August 2022

Key AI/ML use cases in supply chain optimization

Due to the increasing complexities of the supply chains, lack of visibility in consumer goods companies (CGs) has emerged as the most common stumbling block in recent times, thus influencing demand, supply stability, and profitability significantly.

So, the million-dollar question is how CGs can improve visibility, agility, and profitability across the supply chain.

What is supply chain optimization?

Supply chain optimization is fine-tuning a supply chain’s processes to ensure that it functions at the peak of its efficiency. This kind of redesign or optimization is based on specific major performance indicators that consist of overall operating expenses and the ROI of the company’s inventory. The primary idea is to offer the customers the products at the lowest possible cost while maintaining the highest profit margins. However, to accomplish these targets, business managers must balance the costs incurred in manufacturing, inventory administration, transportation, and meeting customers’ expectations.

The need for supply chain optimization in CGs

CGs have been battling several challenges to meet the ever-increasing and shifting consumer demands. Additionally, following the onset of the pandemic, the CGs do not have supply chains that are swift and reactive enough to resist such chaos and complexities. This is perhaps because of how people buy and engage with the brands, which have recently witnessed an extraordinary shift.

So, buying has endured a tectonic change as more product classifications resort to electronic channels rather than brick-and-mortar stores. Therefore, it is now time for a makeover for the CGs to be more laser-focused on modulating themselves as a customer-centric entity through supply chain network optimization, thus reinventing themselves to gain a competitive edge in the market.

Key benefits of supply chain optimization:

There are various advantages of implementing a supply chain optimization, solution some of which are highlighted below:

Artificial Intelligence (AI) and Machine learning (ML) can assist businesses in enhancing supply chain management by making them more resilient to disruptions. As a result, supply chains globally are adopting ML and AI to expand their processes, decrease costs and associated risks, and boost revenue.

Significant use cases of leveraging AI and Automation technologies in supply chain

Demand sensing: AI can remove the speculation in forecasting and prevent supply chain surprises. Demand sensing in supply chain optimization methods plays a crucial role in planning and executing processes linked to supply chain management, thus using AI to manage intricate and volatile fluctuations in customer demand volumes.

Supply forecasting: The material bills and POS data can be easily defined, and correct predictions can be made for supply forecasts per supplier commitments and lead times. So, the organizations must balance demand and transform the business requirements to cover the all-inclusive value chain.

Price planning: A supply chain company must exploit the power of ML and AI to optimize the rise or fall in product prices as per the product life cycles and demand trends.

Inventory planning: Implementing ML can enable a company to predict the demand growth for different products and services, like furniture, apparel, and home applications. Also, it can recognize market zones where there is an over-stocking crisis. It inevitably increases POS with suppliers (as per deficiencies or potential demand shortages) by forecasting supply and demand to ensure that a customer gets the right products on time but without overspending on excess inventory.

Out-of-stock analytics: The structured and unstructured data enable firms to apply supply chain optimization services to predict the cause of out-of-stock goods. It means ML and AI can accurately forecast out-of-stock items, which can help a company strategize the shipments and deliver accordingly. It can also predict the pricing tactic of a given product.

Warehouse management: Identifying the primary stock-out cause and forecasting the exact demand trends with improved lead times from suppliers to minimize the stock-out situation via an AI-powered approach is a vital component of warehouse management. In addition, supply chain optimization enables the programming of vehicles and robots usually used in warehouses.

Production planning: Implementation of IoT sensors and production automation solutions helps to increase/decrease products and enhance the quality according to real-time customer feedback.

Supply chain optimization through TradeEdge Network

TradeEdge Network, an end-to-end and multi-tenant architecture solution, brings execution agility into your supply chains. Its many-to-connectivity ability builds opportunities for swifter action by amalgamating businesses that react instantly to products, services, and information.

For more information, download the whitepaper to learn how the TradeEdge Network helps address demand-supply gaps in near real-time and maximize fulfillment.

So, explore the power of TradeEdge by transforming your value chains.

How can consumer goods companies increase forecast accuracy with demand sensing?

The key concern for most consumer goods (CGs) companies is the constant pressure of handling the ever-shifting pattern of consumer demand behavior. As a result of these market volatilities and data concerns like disorganized data sources, data formats, and data quality, these CGs witness erratic demand signals, which lead to inaccurate demand forecasting.

Additionally, the working investment variabilities significantly affect the business cost, the way consumer service can fulfill orders, and the effect on inventories, thus pulling down the overall productivity. This is where the role of demand sensing comes into the picture.

