How to build efficiency using intelligent automation and cognitive procurement
When we think of robotic process automation (RPA) in procurement, we know that adoption is rising. Many businesses already use RPA in their value chain. For those businesses that aren’t, it is a matter of “when” and not “if” they will use RPA.
In a domain as complex as procurement, robotic process automation ensures that most tasks and processes are automated at a fraction of the cost of adding headcount/resources or deploying new teams. Another benefit of procurement automation is the ability to work around the clock, which significantly reduces dependence on human resources.
The true value of RPA is the ability to repeat complex tasks and follow decision trees effectively. As machine learning, cognitive processing and natural language processing gain traction and advance at an accelerated pace, it is only natural to integrate these systems with RPA to deliver a more effective solution across the value chain.
Let’s dive deeper into why machine learning, cognitive processing, natural language processing, analytics and RPA must go hand-in-hand. We’ll also discuss how learning algorithms coupled with RPA’s execution capabilities are the future of full automation — especially after the pandemic.
What is Cognitive Procurement?
In the field of supply chain automation, cognitive procurement refers to the process of using automation with machine learning, analytics, and other cutting-edge technologies to help automate further, faster, and more efficiently.
Procurement as a process is characterized by large amounts of unstructured data, which may be impossible to process using traditional systems. Apart from solving the problem of unstructured data handling, cognitive procurement also helps:
- Transform all existing purchase and transfer order systems
- Transform supplier onboarding and the associated processes
- Forecast prices and inventory needs
- Create reports with usable data
- Power better decision-making
- Conduct risk assessment to prepare for known threats to the value chain
The best part? A cognitive procurement solution can also connect to external data sources and tie these parameters into the recommendations it makes. RPA alone may not be enough, but when it’s supported with the right data and learning systems the procurement possibilities are nearly endless.
Intelligent RPA and its Role in Cognitive Procurement
Cognitive procurement is often referred to as the final frontier in the procurement process. However, wisdom and experience show that there is still much to learn. In the era of information, we need a system that can handle three aspects of any complex task:
- Research and data processing: This is where analytics come into the picture.
- Learning from past data to make accurate predictions for the future: Machine learning works on the principle that when an artificially intelligent system is given enough data, it can make decisions as good as or better than those made by a person.
- Execution: Any plan is only as good as its implementation, the sheer volume of work and the number of branches in the process. Post-machine learning interventions need RPA to help in seamless execution.
As a final product of automated procurement, businesses with a vast and demanding procurement function can expect to:
- Achieve efficiency in analyzing their data
- Manage their supply risk
- Procure and pause material based on real-time needs
- Plan logistics for better efficiency and optimized costs
- Evaluate their suppliers based on their monthly, quarterly or annual performance across as many parameters as needed
- Provide 24/7 support throughout
Why Should You Implement an Intelligent RPA Solution in Procurement?
According to a KPMG research report, “Delivering Value in Procurement With Robotic Process Automation,” implementing intelligent RPA can cut procurement costs more than 50%, increase return on investment (ROI) by five times and reduce the number of strategic suppliers by nearly 50%. These numbers should eliminate the concern over RPA’s cost-to-benefit ratio, which is a frequent barrier to implementation.
How Should Businesses Decide Where and How to Implement RPA in Their Procurement Process?
Start by reviewing existing procurement processes to identify areas where the scope for automation is high. These tasks often represent repetitive actions that offer less value per extra time unit spent.
However, for an RPA system to work, the process needs to have a clear workflow and lead to non-ambiguous outcomes. Technical specifications include processes that run in relatively stable environments and cases where manual intervention to solve an impasse can be kept low.
Next, identify the processes based on how much business impact automation could create and how much effort might be necessary to implement RPA. With these conditions in mind, classify the tasks into low-impact, low-effort-to-implement processes, which make for good early adoption and trial cases, and high-impact, high-effort-to-implement processes, which can effectively transform the business.
As a process laden with numbers and data, procurement presents the best-use case for implementing RPA in tandem with data analytics and machine learning. Companies that have already done so report unprecedented results across crucial parameters.
A fear of “machines taking over the world” is one barrier to implementing robotic process automation in procurement. But machine control in cases as complex as a global supply chain may be a good thing, as the pandemic’s disruption to this key process has shown.
The human capital that is freed from the clutches of repetitive tasks and handling data too complex to process can now be used in functions needing more human intervention and creativity. This leaves the machines to do what they do best — repeat every process error-free, follow the rules and use data effectively.
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