Category Archives: Technology

Spend Matters Not

Not long ago, sometime after my post where I asked Is it the Case that Spend Matters Most?, the posts “Emptoris: Readying the Spend Visibility Armaments for Battle”* and “Ariba: Not Sitting Still in the Spend Visibility Arms Race”* appeared on Spend Matters and generated quite a buzz. I’m now convinced that most of the providers still have not progressed beyond Spend Analysis 1.0 and, more importantly, that spend, or at least the amount of spend, doesn’t matter.

It’s not how much you spend, how you store it, how you cube it, or how you report on it – it’s how much you get, how you profit from it, and how you improve on it. It’s all about value, profit, and continual improvement. The fact of the matter is, sometimes spending more is the right thing to do. If you’re spending more to build higher quality products that allow you to double your profit margins and drastically increase revenue, compared to spending less, building the same products, and having your profit margins shrink to nil because they are not innovative and desirable compared to the rest of the products on the market, then you’ve made the right choice. Just like you should focus on Total Value Management, and not Total Cost of Ownership, when you make your award decisions, you shouldn’t be focused on how much you’re spending when doing spend analysis. It’s what you are spending it on, who you are buying from, the prices you are paying relative to the prices you could be paying and the rest of the market, and opportunities you have to drive value from the spend.

So, how do you figure this out? Analysis. Flexible, powerful analysis that allows you to aggregate, slice and dice, associate, break-out, normalize, aggregate, and slice-and-dice again. Analysis that allows you as the user to see the data any way you want to see it, any time you want to see it, any how you want to see it. A rigid view on a fixed set of dimensions might tell you that you’re spending 30% more on supplier X, but it might not tell you that you’re spending 60% more on servers, 10% more on workstations, and 10% less on laptops compared to other suppliers. In other words, a rigid cube analysis might lead you to conclude that you should be dropping supplier X for suppliers Y and Z, when really you should only be dropping them as a server supplier, aggressively negotiating with them on workstation pricing, and routing more laptop purchases through them for a larger discount.

I have to agree with Eric (Strovink) of biq (acquired by Opera Solutions, rebranded ElectrifAI). It’s the analysis. The value added services, especially those provided by Emptoris (acquired by IBM, sunset in 2017) in their new release, are great, and they can be used to create some top notch reports that will knock the socks off of your stodgy old CFO, especially compared to what you can pull out of a traditional ERP, but that’s not how you drive maximum performance. Drop the spend. Focus on the value. Which supplier is giving you the highest value ratio (the most quality product for the least spend)? On which categories? Why? Which categories are performing the worst? On which categories? Why? Which suppliers do you have multiple contracts which? Any way to leverage the volume? And so on. You need to be able to build a cube, analyze it, slice off dimensions, extract a sub-cube, aggregate the data, run a report, and then compare it to a report generated off of another sub-cube for a different, but complementary, data set.

After all, it all comes down to the bottom line. It’s not what you spend. It’s not the revenue you take in. It’s not your operating costs. It’s how much profit the business makes at the end of the day and the value it returns to its shareholders. And that requires smart spend management based on actionable intelligence – the kind enabled by next generation spend analysis and visibility solutions. Everything else is just reporting – sometimes really, really, really good reporting – but just reporting.

And if the user can’t hack it … then the user needs to be trained or be replaced. Commodity prices are going up. After the third reverse auction, there’s no more fat left to trim. Once you’ve implemented the latest IT system, there’s little room for productivity improvements. That simply leaves collaboration, innovation, and smart spend management.

On a side note, I applaud Iasta for basing their new spend analysis solution on BIQ’s solution instead of trying to build their own from scratch. It takes years to build a good spend analysis solution, and since they are on-demand, they can easily integrate BIQ’s on-demand solution into their platform and extend it with value added services, which is where the real value is. This complements their core strength, the executable sourcing cycle, with a SaaS solution that helps the user determine the best category candidates for dedicated eSourcing events. Furthermore, as time progresses, they can build up baseline cubes and reports for common categories to jump-start the process for new customers and junior analysts. And, since it’s a partnership and not an acquisition, you don’t have one company swallowing another. Although this sounds great in principle, what usually happens is that the development teams merge, the new blended company adopts “one focus”, and a lot of the distinctive expertise that made the acquired company the best at what they do gets masked or disappears. BIQ is going to continue to build a better, differentiated, spend analysis product, Iasta is going to continue to develop better services around the product for the sourcing professionals it serves with its end-to-end executable eSourcing suite, and everyone is going to win. The only thing keeping it from being a perfect solution is the ability to easily integrate 3rd party data sources for spend augmentation and market-based reporting. But I’m sure that will come in time.

