Category Archives: Spend Analysis

If I Succeeded in Destroying Dashboards, How Else Would I Improve Spend Analysis.

The smart alecks are correct — technically destroying dashboards is not adding anything to spend analysis so I didn’t actually provide a way to improve spend analysis technology, just the results you get from using it.

So if I succeeded and dashboards bit the dust, what would I do? (Besides banning integration points for report writers for all OLAP-based spend analysis products?*) Good question. Especially since there’s about a half dozen logical next steps.

Three things that would be useful if you had a true spend analysis product like Opera’s BIQ would be to:

  • Integrate Easy Should-Cost Modelling CapabilityThis way you can define a cost breakdown for a product or service you are looking to source and have the tool automatically generate an expected cost based upon current data, as well as a price-range, with confidence, based upon low, average, and high prices paid for the raw materials, energy, labour, etc. (provided that the should-cost model permitted base-cost definitions for any cost components you weren’t buying that were bought entirely by your supplier)
  • Optimized Awards Based on Historical Data and Business RulesYou don’t have to send out an RFX to get base market pricing if you are already buying a product, it’s in your transaction store. Nor do you have to run a complex event to determine the lowest cost providers for a market basket. Moreover, if you are buying commodity products and services with list prices, and all your suppliers do is give you a discount of X% for a guaranteed award, you don’t really need optimization to determine the lowest cost as it’s just a simple formula against current pricing. And if your only business rule is 2 or 3 way split, it’s just the 2 or 3 lowest cost suppliers with the appropriate risk mitigation. In this situation, it would be easy for spend analysis tools to build in some simple optimization capability to tell you your lowest cost buy, and if it’s close to your should-cost model, you can just cut a contract without going through a time-consuming sourcing event.
  • True Federation across Related Data SetsMost spend analysis tools are only capable of working on one cube built on one data classification at a time. This means that even though a user can pick the drill dimension order, only one set of data can be viewed at one time. But sometimes you want to drill into greater detail (such as who requisitioned all those widgets from the wonky supplier), and that’s not in the transaction file — so you need another cube with more detail on the invoice (history). Then you drill in on the augmented AP (cube) data until you get to the invoices associated with the supplier, switch over to the new cube and drill down to the line items of interest and retrieve the requisitioners. Another situation is where you are getting a lot of warranty returns, and you want to figure out what batches the returned items are in so you can determine whether or not the batches were bad and it will be cheaper to do a mass replacement (by just putting out a recall) than dealing with one breakdown at a time. In this case, you need to drill into the warranty cube and then branch over into the invoice cube to get the batch numbers associated with the appropriate goods receipts that are associated with the invoice.

These are just a few things that can be done, and all would simplify the life of an analyst. More to come at a later time but first, what would you do?

* If you don’t know why, you don’t know your spend analysis product limitations!

In What Way Would I Improve Spend Analysis?

When it comes to spend analysis there is at least one particularly powerful tool out there that will meet the majority of the needs of any organization and probably at least one tool that will do, with elbow grease, just about any analysis an analyst can think of. Since businesses have wanted reports and analytics since the days of the first spreadsheets, analysis tools are always advancing and most are beyond the ability of the average user to fully utilize their functionality.

So, given this fact, how would I improve spend analysis? And given that this question may imply that I may only make one improvement, just what would that improvement be? Especially since most tools don’t do (true) federation, don’t support full reg-ex (regular expressions), don’t understand semantics, and don’t run fast enough on large data sets — indicating that, as a PhD in CS with deep expertise in analysis, modelling, optimization, and semantics, there are theoretically a number of advancements I could bring to the table if I put my mind to it?

Despite the plethora of options available, today there is only ONE thing I would do to improve spend analysis. I’d make it impossible to do anything but spend analysis. Specifically, I’d make it illegal to include dash-boarding capability in any (spend) analysis product.

Why would I do such a thing? Besides the fact that I’ve been ranting since 2007 that dashboards are dangerous and dysfunctional, I would do such a thing because, among other things, they give you a false sense of security that, if mismanaged, could be so grave that, like the myth of Nero, you would fiddle while the factory burned.

Why would I ditch the dashboards and make it a crime punishable by any fate one could devise that was worse than death to include any capability whatsoever designed to support a dashboard? Because I just read this post on Purchasing Insight on “the inordinate cost of poor spend analytics” that said that it’s reckoned that more than 50% of businesses employ between 2 and 5 people to prepare and create procurement dashboards and spend reports. This is ludicrous. (No, not Ludacris.) If these people are senior analysts, then a large organization is spending more than 500,000 a year on salary and overhead to create dangerous and dysfunctional dashboards that spit out shiny spend reports that, after being analyzed the first time for inefficiencies, provide zero value to the organization. Once the report is analyzed, the inefficiency identified, and the problem corrected, and once this is verified in the next report, no subsequent report is going to tell the analyst, or management, anything new.

