This article is part of an ongoing series called Machine Learning for Manufacturers, which focuses on the uses for artificial intelligence (AI) in manufacturing. It began with our article introducing machine learning and AI, and continues to spell out the many ways that AI solutions are relevant to manufacturing and supply chain operations.
What is Spend Analytics?
Spend analytics (sometimes called spend analysis) involves a lot of analysis, organization, and sifting of a lot of data, specifically, data related to a business’ expenditures.
For manufacturers, there can be an absolute abundance of such data, since there is a near-constant stream of items being bought (i.e., raw materials, from a supplier) and sold (products made in your factory, to your clients) which makes spend analytics an even more daunting, not to mention time- and money-consuming, endeavor than it would be for a business in another industry.
Like most business operations, the goal of spend analytics is to cut down on costs while also managing to boost profits. When conducting a spend analysis, this goal is reached by examining how much you are spending, who you are giving your money to, and whether there is a significant gap between what you are spending and what you want to be earning—the solution being to find a way to close that gap.
Pinching Pennies the AI Way
In manufacturing, like in most industries, money is constantly going out with the hopes that more money comes when.
What differentiates manufacturing from other industries is the frequency of that spending.
Much of a manufacturer’s money goes to downright essential supply chain operations like transportation of finished products to clients or procurement of raw materials for creating those finished products.
This means that it is not a question of cutting out costly processes altogether, but to find ways to make those processes, well, less costly.
Many manufacturers are beginning to realize that, given the prodigious levels of data amounted by nonstop expenditures going into their operations, upping their spend analytics game is the chief order of business for pinching pennies in a time of industry-wide crisis.
To find out the role AI can play in improving a manufacturer’s spend analytics, read on.
Spend This, Not That
The typical AI agent lives and breathes data, and is basically a data-organization and -analysis machine. This is very useful in spend analytics because one of the biggest problems that manufacturers face is organizing and classifying expenditure data.
Classification is so important because it relates to all three of the aforementioned aspects of spend analytics, the How Much, Who With, and Why (Am I Spending so Much for X?)
An AI classification system can accurately sort through the stream of incoming data and organize it all into a data set that gives you a good picture of just how much you are spending in all the areas of the supply chain.
This allows you to take a close look at any discrepancies between your current spending and old spending, or even how you compare to other manufacturers (AI analyzes external data as well as your own internal data.)
AI can even comb through your older data, and sort it through to give you a more accurate picture of your spend history.
AI-Powered Data Solutions for Your Company
The ongoing supply chain crisis has made the time- and cost-efficient uses of AI in the manufacturing process even more relevant than ever, so do not hesitate to make the investment in AI, which has saved many manufacturers from going under in these turbulent times.
To learn more about AI-based solutions, manufacturing-related or not, reach out to Findability Sciences, a leading AI service provider. From predicting customer churn to installing chatbots in your supply chain, you are sure to find what you need at Findability Sciences.
Read other informative articles in our ongoing Machine Learning for Manufacturers series: