Tag: Possibilities

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.

Contact JIFFY.ai for the Help You Need

Now that you know more about the role of robotic process automation in procurement, you’re ready to discover how to transform your business with AUTOMATE. Our easy-to-use intelligent automation platform empowers your teams to innovate faster. Contact us to learn more.

Is your business treating invoice processing as an unavoidable cost of doing business (pun intended!)? You don’t have to when you use accounting process automation!

When seemingly mundane activities are not optimized using intelligent invoice processing automation, you could be leaking significant dollars without even realizing it. Interestingly, it is such tasks that can be transformed to be not only effortless but also provide deep insights into your fund management.

Any opportunity to improve business processes and create bandwidth for other critical activities – especially during the current economic conditions – is valuable and worth pursuing. Manual effort spent on repetitive administrative tasks like managing invoices results in a significant waste of time, effort and money. Studies show that Accounts Payable automation software can reduce invoice processing costs by 90 percent. ¹

It is common for large enterprises to interact with thousands of vendors and pay thousands of invoices per year. Most organizations still require a great deal of manual intervention to manage various aspects of invoice processing even after implementing RPA to automate the process.

How Do Businesses Currently Capture Invoice Data?

Large enterprises receive thousands of invoices every month from numerous vendors, each in a different format. Many suppliers still present their invoices via paper or as a PDF attachment in an email. These delivery methods require a great deal of manual processing, including scanning, data verification, and exception management. The goal of every organization is to get to a point where their Accounts Payable teams can perform end-to-end touchless invoice processing.

An invoice goes through several stages of scrutiny before it gets paid by the account payables department. The dedicated teams that handle invoice processing manually feed the data into enterprise workflow tools like SAP and similar ERP applications to ease the orchestration of invoice processing, including routing the data for multiple scrutiny checks, approval and validation for payment.

Potential Invoice Processing Concerns in Today’s Scenario

A majority of the invoices today are electronically processed where a vendor uploads the invoice on a supplier portal of the organization. Internal systems like SAP have integrated automated invoice processing solutions to manage the invoice data electronically. However, many vendors don’t adhere to the submission process through a supplier portal and end up sending the invoices directly via email. Even invoices submitted electronically are not standard and the same vendor may regularly change formats and fields.

Larger enterprises have teams to manually sort all the invoices, create the invoices in the system, validate the details across the ERP and GRN (Goods Receipt Number) systems and transfer the entries into the SAP system for further processing and approval.

The to-and-fro communication between the business and the finance team for validation and the overhead efforts to perform a match between a number of invoices and associated business units included in the delivery of goods makes it difficult in many cases to validate a one-one match between the purchase order to a goods receipt.

For delivery of services, there is no concept of a GRN. In such cases, the invoice process goes through additional approval. It requires human intervention to route the invoices for the required approval, which further increases manual input.

Manual processing can result in high processing costs, increased risk, process and accounting errors, duplicate and late payments. According to AQPCs 2018 survey² of 1,480 organizations reporting data on the cost to process an invoice, the bottom 25% are spending $10 or more per invoice processed. The median cost to process an invoice was $5.83.

The Challenges of using RPA for Invoice Processing in Accounts Payable Automation

• With the advent of RPA, automation of invoice processing was done based on fixed invoice templates or rules. With the number of vendors increasing, it was difficult to train the automation tasks to handle frequent changes in invoice templates.
• As organizations added new vendors and invoice formats, the RPA system failed to handle the process due to a lack of cognitive ability or flexibility to understand different formats, and an inability to adapt to new features.
• RPA solutions were also weak in ensuring the accuracy of data validation owing to the inability to perform 3-way match in the invoice validation and business rule enforcement throughout the invoice process.
• RPA solutions were not able to handle exception cues or provide many analytic insights of invoice status, improving cashflow or reducing processing costs.

Every touch to an invoice increases costs associated with processing the invoice and accuracy in handling and payments.

Accounting Process Automation for Touchless Invoice Processing

Our Automated Invoice Processing HyperApp with integrated Artificial Intelligence and Machine Learning capabilities can exercise full control over Supplier Portal customizations to ease supplier onboarding and invoice management. It has the built-in cognitive ability to handle complex invoice formats and create templates for new invoices.

Equipped with a powerful validation engine, the validation process is dealt with ahead of ERP processes to avoid downstream updates in the ERP system minimizing errors. The accounting process automation platform allows faster implementation of configurable workflows for new suppliers, product lines or invoices. The potential to provide powerful data analytic features gives the competitive edge for Accounts Payable automation.

JIFFY.ai’s Automated Invoice Processing HyperApp is truly touchless in every sense. Our customers reported significant improvement in straight-through invoice processing, 90% improvement in invoice processing turnaround times and more than 85% efficiency improvement after implementation.

Accelerate your automation journey with JIFFY.ai’s low-code platform.

