How does AI help enterprises accelerate invoice processing?

One of the biggest challenges of any organization’s Accounts Payable (AP) process is capturing, extracting, and processing data from the invoices, especially when the format varies from document to document. Against this backdrop, businesses create customized solutions to resolve this issue. Yet, the companies struggle to keep the momentum and pull off the anticipated invoice-linked business value.

However, there is a way out of everything, which is the case with this challenge. With the technologies evolving every few years, new tools can harness the dynamics of Artificial Intelligence (AI) to identify not just one kind of invoice but all types of invoices in every format. Fascinated by the benefits of AI, many organizations have realized the urgent requirement of AI invoice processing to address the challenges of traditional AP processing.

What is invoice processing?

Invoice processing is the process of receiving, logging, and transmitting supplier invoices and making payments. This complete procedure is overseen through subsequent steps that the AP unit is liable for, from data capture to payment.

Irrespective of the format of the invoice, the data it comprises must be recorded in the general ledger of the organization’s ERP or accounting system. Therefore, invoice processing is a significant element of the procure-to-pay procedure as the final step of any business procurement activity. Once AI technology is applied in invoice processing, it is known as AI invoice processing or automated invoice processing.

Two critical challenges in traditional invoice processing

There are various challenges of traditional invoice processing out of which two significant ones are highlighted below:

Compound invoice data prone to errors

Many companies get the invoices from various suppliers through diverse channels such as image files via emails, PDFs, and XML documents from Electronic Data Interchange (EDI).  Processing all these invoices within a unified system requires a lot of time and manual effort. However, this time-consuming effort can lead to errors and further possible lapses like processing wrong amounts, clearing payments of the same invoice twice, and duplicating invoices.

Expense per invoice processing

Even now, the current invoice processing practice is mainly done manually with all the costs, such as postage, paper, manual hours, and fixing the accurate price of manual invoice processing. Besides this, other hidden costs include late fees, missed early payment discounts, and accounting inaccuracies.

Some other challenges are:

AI invoice processing steps

AI-based invoice processing can automate the data capture and the processing via the below steps:

Benefits of automated invoice processing

Quicker invoice clearing: With the solutions getting smarter day by day, invoices from the supplier need lesser time to process the automated invoice processing system identifies the processes quickly without any human intervention. So, the turnaround time for invoice clearance is less than the earlier processing methods.

Accurate data: Artificial Intelligence and Machine Learning recognize and capture the data by executing neural networks to enhance its understanding and capabilities with each business process document. In contrast to the human data work, invoice processing using ai nullifies the threats of committing errors in repetitive tasks due to the exhaustion factor of the employees.

Enhanced productivity: AI and ML have become the essential touch-point solutions for every organization. As a result, every industry has commenced implementing automated invoice processing software to cut down task completion time. Consequently, the accounts payable staff can move towards the more value-added activities mentioned below.

Increased profit margins: Early or on-time invoice clearance leads to initial payment rebates from the suppliers, thus increasing the companies’ profit margins.

AI invoice processing in the future

Although AI-enabled invoice processing has transformed the payment dynamics across industries, enterprises are still globally in the nascent stages of leveraging AI for invoice processing.

Subsequently, the implementation of AI invoice processing will increase enterprises realizing the advantages of automated invoices, including minimized costs and errors, quicker payments, and streamlined processes. Hence, this will not only lead to B2B efficiency but also leap forward toward cross-border payments and heightened market competitiveness.

Therefore, explore XtractEdge solutions and harness the power of automating your processes and extracting intelligence from your unstructured invoice documents.

Digitizing enterprise data via CMR, RPA and OCR: Find out how

Taking crucial businesses and operational decisions quickly and accurately is a massive challenge for enterprises due to large volumes of fragmented and unstructured data. Nevertheless, as automation is the future, the challenge of unstructured data can be addressed intelligently.

Vying for an urgent need for data digitization, organizations are increasingly shifting their priorities toward addressing the disorganized data challenges. This is where advanced technologies such as Cognitive Machine Reading (CMR), Robotic Process Automation (RPA), and Optical Character Recognition (OCR) come in.

What is data digitization?

Data digitization is the method by which physical or manual data, which includes images, signatures, handwriting, videos, text, and audio) and documents are converted from analog to digital form, making the data readily available and usable by automated machines and humans.

