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Enhancing Power Solutions with an Innovative Admin Portal

In the power solutions industry, efficient administrative tasks are crucial for smooth operations, effective communication, and top-notch customer service. Our client faced challenges with their Admin Portal, which needed to adapt to their diverse products, customer interactions, and dealer relationships. This case study explores the client's issues and the solutions they used. Client Overview: The client has been a leader in power solutions. They expanded into various power products and to manage these offerings, they collaborated with Cognine to develop an adaptable Admin Portal. Customer Needs: As the client grew, they required an integrated solution to handle their products, customer interactions, and dealer connections. They aimed for streamlined administrative tasks like user management, feedback handling, contact management, and communication via a Message Centre. They also wanted to improve the Dealer Portal for better role management. Solutions Implemented: Working with Cognine, the client developed a comprehensive Admin Portal Global Admin Application: A customized hub managed the company's operations. Dealer User Management: A module efficiently handled dealer users, from onboarding to access control. Feedback Management: A system managed customer feedback, aiding product improvement. Contact Management: Tools for managing customer contacts ensured effective communication. Message Centre: Integrated messaging facilitated smooth communication with stakeholders. Role Management: Dealers could control user access for personalized experiences. Automated Testing: Key features underwent automated testing, enhancing reliability. Technology Stack Used: Benefits and Outcomes: The collaboration yielded several benefits: Improved Efficiency: The Admin Portal streamlined administrative tasks, boosting operational efficiency. Better User Management: The Dealer User Management module enhanced the dealer user experience. Customer Insights: The Feedback Management module collected customer feedback for ongoing improvement. Enhanced Communication: The Message Centre fostered better communication with stakeholders. Personalized Access: Role management empowered dealers to provide tailored user access. Increased Reliability: Automated testing improved the Admin Portal's dependability. Conclusion: The collaboration led to an innovative Admin Portal that met diverse needs, streamlined operations, and improved engagement. It showcased the client's commitment to excellence as they continue leading the power solutions industry. Latest Posts Why 2024 is the Year of AI Agents and Compound AI Systems? In the rapidly evolving landscape...Read More From Passionate Engineers to a Thriving Workplace: Cognine Technologies Earns Great Place to Work® Certification Press Release: Hyderabad, India –...Read More Enhancing Developer Experience (DevEx) – How Cognine is Empowering Its Clients for Success What if enhancing your...Read More Code First Vs API First “Code first” and “API first”...Read More Design, Development, and Product Management Can Design, Development, and Product...Read More

