Category Archives: Decision Optimization

(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.

Embracing Complexity

Recently, Supply and Demand Chain Executive ran an article on “Embracing Complexity” that pointed out that supply networks that are becoming increasingly extended and complex; integration between companies and their trading partners is becoming deeper at the systems and process levels; and emerging technologies like radio frequency identification are producing ever-growing mountains of supply chain data and that these and other factors threaten to overwhelm the systems that companies rely on to monitor and manage their flows of goods and 20th century systems may be inhibiting companies from moving toward a 21st century supply chain.

In addition, it presented Lawrence Davis’, a senior fellow at NuTech Solutions (acquired by Netezza Corp), insights into problems with current supply chain technologies. In short, he believes that contemporary solutions do not allow companies to optimize at the appropriate level of aggregation and that companies should be able to use solutions to optimize across their sourcing and procurement, production and distribution processes all at the same time; that software solutions that optimize based on deterministic assumptions about how long it will take for any given process to be completed produce “perfect” schedules that do not allow for breakdowns of machinery, traffic jams, defective parts, and other real-world assumptions; and that stochastic simulations which employ embedded agents that follow the company’s business rules are required.

They got the problems right, but I’m not sure I agree with the proposed solutions. Here’s a short list of reasons why.

  1. Optimizing at the appropriate level of aggregation has always been a discipline-independent problem and we’ve always managed. It’s as much a process problem as a technology problem. It all comes down to using appropriate levels of abstraction that allow us to connect larger and larger problems. And it works. You don’t need to simultaneously optimize all of your categories and all of your lanes – a problem you can’t solve. You can optimize all of your buys using high-order freight approximations, then collectively optimize your freight costs and distribution network.
  2. Deterministic models can be used on approximations and ranges as well as precise models. Yes, the results are still “perfect ranges”, but you can capture most of the likely outcomes. Moreover, none of the technologies proposed will capture every exception and you’ll still need exception management.
  3. Stochastic simulations are a good methodology for determining what could go wrong, but the key is identifying a set of collaborative systems that can embed the company’s business rules – because, as I just said, the processes are as important, if not more so, than the technology.
  4. The technologies proposed – “genetic algorithms”, “evolutionary computation”, and “deterministic simulation” are not silver bullets – just like the ERP was not the silver bullet you needed to manage your supply chain. They have their uses, but they are not that much better than today’s technologies, if they are better at all (as they all have their drawbacks).
  5. You’ll never be able to optimize everything. For that, you’d need a model that accounts for everything (and first of all, we can’t model the market), then you’d need an expensive High Performance Computing Cluster with hundreds (or thousands) of processors and a significant amount of memory, and finally you’d need an algorithm that can take advantage of the highly parallel machines – and you’ll quickly find that most of today’s optimization technologies, or at least the sound and complete ones, do not have efficient massively parallel implementations.

It’s true we still have a long way to go in supply chain, and that we do have to embrace technology, but we have to be careful of over-relying on new technologies, particularly those that have drawbacks as significant as the advantages they are being promoted for, to solve all of our problems. Although some things change, some things will stay the same – and the constant is that no matter what, we are going to need more brain power and good old fashioned human ingenuity to get to the 21st century supply chain.

One can wish it were otherwise, but as a technologist and former academic who could spend countless posts educating you not only on “genetic algorithms”, “evolutionary computation”, and “deterministic simulation” but also on “fractal geometry” (the basis for NuTech’s logo), “chaotic dynamical systems”, and “complexity theory”, it’s not the case. Technology is just a tool – the real solutions will come from the brains who can identify the problems, identify the process solutions, and then put the appropriate technology in place to back it up.

Advanced Sourcing is Where It’s At

“Two Turntables and a Microphone”Forget (e-Sourcing Forum, [WayBackMachine]). Advanced Sourcing is where it’s at, and Aberdeen just proved it again.

Regular readers, especially those who followed my summer series over on eSourcing Forum, will know that my favorite statistic to quote is Aberdeen’s finding (from their “Success Strategies in Advanced Sourcing and Negotiations: Optimizing Total Costs and Total Value for the Next Wave of e-Sourcing Savings” in June of 2005) that the application of optimization tools to analyze total costs, and of flexible bidding functionality to uncover creative supplier solutions has enabled early adopters to identify an average incremental savings of 12% above those that basic, price-focused auctions alone have generated “The Advanced Sourcing and Negotiation Benchmark Report: The Art and Science of the Deal”. This month, Aberdeen released the follow up on this study with which found that enterprises that are employing advanced sourcing techniques are still identifying an average savings of 11.9% per sourcing event. Furthermore, best-in-class enterprises are identifying an average savings of 13.7% per event. Considering that savings from basic sourcing techniques tend to reach saturation after a handful of events, the fact that these companies are not only fighting off stagnation but still thriving is exemplary of the power of advanced sourcing and negotiation, which includes bid optimization, cost modeling, flexible bidding, life-cycle sourcing, and Total Cost of Ownership / Total Value Management scoring techniques.

That’s why I spend so much time on true decision optimization for strategic sourcing – which, as I’ve pointed out before, must include the capability to capture all fixed and variable real-world costs accurately (including flexible bidding, tiered bidding, and life-cycle cost support), to accurately model real world constraints (which impacts cost modeling and TCO/ TVM), and to accurately solve the model (using an optimization algorithm that is sound and complete). True decision optimization for strategic sourcing supports and complements all aspects of advanced sourcing and negotiations, and I’m sure Paul Martyn will have more to say on the topic over on CombineNotes [WayBackMachine] as CombineNet was a report sponsor. (I also expect David Bush will analyze some of the key findings over on e-Sourcing Forum as Iasta was also a report sponsor – so be sure to keep your eyes on that blog as well.)

In the meantime, if you haven’t yet started to use decision optimization in your high-value or strategic events – and statistics are telling me that the vast majority of you are not, start evaluating and test-driving the solutions the market has to offer. After all, It Pays to be World Class.