The need for demand sensing

Demand Sensing is a solution that uses innovative technologies like machine learning (ML), artificial intelligence (AI), and real-time data to predict near-future forecasts and meet consumer demand.

Hence, it is through demand sensing solutions that CGs can create real-time visibility of the ever-changing demand patterns of consumers. In addition, real-time demand visibility augments forecast accuracy, which helps in better business decisions. This further leads to better inventory management and reduced operating costs to help realize key business goals and achieve supply chain resiliency.

The primary goal of demand sensing technology is to conquer latency issues by diminishing the time between an event and the response to that event, which helps planners better anticipate short-term demand changes. Therefore, meeting this urgent market demand is crucial for retaining the competitive advantage. If a company cannot do so, it can turn away from its customers and thus lose revenues.

Going beyond traditional demand planning

The conventional demand planning techniques count on historical sales and shipment data to assess the exactness of future demand. However, there is a shortcoming in this approach that it cannot provide the real-time market volatilities of demand and supply as it is reliant on historical data.

On the other hand, demand sensing utilizes a broader scale of demand signals and algorithmic solutions to make more precise forecasts that factor in real-world events. Moreover, this practice automates the process, via ML and AI, by collating enormous amounts of data from various sources like point-of-sale terminals. This data is then analyzed to get actionable insights or correct demand signals that are then examined to forecast demand.

How can you improve forecast accuracy with demand sensing?

By utilizing point of sale (POS) order data: The most formidable power of the demand sensing tool is its usage of POS data at retail stores, as these data have real-time insights such as products sold, date and time, quantity, and region, to predict the short-term forecast.

Keeping track of competitors’ last-minute offers: A company can be caught off-guard by the last-minute promotional discounts or offers of a competitor. Unfortunately, many of these firms will have no time to provide any response to such situations quickly. However, by applying demand sensing solutions, marketers can forecast the effect on sales and thus adjust the sales forecast accordingly.

Evaluating order history to recognize demand: In upstream manufacturing, forecasting accuracy with demand sensing techniques is often achieved by analyzing historical data, including sales history, open order history, and currently available orders.

Exploiting competitor stock-outs: Production breakdowns, raw material shortages, quality problems, etc. can lead to competitor stock-out scenario. In such a situation, a rival company quickly takes advantage of this stock-out scenario by repositioning its inventory levels.

Identifying macroeconomic indicators: Most CGs are directly impacted by macroeconomic indicators like GDP, the overall stock market, employment data, and housing sales data. However, critical movements of these indicators can be linked to fluctuations in the end customer demand. Hence, preemptive steps can be taken to accommodate the short-term forecast to prevent missed customer orders or surplus inventories.

Adjust forecasts as per weather changes and natural catastrophes: Early arrival or withdrawal of certain climatic conditions and natural calamities directly impact the sales of a product. Nevertheless, modifications in the short-term forecast, production plan, materials procurement, and distribution plans can be altered as per the end consumer demand by applying demand signals effectively.

Explore unlimited possibilities with TradeEdge Demand Sensing

TradeEdge Demand Sensing offers actionable insights by collecting real-time data about inventories and product sales across numerous channels. This end-to-end solution provides granular visibility into network-wide sales and inventory, enabling the businesses to be aware of products sale, location of purchase, and speed of sale. TradeEdge enables CGs to sense and respond to near real-time visibility to demand signals and scale operations to massive growth.

One of TradeEdge’s clients enhanced its forecast accuracy by 20% by using real-time sales inputs (vs. shipment), covering approximately 70% of global sales. In another instance, it enabled one of its global clients to enhance its sales growth by 2% through delivery plans adjustments.

Conclusion

With the supply chain complexity and consumer behavior changing constantly, traditional demand planning approaches have become ineffective as these techniques fail to derive granular visibility and actionable insights, two of the foundation stones for driving business growth.

Adding to the woes, demand planners face many challenges with conventional forecasting to perfectly balance between adequate and surplus inventories due to the non-visibility of demand signals.

Hence, enterprises must transform traditional, linear supply chains into connected, intelligent supply chains to stay ahead of the competition.

Unlock new possibilities with the power of demand sensing

The COVID pandemic was eye-opening for many, especially for consumer goods companies that failed to match a surge in demand for necessities amidst panic buying. The global health crisis is gradually dissipating, but its aftershocks are felt far and deep in the supply chain network. The vulnerability and incompetency of traditional demand planning methods glared at our faces, compelling many enterprises to turn increasingly to more advanced tech-based solutions. Demand sensing emerged as the best bet to prevent the demand-supply value chain from falling apart in the face of similar situations.