Transforming Financial Operations Through Technology

Introduction: Today, business and technology are inextricably linked. And keeping pace with the emerging technology landscape can be difficult for even the most tech-savvy leaders. This case study dives deep into the challenges faced by the client in terms of manual and time-consuming financial processes, lack of data insights, and increased risk of errors followed by describing the solutions provided by Cognine that enhanced the overall functional efficiency of the client. About Client: Client is an American financial technology company that creates and provides financial advisors with wealth management tools and products. Their flagship product is an advisory platform that integrates the services and software used by financial advisors in wealth management. Cognine analyzed the business requirements for their analytical needs, designed data models and developed reports. Implemented CI/CD functionality for auto deployment processes. Technology Stack Used: Implementation: Benefits/Results: Improved data accuracy and increased productivity: Cognine successfully automated Normalization files through UI. With the help of React UI the user can upload file metadata information and download the Normalizer file. Previously, each Normalizer file required manual development. Post automation, using the new UI, users can develop up to 20 Normalizer files at one time which saved user’s time largely and boosted efficiency. Problem Statement: Client received data in various formats from different customers which made it difficult to analyze, understand and reuse it. The incoming incremental data was as large as 100 GB which was being received on to their on-prem servers which had computing challenges on a day-to-day basis. Decision making capabilities were being hampered due to lack of efficient reporting formats making it almost impossible to extract enough insights. Ensuring data quality of the large sets of incremental data had become cumbersome and was affecting downstream systems. There were architectural issues causing latency in the conversion of raw data into customer consumable data. Solutions Provided by Cognine: Cognine provided a highly scalable and robust architecture that supports various file formats with different sizes & data volumes which automated and improved client’s financial workflows, real-time data visibility. The new multi-layer (staging, standardized, curated) and multi-tenant architecture which supports ETL operations to run different teams as well as help different teams access data at different layers based on their needs. An event driven micro services architecture ensured the data load into targets without delay. This impacted the implementation and configurations at environment and object level, such that very minimal changes are needed during the deployment. Embedded Collibra DQ to monitor the DWH jobs and notify users based on the requirement. Reduced Processing time: By creating a standard logging format, we introduced a centralized library for standard logging where we implemented logging format for different steps and saved them into CloudWatch. Quantitative Data Management: There was a huge scale-down on manual intervention on client-specific data extraction. Quick decision making: Data insights fostered the client to take quick decisions and improve their overall financial performance. Conclusion: Data insights are helping the business to take quick decisions based on the highly sophisticated visuals. Manual intervention on client specific data extraction came down extensively as we were able to automate the functionalities. Automating the on-boarding & processing of new client requests was made as easy as just a click and go. This is helping to scale up client’s operations. The marketing team can utilize this to grab the attention of new prospects. Latency of accessing data from Analytical system has been reduced tremendously, it was scaled down to multi-folded level. The cost of the DWH layer has been reduced nearly half per month. This gave financial freedom to the business. Latest Posts The Future of Business is Here: How AI-Driven Intelligent Process Automation is Changing the Game Ready for Smarter Automation? In...Read More The Evolution of AI Agents and What’s Next for Compound AI Systems As we move deeper into...Read More Why 2024 is the Year of AI Agents and Compound AI Systems? In the rapidly evolving landscape...Read More From Passionate Engineers to a Thriving Workplace: Cognine Technologies Earns Great Place to Work® Certification Press Release: Hyderabad, India –...Read More Enhancing Developer Experience (DevEx) – How Cognine is Empowering Its Clients for Success What if enhancing your development...Read More

Change Data Capture (CDC)

Traditionally migrating data changes between applications were implemented real-time or near real-time using APIs developed on source or target with a push or pull mechanism, incremental data transfers using database logs, batch processes with custom script etc. These solutions had drawbacks like Source and target system code changes catering to specific requirement Near real-time leading to data loss Performance issues when the data change frequency and/or the volume is high Push or pull mechanism leading to high availability requirement Adding multiple target applications would need a larger turnaround time Database specific real-time migration was confined to vendor specific implementation Scalability of the solution was time and cost intensive operation Change data capture (CDC) refers to the process of identifying and capturing changes made to data in a database and then delivering those changes in real-time to a downstream process or system. Moving data from one application database into another database with a minimal impact on the performance of the applications is the main motto behind this design pattern. It is a perfect solution for modern cloud architectures since it is a highly efficient way to move data across a wide area network. And, since it’s moving data in real-time, it also supports real-time analytics and data science. In most of the scenarios CDC is used to capture changes to data and take an action based on that change. The change to data is usually one of insert, update or delete. The corresponding action usually is supposed to occur in the target system in response to the change that was made in the source system. Some use cases include: Moving data changes from OLTP to OLAP in real time Consolidating audit logs Tracking data changes of specific objects to be fed into target SQL or NoSQL databases Overview: In the following example we would be using CDC between source and target PostgreSQL instances using Debezium connector on Apache Kafka with a Confluent schema registry to migrate the schema changes onto the target database. We would be using docker containers to setup the environment. Now, let us setup the docker containers to perform CDC operations. In this article we would be focusing only on insert and update operations. Docker Containers: In a Windows or Linux machine, install Docker and create a docker-compose.yml file with the following configuration. Run the docker-compose.yml file by navigating to the directory in which the file was created using command prompt or a terminal and run the below command. Debezium plugin configuration: Once the docker containers are created, we need to copy Debezium kafka connect jar files into the plugins folder using the below command. Restart the kafka-connect container after the copy command is executed. Database configuration: Connect to the postgres-source and postgres-target databases using psql or pgAdmin tool. Create a database named testdb in both the servers. Create a sample table in both the databases. Ex: create table test(uuid serial primary key, name text); In postgres-source database execute the below command to change WAL_LEVEL to logical. Alter system set wal_level=’logical’; Restart the postgres-source docker container using the docker stop and start commands. Source Connector: Using any of the REST client tools, like Postman, send a POST request to the following endpoint with the below mentioned body to create the source connector. Endpoint: http://localhost:8083/connectors Latest Posts The Future of Business is Here: How AI-Driven Intelligent Process Automation is Changing the Game Ready for Smarter Automation? In...Read More The Evolution of AI Agents and What’s Next for Compound AI Systems As we move deeper into...Read More Why 2024 is the Year of AI Agents and Compound AI Systems? In the rapidly evolving landscape...Read More From Passionate Engineers to a Thriving Workplace: Cognine Technologies Earns Great Place to Work® Certification Press Release: Hyderabad, India –...Read More Enhancing Developer Experience (DevEx) – How Cognine is Empowering Its Clients for Success What if enhancing your development...Read More

Is Real-Time Data, The Future?

The transition In recent years, there has been an explosion of interest in big data and data-intensive computing. Along with this, there has been a corresponding increase in the use of real-time data processing systems. Real-time data processing systems are those that process data as it is generated, rather than waiting for all of the data to be collected before processing it. This article discusses the opportunities and challenges associated with real-time data processing. Before moving to the real- data, let's look at some facts: A few fun facts about data 2.5 quintillion bytes of data are being created every day and less than 0.5 % is used Cloud is no more able to handle the pressure created by big data and old data storage Data seems to have a shelf life Bad data can cost businesses more than $3.5 trillion per year Structured data helps in better decision-making in businesses The time to download all data takes about 181 million years Now that the fun facts were surprising to most of you, let’s look at the actual trends, case studies, and challenges further. How can organizations choose and adapt to the dynamically changing Data culture? As per the statistics, in the future, more than 50% of the data is collected or created, analyzed, and stored outside the cloud system. An organization can always start by analyzing its needs and planning the architecture that generates what they are looking for in the future. The increased usage of real-time data is being adopted by an array of industries including but not limited to Banking and finance, Retail, Healthcare, and more industries such as advertising and marketing are poised to adopt this year. Enterprise data management involves the processing of data that involves various activities associated with processing, checking quality, accuracy, security, etc. The data says Enterprises are self-inhibited due to a lack of data availability as and when required in the form that is required to access and understand easily. Not only this has affected their capabilities but also paralyzes their agility and operational abilities. Benefits for an organization: The real-time benefits are real quick. A few of them are listed below. Increased operational efficiency Quicker automated intelligent decision making An enterprise that can project accurate data metrics Helps in every aspect of the enterprise including their products, sales, strategizing, finance, etc. The future There has been a sharp decline in the retail market in the spending of the consumers according to statistics. How is Real-Time data impacting these industries in changing their habits and bringing them back to the usual patterns of shopping? Most retailers are now working on combining real-time data with #ai to give real-time information to the consumer and help in changing the buyer's mindset in rushing to buy the product. When you see this, what do you think it is Only 1 left in stock It means data and AI are working shoulder to shoulder, which is beyond amazing. This had been the real innovation that created an urgency in a consumer's mind to get that last item in the stock. Not only retail, but another example is also the healthcare sector. A classic example is healthcare devices or devices that monitor your health/heart rate. Another massive sector that uses Real-time data is the Financial sector. Now, having said all the above, although real-time data is very useful and works like a magic wand, there are certain limitations and challenges when the ‘processing time’ A few but Real Challenges Although there are a few challenges in Real-time data projects, there are strategic and effective solutions that can make the entire real-time data processing process smooth. A few challenges and solutions are listed below in this article Quality The data quality defines the output of the reports for example in the case of financial projection and business analytics. Not all architectures designed and developed can provide the best quality when it comes to real-time data. An organization needs to be extremely careful while collecting, filtering, and strategizing data. Collection disruptions- data formats When organizations use IoT- Internet of things with their own data formats, it becomes very confusing to these devices, especially with data coming from different sources and multiple formats. This leads to data disruptions due to interactions caused by firmware updates or APIs A quick solution to this can be addressed using batch data processing before the pipelines are created for real-time data. Bad architecture The important part is designing the architecture. If the architecture designed does not give the right results or does not fulfill the requirements of the organization, it is useless and any business can get into losses when the data is not accurate. Using a hybrid system with a mix of OLTP- online collection and storing data and OAP- online analytical processing for batch data processing using carefully designed strategic data pipelines helps with building a good architecture and data loss. So everything links back to architecture. How can we fix this or start with Real-Time data? You can either hire a bunch of data scientists to perform these tasks and build the entire department for change Or Save all the headaches and heartache by booking a consultation with us, plan your journey at quite a cost-effective data processing model right for you at http://cognine.com/contact-us/ It’s the people behind the technology that matters. Latest Posts The Future of Business is Here: How AI-Driven Intelligent Process Automation is Changing the Game Ready for Smarter Automation? In...Read More The Evolution of AI Agents and What’s Next for Compound AI Systems As we move deeper into...Read More Why 2024 is the Year of AI Agents and Compound AI Systems? In the rapidly evolving landscape...Read More From Passionate Engineers to a Thriving Workplace: Cognine Technologies Earns Great Place to Work® Certification Press Release: Hyderabad, India –...Read More Enhancing Developer Experience (DevEx) – How Cognine is Empowering Its Clients for Success What if enhancing your development...Read More

