Today, Machine learning is used in many business processes and is becoming more accepted, especially with every day, virtual assistants like Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana, just to name a few. What businesses don’t know is that Machine Learning also plays an important function for small businesses looking to save money and time when it comes to procuring clients.
Procurement is a major step in the broader story of a business’s health, however, this can be an expensive task for all businesses large or small. Machine learning can be used every time organizations or teams look for ways to save time, drive down costs, optimize efficiencies, provide the strongest quality assurance, and increase visibility among procurement teams. For enterprise-level organizations, this is important as it relates to small, miscellaneous costs that can chew about at the bottom line. Imagine all the unsupervised, seemingly insignificant purchases that occur outside the regular procurement process. This number can quickly get out of control and can account for as much as 40% of gross purchasing volume for an organization. This is an even bigger deal for smaller companies with small budgets where it is much more important to find great value. Procurement mistakes can be expensive, financially, and the time and energy wasted can add up to cost the company even more.
The benefit of machine learning is that it can improve efficiency and assist in making better procurement for business decisions. It is estimated that in the next five years, more than 60% of purchasing managers and officers will use some form of machine learning or artificial intelligence to affect and optimize their buying decisions.
HOW CAN MACHINE LEARNING HELP?
Think about daily business activities that use human intelligence-- these tasks take solving cognitive skills that are hindered by learning speed, pattern cognition, and problem-solving capabilities. Machine learning allows for historical data to be captured and analyzed which allows learning to happen automatically and with manual processes.
Think of how manual, time-intensive tasks can be automated not by employees, but by machines. Things like charting our product trends, sales forecasted and extrapolation, calculations throughout, streamlining fulfillment, and judging price sensitivity. All things can be sorted out by algorithms and machines, as opposed to humans.
ITERATIVE LEARNING AND IMPROVEMENT
Without sorting one-by-one through Choice A versus Choice B-C-D-through-Z, purchasing managers can upload a preferred set of products, and a machine can assess their quality, price, and other data points to find cost-effective substitutions for essential ingredients.
Another great improvement is the customer purchasing behavior to find product recommendations that can be offered to consumers. Through search analysis and natural language processors, algorithms can sort and distill the important keywords of what consumers actually want. For example “Turkey Souvenirs” (for Thanksgiving) vs. “Turkey Souvenirs” (the Country). Machines are smart enough to tell the difference based on historical and current search relevance.
The real advantage of machine learning is that all of this can be done in nanoseconds, compared to the human interpretation which can be costly and time-consuming.
For now, Artificial Intelligence is in the mode of automated and iterative procurement and curated buying, directed by a human.
In the past, as it relates to procurement, companies would invest in business intelligence experts, engineers, scientists, and other data professionals. These individuals would pine over complex data sets, create analysis models, and make an educated guess on procurement improvement processing.
Today, machines can do the work. These technologies can help businesses improve the procurement process, garner efficiencies, prioritize substitutions, and predict trends-- given the right data set and guidelines. Thanks to machine learning, procurement managers don’t need specific expertise anymore. Even simple rules can make vast differences when applied to artificial intelligence.
In the future, machines could be set up to apply their own guidelines and change them to provide their own business goals. Only time will tell.