* All posts prior to 2012 were removed in the Spend Matters site refresh in June, 2023.

Is it the case that Spend Matters Most?

As per my Noteworthy last week, Iasta (acquired by Selectica, merged with b-Pack, renamed Determine, acquired by Corcentric) is about to release it’s new Spend Analysis platform SmartAnalytics, Emptoris (acquired by IBM, sunset in 2017) building on it’s acquisitions of Zeborg and diCarta, just released the new version of its enterprise suite with its new and improved Spend Analysis Solution, and earlier this year Procuri (acquired by Ariba, acquired by SAP) consumed TrueSource to offer TotalAnalytics – and Zycus is gaining ground everyday. It looks like the time has finally come for the big spend analysis vendors. And none too soon. After all, how can you identify your ripest targets for strategic sourcing without understanding your spend? And as Jason points out over on Spend Matters, spend visibility and analytics applications can become an invaluable solution for tactical everyday procurement activities as well as areas that are truly strategic on the board level. It’s definitely a growth area for the eSourcing vendors – especially the on-demand ones.

But is it ready for prime-time? Not only are some of these offerings new and relatively unproven in the field, but they also require a level of sophistication well beyond your simple RFXs and reverse auctions that are still the mainstay of many eSourcing users. And I know there are still many individuals that believe a centralized ERP will give you the spend visibility you need to do proper spend analysis – which is not the case, and you should check out Tim’s response in the comments to Jason’s post for a real world example as to why.

In most cases, I know the solutions are there. Zycus is one of the only remaining pure players and has a very attractive offering based on its success stories alone. Zycus has amassed numerous wins over its seven year history and Procuri has successfully integrated TotalAnalytics to amass some success stories of their own. As noted in Spend Matters, Consider the case of a pharmaceutical company — who will go unnamed — which has used TotalAnalytics to help quantify and accelerate procurement cost savings synergies in three multi-billion dollar acquisitions. This company has used the solution’s capabilities to define and track over 60 category management programs, enabling them to leverage spend and rationalize suppliers across acquired and existing divisions. This leaves us with Emptoris and Iasta.

Iasta’s solution makes the cut since they based it on one of the most powerful on-demand spend analysis engines available, integrated it into their platform, and extended the out of the box reporting capabilities available. I’ll have more to say after the formal release, which is forthcoming in the very near future.

Emptoris has also had some big wins, and has had their eSourcing suite with their initial spend analysis solution ranked #1 by Forrester in Q4 of 2005. So they are definitely a real player, but I’m a little concerned if their new solution is ready for prime time from a usability perspective. It is probably the most aggressive spend analysis offering on the marketplace today, with a new Spend Data Classifier, a new Real Time Spend Classifier, new import / export facilities, and a slew of add-ons for government watch list, credit score, and diversity rating integration, among other features. Now I know that Emptoris knows their stuff, it’s a challenge to find a question on an Emptoris product or capability that Kevin (Potts) cannot answer and Avner (Schneur) is absolutely correct when he says that with accurate and granular spend visibility, companies can gain greater control over and impact on their bottom line through improved sourcing and supply and contract management – and they have already delivered significant results. But when your average eSourcing user is still daunted by basic spend classification and decision optimization (just look at the recent Purchasing Survey), I wonder if they are going to be able to digest Emptoris’ new offering, especially considering Emptoris is still a traditional installed behind-the-firewall application where you only get maximum value from maximum deployment? I know it looks great in a power point presentation, but it can be hard to hide that much underlying complexity. If you’ve seen it in action, used it, or have your own take, please feel free to leave a comment.

The Talent Series VIII: Talent Acquisition Strategies

Back in June, Aberdeen Group released The “Talent Acquisition Strategies” Benchmark Report: Sourcing and Assessing the Best of the Best that address the criticality of investing in a talent acquisition strategy as a way to identify, attract, and engage high performers given that today’s organizations are facing a market with not enough qualified employees to fill necessary job roles, i.e. The Talent Crunch.