What is demand sensing?

A demand sensing solution leverages the power of AI and other technologies to predict near-future demand by aggregating real-time data. It helps overcome latency issues by reducing the time between an event and the response to the same event. Therefore, this approach is ideal for short-term forecasting sales.

Demand sensing tools consider a range of internal and external factors influencing consumer purchase behavior, such as POS data, anomalies in the supply chain, global emergencies, weather trends, economic trends, etc. These factors constantly shape and reshape the demand for goods and services.

On the other hand, traditional demand planning relies heavily on historical data and individual, often biased, opinions of experts which are helpful when predicting long-term sales.

But history doesn’t always repeat itself, especially in the short term. Hence, understanding the demand for goods the following day depends highly on selling the same items the previous day. Therefore, demand Sensing is the only way to predict short-term fluctuations in the market and sales.

Unlock new possibilities with the power of demand sensing

An AI-powered solution like TradeEdge Demand Sensing aggregates real-time data about product sales and inventory across multiple channels. Such actionable insights improve visibility at the empirical level helping planners to understand what is selling, where and at what speed. In addition, it allows for accurate anticipation of short-term changes in demand and forecasts sales with utmost accuracy.

Digitization of supply chains: For any organization, its network of suppliers and distributors is a backbone, supporting the enterprise’s objective of engaging buyers with quality and timely delivery of goods/services. Hence, a seamless flow of communication about what is happening at the suppliers’ end is crucial to prepare for sudden surges in demand for existing or new items. AI-enabled demand sensing solutions can easily break the existing silos by onboarding all concerned parties on a single digital platform. And digitizing on-ground data allows for timely availability of actionable insights into data lakes. Hence, demand planning becomes more accurate, refined, thorough, and ongoing.

Unified view of supply chains: A unified view of sales across all channels is crucial to help enterprises stay market ready all the time. Demand Sensing allows for data digitization and easy accessibility to empower planners to identify the extent and intensity of stock-outs, phantom inventories, and display voids. Hence, they can use more effective measurement and deployment of funds and resources to keep up with the ongoing demand.

Availability of multiple datasets: Existing silos between departments prevent the timely distribution of critical information needed for decision-making. Hence, an all-inclusive view of datasets is paramount for an accurate sales forecast. Since AI works best in the presence of data, the same technology is used to automate the data extraction process from related and unrelated, unstructured, and semi-structured data streams. In addition, AI takes the time and labor factor out of the process, ensuring real-time information about changing market trends is available on time to prepare for the new demand.

Accurate consumer behavior forecasting: Another unique benefit of a demand sensing solution is evaluating the pattern of customer purchase behavior from various sources, including POS data, customer feedback, and online activities. Buyers always demand something better that adds value to the price they pay. Timely anticipation of such behavioral patterns helps companies to make adjustments in the product features on time to foster better satisfaction and engagement with their services. It also gives the company an edge over competitors in the market.

Make more reliable short-term forecasts: Since demand sensing considers various influencing factors and real-time insights to predict short-term sales, the forecast accuracy increases manifold. As a result, companies can stay responsive with timely adjustment of replenishment plans and adequately cater to optimal ordering.

Demand sensing solutions are a game-changer, helping unlock unlimited possibilities by building connected, value networks that support the overarching business strategy, improve operational efficiency, and create new revenue opportunities.

Unlock unlimited possibilities by leveraging the power of Intelligent Automation

Intelligent Automation (IA) is soon evolving as the bedrock of digital transformation as it allows quick and end-to-end automation of business processes. IA enables an automation journey through optimization, discovery, and automation of any front or back-office business tasks and improves the work efficiently across the human-bot systems.

But, before going into details, let us first talk briefly about Intelligent Automation.

What is Intelligent Automation?

Intelligent automation, also known as cognitive automation, is the implementation of automation technologies such as artificial intelligence (AI), robotic process automation (RPA), and business process management (BPM) – to simplify and help make correct business decisions across organizations. In addition, business intelligence automation streamlines processes, frees up resources, and inflates operational efficiencies.

How is Intelligent Automation revolutionizing businesses?

Countless companies are increasingly welcoming automation in their business processes by applying some of the top Intelligent Automation platforms. Also, enterprises need an all-inclusive cutting-edge automated solution to enable agility, augment customer experience, and enhance the output of business processes in this era of swiftly moving business transformations. This is where IA is presently altering the way businesses are performed in almost every domain of the economy.

The implementation of Intelligent Automation is even more significant now as it is imperative for companies to make the right decision via the application of innovative technologies such as Machine Learning, Artificial Intelligence, and RPA to boost operational efficiency.

Moreover, since AI replicates human intellect, Intelligent Automation solutions can deal with activities that consist of a certain extent of precise reasoning, decision-making, and analysis. In other words, IA helps in the comprehensive automation of processes that involve non-routine tasks which frequently require human mediation to achieve a business transaction.

Benefits of Intelligent Automation

How can enterprises unlock unlimited possibilities with Intelligent Automation?

Decrease business process times: Intelligent Automation capabilities concurrently pace up the processes and enhance efficiencies, especially in the use cases where huge data need to be overseen, examined, or moved. Hence, integrating RPA with other advanced technologies like ML and AI makes the tasks considerably less time-consuming and labor-demanding.

Better security, compliance, and privacy: A well-conceived and responsive Intelligent Automation strategy helps protect an organization’s critical data from attacks and glitches, ensuring adherence to compliance and privacy standards.

Delivers more effortlessly auditable data: Business processes are inherently prone to human error and conflicting execution. Without IA, important information can be entrapped in decentralized unit silos, thus making its real value tough to unlock. IA enables the progress of clearly documented transaction trails, thus identifying the things that are working and not working across a company.

Improves customer experience: Once a company implements Intelligent Automation in its business operations, the internal efficiencies get channelized towards outward-facing advantages for customers and clients. The queries are answered more accurately and promptly, with the onboarding becoming less cumbersome and grievances getting settled more easily.

Moves employees to higher-value tasks, yielding more ROI: Intelligent Process Automation allows companies to engage the employees in some value addition tasks rather than repetitive and time-exhausting jobs by allowing bots to perform the recurring tasks. With the application of RPA, mundane jobs can be carried out in a coherent and error-free manner, and employees can be diverted to tasks that generate incremental value for the enterprise.

Saves time and money: An Intelligent Automation program deployed enterprise-wide delivers reduced costs and improves operational efficiencies. Consequently, all the proven benefits add to cost savings, arguably the single biggest reason IA has emerged as a cornerstone of the digital transformation revolution.

Intelligent Automation thus will not only allow the organization to benefit from the automated processes over a long-term period, but it will also enable the company to invest more in cognitive resources and develop optimal strategies to fuel future growth.

Why is Process Discovery an essential step in an enterprise’s automation journey?

There has been an astronomical increase in the focus on Intelligent Automation following the trend of digitally transforming businesses in the wake of the global pandemic. Hence, enterprises have ramped up their investments in RPA and Smart Automation. RPA, also known as Robotic Process Automation, is the first step taken towards achieving some form of process automation covering recurring, rule-based workflows. But, in order for businesses to correctly identify suitable process candidates for automation and scale it through the length and breadth of the company, they need Process Discovery solutions.

Process Discovery steps up to automate the identification of critical processes that can benefit from the touch of Intelligent Automation.

What is Process Discovery?

A successful automation journey begins with Process Discovery. Identifying the right processes is fundamental to ensuring a smooth transition of business processes and enabling end-to-end automation. Process Discovery, powered by advanced AI capabilities, non-intrusively captures human-machine interactions, and execution of each task and creates a practical blueprint with recommendations, thus adding the right processes to the automation funnel.

How does Process Discovery work?

Process Discovery solutions like AssistEdge Discover create empirical data-based business process maps to help enterprises navigate their automation journey from value creation to realization. AI-based Process Discovery tools provide a step-by-step transformation of existing workflows, from capturing data automatically and analyzing empirical data to generation actionable insights.

How does Process Discovery support an enterprise’s automation journey?

Process Discovery unlocks hidden opportunities trapped inside your existing business processes. Such empirical data culminates into a solid foundation for enterprises striving to scale automation transition all through. Furthermore, Process Discovery is directly responsible for driving Intelligent Automation, also known as Connected Automation.

Process Discovery shows you what can be automated almost immediately and what can be used for the pilot to show how other processes can be refined with automation. Here, the learning process happens in two steps: Task Mining and Process Mining.

Conclusion

Process Discovery does the basic groundwork of evaluating how each process functions, based on which opportunities for automation are identified to initiate the journey by deploying RPA bots for basic recurring, rule-based tasks. Following the success of RPA, the following critical candidates are selected to further the automation journey.