As SI has said again and again, the value of spend analysis is actually doing spend analysis, again and again, testing new hypothesis every time they pop into the analyst’s head. Yes, most hypotheses will yield nothing, but that’s not important because it only takes one insight to yield 100,000 worth of savings. If the tool is flexible, powerful, and configured appropriately, the user will be able to explore dozens of different analyses in a week, and if even one yields 10,000 of savings, that’s an (amortized) ROI of (at least) 5X. Spend analysis is analysis. Not dashboards and reports.

So if you really want to improve spend analysis — ditch the dashboards and focus your talent on real analysis. Otherwise, just download a free reporting engine off the internet. You’ll get the same worthless result, without forking out six figures for a tool you’re not really using.

Last Day for Free Sourcing and Procurement Papers from Spend Matters!

As per a recent post over on Spend Matters UK, today is the last day that a set of recent Spend Matters white-papers, made available by sponsors, that is currently free to practitioners goes back behind the pay-wall and this set in particular contains three pieces authored or co-authored by Pierre Mitchell and two authored or co-authored by Thomas Kase. Pierre, most recently of Hackett Group fame, is well know for his thought leadership around Supply Management best practices and Thomas, who has worked for a number of providers, is known for his deep expertise in SPM/SRM solutions. When you can get your hands on their work for free, it’s not something you should pass up.

The papers in particular that are about to become “pro” access only are:

  • How to Justify Spend Analysis to Finance/IT When There’s No Clear ROI
    by Pierre Mitchell
  • Write Better RFPs — How to Get What You Want (and Need) From Suppliers
    by Thomas Kase
  • Metadata Explained: What it Means for Spend Analytics, Supply Risk, Supplier Performance, and More
    by Thomas Kase, Pierre Mitchell, and Jason Busch
  • Procurement Analytics: How to Plan (and Optimize) Your Process
    by Pierre Mitchell

Regular readers of SI know the utter importance of Spend Analysis, a subject which has garnered hundreds of posts on SI over the years. As one of the only two technologies that have been repeatedly to provide an organization year-over-year double digit ROI returns when properly used, your organization cannot afford to be without it! As such, the last thing you want is to be roadblocked by finance when there is no readily apparent savings opportunity (as you need the solution to clean your data to find the savings opportunity). In his piece, Pierre gives us you ten hints to getting your project approved, which can happen quickly if the project is presented appropriately. Always remember, the faster you get the system, the faster you can centralize and cleanse your data and find opportunities, and the faster you can start saving.

Just this week SI was continuing it’s RFX rants, which started back in 2007, about how the vast majority of provider RFPs suck and how you won’t get good results unless you write your own (using the provider RFP as a checklist of some key elements that need to be included, but in a way that makes sense to your organization). In Thomas’ paper he discusses some key elements of the RFP creation process that can make the difference between success and failure in your efforts.

Everyone talks about Meta Data, but not a lot of people really understand what they are talking about. In the collaborative piece between Thomas, Pierre, and Jason, the authors provide a discussion of meta data and how meta data aggregation can paint a picture not readily available from the elements. They then go on to demonstrate how the proper analysis of meta data can yield risk analysis and opportunity assessments that cannot other wise be performed and that can be very beneficial to the business day one. It’s another example of why your organization needs good data and tools to process and mine that data if it wants a true twenty-first century supply chain.

In the last piece on Procurement Analytics by Pierre, he notes that for an analytics project to be successful, you need the right scope. The scope is all of supply management, not just the tactical procurement function. All data collected from the first RFX during the sourcing process through the last on-contract procurement to the final warranty return needs to be collected and stored in one central or federated database so that an analysis can look at all relevant data, not just purchase data. It’s not just how much you paid, but how much you were supposed to pay, what you paid for, and if a different categorization would be more beneficial to your organization. And until you make an effort to centralize, or at least centralize on a common schema even if the data is scattered, you won’t even know what transformations and cleansings need to be done.

If you haven’t downloaded these yet, don’t miss your very last opportunity to do so. These are some great pieces with content that you should know, so read up!

There’s More Than 50 Ways …

… to leave your lover. There’s more than 50 shades of grey (as there is infinite intensity to grey-scale). And there’s certainly more than 50 shades of pay … (see: 50 shades of pay spend analysis many profitable pleasures)

Over on Spend Matters, Pierre Mitchell is penning a series on 50 Shades of Pay: Spend Analysis’ Many Profitable Pleasures where he notes that spend analysis is not a quickie event and nothing could be closer to the truth. Spend Analysis is an on-going process that never ends. There’s always new spend, always new quotes, and always new ways to look at data. It’s wham, bam, spend cube and start all over again. And again. And again.

