Empower Supplier Relationships with Machine Learning
Relationships with suppliers can be complex and your supplier portfolio diverse. As a customer, you want to enhance your value to your supply base and ensure they deliver value back to your business. Only through mutual added value can these relationships thrive over time.
A broad portfolio often spans a long list of categories, geographies and sites. Therefore, identifying risks and opportunities within spend details poses significant challenges. Most organizations rarely possess the resources and bandwidth needed to comprehensively evaluate supplier mix, volume and price shifts, sole source situations and unique product scenarios.
This is where artificial intelligence (Ai) and machine learning (ML) come into play. For example, apply a machine learning model to all of your supplier and contract data. In just a few days to weeks, a 360-degree view of each supplier will formulate and begin to recognize specific patterns in your data to identify risks and opportunities.
Empower your supplier relationships with ML to begin a path towards a brighter future for your company. Read on to find out more!
What is Machine Learning (ML)?
Before we delve further into this topic, let’s make sure you understand machine learning.
Many people hear machine learning and think it would be too expensive, take too long to implement, exert significant strain on tight resources and involve an ongoing need for external support. This not to mention that many often question the credibility of the technology.
Machine learning is actually very much how it sounds. At ElectrifAi, with our SpendAi machine learning enabled software, we cleanse data with remarkable speed across multiple data types and data locations. Made possible by using algorithms that get smarter with every exercise, the model learns which suppliers belong together and what classification they should be organized.
Each time we use the machine, it learns more and more where things belong and understands associations based on words and descriptions. This delivers results that can be acted on in days or weeks and get you exceptional ROI with unparalleled speed and accuracy.
We use ElectrifAi’s machine learning capabilities as a standard of excellence because, bias aside, building models successfully can be difficult. With our proven results over many years, we have the expertise and prebuilt models ready to go, making it easier than others to implement with your systems.
When we talk about industries and verticals, each industry has a different need regarding spend analysis machine learning models. For example, there are big differences in the proportion of direct versus indirect spend across industries.
Most manufacturing companies, depending on what is being produced, are probably between 75% to 85% direct materials and a reasonable portion of variable cost in their model. Service oriented businesses are more likely to spend on indirect materials and suppliers and have a much higher proportion of fixed cost.
Depending on what type of industry you’re in, anything from automotive to steel to chemicals, clothing, textile or apparel, the format of supplier relationships and the processes for sourcing things will vary.
Upstream price volatility can be a concern. How much those direct materials are linked to market forces or to some degree influenced by investor behavior and financial inputs will ultimately impact your strategy. These environments require a thorough understanding of raw material unit costs.
Success or failure of that process has a direct impact on the ability to manage or grow your margins and drive profits. In this case, if you’re in a procurement function, it’s very important to have clean data. Knowing exactly where you buy things and unit prices for the supplies will have a big impact on margins. To do so effectively, aggregate your data accurately and at high quality with attention to detail.
A high-level procurement strategy will include a robust analysis of supplier relationships and identify opportunities to balance sources, improve mix, identify alternatives and mitigate risk. Machine learning can help your procurement process no matter what industry your business is in.
When algorithms spot patterns in data it becomes either a risk or an opportunity. Here’s an example of what’s called a supplier mix.
There are probably more than 3,000 varieties of apples. But let’s focus on just four: Red Delicious, Golden Delicious, Granny Smith and Honeycrisp. A bakery uses these four in their recipes and they need suppliers to obtain the apples. Do they have four different suppliers, capable of supplying each variety with equal quality? Or do they purchase a single, yet different, variety from each supplier?
Your ideal situation is to maximize your alternatives with the ability to buy all varieties of apples from each supplier. Alternative suppliers are important to have in place for a balanced supply base. Mitigating risk with a fair amount of competition for your business is ideal from a cost perspective.
Now we’re going to train the algorithm to a worst-case scenario in which you buy each variety of apple from a unique supplier. At a high level in the category of apples you appear balanced with 4 suppliers satisfying your requirements. With a closer look, the actual situation is 4 sole sourced arrangements that increase risk of supply and reduce competitive activity.
That scenario can change from month to month or from category to category. Our algorithms at ElectrifAi, for example, spot particular scenarios for opportunities or risks. The algorithm in this case would identify your supply base is not diversified enough and highlights an opportunity to diversify. The machine doesn’t make the decision but it does flag the situation for you to review.
Discovering value in your relationships with suppliers doesn’t have to be a chore. Large companies deal with complex portfolios of suppliers and keeping track of them all poses significant challenges and risks. If you combine your data with machine learning, your view becomes significantly more comprehensive.
ElectrifAi can help cast transparency on supplier relationships with pre-built machine learning models. Want to learn more about how our SpendAi can help you tackle the most convoluted supplier and contract issues?
SpendAi generates significant cost saving opportunities by applying machine learning to combined spend and contract data. Accurately and rapidly categorizes spend to produce insights from an unlimited amount of complex data, eliminating the need for error-prone, non-scalable, rule-based technology.
A specialty firm like ElectrifAi can get you set up so you don’t have to worry about when is the best time to review a contract, have I cleaned up the supplier list to make sure there are no duplicate listings for one supplier and so much more.
Get back to growing your business instead of pulling the weeds.