For a few months now, the term Big Data has been a hot topic in the media. Yet what is really new with respect to well-known instruments such as SpendAnalysis and procurement controlling? Big Data in strategic purchasing primarily aims for a higher “analysis intelligence”. The objective is to obtain new, hitherto unknown, conclusions from the data.
In order to achieve this, modern Big Data applications try to use as many data sources as possible on the one hand, and use this data with the help of advanced mathematical algorithms and artificial intelligence (AI) methods for very quick analyses that are suitable for end users on the other hand. Big Data applications in purchasing therefore incorporate both internal company data as well as external data, such as market (price) trends, supplier databases, corporate scoring, risk information, measured data, information regarding cost structures or economic and price level facts.
Intelligent analyses are only possible with high data quality
The particular challenge is to consolidate, categorize, recognize and eliminate data errors of numerous data, as well as to group suppliers or to rationally cluster similar components in an automated manner. It is only with these steps that the quality of the data only improves, making it useable for intelligent analyses.
The data can now be compared and used for benchmark or correlation analyses, as an example. Furthermore, through the classifications and clustering, completely new and virtual purchasing objects are introduced. These, in turn, allow much more intelligent analyses, simulations, forecasts and savings potential calculations. Big Data Analytics hereby replaces perspective work, which was previously reserved for expensive consultants.
Now available in purchasing: the automation of consulting
This effect is referred to as the “automation of consulting” by the experts. Good examples for the new level of analysis intelligence, which is attainable through Big Data, are impact analyses and forecasts that are based on advanced mathematical algorithms such as:
- The forecast of market prices and attainable purchasing prices
- A simulation of the impact of currency exchange rates or changes to the price of raw materials derived from hedging proposals
- The calculation of the dependencies of individual currencies concerning changes in wage levels
- Impact analyses of individual savings levers (such as volume clustering, LCC or collaborative sourcing): Which SavingsEffect do the individual levers feature? Under which conditions do they stop working?
- Automated recognition and monetary calculation of potentials and risks of the individual groups of procurement goods.
Until recently, these were perceptions – if at all – that could only be achieved through a relatively large effort and with manual procedural steps by expensive experts. Big Data Analytics automates the analysis and automatically calculates the results in a matter of seconds or minutes, instead of days or weeks.
We offer you a holistic solution for increased transparency, potential analyses and measuring your procurement success regarding indirect purchasing with our DataCategorizer, SpendControl and InitiativeTracker modules.
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