Monthly Archives: April 2018

… And Keep Your Big Platforms. Big Brains Will Still Win in the End!

Five years ago, about the time when the big data hype first reached insane hype levels, SI published a post that it was sick of all the big data hype and how it is not the answer to all our problems because, not only is this a load of baloney, the reality is that there’s no such thing as big data in business. As we said then, relative to our ability to process it, data has always been big. And, in business, big has always been meaningless. Furthermore, in business, we’ve always been able to process as much data as we need to in reasonable amounts of time if* we make good algorithm and technology decisions

Plus, the fact that all of the hype around big data is often centered around the fact that we will be able to replace science with math and processes with AI programs is even more ridiculous. There is no such thing as artificial intelligence. And even though we’ve finally taken automated reasoning to the point that we have assisted intelligence, there’s a big difference between recommendations from a leading expert system (which not only can’t know when it is wrong but how much it can be wrong the few times it is wrong) and an average, experienced, professional in the domain (who can know how likely they are right, and if they are not likely to be right, how far off they are likely to be).

But even worse than the big data hype is the big platform hype … how mega platforms backed with cognitive abilities can do it all! They can do a lot, but they can’t do it all. And any delusions we might have that they can are only going to get us into trouble. Because as soon as we start trusting them blindly, we’re going to turn two blind eyes and that’s when the 2% failure rate is going to kick in, and materialize in the absolute worst way possible.

In Procurement, it’s going to miss the fact that a new organizational vendor is a very high risk and make a 2 year sole-source award for a small, but critical, custom made component in your (engine/control system) assembly when, in fact, it should be excluding the vendor which just had its credit score downgraded from a B+ to a D-. It’s not going to predict that in all probability, the vendor is not going to be able to secure enough loans to stay afloat (until it fulfills your orders and other customer orders and grows its business after losing a major contract that accounted for one third of its production) and will shut down and stop delivering product in 3 months when no one’s watching. Your production line will go down for 2 weeks while you find a backup supplier to quickly bring a production line up, make a minimal order, and air-freight it to you. If it’s a big automotive production line that goes down for 2 weeks, that’s easily 10M down the drain, and that 1M you saved is wiped out ten times over in a second.

But that’s not the worst thing that can happen from us turning two blind eyes. In this situation, the company temporarily loses some money, and as long as it has enough inventory to keep most of its customers happy, and it can keep its failure quiet, no one notices. Now, if its a medical diagnostics vendor and a (visually-based) diagnostic expert system (designed to help with the identification of all skin conditions) used by a remote doctor fails and mistakes melonoma for a relatively benign lesion, it’s a whole different story. When you consider that skin cancer is one of the five fastest spreading cancers, by the time the patient goes back in and insists something wrong, it could be too late — the spreading could be too far and the patient will be doomed (since melonoma, while only 1% of skin cancer, is not only one of the fastest spreading skins cancers but also has the highest fatality rate and causes the majority of skin cancer deaths each year).

Big Platforms give Big Confidence, but it’s false confidence. I’ll take a real human expert any day. Yes, she’ll make a mistake sometimes. But she also knows when she’s not sure and you should get a second opinion. That’s not always something a system can tell you. It’s above a threshold, below a threshold, or on the line (and no decision is made or classification is given). But that’s not always the right way to look at a situation.

* And, FYI, hiring college drop-outs whose college experience consists of cutting and pasting HTML and javascript code and fiddling with it until it works is not a good technology decision. There’s a big difference between being able to code a web-page and develop a highly scalable, reliable, and efficient enterprise computing system. BIG DIFFERENCE!

Tomorrow is March for Science Day. That IS Important For Everyone.

Why? Besides the obvious that all modern technology is the result of science, you won’t get your next-generation cognitive sourcing platform without more advancement in, guess what, data science.

And, right now, in the US, every year, in addition to having to deal with the introduction, and passing, of more anti-science policies, you also have to deal with the fact that funding for science (education) is diminishing as well and it’s the cornerstone of all progress. What do you think inspires the continual advancement of advanced mathematics and statistics? Scientific need. And where do you think the roots of most of your analytical algorithms come from? Science.

So even though you spend your days slicing data and running reports in the back-office to meet the business goals of savings, reduced inventory turn-around time, reduced, risk, etc. — you’re still using the results of scientific research and progress. Don’t forget that. Or someday Kyle may not be able to flick the internet back on again when it starts to fail. (The sad reality is that because of a lack of science education and knowledge, some people actually believe this is how you fix the internet.)

