Tag: Possibilities

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.

As we navigate a time different from the one we would have liked or wanted, we’re bringing to you a series of blogs, writeups, and LinkedIn resources that we call New Now. In today’s New Now blog, we talk about how businesses can begin to recover and mitigate some of the significant disruption with help from automation.

One McKinsey & Company report suggested, well before the pandemic, that over 30% of manual jobs currently could be automated by 2030. The same report also says that this change could actually bring in more jobs into the economy and that people would need to skill themselves accordingly.

But automation isn’t just a good-to-have feature anymore. For their own well-being and those of the people around them, companies and individuals alike will actively look to automate as many processes as possible, thus reducing the need for manual intervention and the close calls that might involve.

We also need to bear in mind that as all-consuming as it seems right now, the pandemic in its current state will change and subside. What this radical shift really does for businesses is it helps them see what costs they can cut, and where they can better utilize their workforces.

For the fiscal quarters that follow, many industries will continue to focus only on the costs they can cut. Three main solutions can help, and the use of technology and automation can make these processes faster and easier.

1.Analytics for real-time information: Everyone in the retail industry is feeling the heat, but the fashion and apparel industry is feeling it the most. In the business of clothing manufacturing, what is essentially a nice-to-have product in a thriving economy likely will not be purchased in current circumstances. The industry is looking at steep reductions in demand, as well as a restricted ability to predict future trends.

The only fallback, then, is real-time analysis. Fashion and apparel retailers have a tremendous opportunity here – to use real-time analytics and data to predict what they should stock up on, and how much. This could be a big welcome breath for brands that continue to struggle with too much inventory and a severely fragmented supply chain.

Another example is the airline industry. Using real-time data on infection rates and noticing which sectors open up first, they can plan their flight rosters and figure out which staff they need to bring back, and in what time frame. By linking powerful analytics with automated flight rosters, complex, data-driven decisions that now need to be made can be made that much faster.

2.Preserving brand value and customer satisfaction: Much has been said about marketing in the current scenario. For some time now, most businesses have been riding the wave of a thriving economy. For about a year, though, we have heard predictions of a possible recession but certainly not on the scale we see now.

Companies around the globe have to rethink not just what they say, but how well they can walk the talk. Consumers of both B2B and B2C brands are navigating some very sensitive times and simply do not take kindly to an undelivered or under-delivered promise.

The next logical step for brands is ensuring that they can deliver on all the promises they make. We are heading into a territory where every individual is trying to find a new job or hold onto the one they have, and to save money while they can. Something as simple as getting a timely refund can put everyone at ease.

Serving multiple stakeholders in a shorter time window can be achieved using intelligent automation. For instance, Jiffy.ai has been able to help clients in the airline space accomplish improved turnaround times of 300% on ticket cancellations and refunds, while also significantly reducing errors.

Suggested Reading: How Jiffy.ai helped a leading airline company save 2000+person hours and process 90,000 transactions in a week

3.Preparing for the future: Companies today face a twofold challenge – delivering on an authentic customer experience and managing their cash flows to ride out the storm, and regroup for the next phase.

Generating demand is crucial for the cash registers to start ringing again. Unfortunately, demand forecasting will be a real struggle for many industries in the post-pandemic world. Consumer habits have changed, in some cases forcibly, and  wallets have tightened in a tough economy.

AI solutions can problem-solve in real-time when demand forecasting may seem like a mirage. Inventory management can be integrated with AI, helping retailers sync demand and inventory better. AI can also hook new and returning customers with a personalized experience along with identifying gaps in the offerings. This would certainly help businesses bounce back faster.

Looking ahead and planning for the future

Here are some other ways to optimize costs while also maintaining efficiency:

• Adapt to the new virtual culture such that all non-operations staff continue to work remotely, ensuring their continued safety and well-being
• Use automated cleaning and QC tools for spaces where people are needed physically
• Automate complex processes using intelligent automation to help cut costs and improve efficiency

The fact remains that people need to buy things and consume services. The growth will first be visible across essential and semi-essential commodities. Several businesses will have to display tremendous resilience as the demand curve slowly rises. Investing in intelligent automation now can create a path to more efficient processes being run at lower costs, setting the tone for overcoming the current challenges and a viable recovery.

Have you ever said, “Let’s start small and then build it up based on how it goes,”? You sure have. So have most of us. In our world, this is typically how all automation begins.

During the initial days of robotic process automation (RPA), organizations were mostly skeptical. They saw potential but were unsure of real impact.

So, they tried it out for small non-critical functions — they wanted to minimize risks. Understandably. Say, the finance department would automate one task in the Accounts Payable first such as reading data from a file and transferring that to the ERP system. However, other aspects of the Accounts Payable process would continue to remain manual. Also, understandable.

This is what is called partial automation — quite literally, automating just a part of something much bigger.

But why would anyone do that?

In fact, there are plenty of reasons for handling automation this way.

For one, the earliest automation systems could only automate basic screen capture – in other words, anything that couldn’t be seen on a screen would break the process and need manual intervention.

