See Part I for the story to date. Suffice to say that when the following are objectively analyzed, one can expect good market insights:

• financial statements, particularly those from public companies (as false statements are a criminal offence for the CFO and CEO in some countries)
• customer interviews, good or bad, as it’s a third party product/service view
• performance reporting, as any hard metric is objective
• internal stakeholder interviews, where the bias is minimized through targeted questions
• price index data, that can be used to roll-your-own forecasts
• public consumption data from government contracts, as they are great benchmarks

… provided one has the right platforms!

What are those platforms? Well, consider that the following sources are (primarily) numeric:

• price index data
• performance metrics
• public price contracts

And the following sources are primarily (subjective) textual:

• customer interviews
• stakeholder interviews

And the following, final source is mixed:

• financial statements

And that makes it pretty clear you need a platform that has the following if you want to process the price data:

• A Great Open API
  as the price index data will be on multiple exchanges — which use different APIs, security protocols, currencies, and even data encoding formats and you will need to be able to easily retrieve and integrate all of it
• Multi-Level Formula Based Cost Models
  to accurately capture and represent all of the commodity, component, product, and service costs that you need to track for cost estimation and analysis, bill of materials, sourcing, etc.
• Powerful Analytics (Integration)
  you need to be able to store, analyze over time, and use multiple, multi-variate, statistical algorithms to detect trends and project them over time, as well as alter the assumptions, parameters, and model inflection points (due to predicted inflection events)

… and a platform that supports the following if you want to process the textual data:

• advanced semantic processing
  that can extract key topics and opinions and classify them to process or technology, functional area, etc. (as well as identify incongruities)
• advanced textual analytics
  the platform needs to be able to assign general descriptions numeric weights against important factors (perceived risk, customer service level, etc.) to determine if the general view is improving, weakening, or staying static
• advanced sentiment analysis
  that can extract not only general opinions about a supplier, process, etc. but specific opinions about process, technology, etc. components — for example, the stakeholder might be soured on the relationship with a supplier because they have p!ss-p00r customer service but agree they make the highest quality parts (and would be usable if they ever bothered to answer the d@mn phone); just an overall negative sentiment of 0.6 is not that meaningful

… and to process financial statements, the platform needs to merge the advanced textual analytics to populate a standard financial model template, adding in any additional revenue or expense, asset or liability, etc. lines that are missing from the standard model so the books balance and can be analyzed.

So where do you find these capabilities today?

Well, as previously indicated, you will find:

• advanced cost models in direct sourcing platforms that support full multi-level bill of materials
• advanced forecasting in modern analytics platforms that support machine learning
• advanced sourcing support given predictive costs in platforms that support strategic sourcing decision optimization
• advanced document analysis in industry leading contract management solutions (which can be adapted to parse and analyze and break apart and score any document type given a template and samples)

In other words, modern Analytics, Optimization, and Contract Analytics solutions. And this is just another reason SI has been preaching advanced optimization and analytics since day 1.

Last year, about this time, we wrote a piece on Supply Market Intelligence and how it was Harder Than it Looks because there are a number of sources that might yield intelligence, including:

• Suppliers,
• Internal Sources, such as
  • internal stakeholders
  • performance reports
  • SRM programs
• External Sources, such as
  • news feeds and alerts
  • price index forecasts
  • blogs and social media
  • peer companies
  • research services
  • advisory programs

… but not all are equal. And not all are fully accurate. For example:

• Supplier company websites only show you what the supplier wants you to see, which is typically not the full picture, and maybe not even a true part of it
• Internal Sources, such as
  • internal stakeholder interviews capture bias as well as expertise
  • performance reporting can only report on hard metrics the organization had the foresight to capture
  • SRM programs — and the insights yielded — vary by company and supplier
• External Sources, such as
  • news feeds only cover the stories that interest the journalists
  • price index forecasts use in-house algorithms that are not disclosed that may not be accurate
  • blogs and social media cover the stories that can be sussed out by the bloggers and analysts

But some of them contain valuable data that when appropriately, and objectively analyzed, can yield good insights, as per our follow up post:

• financial statements, particularly those from public companies (as false statements are a criminal offence for the CFO and CEO in some countries)
• customer interviews, good or bad, as it’s a third party product/service view
• performance reporting, as any hard metric is objective
• internal stakeholder interviews, where the bias is minimized through targeted questions
• price index data, that can be used to roll-your-own forecasts
• public consumption data from government contracts, as they are great benchmarks

… and so on. But it can be pretty hard to make sense of all this … unless you have the right platform with the right capabilities. Now, it might not be a single platform from a single vendor and instead be a base Sourcing / Procurement platform augmented with multiple best of breed modules and API services from multiple vendors, and that’s fine. The point is that it’s possible to make sense of this with modern technology. What technology? How? That’s the subject of our next post.

AI is the buzzword, or, more precisely, the buzz acronym. Just about every enterprise vendor is claiming they have AI, even if all they have is RPA (and even if what they have is pushing the definition of RPA). However, whether your vendor has AI or not (and the answer is that they probably don’t, as most of the best vendors just have ML, possibly enabled by AR, but probably not), it is coming, and if you don’t adopt (at least) the (precursor) technology available today, your Sourcing and Procurement organization may be left in the dust.

And by now you are probably firmly bamboozled, so let’s set the record straight, starting at the bottom of the AI technology ladder.

At the bottom of the technology ladder we have RPA, short for robotic process automation, which is generally used to automate what would otherwise be very manual processes, usually by way of a rules-based workflow engine.

On the next rung we have ML, short for machine learning, which applies (usually improvements on, or variations of) open-source or standard algorithms that can extract a model from a set of inputs to produce the associated outputs with high probability. The better platforms use machine learning to tune, if not define, the rules used by the workflow engines embedded in the platforms.

Sometimes the mix of ML and RPA is so good that for certain, focussed, applications that the platforms almost seems intelligent, and this is often what passes for AI these days. But it’s not real artificial intelligence, it’s assisted intelligence as it helps you do a better job, but your intelligence is still required to identify the right recommendations and approve the right actions.

The next rung up is AR, automated reasoning, which can take a set of assumptions, encodings of logical rules and predictive models, and compute derivations that can surpass even a human expert most of the time for very well (and narrowly) defined applications or problems. It’s basically the modern equivalent of an expert system that can compute millions of inter-related logical inferences until new realizations are discovered.

The next rung up is the version of AI that exists today, augmented intelligence, which expertly integrates RPA, ML, and AR to produce applications that more-or-less mimic what an expert would do the majority (but not all of) the time. And that allows an organization to automate some low-value tasks that would otherwise require manual effort as they were generally identified as strategic, but not always worth the effort.

If it existed, the next rung would be the AI that is touted, true artificial intelligence, which does not exist today. (And that’s a good thing, because if there was true AI, would the C-Suite need you? Yes. But would they realize it? Probably not.)

But the final rung, and where everyone wants to get to, is cognitive. AI technology that is not only intelligent, and that can make great decisions unassisted every time, but make the decisions the best human buyer for every situation would make considering all hard and soft variables.

And that’s the technology ladder you are dealing with, and now you know that where you are is likely not where you want to be. But don’t fret, things are getting better. Stay tuned!