Procurement organizations generate and store massive amounts of data, which is often widely dispersed across different systems, operations and geographies. But how can procurement take control of all this data? How can it be used to gain insights that will enable better planning and effectiveness?
Before there was big data, there was just … data. Procurement organizations compiled mainly internal, structured data from transactions, operations and partners through laborious processes that often took weeks to complete.
Today, sources for data have expanded to include a much more disparate set of both internal and external, structured and unstructured data obtained from automated processes executed within hours or, in many cases, in real time.
Applying Analytics to Procurement
Big data technologies and analytics for procurement organizations can be applied in the following areas
- Self-Service Analytics and Reporting – Enables procurement to develop reports and perform analysis on spend, supplier base, compliance, and other spend-related data.
- Predictive Analytics – Typically support planning or provide the most likely outcomes based on actual conditions. Procurement can forecast spend based on anticipated changes in the business and pricing based on external factors.
- Scorecards – Easy-to-read, real-time graphical snapshot of performance outputs of procurement based on a set of effectiveness and efficiency metrics. This enables instantaneous, informed decision-making.
- Risk Analysis – Identifies business risks to the supplier base, including pricing risk, compliance risk, geographical risk, disaster risk and others. Also helps define preventive measures and detect countermeasures to successfully deal with risk scenarios.
- Data Mining – Analyzes large amounts of data to identify trends that offer meaningful insight. Procurement can better understand spending, supplier performance and external pricing trends.
- Slice-and-Dice Analysis – Analytical tool that presents data according to different criteria. Procurement can examine spend information by supplier, geography and category.
Strategic Purchasing
Big Data in strategic purchasing primarily aims for a higher “analysis intelligence”. The objective is to obtain new, hitherto unknown, conclusions from the data.
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.
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.
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 savings effect 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.