According to Aberdeen, talent acquisition involves the planning, sourcing, assessing, hiring and on-boarding of top talent. Sourcing candidates is a way to identify and attract qualified individuals whether they are actively looking or not and assessment involves the skills tests and behavioral assessments necessary to evaluate the ability of the candidate in a given role.

As usual, Aberdeen found that there is a sharp distinction between best performing companies who are tackling the talent crunch and average players who have done little more than adopt a talent mindset. Best performing companies distinguish themselves by leveraging technology to manage the sourcing, assessment, and hiring process and creating long-term strategic plans for talent acquisition that:

  • improve their corporate brand
  • create a pool of qualified candidates
  • improve their strategic workforce planning
  • utilize technology

As proof that a talent acquisition strategy works, Aberdeen offers us the following statistic: 59% of high performing companies have increased their overall workforce performance after implementation of a talent acquisition strategy compared to 41% of industry average and 33% of laggard companies.

The report found that Job Boards and Employment Websites are number one – with companies spending over 80% of their talent acquisition budget on job boards and company employment websites (according to the “Enterprise Talent Management” study), which is probably due to the fact that job boards have an increase in the quality of hire (48%), a decrease in the cost per hire (38%), and time per hire (44%).

Furthermore, the report found that 90% of Best in Class companies have aligned talent acquisition to their company’s overall strategic plan. Furthermore, best-in-class companies have a yearly hiring management plan that covers all hiring levels and includes contingent plans for unanticipated hiring needs.

The Aberdeen report offers the following recommendations for action:

  • align talent acquisition strategy with the overall corporate strategic plan
  • measure workforce performance based on quality of hire over cost per hire and time per hire
  • recognize that “one size does not fit all”: what works for talent acquisition in one company might not work for every company
  • eliminate paper and spreadsheet based processes and use technology solutions
  • focus on a long-term plan for talent acquisition
  • manage the whole workforce

These are all good recommendations, but you should also note the following:

  • job boards and employment sites are great, but with their increasing popularity you need to remember that the same candidates they deliver to you will also be delivered to dozens of your peers, so make sure you have a compelling brand to fall back on
  • your best channel will always be referrals from your own top employees, make sure to track each and every one – even if a candidate referred to you is not available now, or not the right candidate for the position you need to fill today, it does not mean that she will not be available tomorrow or the best fit for the next position that opens up
  • metrics are good, but positions filled with highly capable individuals are better – and it’s really hard to measure “quality” (on the other hand, productivity is often more easily captured if you make a product or bill a service)
  • although spreadsheets are not the best solution, don’t throw away Excel just yet – a good product will integrate with Excel and save your staff from having to learn a new interface (and save you training time and dollars)
  • one size may not fit all, but that doesn’t mean you shouldn’t at least explore ideas that have worked for other firms – sometimes only a few small tweaks are required

Noteworthy (Developments in the e-Sourcing Space)

Rearden Commerce (rebranded Deem) announces a new relationship with American Express Business Travel that will resell the Rearden Commerce platform under the name American Express Intelligent Online Marketplace or AXIOM. There is quite a lot of buzz, including:

  • Rearden Commerce Press Release
  • Wall Street Journal Article
  • Spend Matters Coverage
  • American Express Flash Introduction to AXION
  • Sourcing Innovation’s Rearden Commerce Introduction
  • Prior Spend Matters Rearden Commerce Coverage*

Emptoris (acquired by IBM, sunset in 2017) launches a new version of its new integrated suite this week with enhanced spend analytics and spend management capability. Check back here on Sourcing Innovation later this week. I’d also keep an eye on Spend Matters which has had some great coverage of Emptoris in the past.

Iasta (acquired by Selectica,merged with b-Pack, renamed Determine, acquired Corcentric) just launched it’s brand new website in preparation for its forthcoming SmartSource 7.0 release which will integrate with their new and improved SmartAnalytics and be supported by their new Spend Velocity programs. Also, hidden betwixt the pages is their announcement of their new annual Iasta reSource user group conference next May. With the Indy 500 only two weeks after the conference, there are sure to be some great lead up events going on in town at that time. I’ll be covering the new Iasta release here on Sourcing Innovation in a week or two, so keep an eye out.

* All posts prior to 2012 were removed in the Spend Matters site refresh in June, 2023.