Unleashing The Potential of Digital Assistants in Fintech

So, what’s the difference between Chatbots and Digital assistants? There are disruptive technologies that have positively impacted fintech, but none of them has revolutionized the financial world quite like smart AI, chatbots, and voice-enabled digital assistants. In making key financial decisions, the presence of a digital assistant through conversational AI enhances customer intimacy while optimizing costs & revenue to a large extent. Digital assistants help FinTech companies to collect and process a large volume of information about customers’ needs, requirements, past transaction history, etc. They offer extremely personalised information about customers. With the detailed set of data, FinTech companies can easily amplify and automate operational performance. Functions like analytics, billing, collections, renewals, upselling, and cross-selling of services are automated and organised in such a way that the personnel can rely on the digital assistants completely. While both chatbots and digital assistants help in performing tasks as instructed and answering questions, chatbots are limited to making a conversation, making recommendations, and checking on statuses. Digital assistants use AI to understand customers’ speech and text and have the ability to understand complex questions and even localized slang. Digital Assistants can also advise the right service or product to the consumer based on the customer’s emotional sentiment. Which digital assistant do I need? Adopting a digital assistant won't solve all of your company's problems or reduce expenditures. A crucial factor to take into account is redefining organizational process trouble spots, using a step-by-step analysis process, knowing what you need from your digital assistants, and tailoring them to meet those demands are the key points to consider. The best digital assistant for you will depend on your needs and the scorecard, which you should create. A custom-designed digital assistant might be the solution in many cases. Increase the ROI: Digital Assistants bring a great ROI to any organization by cutting costs and time while providing great customer satisfaction. They also help in Sales, marketing-offering new products and promoting new services, lead generation, and an overall reduction in the company costs and increase in ROI. Here is a brief comparison: Wrapping up Digital assistant technology is opening-up an overwhelming world of innovation, optimization, and opportunity. If you are lost in translating how this technology shift could open new opportunities and transform their business model, let us TALK!

