women?” Well, duh. Those were easy answers.

My goal was to attend one of the premier engineering schools in the US. I wanted MIT or CalTech or Stanford or Carnegie Mellon. I’d be happy with Rice. If my grades or scores weren’t good enough for the snooty super-competitive schools, I’d try for Rensselaer or Northeastern.

The system ended up choosing an entirely unsuitable school, evidently equally weighing my academic preferences and my social and geographic preferences.

What’s my point? Well, in a microcosm, this has been the essential problem with optimization. When you provide a “constraint” — and let’s be precise, here, the term really is “constraint” — an optimizer will not look outside that constraint for options. It cannot. It is a mathematical engine, and it can’t read your mind and figure out which is a “soft” requirement and which is a “hard” requirement. As far as it’s concerned, they’re all requirements, and, by whatever God you (don’t) believe in, it will find a solution that fits those requirements, if there is one.

That’s one reason why optimization has struggled to find its way.

I was listening to my wife talking to a survey telemarketer the other day. She said, “I really don’t have an opinion about Blue Cross’s responsiveness to patient needs. I’ve never had Blue Cross.” There was a pause. Then she said, “But how can I have an opinion on a 1 to 10 scale, if I’ve never used them?” There was another pause. She said, “OK, but ….” There was another pause. She sighed, and said, “OK, 5.”

What’s my point? Well, do you really know the answer to what kind of constraints you should impose on your optimization model? Or are you supplying an answer because you don’t know the answer, but you have to supply something? And after the optimization model has solved, can you remember all the places where you guessed, but you didn’t really know? What if you forgot one of those places? And what if that one guess caused the model to solve in a really non-optimal way (non-optimal from your perspective, not its)?

That’s another reason why optimization has struggled to find its way.

The breakthrough has come with what I’ll term “guided optimization”. If you hike in the White Mountains of New Hampshire, for example, you have a large number of excellent trails to choose from. Many of them are safe climbs that lead to outstanding views and vistas; but others lead up steep, often wet cliffs that are unsuitable for casual hiking. You need a guide; in this case, any of the excellent guide books from the Appalachian Mountain Club. In the case of optimization, your guide usually needs to be an experienced practitioner who can help you set up your model, show you how to move constraints to find inflection points in your model, and so on. (The good news is that lots of vendors provide guided services now, and it isn’t that expensive. Especially when you consider that optimization can be incredibly valuable.)

Companies that provide guided optimization services, like Trade Extensions, have enjoyed solid growth and have left a legacy of satisfied customers. You can always use optimization software on your own (Trade Extensions is no exception); but until you really understand what you’re doing, it can be unwise.

Optimization vendors have claimed for years that their systems are usable by novices. I don’t dispute that there are cases where this is true, and has been true. But for me, it’s a case of crying wolf: there have been so many claims, for so many years, with so many tears, that I’m solidly in the “get a guide” camp. I do hope, though, that optimization vendors will take additional steps to make guidance unnecessary. the doctor has assembled a pretty comprehensive list of what needs to happen.

At the end of the day, if you can’t do analysis yourself, you’re less likely to do it at all; which, as you’ll see in the next installment, is the theme of this series.

Next: Analytics II: What is Analysis?

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