In McKinsey's exploration of the Big Data on the future of commerce (private and public sector together), the authors of the foundational study offer a number of higher-level observations exploring how Big Data will impact business and government. One of their specific observations is that Big Data will play a key role in "replacing/supporting human decision making with automated algorithms." This is something we've observed and explored significantly on Spend Matters, including previously in this series, in the area of sourcing optimization. But the concept of having algorithms replace human decision-making goes far beyond sourcing. In fact this is precisely the vision that Opera Solutions, and to a lesser degree IBM, are hoping to bring to procurement in the coming year.
As McKinsey observes, "sophisticated analytics can substantially improve decision-making, minimize risks, and unearth valuable insights that would otherwise remain hidden ... In some cases, decisions will not necessarily be automated but augmented by analyzing huge, entire datasets using big data techniques and technologies rather than just smaller samples that individuals with spreadsheets can handle and understand. Decision making may never be the same; some organizations are already making better decisions by analyzing entire datasets from customers, employees, or even sensors embedded in products."
In the recently published research brief on Spend Matters PRO,
The Meaning of Big Data for Procurement and Supply Chain: A Fundamental Information Shift, we suggest that one area where automated algorithms and systems may replace existing (or non-existent) human involvement is in the case of better implementing, managing, measuring and forecasting savings and cost avoidance. Here, we suggest that organizations are beginning to factor "in underlying changes in the market (currency market swings, commodity volatility, etc.) and adjust strategies as underlying conditions and information sets change ... only a select few have the ability to adjust strategies in mid-course (e.g., changing the amount of spend from month-to-month that is bought on contract vs. on the spot market) to take advantage of arbitrage opportunities while factoring risk tolerances into account.
In summary, when thinking about procurement savings implementation, machine-driven approaches combined with human analysis as a final component will be especially valuable when considering the need to combine, report and analyze multiple datasets leveraging internal cross-systems data combined with external market information to track and manage savings.
Next up on McKinsey's list (and our procurement-related extension and exploration): "Segmenting populations to customize actions."