The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for data scientists implementing different use cases using machine learning. It is aimed at procurement professionals and data scientists who would like to use procurement analytics for making better business decisions. With so much data available nowadays it’s important to know how procurement analytics can help you make the best decision possible based on cost, quality,
Procurement analytics allows you to use data very effectively in procurement decisions. It helps procurement professionals make an informed decision on the basis of cost, quality, delivery, etc. by using data science and machine learning. This results in increased savings for your business along with improved customer satisfaction.
What are some unique use cases for procurement analytics?
Procurement analytics use cases are centered around the following process areas:
- Demand management
- Spend analytics
- Category management
- Supplier management
- Contract management
Spend & pricing analytics use cases
Here is the list of use cases related to spend analysis, cost optimization:
- Spend analysis: Spend analysis allows you to understand where the money is being spent. This helps in understanding what areas require more attention and how much resources are going into a particular area/department/vendor etc. It helps in understanding the maverick spenders and provides a better procurement strategy.
- Purchasing trends analysis: Purchasing trend analysis helps in identifying what products are bought the most by analyzing purchasing data. It’s useful if you want to know how many of a particular product was purchased, who all bought it.
- Cost optimization: Cost optimization techniques can help with figuring out which supplier can provide the best price and why. It also provides a comparison between different suppliers to identify who’s offering better prices or services. Cost modeling is one of the most used procurement analytics applications.
- Pricing benchmarking: Pricing analytics provides information on how much a supplier has raised prices over time, thus helping you identify whether or not it’s reasonable for them to demand an increase in price from their customers. This information is useful for discussing the next contract with their suppliers. It helps them justify why a new price may be too high or low and come to an agreement on what’s best for both parties.
Supplier management use cases
The following are some unique use cases related with supplier relationship management:
- Supply risk management: Advanced analytics techniques such as machine learning models can be used for identifying risks associated with various suppliers which may help companies in avoiding supply chain disruptions. Supplier risk management is one of the procurement analytics applications that can be used to better understand their suppliers and ensure an uninterrupted supply chain.
- Supplier enablement: Supplier enablement is related with leveraging supplier data for effective negotiation with them. The procurement professional is able to make the best decision by using data on a supplier’s performance, financial stability, etc., as well as identifying the best suppliers for specific procurement requirements. It also allows learning about new vendors and untapped geographies which can help in widening their supplier base, thus providing a better customer experience.
- Supplier scorecards: You can train machine learning models for understanding which supplier is performing well and what areas need improvement. This information can be used to develop suppliers by helping them improve their processes or products. It may also identify risks associated with certain suppliers, like the one that may not be able to meet the procurement requirements or deliver late. You can prioritize suppliers based on their risk assessment and contract terms, thus helping in effective procurement decisions.
- Supplier performance analysis: Advanced analytics techniques can be used to come up with standard measures / KPIs to compare different suppliers based on their financial health, risk management practices, etc. This information is useful for procurement professionals when they need to choose between multiple suppliers.
Category management use cases
The following are few unique use cases related with category management:
- Category management: One of the key ask for category managers is to identify the procurement trends in different categories. This is where analytics can come to rescue. It will help category managers understand what products and services are selling well, how often they’re being purchased, etc. Catagory analytics covers forecasting and procurement planning.
- Scenario planning: Businesses need to look at future scenarios before making procurement decisions that will affect them over an extended period of time. Advanced analytics can help understand how various scenarios might affect different aspects of supply such as cost, quality, etc. This allows them to make the best decision based on all possible future outcomes and ensure procurement success in case of any eventuality.
Sourcing use cases
The following represents sourcing related use case:
- E-sourcing: Sourcing manager is required conduct e-sourcing surveys and identify what suppliers are worth targeting based on their performance, cost structures, and more. This is where advanced analytics techniques can be useful. This information is useful for procurement professionals when they’re looking to outsource a specific task or need reliable suppliers in the future.
Other use cases
The following is the list of other uses related to demand management, contract management etc.
- Demand management analysis: Analysis of demand management is useful in understanding what products are bought when, how many of them were purchased, etc. This helps in identifying what products and services are in demand at that point in time.
- Contract analytics: Machine learning models can be used to analyze contracts and their performance. This helps in understanding which contract is being executed well or needs a change. Contract summarization and analysis of procurement data can help companies in identifying the risk factors involved with a particular contract.
Procurement analytics provides a lot of useful information that procurement professionals can use to make better decisions and achieve their business goals. It helps them understand supplier performance, risk management practices, etc., which save time and costs in the long run. This is why procurement analytics has become such an important part of modern procurement strategies.