To scale automation enterprise-wide, businesses should start by discovering process candidates and evaluate each success to automate those next in line. It is a gradual journey, but with the help of advanced technologies, the effort and time needed to identify each process architecture are aptly negated. Hence, seamless end-to-end automation transition becomes achievable at no cost, no error.

Benefits of Cloud RPA for enterprises

Cloud RPA is expanding quickly, with enterprises steadily realizing that Cloud RPA enables them to automate business processes faster, cost-effectively, smartly, and more securely compared to the on-premises RPA. This is undoubtedly a step ahead for enterprises in their digital transformation mission.

What is Cloud RPA?

Cloud RPA refers to Robotic Process Automation that’s accessible online or on cloud. RPA on cloud brings in all the responsive attributes of RPA through the cloud’s facilities. Conventionally, the RPA technologies were utilized as on-premises solutions for enterprises.

However, with the assistance of cloud-based RPA, enterprises can maintain their on-premises software solution and drift the entire infrastructure to the cloud platform. As a result, it allows businesses to scale quickly and significantly enhances the speed at which companies can design, implement, and manage applications.

Major benefits of Cloud RPA

Can be executed and deployed easily: One of the significant aspects of RPA cloud service is its ease of operation and fast deployment of technological solutions. Cloud RPA eliminates the roadblocks to implementing automation by hosting and managing the lot on a cloud platform. The connection of pre-built and augmented cloud services is effortless and extremely fast, contrasting to an on-premises solution. All the automated activities can be shifted to the cloud in just a few seconds and positioned instantly. The company needs only a one-time setup support system.

As a result, all the automated tools can be run instantly, employing a pre-built network segment. This step will reduce the need to have a significant IT department for continuous support and maintenance, thus substantially speeding up the current automation process.

Reduced infrastructure expenses: Robotic Process Automation on cloud reduces the cost immensely. Additionally, the technological infrastructure needed to establish and maintain Cloud RPA is much less in contrast to any on-premises enterprise technologies. Consequently, the requirement for IT support becomes minimal or nil due to the absence of any software, minimizing the technology infrastructure cost and the total cost of ownership to a large extent.

Accessibility from remote areas: Remote accessibility is one of the most significant benefits of using cloud-native RPA as it does not require downloading or installing on local devices. One of the examples of such functioning is email, which can be accessed from any device and location. Hence, team connections become more efficient and unified.

Increased data safety: Cloud RPA offers an ideal hybrid technological solution for dual delivery with the combined platform subject to strict regulatory compliance. Hence, the data extraction, processing, and migration are safe and highly free from errors. Furthermore, this amalgamated model maintains all the information on-premises while the data can be automated from the cloud.

Scalable and agile: Cloud-based RPA is a highly scalable business solution. However, in the beginning, few business processes can be automated to scale up the business without the intervention of any additional computing resources. Moreover, the Cloud RPA tools get updated regularly so the user can use the latest tool with constant and automatic updates. Subsequently, the user can get the bandwidth to work on groundbreaking solutions and deploy the latest products quicker to gain a competitive edge.

Leveraging AssistEdge Cloud RPA

For its features and benefits over traditional on-premise solutions and its unique deployment models that are apt for businesses and automation needs, enterprises must explore the power of AssistEdge Cloud RPA, an all-inclusive cloud automation solution.

By implementing AssistEdge Cloud RPA, enterprises can easily capture the automation industry’s most advanced RPA delivery capability and deep cloud expertise. AssistEdge Cloud RPA has deployed 50,000+ BOTs across various business domains so far. It brings together the authority of AssistEdge, cloud infrastructure, and Intelligent Automation, enabling enterprises to access real-time supervision of automated processes.

Cloud RPA in the future

Cloud capabilities unified with the business intelligence of RPA can effortlessly and drastically transform the processes of a company eager to incorporate this solution in the future. While any enterprise’s management emphasizes higher-level strategic decisions, operational and tactical business functions can be effortlessly automated by implementing Cloud RPA solutions. It is now the perfect time for enterprises to leverage the power of Cloud RPA platforms to deliver quick results and grow at an uninterrupted speed in the times ahead.

How can a responsive route planning solution help unlock unlimited possibilities?

The e-commerce industry is flourishing rampantly while customer preferences are constantly shifting with the expectation of their ordered goods’ delivery bandwidth getting reduced to ‘few hours’ or ‘same-day.’ As a result, route planning solutions for identifying the most beneficial route have become one of the most sought-after resolutions for logistics companies in recent times.

However, let us first delve into the fundamentals of route planning and its significance in logistics before discussing it at length.

What is route planning, and why is it essential?

Route planning is the method of figuring out the most profitable route, including maximum stops with the least amount of time consumed for the delivery of products. Businesses use real-time delivery route planning solutions to identify the best way out for deliveries.

A responsive route planning is vital in logistics as the companies must adapt to the customer delivery supplies, route availability and accessibility, and vehicle problems to guarantee reduced operational cost and improved customer satisfaction.

Going beyond traditional route planning

Transforming traditional, linear supply chains into connected, intelligent, supply networks is more important than ever.

Traditional or manual route planning, and multiple stops scheduling takes hours that are not productive and efficient. In addition, the manual approach fails to handle real-time events that impact daily business productivity.

On the other hand, a responsive route planning solution considers weather, traffic, customer availability, real-time order tracking, and route optimization to help companies regularly track the changes that impact the delivery chain in real-time.

Some challenges of traditional route planning:

Significant benefits of a responsive route planning solution

Automated route planning: A business route planning solution considers the historical data, weather conditions, and traffic to identify the shortest route between the warehouse and the customer’s place, which helps in saving time and fuel and eliminates bias.

Real-time visibility and tracking: Getting real-time updates of the position and fleet obtainability via route planning and optimization solution will help better control the fleet and drivers. It will also enable the source to connect with the driver directly to ensure that they do not take any needless breaks on the way and guide them through any hindrances that will help maintain delivery timelines.

Pick up and replace: Nowadays, companies can interact with their delivery officials via mobiles, thus allowing them to manage and regulate job allocation, sequence, routing, and real-time customer communication.

Increase in outlet coverage and reach: The outlet coverage and reach can only be augmented by balancing the visit regularity for high impact to low performing outlets through an effective business route planning solution.

Increase in sales rep efficiency and productivity: By implementing route planning solution, sales rep efficiency and productivity can be improved remarkably as the solution speeds up the process, thus minimizing the time consumption.

Unlock new possibilities with TradeEdge Route Planning

TradeEdge, a smart supply chain management platform, enables large enterprises to gain maximum route visibility, thus enabling them to deliver products faster. Its enterprise-grade solution, TradeEdge Route Planning, helps unlock the true potential of distribution networks for businesses.

Driven by AI and ML algorithms, it is a responsive and scalable digital solution that keeps companies in total control over their route planning activities.

With the snags increasing in the route planning activities, it is now the perfect time for logistics companies operating in real-time to opt for a solution like TradeEdge Route Planning to chart the optimized routes for enhancing delivery efficiency and decreasing the operational costs.

What is Process Discovery and its role in an enterprise’s RPA journey?

Robotic Process Automation, or RPA, is the critical competitive differentiator for organizations, growing by leaps and bounds following the urgency to automate repetitive back and front-end processes. Process Discovery, on the other hand, is considered the means to shorten the transformation curve to automation.

The COVID pandemic had been the stimulator that enforces businesses to ramp up their digital transformation efforts. Adopting automation to speed up recurring processes is the first stride taken in the said direction. But which business process will benefit more from RPA solutions remains a challenging decision to make. Hence, ML-powered Process Discovery tools are opted by enterprises to help them select the suitable candidate for automation.

What is Process Discovery?

There are probably numerous processes working simultaneously within an organization – each following a unique and distinct roadmap. To bring certain key areas under direct automation, organizations are faced with the challenge of narrowing down to those processes for which automation will deliver the most significant benefit.

Business Process Discovery is the medium helping organizations discover how every process within the company is executed and which process or processes need priority improvement and automation.

Process Discovery tools are incorporated to identify the most recurring and repetitive tasks that are error-prone, time, cost, and labor intensive. By transitioning such processes to automation using RPA bots, companies can meet heightened customer expectations and deliver better employee experiences.

How does Process Discovery work?

Automated Business Process Discovery tools and methods are employed to monitor an organization’s business process using bots for a specified period. They silently run in the background of employees’ systems, collecting valuable data and recording workflows without hampering daily work. These bots carefully note down various applications employees use in different ways to complete specific tasks. Then, advanced ML algorithms are used to analyze the data, identifying workflows that can be moved to an automation tool for seamless journeys.

Benefits of Process Discovery

The following are key benefits of Process Discovery:

How does Process Discovery affect RPA success?

RPA involves automating rule-based recurring workflows. Unfortunately, an organization can comprise millions of such processes involving too many human hands and productive hours. These are repetitive and monotonous, forming an integral part of each system and business process but adding minimal value to employees’ individual growth.

