Category Archives: Decision Optimization

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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Headline From the Land of D’OH: Bypassing Proven Supply Sources Invites Unacceptable Risk

Really? I did not know that! Tell me more!

Don’t we all know this by now? Needless to say I was a little disappointed when I saw this headline in Industry Week on “bypassing proven supply sources invites unacceptable risk”, one of my favourite publications in recent times as they usually avoid the obvious diatribe I expect from the WSJ (and used to expect from the now deceased Purchasing) and focus on the core issue, such as how to maintain quality and proven sources of supply in tough times.

There wasn’t a single sentence in the article that I don’t think we all know by now. We know cheap often translates into poor quality, lack of service, and all too often as of late, recalls. We know that production line downtime costs tens or hundreds of thousands of dollars. We know that moor Procurement needs to meet regularly with Engineering and they have to work together to maintain the necessary budget.

What we need is advice on how Procurement can stave off the incentive to “go for the lowest cost no matter what” when the top line design and production managers know that the associated costs of such a decision far outweigh the savings. We need some advice on how Purchasing can qualify the total cost and risk associated with a decision and show that “the 10% cheaper solution will in fact cost the company 10% more”. We need a discussion of cost modelling, optimization, and simulation that can be used to demonstrate the true total costs.

And when health and safety is on the line, we need the reminder that even the simplest of parts can spell disaster. Remember, it was a single O-ring that resulted in the Challenger disaster. That’s right, a single vulcanized rubber part brought down a Billion dollar piece of equipment. We can’t overlook quality, but until the cost of poor quality is quantified, uneducated business leaders will continue to do so. So let’s teach our buyers about cost modelling and optimization every chance we get. It’s the only way we’re truly going to end this view of relentless cost cutting as business as usual.

Optimization is Supply Chain’s Simulation

A recent article over on Industry Week pointed out how Simulation is “Empowering Product Engineers to Save Time, Money (and the Planet Too)”.

According to the article, simulation helped:

  • NatureWorks design a new biodegrade chip bag for SunChips,
  • Balzer Pacific Equipment Co. design and manufacture a new barge to carry 6,000 tons for a client that required less steel and saved $20,000 in steel costs, and
  • Unverferth Manufacturing Co to create a new strip-till subsoiler in only 3 months that was ten times stronger than its predecessor, required less parts and cost thousands less to make.

Optimization offers similar benefits to your supply chain. It:

  • allows you to redesign your supply chain network to be more efficient,
  • allows you to source the optimal amount of product at the optimal cost, which saves you a ton of money,
  • and allows you to complete sourcing projects in weeks that used to take months.

So stop waiting and just do it.

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