The data is accumulated and organized in databases, repositories, lakes, data marts, and storage warehouses. Some of this information is accessible, however unstructured and dispersed, in several digital formats, such as emails and pdfs. Additionally, many data are found in non-digital paper-based documents as well.

Why is data digitization important?

Data Digitization is the first and foremost step each company should start in the digital transformation journey. However, sole digitization is not enough for the seamless data extraction flow. Hence, the digitized data must be in unison with the combined technologies of automation and artificial intelligence to avail benefits in quicker processing time.

Below are some of the benefits:

In the modern-day business game, companies require the assistance of analytics-based data for end-to-end business process automation. However, the issue here is that these technologies are effective only in the case of structured and digitized data.

For businesses to achieve end-to-end process automation, they need analytics-based insights to provide a refined structure to the data digitization process. However, both of these technologies flourish only in the case of structured and digitized data. Woefully, most of the data are present in the unstructured format, and converting them into usable mode is one of the most challenging aspects of this process. This is where AI-powered data digitization software solutions like CMR, OCR, and RPA prove crucial.

Data digitization via CMR – A model beyond RPA and OCR

Automation such as CMR, OCR, and RPA is undoubtedly the prospective solution for nullifying the challenge of unstructured and scattered data. However, OCR and RPA are merely short-term resolutions. RPA is not at all programmed to be able to read the information but only react to the available data. So, the scope of automation in RPA is limited.

On the other hand, OCR converts the scanned documents into machine-readable text, but it can work only if the documents share the information. Moreover, in one of the major surveys, respondents said that OCR provides only 50% accurate process information.1 Hence, it can be inferred that this is one of the primary reasons for such automation not being able to scale heights.

In such a situation, embedding CMR into the process is the only answer that results in quicker and high-quality data digitization techniques that can reduce the billing errors and turnaround time by 63%.2 Also, business process decisions revolve primarily around unstructured data, and CMR handles this challenge more effectively when compared to OCR.

Data digitization services by CMR eliminate the human involvement in the case of inbound document capture and extraction by data digitization software and authenticating the required data. Instead, CMR encapsulates the unstructured data points from several file types and identifies the patterns by evaluating them.

Data digitization with CMR – A must solution for the future

With the need to make insightful decisions quickly, companies are exploring their transformational journey with new evolving technologies, although some are still in the initial stages. Earlier, OCR was the one that was the most sought-after technology. This was followed by the emergence of RPA. But, due to certain shortcomings, it gave way to CMR, which is believed to be the most apt solution for overcoming the ever-mounting data challenges as it applies the ML models to capture, analyze and process data.

Therefore, the data capturing, and extraction process is bound to become more efficient, provided the organizations get hold of the right capabilities and digitization platforms such as XtractEdge for end-to-end business process automation and making a correct and timely strategic decision.

How does Intelligent Contract Analysis enhance business prospects

The pandemic has forced organizations to reconsider their business strategies and move towards a more streamlined approach to adaptability and survival. This existential ambiguity has pushed the legal and contractual bindings into the scrutiny zone from a client and vendor management point of view. Against this backdrop, the companies are gearing up for an overhaul of their traditional approach by unlocking information across their legal systems through Intelligent Contract Analysis.

Definition of Intelligent Contract Analysis

Intelligent Contract Analysis uses Artificial Intelligence (AI) and Machine Learning (ML) algorithms to change static contracts and bonds into valuable, actionable business resources. It provides the parties involved with unrestricted visibility into the contracts so that the parties can avert contract breaches, reduce costs, and increase productivity.

Difference between contract analysis vs. contract review

People assume contract analysis and contract review to be the same. However, both are entirely distinct processes. Contract analysis is separate from contract review as contract analysis happens post-signature while the contract review process takes place before the signing of the contract. Additionally, contract analysis is extracting and assessing large volumes of data from contracts. On the other hand, contract review includes the process of verification and approval of specific terms within a contract.

Why do you need Intelligent Contract Analysis?

AI-powered contract analysis has become even more significant as the traditional Contract Lifecycle Management (CLM) often falls short of sorting out the enormous volumes of contractual data. The antiquated contract management tools face a lot of challenges, some of which are mentioned below:

However, these challenges can be successfully tackled if one utilizes automated contract analysis effectively.

