Procurement in the Age of Big Data ‚Ä®Challenges and Opportunities
Mo Data stashed this in Big Data in Supply Chain Mgt and Heavy Industry
By¬†Roshnee Mistry, Content Leadership Team, Zycus
According to a Computer Science Corporation study, by 2020 there will be 44 times more data than there was in 2009, and a third will move into the cloud.
Procurement analytics reveal a shift from a historical spend analysis with structured data to a predictive supply analysis with the ability to make sense of unstructured (transactional) data. This trend will grow to become a core competency for an organization. According to a Hackett study, analytics is a top priority for a lot of procurement leaders in 2014. Top CPOs intend to make spend analytics, contract management, supplier management and performance management analytics a determining factor of their future performance. It is also important to note that World Class procurement organizations are more likely to have less savings leakage due to better line item analytics.¬†
There are huge streams of data out there, and part of the challenge is to understand the usability of this data in our day-to-day procurement requirements and fine-tune the supply chain. This sea of data calls for new approaches to be adopted in order to build up deep spend and cost analytics capabilities to help drive better decisions and outcomes.
Some of the key challenges are:
- Understand the data collected around the life of a supplier from¬†Registration,¬†RFX,¬†Contract,¬†Performance,¬†Payments,¬†Termination.
- Transfer the customer data right back into the organization to drive better decisions, such as network optimization, product lifecycle management, internal business savings, capital improvements, etc.¬†
- Use collaboration data for collaborative planning around suppliers.
- Use of unstructured marketplace data which can help in planning scenarios, mitigating risks in the supply base, understanding the spending trajectory and sharpening the category strategies.¬†
Making Sense of Big Data
To create value out of this host of information, one needs to understand how this data links back to the business intelligence/database that exists in the company. The next step is to prioritize the types of data and not just be overwhelmed by a sea of data. One needs to have an objective and then work around the data to see how it can build insights into the strategies to realize the goal. This will drive the analytics capability in making sense of the clutter of data/information.¬†
Supply data analytics will help to link this data back with the legal structures, understand the history of contract/supplier usage, provide visibility on the adherence to procurement processes regarding governance and compliance requirements, understand the signatories, assess risks to the organization and gain insight into Industry and market information, to name a few of the outcomes. Harnessing this massive amount of datasets in a productive manner can make this information an asset and aid in arriving at better decisions.
Procurement needs to go beyond spend analysis and start looking at the business objectives and requirements, and that will lead them to pursue the right data leading to productive discussions beyond spend analysis and contract compliance. Business Intelligence/Management Intelligence should enable processes to gather, analyze and synthesize data and information, thereby creating knowledgeable buckets around risk management, cost improvement, supplier relationship management, etc. Some of the sources of market and supplier information come from trade journals, internet, news, industry associations, company annual reports & 10-Ks, consultancies and research providers. Supply chain analytics are now looking at advanced functionalities that aid in cost mapping and modelling at a very early stage and help drive scenario-based outcomes, thereby enabling analytical solutions to supply chain problems.¬†
Finally, it is our lack of creative thinking that could limit the plentiful data/information relationships/models that could be generated.
Some of the key questions, if answered, can help in applying evolved procurement analytics tools¬†
- What are the opportunities to turn to new applications in spend analysis to drive improved supplier performance outcomes and sourcing strategies?
- Are we moving away from supplier management by ‚Äúspreadsheet‚ÄĚ to management in the cloud?
- Do we understand all the cost elements that can drive total cost to serve, total cost of ownership and target costing for new product development applications? For example, cost of goods, labour costs, overhead costs, etc.
- Do we know the key emerging areas of supply chain analytics that will drive supply chain innovations, such as first market view, early technology indicators?
- Who should own and drive supply chain analytics: Manufacturing, IT or Business Management?
Current and Future Focus
Well, the future definitely looks extremely good for integrated procurement analytics with internal focus and inputs coming in from various external sources. However, big challenges in the form of data quality and cleanliness of data still persist as key pain points. At the root of all procurement or strategy are the people who drive innovation and derive value for the company. This is the time for training and improving the skills of procurement teams in order to craft the backbone of your organization‚Äôs integrated procurement analytics approach. It will drive you into the future of big data analytics.
For further reading download the whitepaper -¬†The evolution of spend analysis and the rise of big data.