CombineNet IV: BoB’s Unique Talents

Disclaimer: This blog, including this post, is not sponsored by CombineNet (acquired by Jaggaer). The author is not employed by, contractually engaged with, or affiliated with CombineNet. Any and all opinions expressed herein are solely those of the author. Furthermore, the opinions expressed herein should be contrasted with the opinions of other educated professionals before the reader forms his or her own opinion. Finally, the author is neither endorsing nor dissenting the use of CombineNet’s products or services – merely trying to spread awareness on the importance of optimization and the relative uniqueness of an offering like that of CombineNet. This disclaimer holds true for each post in this multi-part series and will be repeated.

Warning: This is a lengthy post.

In my last post, I outlined in some detail a problem that I felt not only required BoB (Best of Breed) but required CombineNet in particular for an optimal solution. What I did not convey is that not only are there other problems out there that I could have chosen, but there are a significant number of supply-chain related problems that often require BoB.

Today I am going to discuss six problems that generally require a BoB solution. This does not mean that you would necessarily require CombineNet (there are some other optimization vendors that can tackle a few of these), but that you would require a best of breed optimization solution (similar to that offered by CombineNet) to tackle these problems and be assured that the solution you achieved was optimal.

The problems I am going to discuss are:

  1. Distribution Network Design
  2. Large Combinatorial Problems
  3. Large Non-Homogenous Logistics Problems
  4. Non-Traditional Sourcing Problems
  5. Very Large (Traditional) Sourcing Problems
  6. Regret Minimization Problems

Distribution Network Design

Most large retailers or distributors have large distribution networks – often dozens of locations throughout a single country or region. However, this is generally not optimal. For example, in a talk at INFORMS given by a practitioner at APL Logistics, they described how they analyzed the distribution network used by JC Penney that had almost 60 DCS (and cost over 330M / yr to operate) and using in house proprietary meta-heuristic optimization algorithms, they deduced an optimal distribution network that had only 8 DCs, saved over 30M dollars, and, on average, shaved over a day off of standard delivery times! What the presentation did not dwell on (since INFORMS is an OR conference) is that these problems are usually humongous and insanely difficult to model, yet alone solve with your average off the shelf optimization problem (as bad as the multi-level make vs. buy problem discussed in my last post) and without a best of breed solution, your chances of finding the truly optimal solution are often slim.

Large Combinatorial Problems

Large pure combinatorial problems are much, much harder than large pure linear problems (which optimizer’s like IBM iLog’s CPlex can often cut through like a hot knife through butter on today’s high end machines) and significantly harder than general MIP problems. The reason is that these problems contain very large numbers of binary variables, and the best generic domain-independent techniques available are generally no better than greedy branch and bound, and for even a thousand binary variables, that could be 21000, or over a trillion evaluations. An example of a large combinatorial problem is a large marketplace auction where all the participants bid on fixed size lots which are non-decomposable. In other words, CombineNet’s (original) definition of an exchange.

How much better are best of breed solvers on large combinatorial problems? Let’s consider CombineNet’s best of breed solution customized for exchanges. According to CombineNet: “The resulting optimal tree search algorithms are often 10,000 times faster than the state-of-the-art general-purpose MIP solvers on the hard instances of real-world market clearing.” As I have expressed to CombineNet personnel directly, I doubt that this is the average case, but I know beyond a doubt that well defined algorithms can easily shave a factor of 100 or more off of solution time, and that this can be scaled up to almost 1000 on a multi-core machine with a smart parallel implementation. In other words, don’t always expect the best case, but the average case performance of BoB on these problems will demonstrate significant improvements.

Large Non-Homogeneous Logistics Problems

This is similar to a variation of the multi-level make vs. buy problem discussed in the last post. However, in this situation you are trying to optimally bundle your deliveries across product, and sourcing categories and choose the optimal carriers and distribution network independent of your suppliers. Given the non-uniform quotes (weight, volume, LTL, FTL), lot sizes, and various charges and surcharges often imposed by freight carriers, forwarders, loaders, unloaders, warehousers, etc., this problem can become really surly really fast on a large buy. Moreover, unlike the sourcing case where it often makes up a low percentage of your spend and a high order approximation is more than sufficient, when you aggregate your logistics across multiple categories, even a fraction of a percentage point can become significant.