Optimizing Driver Retention Through Technology

Looking for truck drivers?Sorry, our truck drivers are not available.These are some common statements that most Trucking USA companies face. Ever wondered why???Trucking companies play a key role in the development of trade across borders. However, the success of any trucking company depends on two factors, i.e. Man and Machine.Driver RetentionDigital LogisticsA recent survey by the American Trucking Association reveals that the US market is facing a nearly 60,000 driver shortage, and the numbers will soon reach 1,00,000 by the end of 2022. Further analysis shows that the number of drivers who quit within the first three months of their joining is more than 30%; the ones who leave within the first six months is more than 50%, and the requirement for new drivers is always increasing at alarming rates.Drivers quitting at the very beginning of their careers seem to have dented the company's reputation in the market with dropped profit margins and turnover. Onboarding a new driver can cost a company more than 8,000$. Adding to this is the time spent training the driver on the company's vehicle specs, delivery routes, and understanding of the company's working style. After all, when the driver leaves within 6 months of joining, it proves to be a complete loss. These numbers are alarming for trucking companies as they know the importance of retaining drivers. Before we understand how to address the problem, it is important to understand why it exists.Here is a simple "Why-Why Analysis to drivers quitting jobs."3 aspects define Driver Retention:PayEfficiency of workDignity at workLack of digitalizationPoor Pay: The drivers feel that the salary they get and the effort they put in are underpaid. Therefore, most of the drivers quit. On the other hand, the drivers hired as replacements quit in a short span because the trucking companies pay less to new drivers than the drivers they replace.The efficiency of work: Driving trucks calls for a lifestyle change. The long travel hours and the risks involved in the traditional routing process can be taxing for drivers.Dignity at work: It's necessary that trucking agencies look at their drivers as their representatives and not as mere drivers.Lack of Digitalization: The drivers feel stressed handling all the paperwork, customer management, and payment process manually.The above is the holistic view of the problem; In this article, you will understand how technology in different formats can impact drivers' efficiency and the company's paying capacity.Here are some technological insights into what is needed for logistics and transportation companies to retain drivers:The first and the most effective tool is establishing a good Routing App. What is a routing app? A good routing app is an interface created based on the driver's inputs of all the details in terms of driving time, type of terrain, the kind of fines and traffic delays and more the drivers face in all the routes they travel. Creating this app will ensure that the trucking agency, the customer, and the driver are on the same page regarding delivery deadlines. In other words, the drivers are relieved from the stress of meeting unrealistic delivery timelines, which causes working overtime and accidents.With technology growing leaps and bounds, it's time to move from the manual process and plug-in RPA into logistics. RPA in logistics management helps increase efficiency and productivity, saves time, reduces errors, and increases company profits. For a 3PL provider based out of the US, Cognine helped implement the RPA platform that automated order creation, rate lookups, and payment status updation without any manual intervention, which 8FTE earlier performed.Automation of operations has become the need of the hour. Cognine built a natural language processing(NLP) engine that made a fleet manager's day easier in data extraction and management. The model developed processes 99% of the rate requests, with the AHT being reduced from 500 sec to less than 50 sec. Pay the driver his worth. Wondering how to measure his performance??? That's where embracing Driver Management App helps you track and measure driver performance. We help trucking agencies develop an effective DMA to look at the drivers' productivity, track settlements, plan their routes, upload dispatch expenses, e-sign BOLs, and send automatic load status notifications to customers.ConclusionYes, Automation and Digitization are the keys to improving the process and retaining drivers. But, choosing the apt options that suit your organization is the key to making the best of the automation. With a thorough understanding of the logistics and transportation industry, Cognine constantly strives to create customized business solutions.We consider the drivers' and organizations' perspectives to ensure that the technology makes their lives easy.With Cognine's intervention, it is always a win-win solution.

