If you’ve read the book or seen the movie Moneyball in the past few years, no doubt you’ve considered the opportunities to take an advanced analytical approach to your procurement activities. You’re not alone.
A recent survey published by Accenture measured the opinions of over one thousand senior executives at large global organizations. Of them, 67% were either in serious conversations to implement analytics in their supply chain, or had an active organizational initiative to implement supply chain analytics in the next six to twelve months. An additional 17% had already implemented analytics into their supply chain processes.
That makes big data a big trend in the world of Supply Chain Management.
Certainly there are several reasons for companies to look to analytics as the future for procurement optimization. As Trkman et al. summed up in their 2010 paper on the impact of business analytics on supply chain performance:
“In the modern world competition is no longer between organizations, but among supply chains. Effective supply chain management has therefore become a potentially valuable way of securing a competitive advantage”
While few CPO’s would debate that analytics can offer significant competitive advantage, the Accenture study makes it clear that many companies are still hesitant to implement big data solutions. Principal concerns include the large investment requirements of implementing big data solutions and the associated security and privacy risks.
Clearly the size and scope of big data is overwhelming to many procurement professionals, but perhaps this is more a perception driven by the connotation of the word “big”. In our experience size doesn’t matter – the opportunity comes from focusing less on the mass of data and more on the intelligence – and the decisions – derived from it.
Don’t mistake the point here – truly exceptional procurement decisions can’t be made with insufficient data. In a world where currency volatility alone can be the difference between millions in savings or millions in losses, accurate and timely information is critical. The point is rather that the (often reasonable) fear of implementing expensive big data solutions may cause you to miss out on competitive advantage.
So how can you get started?
In a 2013 InformationWeek Article, Mare Lucas, CMO of big data company GCE suggests taking “baby steps”. While his example discusses analyzing a small amount of sales information in a big data software system, the same approach can be applied in a purchasing environment and in the absence of an expensive data solution.
In our world, a “baby step” approach may be to simply gather all organization-wide purchasing data for a single product category to see what you can learn. For example, consider the amount spent in a year on travel expenses alone in a global organization and the learnings that could be realized from a detailed analysis:
- Which departments are flying economy? Which are opting for more luxurious options?
- How much is spent on change fees alone?
- Which units are taking advantage of teleconferencing or webinars as opposed to face-to-face meetings?
- What are the additional baggage charges?
- Taxi vs. Uber vs. rental cars & parking
- Hotels with included breakfasts vs. purchased separately
- Per diem allowances vs. as realized expenses
- What aggregation opportunities exist for savings with a third-party travel agency, hotel chain or rental car operation?
Another “baby step” route is to aggregate and analyze customer sentiment in a particular region or market to identify opportunities for improving product quality or competitive pricing by sourcing higher quality or less expensive vendors. Does reducing your cleaning bill save you money or reduce the customers visiting your branch? Is customer satisfaction declining as more product is sourced from offshore vendors or can the associated cost savings be realized without hurting brand perception?
Neither of the above examples necessitates huge cloud data investments necessarily, but they could illuminate significant opportunities for savings or competitive advantage. More importantly, they may offer a compelling business case to invest in bigger data solutions in the future and in turn, remove the big concerns currently slowing your move to big data.