Benefits of Intelligent Contract Analysis

There are numerous advantages of AI-powered contract analysis. Let us consider some of them in brief:

Helps in making timely and better decisions: The application of contract analysis software enables companies to make on-time and better decisions in the future by evaluating the tactics that have worked in the past and failed. A contract analysis tool can assist in finding out the most efficient method for executing them in similar future contractual documents linked to the redlines and contract negotiation data. Intelligent contract management enables companies to process contracts at incredible speed and accuracy.

Increased visibility: The contract analysis platform guarantees that unstructured and fragmented legal agreements data is captured effectively, transformed into usable information, and finally shared with the relevant department on time.

Recognizing innovative opportunities: Intelligent contract analysis also makes it easier for legal and business teams to detect new prospects for capturing revenue. Without such changes to consider critically on already executed contracts, these latest opportunities will most likely slip from the hands of the involved parties.

Mitigating the risks: Besides identifying opportunities, AI contract analysis also enables organizations to recognize the risks.

Elucidating this, let us consider a scenario.

Suppose contracts with specific kinds of enterprises come across hurdles with some clauses, including these firms can lead to a fair number of risks. Hence, to nullify this, the terms can be modified marginally, and the companies can go ahead with the signing process, thus reducing the time consumed to sign. So, applying contract analysis enables tracking these danger zones very early.

Explore AI-powered XtractEdge Contract Analysis

XtractEdge Contract Analysis uses ML techniques to transform the practice of evaluating and assessing legal contracts.

Illuminating this, here is the automation use-case of the contract analysis process for one of its telecommunication clients. By leveraging XtractEdge’s Text Analytics and Computer Vision-based capabilities, the client, one of the largest telecommunications companies in the world, automated its contract review process resulting in $20 million in savings and a 60% enhancement in employees’ productivity rate.

Intelligent Contract Analysis is the future

Although the focus on legal binding contracts has become more prominent following the pandemic, the contract analysis process has long been under scrutiny. Unfortunately, it was far from being an efficient one. Nevertheless, the fast-emerging technologies of Artificial Intelligence, Natural Language Processing, and computer vision will streamline the labor-intensive contractual negotiation with insufficient information.

Hence, organizations can breathe a sigh of relief by understanding the requirement of getting acquainted with the risk clauses like survival agreements, liability limitations, and force majeure with their contract partners. Therefore, AI-powered contract analysis is undoubtedly the eye-candy for the future as it infuses efficiency into the contract management system by automating the evaluation of unstructured data with the assistance of computer vision and NLP techniques.

Benefits of supply chain management in manufacturing

The onset of the pandemic has changed the course of the business game in the last three years, creating mayhem across all industries. However, turbulence was more prominent in the manufacturing and supply chain industries. This is because industrial productions were either stopped or delayed owing to supply chain shortages and production interruptions that have adversely affected the businesses globally.

In the backdrop of these disruptions, organizations are eyeing for digitization of their supply chain management in the manufacturing sector for a streamlined and efficient distribution network in 2022.

Before heading too deep, let us first have a clear-cut idea about logistics and supply chain management.

What is Supply Chain Management?

Supply Chain Management (SCM) is an organized method of supervising and overseeing the dissemination of goods from the raw materials producers, manufacturers, and finally to the end customers.

It includes planning, strategy designing, implementation, control, and supervising sequential supply chain actions to create a global logistical competitive infrastructure, orchestrating supply and demand, and evaluating the performance globally.

Some key benefits of Supply Chain Management in manufacturing

Higher efficiency: A well-planned strategy related to resource procurement, logistics, and delivery and an efficient supply chain management solution can forecast the demand trends of consumers more precisely, thus enhancing the company’s efficiency.

Customer retaining: An efficient supply chain management system helps organizations respond to consumers’ demand signals on a timely basis. This allows the companies to tap new clients and build loyalty toward the brand.

Improved risk mitigation: Digital supply chain in manufacturing mitigates the risks involved proactively.

Enhanced Relationships: Supply chain management involves cultivating rewarding relationships with the distributors and the suppliers. Hence, bolstering associations with dealers via active communication helps improve productivity and reduce costs.

Decreased inventory and operating expenses: Well-planned and implemented inventory management can help to minimize the costs through Supply solutions Chain Management.

Lowers legal obligations: Efficient supply chain helps companies to avoid damaging publicity and legal obligations that arise from global CSR standards violations.

Minimizes interruptions: Effective communication leads to decreased discrepancies in logistical errors, distribution, and delays in production and shipment.

Continuous cash flow: A flawless supply chain where customers streamline the invoicing processes efficiently, not only decreasing overhead costs but also guaranteeing enhanced cash flow for an organization to operate.