For more information, see March for Science

Are You Ready to Get Analytical But Don’t Know How? Read On!

Now that you’ve read our last three posts and understand that you need to get more analytical if you want to get cognitive, hopefully you’re ready to dive deeper but just don’t know how to do that.

The four part answer is almost as easy as it was for optimization, just a bit more nuanced. What’s the nuance? Figuring out if your provider offers a modern spend analytics platform or is still a generation (or two) behind (when you are still behind yourself) is the nuance. So how do you determine if a vendor at least passes the sniff test? We’ll get to that, but first, let’s talk about where you start.

At a high-level, the four-part answer is almost the same as optimization. Just the vendor names change.

1) If you are using a sourcing or analytics platform from a modern provider with modern (next generation) analytics capability, use it (and acquire the module if necessary).

Who are the vendors? While we can’t say this list is thoroughly exhaustive, if you look at Spend Matters Deep Solution map, you see that the following providers make the map: AnyData, (SAP) Ariba, (Opera) BIQ, GEP, iValua, Jaggaer, Sievo, Simfoni, SpendHQ, Synertrade, and Zycus. Not all are equal, and this list is likely not exhaustive, but depending on your organizational needs, a sub-set of these providers is likely your starting point. (What Sub-Set? Depending on whether you are data, function, process, technology, configurability, or services oriented, the sub-set will vary. And practitioners who want to know which vendors match which subset can contact Spend Matters.) And if you are a do-it-yourself type, you could probably start with a platform like Spendata.

2) If you are not using a modern analytics platform or a modern sourcing platform with analytics, get a modern analytics platform or a modern sourcing platform with analytics, your choice.

Again, you can start with the dozen of providers above, which you can quickly narrow down depending on whether you prefer best of breed or sourcing suite and whether you favour technical orientations or service orientations. If the list is still too large, find the subset that bests fits your organizational size, industry, category focus, geography, and culture and focus in on those.

3) If you are using another sourcing or analytics (reporting) platform that is not meeting your needs, and can replace it, do so.

As with the optimization providers, a few of these providers have a considerable portion of their customer base that consist of customers that switched from another provider with a solution that didn’t meet their needs and, thus, have a lot of experience with change management, fear squashing, migrating your data over, and getting you up and running on the right processes quickly. Simply craft the right RFI and you will quickly zero in to the handful of providers that will likely be the best fit for your situation.

4) If you are using another sourcing platform or reporting platform that is otherwise meeting your needs, or can’t be replaced at the present time, or both, augment it with a pure-play deep-dive best of breed modern analytics solution.

So if you are in the situation that you just bought a best of breed Source-to-Contract or Source-to-Pay solution and can’t replace it, or you have a first generation BI tool that produces reports the executives love but doesn’t meet your needs, augment it with a point-based best of breed solution. From the above list,
AnyData, (Opera) BIQ, Sievo, Simfoni, SpendHQ, and Spendata fit that bill.

But what about the “sniff test”?

How do you differentiate a last generation solution from a current generation solution? Three tests. Have them, in front of you, in a live demo:

  • Build a Cube with Derived Dimensions and a new Report on the Cube on the Spot
    if they can’t do so (in 15 minutes), they are a last generation platform that can only work on pre-defined and pre-built OLAP cubes
  • Run a categorization exercise on at least 3 months of your transaction history / invoice data and at least 100,000 transactions
    if they can’t either use their AI, or powerful (collaborative) filtering and priority based rule definition, and get to the 95% mark in an hour, it’s not for you … (and, trust me, you don’t need AI to get to the 95% mark if the rule definition capability is appropriately defined)
  • Map the cube to a new taxonomy, create new derived dimensions, and create a set of filters that will allow comparison reports to be run between the cubes
    let’s face it, there is no one size fits all taxonomy for analysis, and this is the kicker test to see if the platform can support any taxonomy that is needed, run any analysis you want, and allow you to run comparison reports both as checksums and as differentials to figure out where the opportunities are hidden

All this should take less than a morning or afternoon. But it means the provider deserves to be on your short list.

Are You Ready To Get Optimized But Don’t Know How? Read On!

Now that you’ve read our last two posts and understand that you need to get optimized (and analytical) if you want to get cognitive, hopefully you’re ready to get optimized but you just don’t know how.

The four-part answer is pretty easy.

1) If you are using a sourcing platform from a modern provider that offers optimization, acquire the module and start using it.