Digital Transformation in the Utilities Sector

While trends in the power and utilities business models have been exempt from massive overhauls for a while, the influence of technological innovations is slowly starting to change this factor. Due to this, digitization’s visionary approach to technology lays the groundwork for new capabilities that are expected to accelerate exponential growth in pressure-pumping market value for decades to come.What is digital transformation in the power sector?Rapid transition to clean power is helping businesses reduce their carbon footprints and many are also racing to catch up with the rising sophistication of digital technologies. Companies must ensure that they have the right technology in place to support a digital transformation strategy and leverage it across their entire operations. The power industry is leading the way in this direction, with renewable power at the center of many providers’ strategies.This translates into a newer, more diverse set of assets to manage and integrate with the rapidly ageing set of existing assets worth about $1.2 trillion. In addition to online tools that automate business processes, programmable automation (PA) and cognitive computing are quickly entering the mainstream as firms realise these technologies can help them reduce costs and improve performance. But moving from traditional asset management strategies to develop comprehensive digital strategies with robust data governance and cybersecurity at its core will mean going beyond software alone:The need of the moment: Companies need more robust analytics capabilities that can support key business processes such as fuel planning, risk management, maintenance scheduling, and asset monitoring as well as infrastructure planning for new projectsDigital paradigm shifts in power transformation: The power and utilities industry today faces a host of challenges that are making the business more difficult to succeed in. On the one hand, high oil prices, a growing population, and widespread use of electricity are making it imperative that businesses adapt to reflect market demands. Meanwhile, industry professionals lack the agility and resilience to effectively deal with these new demands.At this point in time, what's more important is how your business will be better able to adapt to changing conditions and capitalise on opportunities as they emerge.Let us look at some digital paradigm shifts that are game changers in the vertical: Industry 4.0:Industry 4.0 has revolutionised the way manufacturing operations are run. Industry 4.0 is the transition from a manufacturing operation to an intelligent operation. Data-driven, automated, and ever-improving technology will make factories leaner, more efficient, and profitable.Today, there is more opportunity than ever before in this industry as digital twins and IoT devices bring us ever closer to fully autonomous systems fuelled by massive amounts of data. This gives more power to utility companies when it comes to gaining a competitive advantage over their peers. However, cyber security becomes even more important through such innovations.2. Compliance:The industry is facing an uphill battle in order to meet new emission regulations and sustainability targets. Oil and gas companies are being tasked with implementing smarter, more efficient operations. With the heat on for future-ready leaders, getting creative with available technology solutions will be key in our efforts to meet air quality goals. Changing industry needs:The shift toward alternative power sources has not only affected the business but has also opened new opportunities for companies in the industry. It has shown the customers what is needed and how their lives will be improved. The shift towards clean power has created new revenue opportunities for utility companies. Customer accessibility and sustainability have driven the development of IoT devices, which allow power companies to connect with customers and influence behavior. In addition, IoT devices are helping reduce the environmental impact that extraction creates. Digital asset management:Digital innovation is playing a key role in the transformation of asset management in the generation segment. As the industry moves toward digitization in the sector, digital asset management can play a key role in the transformation of asset management, helping reduce O&M costs, boost reliability and profitability, and lowering greenhouse gas (GHG) emissions. Data management:We are now entering the era of smart grids. Companies are facing the challenge of collecting, analysing, and acting on all their valuable data. Connected devices help them to collect and analyse data from multiple sources and make it accessible to other departments of an enterprise. Infrastructure set in place that communicates with data centres provides information on electricity consumption; sensors enable real-time monitoring of facilities and assets on the premises; IoT-based solutions offer comprehensive insight into the performance of plants, equipment, and networks as well as usage patterns at every stage of the supply chain.Wrapping up…The power industry is open to a great number of digital transformations, the most important of which is the fact that it allows companies to open new horizons of digital opportunities and reach a new level of global economic growth, and effective operation.With the transition to digitization underway, IT architecture is playing an increasingly important role in ensuring that generation and consumption interact smoothly. Not only does the utility sector need to find new ways to manage their business, but they also have a duty to inform their consumers about new technologies and service offerings so they can make informed choices.

