It's been stated many times that increasing use of data will improve various industries and functions, procurement definitely among them. The question then becomes more granular, concerning the exact ways in which access to better, faster or more accurate information will help the day-to-day operations that keep these departments functioning.
Leaders already have one of the ingredients for good data use: A contemporary supply chain is made up of a huge number of moving parts, all of which are great sources of information. Once supply chain executives find a way to harness and crunch the numbers generated by their operations, they can learn a lot about their businesses, and use that knowledge effectively.
AI and data analytics in sourcing
A recent Spend Matters piece focused on the use of high-powered artificial intelligence algorithms in procurement to improve the calculations that go into strategic sourcing. Today, supply chain officials make decisions based on retrospective data. Advanced systems, however, can go faster. Systems powered by machine learning and AI can work with real-time input rather than just crunching old data. Procurement teams can turn this extra edge into better decisions.
Furthermore, Spend Matters explained that powerful analytics have uses long after a contract is awarded. The continuing relationship between supplier and company is open for analysis, ensuring that the value has stayed constant, and that the business is indeed saving money based on its choice of partner. Before, during and after the act of selecting a supplier, companies are best served by fresh data input rather than retrospective figures.
The relevant data doesn't just come from historical analysis of suppliers or the companies themselves. Spend Matters pointed out that modern algorithms can crunch the numbers generated by overall market activity as well. Driven by machine learning and AI, powerful data analytics programs are able to make more accurate predictions than ever before, based on both the timeliness and variety of variables considered.
Various kinds of analytics
There is more than one kind of analytics at play in the sourcing and procurement world. As EPS News recently pointed out, multiple technologies that fit this description recently made appearances in Garnter's periodic analysis of supply chain tech readiness. The "Hype Cycle" tracks concepts as they make their way through the pattern of rising expectations and finally find their place in the industry. Different types of data use appeared scattered along the curve.
For example, Gartner sees true artificial intelligence in the "innovation trigger" stage of development. It could be 10 years before this technology becomes as common and well-known as descriptive analytics, which has reached the "plateau of productivity. Other forms of analysis counted include big data, which is two to five years from workaday usage, and prescriptive analytics, which should be mundane in five to 10 years.
Data analytics is in an interesting place, as both the present and the future of supply chain efficiency. Companies are crunching their numbers today, but as the technology involved becomes better and more affordable, they will grow progressively more insightful. When it comes to gaining valuable insights, the potential for improvement is staggering.