If you’re already using (SAP) Ariba, Coupa [Trade Extensions], EC Sourcing [with bidmode Inside], Jaggaer (Indirect/Direct/Advantage), Keelvar, or SynerTrade, acquire the sourcing module, turn it on, and start using it. We know that not all platforms are equal (as made clear by the Optimizer Persona in the Spend Matters Solution Maps), but all are more than enough when you are just beginning your sourcing journey. Plus, the majority of these providers are all actively developing their optimization solutions and should stay ahead of your optimization needs.

2) If you are not using a sourcing platform, get one that has decision optimization.

We gave you six names, and these six names can all help you. While we have our preferences, the right solution is utterly dependent on your organization size, industry, dominant categories, geography, and culture and which provider matches your profile the best. There’s only six names, and a relatively short RFI should allow you to quickly zero in on the 2 or 3 that are most likely the best for you.

3) If you are using another sourcing platform and it is not meeting your needs and can replace it, replace it with an optimization-backed sourcing platform.

A few of these providers have a large customer base that consist of those that have switched from another provider with a solution that didn’t meet their needs and, thus, have a lot of experiencing with change management, fear squashing, migrating your data over, and getting you up and running on the right processes quickly. Simply craft the right RFI and you will quickly zero in to the 2 or 3 providers that will likely be the best fit in this situation.

4) If you are using another sourcing platform and it is meeting your needs, can’t be replaced at the present time, or both, augment it with an optimization-backed sourcing solution just for those events where optimization is a must-have.

You just bought Source-to-Contract or Source-to-Pay Solution X a year ago and you know that Finance / Operations / etc. will not approve a new solution for at least a few years because they still believe systems should last five to ten years. In that case, you get a pin-point solution that you use to augment your current solution as a bolt-on. Two of the providers in particular that we mentioned — EC Sourcing with bidmode Inside and Keelvar — are small, mid-market focussed, pin-point best of breed optimization-backed RFX solutions that start in the six figure range (or five figures on an event basis) that can be used to augment a traditional Sourcing platform at a low cost and deliver a high value.

And, no matter what Don’t Say It’s Not That Easy. It is. Yes it’s work to create the technology RFX, reach out to the vendors, make the short-list, do the negotiations, select a (new) vendor, create a transition plan, create an integration plan, and get it done. But making the decision to get a platform that will save your organization an average of 10%+ year-over-year and taking action to do it is easy. And there’s no situation there isn’t an answer for. So, just do it. You won’t regret it.

You Want to Get Cognitive? Then Get Analytical!

As per our post yesterday, the new “cognitive” buzzword is getting a lot of people interested in modern Sourcing and Procurement technology, and that’s a good thing, except when it isn’t. (How can it now be? Not all providers truly offer cognitive capabilities, not all are equal among those that do, and not all are right for your organization.)

And unless you truly understand what cognitive sourcing can do, when it should be used, what technologies you need to power it, and how to properly apply it, the answer is no cognitive sourcing is right for you.

In yesterday’s post, we noted that there were five (deep) technology requirements that a cognitive sourcing platform had to meet to have any hope of truly being cognitive and zeroed in the optimization requirement to indicate that before you even think about getting cognitive you better acquire, and master, strategic sourcing decision optimization because you can’t really properly apply what you don’t really understand, and the vast majority of organizations don’t really have a clue what this is because they don’t have it.

But optimization is not the only area that the average Procurement organization doesn’t have a good grip on. Spend Analytics is another area. Most organizations that have “spend analytics” solutions really have first generation “spend reporting” solutions that are nothing more than a set of canned reports and a few mildly alterable report templates that are customized to certain categories or segments of the supply bases. That’s not analytics.

Regular readers of Sourcing Innovation know that true analytics is the ability to create your own cubes, derive your own dimensions, define your own (pivotable, filterable) reports, and drill across data elements until you find opportunities that cannot be exposed by a canned report. (See the Spend Analysis archives for over a decade of great insights.)

Next generation cognitive systems find opportunities by doing more than just running a set of canned reports on a monthly basis and looking at trends. They are regularly running running variations of dozens, if not hundreds, of analytics on purchase data against deep should cost models populated by ever changing commodity and market costs feeds and looking for variations and emerging trends that could signify potential opportunities as they emerge.

But to understand what’s an emerging opportunity vs. a blip and what is small enough to allow automated platforms to procure and big enough to justify a deep strategic sourcing event or second look at the market, you need to understand just what analytics can do and how to best apply the insight gained from, and the capabilities provided by, a modern cognitive platform — and that requires hands-on experience.

So get a modern spend analytics solution and get your hands dirty in the data. Then maybe, someday soon, you can think about getting cognitive.