The basic idea of Big Data is that analysis effort focused on analysing large quantities of interrelated business data can provide enormous amount of business value.

The fundamental driving principle is that trends and correlations discovered from the data analysis can propel significant business level insights.

Applying a Big Data based methodology to procurement data can extract significant insight and value. Here are 5 ways in which it can.

1- By Empowering Advanced ERP systems like SAP Ariba

SAP is a leader in application software with products encompassing a variety of areas including Analytics, Content and collaboration, CRM, Data management, Enterprise management, Human Capital management, Product LifeCycle Management, Supply Chain management and Supplier Relationship management.

SAP Ariba is the most prominent supply chain management solution in the world. Be it Spend & supplier management, Financial Supply chain management or E-Commerce and account management, Ariba has you covered.

Even with extensive usage of ERP systems, it is estimated that anywhere between 20 and 50% of working time in the procurement function is spent on activities related to searching for information.

Given the fact that Big Data solutions correlate and aggregate all the relevant information, they can help to greatly speed up the pace of procurement related activities and reduce the time needed to complete the same. It has been estimated in some studies that there can be an improvement in the efficiency to the extent of 10-30%.

2- By helping manage supply chain related risks

One significant area of application of Big Data is in identifying events and trends that act as a warning sign for supply chain related risks.

Take the case where a company is able to put in place a system to monitor all publicly available news outlets for any critical keywords or phrases associated with supplier names or the names of sourcing markets.

A system like this can help to keep track of risk profiles for both suppliers and markets. A system like this would be enormously useful in managing supply chain risks. When amped up with an SMS marketing and alert solution, this can help enterprises reinvent they maintain vendor relations and customer relations.

3- By reducing sourcing costs

Big Data can be an excellent avenue for identifying opportunities to reduce sourcing costs.

Suppose you take the case that an organization is procuring Calcium carbide pellets. If the databases are automatically linked with market price data for calcium carbide and data related to macro-economic forecasts, a Big Data solution can alert the organization in real-time to opportunities for renegotiating contracts.

An analysis by Arthur D. Little shows that Big Data has an impact on about one third of all the improvement levers in the ADL procurement toolbox, which can in turn lead to reductions in sourcing costs in the range of 2 to 5% annually.

4- Providing alerts by leveraging knowledge of inter-linkages

One of the biggest areas in which Big Data can transform procurement is with regards to discovering inter-linkages between the prices of various commodities.

Consider for instance that a refinery which is sourcing crude oil, natural gas and rubber has put in place advanced Big Data analytics capabilities in its procurement function.

Such analytics can bring out linkages between the price trends of crude oil with the price variations for natural gas and rubber.

Based on these linkages, it then becomes possible to predict the price movement of natural gas and rubber with a certain degree of certainty.

5- Moving from guesstimation to estimation

Big Data helps to introduce a solid numbers-based grounding to a lot of guesstimation that can happen in terms of calculating and predicting prices in sourcing.

While ERP systems have brought in a lot of rigor to the procurement process, Big Data applications increase it even further by giving numerical basis to assumptions regarding inter-relationships among commodity prices.

This also adds to the robustness of predictive models, financial projections and data used during negotiations.


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