The Final Stage on the Spend Visibility Curve: Predictive and Proactive

I’ve given entire talks on how procurement organizations are turning to new types of technologies and initiatives to become more predictive and proactive in their overall efforts. Yet this argument also forms the basis of the final stage, or Level 4, in the spend and supply visibility maturity curve that I presented on the Spend HQ webinar, The Spend Visibility Curve: Where Do You Stand.

The initial three maturity stages I explored from this talk were sourcing and measurement, spend and supply and continuous improvement. Today we come to the final stage: predictive and proactive. As with Level 3 maturity (continuous improvement), this final stage of data and analytics maturity is really not just about an overall approach to examining spend and supply data. Rather, it is about changing how the procurement function looks at impacting the business.

In short, Level 4 maturity is about applying a forward lens. It is fundamentally about looking forward versus a rear-facing view, which categorizes spend and broader supply analytics programs in 99% of cases. Level 4 also encapsulates all the previous stages — it assumes you’re engaging in all the data analysis and subsequent activities that we outlined previously in this series. (See the links above.) The fundamental concept underlying predictive and proactive spend and supply analytics is influencing spend before it happens.

Here we add a new layer to using data that focuses on forecasting and scenario analysis. In other words, at Level 4 maturity, procurement organizations are actively engaged in developing a forward-looking perspective that provides not just answers but also the logic behind a forecast, to help the broader organization plan for the future. Certain areas stand out as first priorities for forecasting, including commodity prices, capacity, demand and inventory.

But there are many more areas that procurement can begin to forecast if it has strong underlying data sets and the analytical tools to develop predictive models. Such initiatives might include forecasting:

  • Budget and budget impact of specific initiatives
  • Supply risk
  • Overall demand — an organization may already be doing on the factory or plant level for direct spend, such as incorporating procurement into sales and operations planning (S&OP) initiatives, but it can also be done for indirect and services as well
  • Future supplier performance, including adherence to quality/SLA requirements
  • Internal performance versus market
  • Peer performance versus market

Finally, at Level 4 maturity, savings from spend/supply analytics can primarily be attributed to collaboration and engaging with the data in new ways to forecast likely — and unlikely — scenarios and outcomes and to take action to influence future states.

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