Intelligent Automation – Future Of RPA

Adoption of emerging technologies across industries is rising at a break neck speed. Besides digital transformation, organizations are pushing into digital optimization initiatives like Machine Learning, AI and automation to become more competitive, resilient and efficient.Robotic Process Automation (RPA) has been one of the most successful and widely adopted automation tools. According to the latest forecast from Gartner, Global RPA software revenue is projected to reach $1.89 billion in 2021, an increase of 19.5% from 2020.Over the rest of the article, we will focus on how we help enterprises with:Facilitating RPA implementationChallenges and strategic navigationFuture of RPA1. Facilitating RPA implementationOver time, having worked with various clients across the industries, one of the most important imperatives for successful RPA implementations has been management buy in.Getting started with RPAIn this step, you will lay the foundation needed for successful RPA implementation. At the end of this phase, you would have a completed a pilot to show the benefits of implementation.Opportunity discoveryWe work closely with your teams to identify gaps, savings potential and ROI as compared to the peers and industry benchmarks. This includes data collation, workshops with your team and value stream mapping. Study data collated to confirm on the opportunities identified.Platform selectionOnce you have identified the opportunities for automation, the next step is to pilot the process. Having worked with leading automation software providers across the ecosystem, we help you identify the right tool for automating the identified opportunities. This includes considerations ranging from no code/low code platforms to cutting edge automation using computer vision, NLP and AI.POC executionBuild automations, technical flows quickly for the identified automation opportunities. Collect the results, evaluate the feedback and build score cards to measure long term success and focus on creating an opportunity pipeline.Scaling across the enterpriseTo scale off on the back of successful pilots, organisations need a team responsible for opportunity pipeline creation, automation governance, process assessment and enterprise-wide support. This team ensures efficient usage of RPA resources, increased integration/access to new technologies with in the enterprise and increase in the throughput capacity.We work with you to build teams/capabilities to support continuous improvement, identify cross enterprise opportunities, help you reengineer the processes and track the results after deployment, to ensure you realize the full potential of your automation effort.2. Challenges and strategic navigationWhile RPA adoption has been gaining significant attention in some industries, there have been a plenty of failure stories too, exceeding the implementation time, cost and overall ROI. According to Gartner, “By 2021, 50% of RPA implementations will fail to deliver a sustainable ROI.”Below are some of the most common challenges you will likely face if you and your company choose to implement RPA.Process IssuesIt is recommended that you map your automation journey, identify gaps across various departments and saving potential before you set out on your automation journey. While most enterprise’s successfully implement pilots, they lack a clear opportunity pipeline to scale the efforts.Tasks that are repetitive, rules-based, high volume, and that do not require human judgement are the ideal candidates for automation using RPA. This can include activities involving moving files/folders, copy & paste data, scrape data from web, connect to the API’s, extract and process structured and semi-structured content from documents, PDFs, emails and forms. RPA implementation might be difficult with the process that are non-standardized and require significant human intervention.Redefining business process for efficient use of bot’s time or modifying the business process itself might speed up implementation. For example, it might prove to be efficient, if you could get all the data first, feed it into the application and then call the next flow instead of calling the next flow after every single data entry point.It is easier to reach automation levels off 70-80% for most applications and the remaining 20% might require significant investment of time and cost due to the complexity, throwing out the whole purpose of automation. Hence it is crucial to make a cutoff between desirable level of automation versus efficient level.2. Organizational pitfallsFrom getting the management buy-in, it is important to rally support from IT department to successfully execute RPA projects. IT department plays a crucial role in speeding the RPA implementations with resource allocation, exposing API’s or even building certain custom scripts over components. Some of the other IT support functions that play a key role include RDP access, network stability, bot run context and issue resolution time.3. Technical IssuesIt is advisable to choose low code/no code RPA solution over some the outdated solutions available in the market. It is easier for your internal teams to adopt or transition later, should you work with an outsourced service provider to develop the initial components. It also helps you keep the development costs under control.Some of the other best practices include:Initialising certain applications before handImplementing best practices like modularity, re-usability and efficient looping into the codeSecuring credentials using orchestrator 4. Post implementation adoptionScalability, Maintenance and de-commissioning the process are the three most important post implementation challenges. We have covered scalability in the earlier part of the article (RPA centre of excellence is most important to address scalability). Changes in business processes or applications require the components to be modified. Since most the bots are programmed using best practices it is relatively easier to re-configure and change as per changing business needs. As the process evolves over a period of time with the changing business needs, we should also be able to analyse when to de-commission a certain process based on the complexity and effort it takes to maintain and bot’s run time.3. Future of RPARPA market is expected to grow at double digit rates through 2024 as per predictions from Gartner. Here are the trends that are expected to shape up the RPA market in the short term.Non-IT Buyers/Low Code and No code PlatformsThe RPA adoption is over 90% in certain industries and most of these revenues has been coming from the IT buyers. Over the course of the time, business buyers are expected to drive the revenue growth for RPA players given the complex business landscapes and for simple reason there aren’t enough programmers around the world to meet these demands. By 2024, half the new RPA revenue is expected from non-IT buyers.While the existing pure play RPA leaders like Ui Path, Blue Prism & Automation Anywhere are working on simplifying their platforms, some of the tech giants like SAP, Salesforce, Oracle, ServiceNow, Google and AWS are focusing on low code RPA platforms. There also has been some of the innovative starts ups that are already making some big success stories in the no code RPA space.Cognitive AutomationLeveraging NLP, AI and ML with RPA enables enterprises to expand the scope of process it can automate. While some the tools provide these features by default, some of them may require custom coding or installing certain plugins from the RPA market place.Process Modelling AutomationOne of the top priorities for RPA research is auto-extraction of process knowledge from logs and videos. The workflow creation and process definition acts as a bottleneck in the creation of opportunity pipeline and is manually intensive. Automating the process modelling can speed up RPA implementation and deliver substantial ROI.