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References

¹https://www.industryweek.com/finance/article/22010161/the-cost-of-paperbased-invoicing

²https://www.apqc.org/system/files/K07216_Cost%20to%20process%20accounts%20payable%20updated%202_26_18.pdf

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: Beyond the Pandemic: Give Wings to Your Digital Transformation Goals with Intelligent Automation

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.

Almost all business today is being conducted on web-based systems. Collection and collation of data are done on the cloud, form filling, data extractions, website testing and a large number of other repetitive tasks have all become web-based. Most businesses have started moving virtually all their operations on to the cloud in search of greater efficiency at a lower cost.

Organizations are increasingly finding that most infrastructure, software, and processes are now only being offered on web-based platforms. Now, with the emergence of Robotic Process Automation (RPA) as one of the key drivers of efficiency, the cloud and RPA have become closely linked.

Technologies like the cloud and the Internet of Things are helping businesses and individuals become more interconnected. Being able to send, analyze, and interpret data on the go is one of the key drivers of IoT, and the cloud helps facilitate this. With RPA, all of these tasks can be more efficient.

Web-based RPA has changed the way people think of robotics. The strategies used have to be changed to adapt to the cloud. By using a web-based RPA, customers can see quicker results from using robotic automation.

RPA on Cloud– Quicker, more convenient solutions

Developing on-site RPA solutions for businesses can be cumbersome and time-consuming. This is why automation companies are making a move towards pre-built, customizable cloud-based RPA solutions, where a network segment has already been built into the cloud. This way, a client can start using the bots as soon as they start using the cloud.

Web-based RPA also allows for customizable solutions, where customers can choose specific processes to focus on so that bottlenecks can be addressed easily. By maintaining a library of ready-made RPA components, web-based systems can combine a number of automated processes to efficiently automate new processes. This provides a higher degree of personalization without installing an unwieldy system-based RPA solution.

Scalability

As businesses grow over time, there is often a need for new bots to increase efficiency. By adopting a web-based RPA solution, it is possible to put new bots into service when they need it. Businesses often will not need many bots when they start out, and their needs will increase slowly, but surely with time. It is also possible that certain bots will become obsolete and will need to be phased out. For example, if a certain department shuts down, or service is stopped.

Since the process of upgrading and updating cloud-based systems is a lot easier than system-based bots, a web-based RPA system can be a lot more flexible. It also takes away the need to buy a higher capacity than what is needed when you first install an RPA system.

Lowering costs

Easy customization and flexibility mean that companies can pick and choose what they want to automate. This means lower costs of licensing, and operations. By taking some simple steps to understand and identify what they want to automate, where the technology can be used, and the potential savings, a web-based RPA system can help companies save a lot of money. In a system-based RPA, companies will often have to purchase a lot more licenses than necessary to avoid high installation costs, and increased downtime, the cloud-based system can provide extremely cost-efficient.

Safety

The cloud has become ubiquitous. Almost all companies rely heavily on cloud-based solutions for more efficiency. By using robots instead of humans, they can also increase the safety of their operations. The robot’s access rights and scope of work can be clearly defined, so as to minimize any potential problems and minimize human errors.

The ability to scale and replicate cloud services improves the competitiveness of businesses.

For a detailed discussion, write to marketing@jiffy.ai

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.

Great people make great workplaces, and great workplaces make amazing products.

December is typically a reflective time of year for most people and this year is – by far – no exception. Indeed, the pandemic and our response to it gives us further reasons to reflect, offer thanks, and look forward with hope.

This year, JIFFY.ai announced our Series A funding, received incredibly positive client reviews, and most importantly, managed to create real success stories for several clients reeling from the pandemic’s impact. 

As I write this article, we’re also very excited to be finishing off the year on such a positive note because our workplace has been voted as one of the best, most trustworthy workplaces in the Great Place to Work Survey 2020. 

Every year, more than 10,000 organizations from over 60 countries partner with Great Place to Work® Institute for assessment, benchmarking and planning actions to strengthen their workplace culture. Great Place to Work® Institute’s methodology is recognized as rigorous and objective and is considered as the gold standard for defining great workplaces across business, academia and government organizations.

Specifically, the metrics around the pride we take in what we do and the camaraderie we share are truly heartening. The free-flowing conversations, the honesty, the friendships we build at work – whether online or in person – these are the things that truly matter to us. While it is often hard to quantify great culture, we’re pleased to report that we’re building a true family here at JIFFY.ai.

From the days of our company’s inception, our leadership team has made building a people-driven culture a key tenet of what drives us. And in the year of the pandemic that forced us to connect remotely and often figure things out over a video call, a culture of putting people first has only gotten stronger. 

Being nimble on our virtual toes has been an unprecedented challenge, but when it comes to service, every team member has found it in themselves to rise to the occasion and do what would have once seemed impossible. These attributes are reflected in our high ranking in the Trust Index score, and we couldn’t be happier!

To many more years of changing the world of intelligent automation and remembering to do it with a smile! The JIFFY.ai team wishes you a safe and happy festive season ahead.