New Procurement Management Thinking: Theory-Informed Practice and Proactive Data

We recently skimmed the surface of the evolution of management thinking in procurement, commenting in part on how this area does not receive enough academic and non-academic attention today. In the practitioner world, there are some great examples of some of the best minds in the business applying theory into practice. This includes some of the fascinating work Jason Magidson and Sammy Rashad have done – ironically both are “graduates” of some of the largest procurement teams in the pharma business – leading organizational process and design for GSK and Novartis, respectively.

My commentary on this topic was sparked by a piece I read by someone who I can tell is truly into the topic, Gerard Chick (who recently contributed a post to Procurement Leaders on the topic). One suggestion that Gerard offers to adapt to the current paradigm of individual knowledge workers – in our view all too often working on indirectly hoarding knowledge rather than leveraging systems and processes to share it – is to “develop a mind-set of ‘theory-informed practice’ by analyzing data from your organization and your networks and using analytics and ‘big data’ for innovation, competitive advantage, and productivity. Best practice databases tend to lead you to rely on what you ‘learned’ in the past.”

This concept of using data more proactively than relying on the “best practice” database as Gerard terms it ties directly to a talk I gave last Friday at an ISM conference focusing on what’s next in risk management. In my talk, I shared the term “3 P’s” – no, we’re not talking about the “marketing P’s” – that I thought were defining the future of best practice: predictive, proactive, prescriptive. As Gerard observes, the biggest danger around knowledge management is simply collecting data for data’s sake. Rather, we must think beyond data collection and prioritize the visualization, presentation, and predictive nature of what individual and combined elements in a broader data set can tell us—as opposed to relying on increasing information loads to query “what we’ve learned in the past.”

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