Category Archives: Decision Optimization

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.

Missing the Point … or … The Right Way to Handle Freight

Last week I summarized my comments on how Sometimes 80% is enough here on Sourcing Innovation. I did this for multiple reasons – it seems that not everyone gets the point that with regards to optimization, not only is 100% unattainable, but even striving for 100% is often ludicrous.

The reason for this is that you are never optimizing against actual data, but estimated data. Remember, when you are sourcing, you are sourcing against forecasted needs, on forecasted schedules, with forecasted shipment levels associated with forecasted freight costs. Your demand probably will vary slightly, and may vary significantly, your schedules will need to be accelerated or decelerated when demand spikes or drops, your shipment sizes will also vary with seasonal demand variations, and with freight surcharges the norm these days, your freight rates will never be locked in stone. Thus, even an “optimal” solution is not optimal.

Moreover, striving for an optimal solution instead of settling for a (very) near optimal solution may actually decrease the quality of your solution. For example, let’s say your supplier gives you a significant discount (in the form of a rebate) of 10% if you buy 60,000 units, and your anticipated demand is precisely 60,000 units. Let’s say you award the supplier the business, but your forecast was over by 5% and you only buy 57,000 units. Let’s also say that the second cheapest supplier was only 3% less expensive. In this situation, your search for the ultimate solution cost you 7%!

As another example, let’s say a certain carrier will beat every other carrier’s truckload rate by 10%, where the truckload rate applies if you fill 75% or more of the truck. Let’s also say that we have the situation where your expected shipment is 80% of a truckload, that 25% of a truckload costs 20% more than the average shipping cost across your other carriers, and that your shipment size varies significantly by season and promotion (because you are in the food service industry, for example). One week you’ll ship 80%, the next week you’ll ship 60%, and the week after you’ll ship 120%. Chances are good that, in reality, you will not be shipping truckload half the time and paying on average 10% more. (If you are paying 20% more half the time, you’re paying 10% more over all.)

So this brings me back to the title of my post – the right way to handle freight. First of all, let’s note that when dealing with freight, you have one of five situations:

  • Freight is a small percentage of total spend, less than 20%
  • Freight is a moderate percentage of total spend, 20% to 40%
  • Freight is more or less equal to total spend, 40% to 60%
  • Freight is a large percentage of total spend, 60% to 80%
  • Freight is a majority percentage of total spend, greater than 80%

The first case is the most important case. Why? Because it is this case that I find to be the most mishandled and misunderstood. I know for a fact that many corporations have thrown away millions, if not tens or hundreds of millions, of dollars because of their belief that freight optimization needs to be perfect even when it falls into this case and have put off acquiring a decision optimization solution in hopes that the perfect solution will come along soon.

This is the case where the “sometimes 80% is enough” rule comes into play. If someone provides you with an optimization solution that can handle your buy almost perfectly but only handle freight 80%, don’t dismiss it as imperfect and pass up an opportunity to save millions just because it’s not perfect in your eyes. Do the math! If freight is at most 20% of your spend, and the solution is expected to be at least 80% accurate, then the solution computed by the optimizer will be at least 96%. If freight is at most 10% of your spend, then the solution computed by the optimizer will be at least 98% optimal. If your non-optimization assisted solution doesn’t even approach 90% of optimal, why would you pass up an opportunity to save an extra 6%-9%? After all, as per my arguments above, I’d argue you are never going to achieve more than 98% (on average) in reality anyway! So don’t look for perfection when evaluating optimization solutions – chances are you will not find it (even though some solutions might come quite close) as it’s still a maturing and improving technology.

What about the other cases? The fifth case, where freight is the majority of your spend is also easy – you simply invert the problem and source freight lanes, and treat the product buy as freight.

The middle cases are harder. As for cases two and four, where freight is a moderate or large percentage of spend, the best way to handle these cases is to combine the categories with similar categories that can, or will in all likelihood, be shipped on the same trucks or in the same lanes. Preferably, those categories where, in the second case, freight is significantly lower and bumps you back into the first case or where, in the fourth case, freight is significantly higher and bumps you up into the fifth case, as we already know how to handle these cases. Case three is the toughie – product cost and freight are almost equal. What do you focus on?

This is the case where you do enterprise-wide freight optimization. You optimize all of the product buys, amalgamate all the freight requirements, and then optimize the freight. Unless, of course, your spend is significant enough, your pocketbook deep enough, and your patience long enough to throw CombineNet’s top-end optimization platform at it. (It really depends on your organization size – if you are a large organization, the cost of CombineNet should be inconsequential, especially considering the potential savings. If you are a small organization, the difference between the cost of the solution and the expected savings is not likely to be significant. If you are a mid-size, it depends on category size and characteristics.) On an ultra-high end server, their platform can certainly handle most of the problems you can throw at it, but not all problems solve in less than a second … a large and complex enough problem will take minutes, hours, or even days regardless of how good your optimization platform is. (However, if it takes more than a few hours, chances are your model is not the right one.)  Also, since their solution is not part of any suite where your data resides, there will be some integration time.  (But that’s a small price to pay to save $$$!)