Editor’s Note: Today’s article picks up where a previous article, The Rise of Analytics across Procurement Operations, left off last month. There is an enormous amount of value in Big Data, and for those that can harness its power, they can do truly amazing and innovative things with it. We hope you’ll enjoy today’s article.
For those procurement organizations that are mature in their processes, tight in their execution, and advanced in their skillsets, the move beyond basic data management presents significant opportunities to turn Big Data into predictive, forward-leaning analysis and strategic planning. Savvy Chief Procurement Officers (CPOs) and their teams have been taking multiple streams of internal and external data, including structured and unstructured data, and combining them in centralized data management dashboards that leverage powerful BI and analytics tools. There, teams cleanse, categorize, and enrich this data, run it through complex algorithms, and derive 360-degree views of their spend, categories, suppliers, and geographies, as well as their internal team performance and resources. In doing so, progressive CPOs and their teams are gaining holistic views into enterprise and supplier risk, and developing strategies to mitigate or avoid those risks.
Other procurement teams are developing data models that use years of historical spend, commodity, and supplier data as a way to better anticipate pricing trends and market shifts in the development of optimal category strategies. As the data quality improves and as the tools and capabilities in use by procurement teams also advance, a “Moneyball” effect allows procurement practitioners to automate category management and purchasing behaviors by setting automated, data-driven models to execute tactical buying and order refilling.
Much in the same way hedge funds and sophisticated investors have developed automated trading systems that use pre-defined trading rules to automatically determine when to buy and sell stocks, procurement departments (with IT support) are starting to build data models for various categories, commodities, regions, and suppliers, and delegate buying decisions to these models under optimal circumstances – for example, when a particular commodity price drops below market average, or when its quality per price reaches an optimal level based on historical trends.
Additionally, next-generation procurement analytics tools, particularly predictive analytics and artificial intelligence (“AI”), are driving agility, efficiency, and innovation deeper into the source-to-settle value chain. Thanks in large part to sourcing and procurement analytics providers, like Sievo, The Smart Cube, and Tamr, these solutions will continue to remove the “man in the loop” and free him or her to perform strategic business planning and negotiation. And, as “best-of-breed” solution providers, they are giving end-to-end suite providers a run for their money in the supply management solutions market.
In 2019, procurement strategies that avoid or bypass data-driven logic and insight are anachronistic signs of an earlier time when procurement was a back-office function, marginalized, irrelevant and without standing. The proliferation of data in business requires analytics to be on the rise in procurement and for CPOs to have a Big Data plan. Procurement teams that can harness the power of multiple data streams without getting soaked stand to benefit the most by the rich intelligence that it provides, and the enhanced decision-making and impact on savings and risk that can result.
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Tagged in: Chief Procurement Officer, CPO, Innovation, Process, Solution Providers, Source-to-Settle, Sourcing, Strategy, Supply Risk, Technology
