How to build efficiency using intelligent automation
When we think of Robotic Process Automation (RPA) in procurement, we know that adoption is already on the rise. Many businesses are using RPA in their value chain, and for those that aren’t yet, it is a factor of ‘when’ and not ‘if’ they will use RPA at some point in time.
In a domain as complex as procurement, RPA ensures that most tasks and processes are automated at a fraction of the cost of adding headcount/resources or deploying new teams. The benefit in addition to using a computational system is also being able to work around-the-clock (thanks to RPA), which significantly reduces dependencies on human resources.
The true value of RPA is being able to repeat complex tasks and follow decision trees effectively. But with machine learning, cognitive processing, and natural language processing gaining traction and advancing at an accelerated pace, it is only natural to integrate this with RPA to deliver a more effective solution across the value chain.
Enter intelligent automation.
Now let’s dive deeper into why machine learning, cognitive processing, natural language processing, analytics and RPA must go hand-in-hand, and how learning algorithms coupled with RPA’s execution capabilities are the future of full automation.
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, sometimes entirely
- Transform supplier onboarding and the associated processes with automation
- Forecast prices and inventory needs, create reports with usable data and 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 sources of data and tie these parameters into the recommendations it makes. RPA alone may not be able to do so, but when supported with the right data and learning systems, the possibilities are nearly endless in the space of procurement.
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 us that most of the quantum of human knowledge is actually ahead of us. 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 to work with, it can make decisions that are as good as, or better than, their human counterpart.
- Execution: Any plan is only as good as its implementation, and the sheer volume of work and number of branches in the process. Post-machine learning interventions need RPA to help in seamless execution.
As a final product, 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, and provide 24X7 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 deliver over 50% savings in procurement costs, increase Return on Investment (ROI) by 5X, and reduce the number of strategic suppliers needed by nearly 50%.
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 for an impasse can be kept low.
Next, identify these processes based on how much business impact using automation could create, and how much effort might be necessary to implement RPA in this process. With these features in mind, the tasks can be classified 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. One of the barriers for RPA implementation is worry around the cost-to-benefit ratio, which these numbers quickly disprove. The next barrier is a fear of ‘machines taking over the world’, which 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 has been 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.