Non-Traditional Sourcing Problems

Most Platform Optimization Engines are optimized for traditional sourcing problems – this means that they are generally not optimized for non-traditional sourcing problems. (Why should they be? Most of the problems you face are traditional everyday sourcing problems.) But every now and again you might have a non-traditional sourcing problem. One example – cell phone plan optimization. Cell phone plans are expertly crafted to be as confusing as possible to make sure the carrier maximizes profit at your expense. If you’re a small company, it probably isn’t worth the hassle trying to figure it out and standardize on a common carrier plan – the costs of manpower and resulting therapy costs will probably outweigh the savings, but if you are a large company, you can save hundreds of thousands of dollars, if not millions, with the right company wide plan (which will probably consist of different sub-plans for different groups and individuals, but all on the same corporate contract). That’s why Soligence has a solution just centered around cell phone plan optimization.

Another example, as conveyed to me by Paul Martyn himself (CombineNet’s Chief Marketing Officer and premiere evangelist on the CombineNotes blog) is energy utilization optimization. If you are a large corporation that produces energy for your production operations and consumes it from the grid, whether you realize it or not, you have a sophisticated form of an energy trading problem. If you can produce enough extra energy to add to the grid, should you, and when? If you have the potential to store energy, then adding energy to the grid at peak hours and only siphoning off extra energy at non-peak hours could slash digits off of your energy bill. Furthermore, shifting your operations so that your maximum energy utilization only occurs at non-peak hours could also save you bags of money. Considering this isn’t a traditional energy trading model, traditional production model, or traditional operations research planning model, its easy to see why you might have to call on BoB.

Very Large (Traditional) Sourcing Problems

POE, especially a well designed and implemented POE that uses real optimization technology as its underpinnings, excels at traditional sourcing problems. It’s what POE was built for. That being said, POE is built on off-the-shelf optimizers, and off-the-shelf optimizers are designed for general purpose needs. That means that they will hit their breaking points before a custom designed best of breed solution, even though they improve every year. For example, leading solvers can easily crunch MIP problems with hundreds of thousands of variables and pure LP problems with millions of variables, but once your MIP problems contain millions of variables and your LP problems tens of millions of variables, you’ll start to notice a performance degradation, which could be rapid and considerable when you get close to the underlying solver’s breaking point. When your encounter the odd problem that is this large, a best of breed solution that also integrates domain intelligence can save you a lot of time and rapidly increase your chance of finding the optimal business solution in the finite timeframe that you have to make a decision.

Regret Minimization Problems

Most of the time you know the problem you need to solve, the associated constraints, and the associated costs. But every now and again you don’t. For example, you need to rationalize your supply base but do not know the optimal number of suppliers. In most products, you have to choose a number or run multiple scenarios with different numbers and then take the best one. Although this approach can be effective, it doesn’t help you understand why a certain solution is best or guide you to the best solution. A best of breed solution that manages the search algorithm can not only guide you to a potentially optimal solution but inform you of nearby solutions that are invalidated by your soft or uncertain constraints. This is very difficult for a platform optimization engine – since it generally cannot guide the search. The best it can do is run different scenario formulations, show you nearby answers, and identify constraint conflict sets. It’s a good approach, but for a strategic problem, the more you know, and the faster you know it, the better you are.

By now, you’re probably asking does BoB have the upper hand? Well, even though BoB can solve a slew of problems that POE cannot and it’s always theoretically possible to tone down a solution, whereas it’s not always theoretically possible to built up a solution, the reality is that, as I’ve said before, when it comes to optimization, one size does not fit all and using a sledgehammer on a finishing nail is not always effective. Furthermore, these are not your everyday problems. It often takes years to realize the savings from distribution network redesign, reverse auctions and sealed bid negotiations are generally not combinatorial exchanges, you do not redesign your transportation network after every sourcing event, most of your sourcing problems are traditional, most of your sourcing problems are of a manageable size with proper strategic sourcing, and if you don’t know what your constraints should be on a regular basis, you have deeper problems you need to solve first. That’s why an upcoming post will focus on POE, the everyday hero, in more detail. When combined with this post, you should have a better understanding of where each technology can be helpful to you.

Disclaimer II: Although this blog, including this post, is not sponsored by CombineNet and the author is not employed by, contractually engaged with, or affiliated with CombineNet, the author is going to report, in full disclosure, that CombineNet did allow the author to use one of their free registrations at INFORMS (of which they were a sponsor), as well as buying the author lunch.