Hence, to speed up such time-intensive tasks, companies are deploying RPA bots, but the latter’s success hinges on being able to identify the right processes to automate accurately. Chances are high for RPA failure if Process Discovery fails to select the deserving processes for automation.

Automated Business Process Discovery leverages intelligent cognitive technologies to evaluate employees’ desktop data and interactions like keystrokes, mouse selections, applications used, pages visited, field entries, and handle time. It continuously maps and ticks off priority processes to create a stream of RPA-friendly process opportunities. This eventually enables businesses to achieve optimum operational efficiency by arresting human errors common in recurring tasks, time, and cost-intensive factors.

Intelligent Process Discovery maximizes ROI by unlocking the actual value of RPA only when the latter is deployed in fitting processes and workflows. Hence, Process Discovery is the first calculative step toward achieving end-to-end business process automation.

Data Digitization with CMR – A step beyond RPA and OCR

Handling unstructured data has become a pressing problem for organizations, especially document-centric enterprises. Data digitization using the power of AI capabilities and automation is the only way to make crucial information available on time to cater to data-driven decision-making. Enterprises, therefore, need to step beyond RPA and OCR technologies to address the data availability challenges.

What is data digitization?

Digitization of data refers to converting text, images, audio and video clips, and other forms of manual records into digital formats and stored in the cloud. This analog to digital data conversion is essential for businesses that handle bulks of paper-based and digital documents, including PDFs and emails, daily. These documents are goldmines of valuable information, which, if harnessed on time, can help with impeccable decision-making and, at times, provide an edge over the competition.

With the help of modern technologies like AI and Automation, data is not only converted into its digital version; granular insights are extracted automatically, processed, and classified into specific datasets and presented in structured, consumable formats. Moreover, since such datasets are shared in the cloud, authorized departments or persons can quickly leverage them in their operations.

Here, AI and Automation solve the unstructured data challenge effectively. For instance, OCR or Optical Character Recognition is a dominant solution to convert scanned documents into machine-readable texts. But OCR has its own set of limitations. Thankfully, other data digitization solutions such as CMR or Cognitive Machine Reading are available to enterprises today.

What is Cognitive Machine Reading ?

CMR or Cognitive Machine Reading easily overrides the various deterrents of the unstructured data digitization process and fosters a seamless extraction of granular insights even from credentials. Furthermore, unlike the rule-based OCR, this data digitization solution is created out of proprietary pattern matching using methods focused on content-based object retrieval, which renders the level of precision in unstructured data extraction.

Therefore, it is more reliable than OCR and other related tech-based solutions.

Why should enterprises consider CMR over RPA & OCR for data digitization?

OCR is inherently inadept at handling unstructured documents like contracts where there is no template guiding the OCR in the right direction. In order for OCR to understand where to look for data in documents, the latter must be consistent and structured, following common document standards.

On the other hand, RPA goes beyond OCR with its ability to integrate applications with legacy systems to accommodate seamless process flow. But even this technology is not enough for data digitization projects. Most importantly, RPA is not designed to read data. It acts on data.

Enterprises can embrace Intelligent Document Processing from the initial point of entry, via upstream processing, and through to the desired output.

Automation, AI, and ML are the best bet for unstructured data digitization challenges. CMR incorporated with the above-mentioned tech capabilities is presently the only solution available for enterprises. This new approach matches the digital ecosystem gradually building around us. Machine Learning-based models know what data they need, where to find it, and how to process it, and they have the power to overcome the limitations of rules-based approaches to data extraction.

The timely availability of data not only empowers enterprises with data-driven decision-making, but it has also been proven that companies that can better mine customer data can aptly predict shifts in demand and meet emerging needs. But unfortunately, due to the unstructured nature of such data or the absence of proper strategies to address it, data digitization and availability continue to bother enterprises. The advent of CMR has solved many of their concerns, yet it is a new technology that tags along with a new set of challenges.

Fast-tracking new business processing with AI in commercial insurance

With the unstructured data increasing exponentially with each passing day, the insurance industry, especially commercial insurance, has been grappling with some fundamental challenges recently. Hence, painting a long-term roadmap for enabling commercial insurance firms to digitize their unstructured and semi-structured data by implementing Artificial Intelligence (AI) has become the need of the hour.

In the wake of these turnarounds, it is now imperative for commercial insurers to set up a unified digital strategy and a well-laid-down makeover plan via AI and machine learning (ML) applications. Subsequently, AI in commercial insurance can help insurance carriers to hone the functional structure for fast-tracking the business processes to accelerate the desired insurance growth trajectory.