RPA Solutions for 3PL provider

Client focus Client is a leading North American provider of logistics technology and transportation management services for manufacturers, retailers, chemical and consumer packaged goods companies. Challenges The client had been experiencing steady growth over the last 5 years. However a key concern going forward was, how to grab new market opportunities fast enough and press the accelerator on future growth. Manual processes across rate lookups, route optimization, audit logs, order management and several other areas served as biggest roadblocks in its pursuit for process excellence and future expansion plans. The client’s management team has realized that technology intervention was needed to automate some of these repetitive mundane tasks across the above areas to increase customer satisfaction. Solutions Cognine has started RPA implementation with 2 bots across order management, freight forwarding and rate look ups. The bulky, repetitive, rule based processes across these areas served to be an ideal candidates for automation. Ui Path tool was chosen for its resilience and ability to perform in various environments and proven NLP capabilities. The bots were responsible for handling the process of order creation (orders received by email –> involves NLP based text extraction) in the ERP system, getting rates from various freight forwarding companies and inserting in to the portal, and getting the latest status of the client’s customer shipments all with out manual intervention. Key Benefits The RPA platform has increased the accuracy levels and cut the time needed to perform certain tasks by up to 90%. The resources that were performing these manual tasks earlier are able to focus on other value adds to customers.

White Paper | 2024

AI in Business

Navigating the influence of AI on business landscapes

Blogs

Enhancing Power Solutions with an Innovative Admin Portal

Finance

Transforming Financial Operations Through Technology

Blogs

Change Data Capture (CDC)

Blogs

Is Real-Time Data, The Future?

Finance

Unleashing The Potential of Digital Assistants in Fintech

Logistics

Optimizing Driver Retention Through Technology

Blogs

Digital Transformation in the Utilities Sector

Blogs

Intelligent Automation – Future Of RPA

Logistics

RPA Solutions for 3PL provider

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