Category Archives: Decision Optimization

Even Heroin Smugglers Need Freight Optimization

According to a recent article on Yahoo! News, Tajik police have arrested a woman for trying to smuggle heroin in a refrigerator through express delivery firm DHL. Apparently, the DHL office in the Tajik capital Dushanbe grew suspicious after noticing that its transportation cost to Moscow exceeded the actual cost of the fridge by several times. Upon a search, they found 17.4kg of heroin hidden in the innver cover plate.

What was she thinking? Or more importantly, what was she not thinking? “Let’s see … $200 fridge … $500 shipping … makes sense to me!” What isn’t wrong with this picture. First of all, as a consumer, you should never pay more to ship a commodity than you pay to buy it. Secondly, you should never import something you can buy locally. (Who wants to deal with customs and import duties when someone else can do it for you?) Furthermore, if you have approximately 2.2M* worth of heroin, certainly you could afford to buy and ship a small car, which would cost roughly 1% to 2% of the total value and have a shipping fee only one tenth of its value – which would surely not be as suspicious since shipping would be much less than the value and people import cars significantly more often than they import fridges.

So, I guess there are two lessons here:

  • If you’re going to smuggle drugs, make sure you smuggle them in an item where the shipping cost doesn’t (significantly) exceed the item value.
  • If you’re going to buy and ship internationally, make sure you’re not paying too much for freight, or risk getting your shipments stopped and search by ambitious agents looking for the next bust.

* Based on an estimated street value of roughly $125/gram, which appears to be the median value returned from various sources in a Google search on June 1, 2007.

CombineNet IX: Interlude – We Can Optimize Anything

CombineNet is on a quest to conquer the Optimization World! Hopefully I’ll be able to post more details soon, but for now, let’s just say that it sounds like their new slogan is “We Can Optimize Anything“. To that end, I hereby proffer* the following for their new theme song (assuming, of course, that they can get the musical rights from the Tragically Hip for Blow at High Dough).

They had a problem once … in my home town
Everybody affected … from miles around
Energy crisis … not enough silver bling
Well we ain’t no day traders, but we can optimize anything

Get it out .. get it all out
We’ll stretch that bling
Make it last, we make it last
To well beyond the market bell rings
Well the stock-car driver likes his rhythm,
never likes the stops
Throes of passion, Throes of passion
When something just throws him off

Sometimes .. the faster it gets
The less you need to know
But you gotta remember
The smarter it gets, the further it’s going to go
When you optimize so
When you optimize so

Whoa baby you’ll feel fine
You can trust that it’s genuine
Dollars and Cents, Saves Dollars and Cents
Yeah, Every time you optimize
Nobody solves it as good as we do
It is one kick-ass tool
‘Cause we solve so fast, solve so fast
Makes everybody drool

Sometimes .. the faster it gets
The less you need to know
But you gotta remember
The smarter it gets, the further it’s going to go
When you optimize so
When you optimize so

Out in the market, same Elvis thing
But they can’t catch us, ’cause we can optimize anything
‘Cause we can optimize anything

Sometimes .. the faster it gets
The less you need to know
But you gotta remember
The smarter it gets, the further it’s going to go
When you optimize so
When you optimize so

Out in the market, same Elvis thing

* Copyright 2007. All rights reserved.

No Advanced Sourcing at Oracle

I was recently asked what I thought about Oracle’s Advanced Procurement Solution, and its optimization offering (new in version 12.0) in particular. The short answer is that it doesn’t make the cut in my book. The core sourcing cycle consists of spend analysis, RFx, auction / bid collection, decision optimization, and contracting. In order to be considered an advanced sourcing application, in my book, the underlying spend analysis technology, decision optimization technology, and contract management technology must support advanced capabilities. Although Oracle Procurement Contracts might be considered a fair contract management tool, it is certainly not advanced and it is by far the most developed of the offerings.

Oracle’s sourcing optimization product is pretty basic. It only supports three constraints: header, which can be used to constrain the maximum award amount or exclude one or more suppliers by scores, line constraints, which determine which, and how many, suppliers can split a line item award, and to what extent, and supplier which can be used to globally limit the number of suppliers and the award to certain suppliers. Basically, they support basic exclusion, capacity, allocation, and qualitative constraints. Not bad compared to most of the “optimization solutions on the market, but not really advanced sourcing.

Regular readers will note that I have four basic requirements for a true strategic sourcing decision optimization product:

  1. solid mathematical foundations
  2. true cost modeling
  3. four key categories of sophisticated constraints:

      capacity, basic allocation, risk mitigation, and qualitative

  4. sophisticated what-if capability

Since it uses ILog CPlex, it meets the solid mathematical foundations (provided that the underlying model is a true representation, and not a heuristic simplification), it has basic what-if capability, it mostly meets the minimum constraint requirements, but definitely falls short on true cost modeling. In order to allow for true cost modeling, a decision support must support tiered bids (or discounts that can model the tiered bids), flexible discounts, separate cost components (or at least flexible adjustments), and fixed costs. To the best of my knowledge, although Oracle’s tool does offer some volume discounts, it does not support multi-level tiered bids, flexible discounts, separate cost components (and at least freight should be supported), or fixed costs. In the constraint department, they do support basic capacity, allocation, risk mitigation, and qualitative constraints, but they are all tied to an item or an entire order. Qualitative constraints should be definable at the supplier, item, or location level, a concept the tool doesn’t yet support to the best of my knowledge, risk mitigation should be definable across item, group, or order, and only item and order appear to be supported, and capacity, allocation, and exclusion should be equally as flexible. Plus, the Oracle tool doesn’t have any constraints beyond these absolute basic constraints.

