Category Archives: Decision Optimization

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]

Rapt up in Revenue

When I was in sunny California last, I had a chance to sit down with Rapt (acquired by Microsoft) and talk about their rather unique solutions that revolve around pricing strategy, decision analytics, and price optimization that, when combined, can help a company maximize their revenue opportunities.

Rapt’s sophisticated software platform, that integrates more statistical, analytical, and optimization algorithms than you can shake a stick at, was designed to uncover the many complex supply, demand and price relationships that, when harnessed, predictably improve profit and market share. Unlike simpler modeling tools and platform, Rapt can break down products, or SKUS, into features and analyze the impact of each feature on demand. This is one of the reasons why their solution is becoming popular in high-tech.

Let’s say you have three laptops, the Pinta, the Nina, and the Santa Maria, and each are selling quite well. However, like all electronics today, their life-cylce is limited and you need to design your next generation laptop. Each has a different processor, CPU, hard drive, display, and battery life. How do you determine the best configuration for your new laptop? Rapt’s forecasting engine can integrate your historical sales data with marketplace data, analyze the sales patterns and trends at the feature level, determine which features (CPU, hard drive, etc.) are the most popular, determine how much each feature influences the overall sale, and tell you which combination of features would sell the best in a laptop. You can then use it’s Price Director solution to determine the optimal price-point for your product. This product contains advanced algorithms that work on order, inventory, and market data to extract the elastic and cross-elastic effects among products, their attributes, and consumer demands which it can use to determine the optimal price points for revenue or market-share optimization.

However, one of the most interesting facets of our discussion centered around the fact that the largest uptake in their rather unique solution offering was not in consumer goods industries, but in media, and new media in particular. MSN, Yahoo!, CNET Networks, NBC Universal, The Weather Channel, and MTV Networks, among others, all use Rapt’s solution to determine how to price their advertising, which is defined by high variability in demand, uncertain availability of supply, and the rapid innovation and evolution of medium capabilities. If they can tackle one of the most challenging pricing problems out there, surely they can be helpful in more traditional industries. But then again, many companies in these traditional industries most likely have not yet adopted decision optimization in their award process, should-cost modeling in their product design process, or advanced spend visibility solutions in their strategic sourcing process. All I can say is that … the technology’s finally here, let’s start to use it!

Sometimes it’s okay to get Rapt up in revenue

These days it seems like everyone is focussed on cost savings. This is not a bad thing, considering the vast majority of companies are not best-in-class, which means the vast majority of companies, on average, are probably spending too much on their purchases. But despite some vendor claims that revenue is, and will remain, flat, or that there’s nothing you can do about it since the market sets the price and constitutes the demand, this is not true.

We all understand that the fundamental goal of business is to make money, or profit, and we all learned the same calculation in our first business class: Profit = Revenue – Cost. This tells us that, as a business, there are two levers we can manipulate to increase profitability, Cost and Revenue. Now it’s true that we as sourcing and procurement professionals have a lot more control over cost then we do on revenue, but that does not mean our focus on cost should be myopic. We should also understand the revenue side of the equation and work with marketing on the pricing side of the equation, because neither the market price, the highest price marketing predicts they can get, nor the price at which demand (or consumption) is maximized is the optimal price.

If your goal is to maximize profit, the optimal price is the one where the profit equation is maximized, and this means this price is determined as much by cost as by revenue, and we all know that the cost for a product is not fixed – it depends upon the supplier we use (which determines a host of physical attributes such as quality, appeal, etc.) and, more importantly, the quantity we order. Generally speaking, the cost per unit will decline if we order more units, but this is usually only true to a certain point. Each supplier has a base capacity they can produce on their production lines during their regular hours of operation. To exceed this capacity they will have to add shifts, add lines, or both – which will increase the cost per unit. Or if your product requires a raw material in short supply, costs will increase as you try to divert supply away from your competitor, and there will be a point where you just will not be able to secure more material.

Is marketing, or if you’re big enough, product pricing, going to understand all of the factors that contribute to product cost – and, if so, are they going to understand the factors and inter-relationships as well as we do? Probably not. And that’s why sometimes we need to get Rapt up in revenue – to make sure that not only does the organization choose a price-point that theoretically achieves their profit, margin, or market-share goal (which, without our assistance will probably be based on cost-data that is only an approximation, and not necessarily a good one), but that the price-point is realistic and that the forecasted demand can be met in the intended time-window.

Furthermore, as the users of some of the most advanced analytic and business intelligence tools in the organization (spend analysis, cost modeling, and decision optimization, for example), we are much more likely to understand that our historical data alone is not necessarily sufficient or accurate enough to predict future demands, that different product features and price-points will have a considerable impact on actual sales, that costs can vary significantly by feature and demand level, and that the only way to analyze all of these variables and make the best pricing decision is to use a good decision support tool based on sophisticated analytics and optimization to model the different scenarios at different price points and obtain a true picture of feature – price point – demand level correlation.

And that’s why tomorrow I will introduce you to Rapt (acquired by Microsoft) a decision analytics and price optimization solution provider whose goal is to help companies maximize their revenue opportunities.