Some of them are financial — end-to-end automation is more expensive and incurs higher opportunity costs to run business-as-usual in the interim because every sub-task would need investment in a bot. Partial automation, on the other hand, was cheaper. Organizations could pick a few bots for shorter processes and pay-as-they-go. This also helped them understand the effectiveness of automating and calculate ROI in the longer term.

Some industries worried about security. A bank would use RPA tools to move data from a front-end system to a legacy back-end system but wouldn’t let bots analyze their customer data. Even to this day, security remains an important reason companies choose partial automation. Why risk exposing critical data while their mandate – bolstered by regulatory requirements – is to protect it and keep it confidential?

Some others just weren’t ready for end-to-end RPA — automating a process end-to-end would necessitate standardization of formats, fields and rights, and that requires an investment of finances, as well as time and energy from their internal teams.

It also didn’t help that monitoring each automated process or bot was not easy. So, there was greater risk of broken automation if the scope was end-to end.

The initial RPA landscape had its limitations, lacking seamless integration with the human input when the time came for decision-making and without a human-in-the loop concept.

Most also feared that they might not have the people trained and equipped to intervene and improve the end-to-end RPA, making it a bigger risk. Partial automation is less demanding.

To be clear, in all these cases organizations certainly understood the value of RPA, invested in partial automation and derived value from it. Most of them are “somewhat happy” with the results their RPA systems are delivering.

Partial automation only provides partial success. Why?

Process measurement issues: Partial automation meant that a major part of the processes still had to be done manually, so there was no way to measure the ROI per process or per team/department. In other words, there was no way to make a strong case for automation because the results couldn’t be measured objectively.

Efficiency deficit: The improvement in overall process efficiency, while automating only a part of it, can often be so minimal it doesn’t seem worth the effort.

Savings deficit: As efficiency is only marginally improved, cost savings also end up being marginal.

Stagnation: Partial automation can be a dead investment without the bot’s ability to learn, adapt or grow with the needs of the organization. Likewise, it can be a dead investment if the organization doesn’t have the ability to see and manage how automation is being applied across the enterprise.

Resource blocking: Without the ability to improve intelligently, partial automation still needs people to fill its gaps. This means that people continue to work on mundane tasks, leading to low productivity, fatigue and dissatisfaction.

Right, so is Intelligent Automation a possible end-to-end solution?

Intelligent or Cognitive Automation in its simplest form, is an intelligent version of RPA — one that can learn from the data and apply it to present needs. Automation can become limiting when not supported by the learning capabilities of AI, which is where intelligent automation comes into the picture. It is flexible enough to understand and adapt to non-templatized data inputs. It can process structured, semi-structured and unstructured information with ease.

Take Jiffy.ai’s cognitive automation tool, for instance. It is able to read and extract non-templatized information. Even in cases where Jiffy.ai doesn’t understand or cannot read certain parts of the document, it will extract all the other parts and reduce manual intervention to a bare minimum. This way, with cognitive RPA, you can automate the entire process, not just a part of it.

With its ability to learn, cognitive RPA is also scalable. As a business becomes more complex and processes more intricate, cognitive RPA can learn and grow along, making the ROI significant in the long term. For instance, intelligent automation systems that trigger alerts to floor supervisors in a manufacturing unit can learn to spot newer anomalies over time, making all aspects of productivity, quality and capacity predictable. Enterprises are addressing their requirement for end-to end automation using a combination of RPA tools (for repetitive tasks), BPM tools (for process management), OCR , IDP tools (for document extraction), Data platforms for data streaming and beyond.

Instead, a platform that makes all of these features available in a single stack can help save costs and time, and also translate to easily calculable returns over a period of time. This way, they can adopt cognitive RPA for all processes, interconnect them and enable them to work in tandem.

Cognitive RPA also comes with basic skills. Pre-built RPA systems, customized for industries and functions, are now available with the ability to hit the ground running immediately. Once installed, they are in auto-pilot mode needing very little help from people, even for setup, training or maintenance.

With prior knowledge, pre-built cognitive RPA solutions can automate end-to-end with a more meaningful understanding of the process landscape.

With cognitive RPA, the solution is no longer piecemeal. Unlike partial automation, cognitive automation impacts the entire value chain.

Today’s context

The global situation businesses face today is a reason for organizations to take seriously how end-to-end automation can help them to be more resilient in the face of crisis.

As an example, a large automaker based out of Europe has worked with Jiffy.ai in automating their financial processes. This truly helped them recently when there was no business shutdown in their country, and they continued to send in their documentation to Jiffy.ai’s offices where physical offices were shut down. Thanks to automation, backend support continued seamlessly while production continued as planned.

It is completely understandable if you have a partially automated system now. It made sense in its day. But today, to see the real value of automation, end-to-end cognitive automation is the way to go. With a clear view of the entire system, end-to-end RPA will be able to bring together various processes into a smoother journey, be it for your customers, vendors or employees. It will also future-proof you as the system understands your existing processes and can expand to accommodate newer ones.

If you have adopted partial automation and aren’t fully realizing its potential, speak to one of our consultants to explore newer avenues. We understand where you are and we’re happy to help.