And it must be an evolving competency that is refined over and over again. There are many reasons for this, and, as Pierre pointed out,these include the facts that:

  • You can’t manage and improve what you cannot see
    and the more you see, the more you manage, and the more you’ll manage, the more you’ll see …
  • It’s a fundamental part of corporate strategy as the more you see and manage, the better you’ll be able to manage your resources and opportunities, which is what corporate strategy should be focussed on.
  • Managing your spending includes internal spending too even if you can’t control it, you still need to understand what it is, where it is going, who controls it, what could be done about it, and what the recommendations should be.
  • Spend analysis is a gift for your partners – not an IT project because it really is decision support for the organization.
  • Spend is the flip side of supply and it is about maximizing bang (supply value) for the spent buck (spend magnitude). It’s the other end of the source-to-settle process. And to optimize your Supply Management, you need to optimize your entire source-to-settle process.
  • Finance will get even more turned on by spend analysis than you if you do it right. They like shiny reports — and a good spend analysis solution can produce them en masse. (The reporting engine is actually the least useful part of a good spend analysis tool, but just like Sonny goes cuckoo for cocoa puffs, finance and the C-Suite love their reports.)
  • Spend analysis shines a bright light on the master data problems which can be cranked up to the point that it’s blinding. Some people may be embarrassed at the mess master data is in (because they spent millions on a broken ERP), but what’s more import, their pride or your bonus (which requires the company to be profitable)?
  • It’s incremental in nature – the Trojan rabbit of procurement transformation. Just like the rabbit of Caerbannog, it looks cute and sweet, but, in the right hand, spend analysis is a vicious killer of inefficiency and waste.

If Pierre manages to write 50 pieces, it will shape up to be a great series for those of you who have Plus membership. For those of you who don’t, I’ll remind you that SI co-wrote the book on Spend Visibility with Lexington Analytics 3 years ago, and this book, which has garnered over 10,000 downloads since its release, is still available for free. I’m sure Pierre will get to more advanced topics in the later part of his series then what we covered in the book, but it’s a great start. And maybe by then you can convince your boss to pay for the Plus membership to read Pierre’s posts.

Could You Be Doing It Right? Part III: Big Data

In last Friday’s post, we asked if you were doing it wrong. In particular, we mentioned category management, supply chain risk monitoring, and big data, and asked if you were doing them wrong. We noted that even though a number of companies have jumped on these runaway bandwagons, most have yet to grasp the reigns and take control of the wagon and get it on the right track.

Why is that?

Fundamentally, it’s the same reason that there are no world class Procurement Organizations in Asia Pacific — the classic Triple-T problem.

  • Talent
    the organizations don’t have the right talent to properly manage the initiative
  • Technology
    the organizations don’t have the right platforms to capture the right data and support the right processes
  • Transition Management
    the organizations don’t have the right processes in place to handle the necessary organizational shift to properly manage the initiative

Once the talent, technology, and transition management is in place, the organization has what it needs to fully embrace the initiative and take it to the next level. And do it right.

Where should your Supply Management Organization start? By identifying the core capabilities that are required in each “T” category and finding the right talent, technology, and transitions management for the initiative, the organization will be well on its way.

In the rest of this post, we’re going to talk about the requirements for an organization to get on the right category management track.

Talent for Big Data

Good big data scientists need the following hard and soft skills:

  • Algorithms
    there’s no magic algorithm where big data is concerned as every problem is unique and requires a unique (variant of an) algorithm
  • Domain Knowledge
    the scientist needs to know when she can be confident in the data and when she can’t; if there is not enough data, or the data is too random or skewed from expected patterns, then the scientist needs to know to trust judgement over data
  • Technical Skills
    the scientist needs to use sophisticated tools to perform her analysis
  • Logic
    the data, and algorithms, are very precise and the data scientist needs to be as well
  • Teaching
    since the majority of organizational employees will not understand what the big data scientist does, she will have to be able to explain what is needed data-wise, what the meaning of the results are, and how confident the organization can be in the results in simple terms
  • Perseverance
    since big data isn’t as simple as just dumping a bunch of data into an algorithm and accepting the result; the first, second, and tenth try won’t always generate a useful result — sometimes the data scientist, like an archaeologist, has to dig, dig, dig

Technology for Big Data

Appropriate technology platforms for big data will have at least the following features:

  • Big Data Stack
    You need an infrastructure that is scalable, replicable, and fault-tolerant.
  • Domain Specific Algorithms
    That can run on the stack and analyze the right data in the right way to generate some useable facts.
  • Powerful Reporting Engine
    That can not only generate reports useful to the scientist but to others in the organization.
  • Powerful ETL Middleware
    As you will need to extract, transform, and load data from a wide variety of sources.

Transition to Big Data

In order to transition to an organization that properly uses big data, the organization needs to hire someone with good change management skills and give that person the tools and C-suite support he or she needs to get it done. That person also needs to be a natural born leader and someone who can work with teams to get it done.

This isn’t a complete (laundry) list of what is required for big data, but it’s a good starting point. Get the right talent, technology, and transition management in place, and your organization will be well on its way to big data* success.

* Especially if you hire a good big data scientist who recognizes that sometimes the data doesn’t have to be all that big to derive a useful fact!