CombineNet Comunique VIII: CombineNet Energy

Recently, CombineNet (acquired by Jaggaer), one of the few companies lucky enough to have inspired a whole series of posts (I, II, III, IV, V, VI, VII) on this blog, launched CombineNet Energy which, although it sounds like a new sports drink, is actually a re-launch of their advanced sourcing application platform with built-in decision guidance systems for the energy sector.

As you may know, back in 2003 CombineNet, in a leading initiative, formed an Energy Division, which, considering the recent energy crisis, doesn’t look nearly so crazy or risky in hindsight. According to their

solution overview, in a competitive energy market place, shareholders insist on strong financial performance, customers expect reliable and affordable service, and regulators require detailed reporting and compliance. In this pressured environment, CombineNet Energy offers proprietary, optimization-enabled decision guidance technologies that enable energy executives, policy makers and business managers to address the most complex strategic, financial and operational issues confronting the energy industry. CombineNet Energy’s advanced technologies help energy companies increase operational efficiencies and maximize profits.

In addition to their advanced sourcing platform, based on their Expressive Bidding and Scenario Builder offering, they are now offering a Gas System Guidance System, GS2, that they are promoting as an entirely new breed of operational and financial planning tool for the natural gas industry that simultaneously analyzes all elements of the vertically-integrated natural gas operation, including supply, transmission/compression, storage, operational solutions and contractual constraints, to guide weekly, monthly, and annual operational decisions that maximize profits and efficiency. It sounds very interesting – pipeline optimization is a tricky problem. At any one time, you can only pump one-way, and it’s hard to predict exact demands in advance. They even have a Video Demo, which is kind of neat.

The also have an on-line flash-based value-assessment Calculator that an energy company can use to estimate it’s potential cost reductions using the CombineNet tool (which is quite important because this type of technology comes with a hefty price tag, and not every one understands that the rewards can be many times greater than the investment). For example, given the number of Local Distribution Companies, the annual operating revenue, annual gas purchase expense, annual volume of gas withdrawn, the weighted average cost of gas, the annual transportation expense, and the property, plant and equipment balance, it is able to calculate an estimated range of savings that would be obtained using CombineNet’s solution. Check it out.

I’ll eventually get to my promised “Powers of POE” piece …

(Spend) Analytics vs. (Decision) Optimization

I had a very interesting conversation with Eric Strovink, my co-author of the “Spend Analysis and Opportunity” e-Sourcing Wiki [WayBackMachine] about the power of analytics and how they can reduce the complexity of award optimization when applied after an RFP or Auction to the point where, in his view, optimization might not even be needed at all.

Most of you probably think advanced analytics, like those provided by BIQ (acquired by Opera Solutions, rebranded ElectrifAI), are only for Spend Analysis. In this regard you’re wrong. Dead wrong. But I understand why. Most spend analysis products are built on the idea of one cube built on historical transactional data merged from the ERP, AP, and any other spend database you have in your possession, spend, and augmented with external sources – which is then essentially frozen (even if it is updated daily with data). The good ones come with dozens of built in best-of-breed reports, and allow you to build hundreds more – but on that one cube. Therefore, you can’t use it on your RFP or auction data, because it’s pre-award data – and not the transactional data that’s allowed in the cube.

But what if instead your organization had a spend analysis product that allowed you to build a spend cube any time you wanted – on any data you wanted – on any dimensions you wanted – and then throw it away when you’re done? Then there would be nothing to stop you from building a cube on your RFP or Auction data, building reports by supplier, by cost, or by property (minority supplier, quality, historical on time delivery), building cross tabs and tree maps, and then changing the cube to look at the data a different way.

You wouldn’t need optimization or a plethora of deterministic reports to find out who the lowest cost supplier was, who the highest quality supplier was, who the lowest cost supplier was relative to your quality metric, or any other query that can easily be answered by rank and cross-tab queries.

You’d still need optimization, because it couldn’t tell you the best way to make the 50-30-20 split between three top suppliers subject to your qualitative and on-time delivery requirements when your freight costs vary to each local ship to location, but it would greatly simplify the optimization process. First of all, you could easily see which suppliers do not make the cut in quality or in on-time delivery metrics and eliminate them with a couple of rankings. Then you could quickly analyze total cost rankings based on presumed 100% awards to each suppliers and quickly determine that you could only do the split between three of the top five bids, since the rest of the bids are just too high consider. Furthermore, you could eliminate the need for the quality or on-time delivery constraints since you have eliminated all suppliers that do not meet the requirements. Now you have reduced model size and model complexity, and significantly decreased solve time.

In addition, with all the insight you are able to gain with true analytics on the RFP or Auction data, you are much more likely to get the model right the first time. No more running a model, getting a solution, deciding the solution doesn’t quite work, adding a constraint, and running again. Furthermore, you no longer have a need to run a model with the just the quality constraint, or just the on time delivery constraint, or just the 50-30-20 constraint to determine how each constraint impacts the “best award”. For a sophisticated model where you might have run a few dozen what-if scenarios in the past to understand the interaction of the various costs, factors, and constraints in your quest to build the “right” model, you might now only need a few what-if scenarios to get it right.

And, more importantly, optimization becomes a lot friendlier. You know you have the right data, you know you have the right costs, you know you have the right factors, and you know you have the constraints. In other words, you know you have the right model – which means you know you have the right answer, even if you are a novice! No longer do you need to rely entirely on the math to get it right – the math is only needed in the final step!

That’s Spend Management 2.0! (Sorry Tim*.)

* Minahan of Supply Excellence [WayBackMachine]