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.