However, before going too deep, let us first understand the essentials of commercial insurance.

What is commercial insurance?

Commercial insurance covers businesses and corporations, generally designed to protect the company, its employees, and its ownership. As there are various types of businesses with different necessities and situations, commercial insurance can be of numerous shapes, sizes, and colors.

Commercial insurance helps businesses stay protected against hazards that could impact their target realization. In addition, some types of business insurance protect the company’s reputation and well-being, while the rest preserve the financial attributes.

The need for AI in commercial insurance

Accelerating the new business processing with traditional automation such as Robotic Process Automation (RPA) has been a challenge for many commercial insurers. As a result, insurance carriers are shifting towards AI to get a comprehensive view of the submission to make smarter business decisions. However, adopting the technologies among commercial insurers has been the primary roadblock.

To resolve such challenges, insurance firms will need to apply the right solutions, such as XtractEdge Commercial Insurance, to augment the underwriter productivity and the response time by offering solutions that will enable firms to change the commercial lifecycle for making correct business decisions. XtractEdge boosts efficiency within commercial insurance by employing IA in business processes.

Additionally, expanding AI and ML to spontaneously automate the required processes, rather than using the solution for a specific set of rules, offers a broader consolidative strategy, provided the goal is to eliminate the labor-intensive manual review process. Also, there has been a constant problem of investing time on compound tasks that may not be apt for commercial insurance.

Unlocking unlimited possibilities in commercial insurance with AI

New sources of underwriting data: One of the most significant advantages of using AI in insurance is that it can turn the earlier unreachable sources of underwriting understandings on small commercial ventures into actionable intelligence. These data can be in words, videos, audio files, pixels, and web pages – mainly in an unstructured format.

New information from already used data: Commercial insurers build and maintain their own data sets and evaluate them to predict the losses better or enhance other results. The infusion of AI in insurance underwriting enables the insurers to offer innovative ways to assess this information compared to the linear models. However, these predictions can expand to several underwriting functions like coverages an insured may require built on the type of business it runs.

XtractEdge Commercial Insurance by EdgeVerve provides value for the enterprise and expands the underwriting capacity by 50%.¹

Better consistency: The applications of AI in commercial insurance will surely provide more consistent actionable data compared to those developed by human underwriters. For instance, AI-powered algorithms that apply groupings and suggest the coverage options will make expected decisions whenever it confronts comparable data, but it is not the case with humans.

Enhanced efficiency: With an all-inclusive dataset containing the traditional sources, the execution of ai in insurance claims can be applied to prefill the application details by means of minimum consumer information to find possible claims.

XtractEdge Commercial Insurance by EdgeVerve helps increase the efficiency of the claims processing dramatically. With the right set of tools, the insurance carriers can reap the benefits of a 5X decrease in application processing time.²

Enables insurers to make the right decisions: The execution of AI assembles and offers automated information to the customers. This information engraves new data points that optimize the process of making appropriate decisions.

How can commercial insurers accelerate new business processing through AI?

Implementing a cohesive strategic plan: To tackle the challenges presented by unstructured data from various sources, the insurance agencies need to build a long-term coherent strategic plan for a multi-year transformation touching talent, operations, and technology. Following this, the companies must decide how to apply the technologies to materialize their long-cherished goals built on the insights from using AI in insurance.

Using AI technologies smartly: The customer-experience teams of the insurance carriers must invest the resources and time to create an in-depth understanding of AI-related solutions. This will help companies identify the hypothesis-driven scenarios to understand the subtleties involved and use the technologies intelligently.

Producing a comprehensive data strategy: Insurance firms should build a structured and actionable internal and external data strategy. Internal data should be structured to procure new analytics insights and capabilities quickly. However, the companies should concentrate on securing access to data cost-effectively that complements the internal set of data.

Building the right pool of technology and talent: The future insurance firms will need the right mindsets and skillsets as the future generation of insurance staff will be high in demand due to their technologically sound and adept abilities. Moreover, they should be able to work on something that will blend machine-backed and semi-automated tasks that continuously evolve.

The future of AI in commercial insurance

Implementing AI in commercial insurance will undoubtedly be rampant as there will be more evolvements of technologies in the coming times, thus leading to disruptive alterations in the insurance industry. Therefore, it will be apt to infer that the winners in AI-based insurance will be the ones that innovative platforms like XtractEdge to generate ground-breaking products from new data sources, reorganize processes, and go beyond customer expectations for tectonic adaptation to new technologies in the coming decades.