Enhance your supply chain visibility with TradeEdge

TradeEdge, a cloud-based and highly configurable supply chain management platform, empowers producers to get the information instantly and smoothly. It is intended to expand the efficiency and output of manufacturers. It also helps them gain maximum channel visibility and fast-track the supply chain digitization.

In one of the use cases, TradeEdge increased employee productivity by 70% via real-time visibility of consumer sales for a reputed food and beverage company. It also helped a leading consumer goods company, one of the global clients, to improve its case fill rates by 10% and reduce the non-productive inventory by 3%. Additionally, it assisted a premier international brewer to decrease in stock-outs level by 20%.

Future of Supply Chain Management in manufacturing

Going ahead, it is very likely that organizations will be applying the right tools for better functioning of the supply chain management manufacturing. AI and ML-driven solutions will control the operations of all the future supply chain processes, especially in manufacturing. By implementing these purpose-built automated supply chain solutions, suppliers and manufacturers can gain a more comprehensive understanding of their inventories and resolve issues before they arise.

Additionally, deep learning solutions of this kind will be applied to evaluate the ecological impact. Hence, with these kinds of insights, the companies can respond and adapt to the rapidly changing concerns and avoid mass disruption in the future.

Online sales reshaping demand-driven supply chain network

With the exponential growth in e-commerce due to the global pandemic, consumers are embracing mobile operations and e-commerce channels to interact, transact & shop, and make purchasing decisions. Subsequently, communication between the organizations and consumers has become faster and more efficient.

Sensing this, sales channels have begun to evolve with streamlined strategies and efforts to tap the current emerging market potential. However, to capture the imagination of these digital consumers, the sales channels require a sleeked Demand Driven Supply Chain Network (DDSCN) strategy.

What is a demand-driven supply chain network?

Are you looking to gain clear visibility into consumer needs and behavior? Do you want to gain accurate demand forecasting?

Demand-driven supply chain network can help. It’s a system of supply-chain management that entails the creation of supply chains for reacting timely to demand signals of the consumers. It spans the entire supply chain from manufacturing, warehousing and logistics, to promotions, distribution, retail and consumer touchpoints, including digital and social media.

A demand-driven supply network (DDSN) ensures that all supply chain activities are based on actual consumer demand, ensuring that the consumer finds the right product in the right place at the right time.

The need for adopting demand-driven supply chain network

The earlier supply chains were supplier-centric that followed a linear, sequential and a static approach. The customers’ preferences are becoming more dynamic, multidirectional and altering constantly with technological advancements leading to dynamic transformations in the behavior of the consumers.

Hence, it is important that the supply chain managers make quick decisions to gain an edge over the competition Moreover global distribution planning teams must shift their concentration to individual customer requirements. For this to happen, a demand-driven supply chain is mandatory as this approach has more flexibility when compared to the traditional supply chain, which is supplier driven. Companies can adopt this strategy to accurately sense and respond to actual consumer purchases and behavior, resulting in lower inventory management costs.

Let us consider one demand-driven supply chain example below:

By implementing TradeEdge, an intelligent supply chain management platform, a leading company improved its forecast precision by 20% by using real-time sales inputs (vs. shipment) covering approximately 70% of global sales. It also enhanced one of its clients’ efficiencies by 100% after eliminating the out-of-stock and phantom inventory level.

TradeEdge, with its cloud-based solution, empowers brands to gain full visibility, develop retail execution and tap new markets quicky.

Benefits of successfully implementing a demand-driven supply network

Advantages of engaging with digital consumers via DDSN

A demand-driven supply network contributes helps companies:

To productively employ DDSN plans, consumer product firms must consider the following factors:

Future scope of demand-driven supply chain management

A demand-driven supply network (DDSN) strategy with the right implementation approach and technologies can help overcome supply chain challenges, from fragmented physical supply chains to limited visibility. DDSNs leverage collaboration and workflow tools to facilitate joint planning and execution with upstream suppliers and downstream customers, which enables organizations to receive timely and accurate data from distributors to deliver the market insights required for decision-making.

By applying these solutions to a demand driven supply chain strategy, product-based firms can immediately adjust to the ever-changing, real-time demand signals of the customers. These integrated supply chain networks can react to the signals with superior swiftness compared to the traditional approach.

How does supply chain collaboration improve sales in emerging markets?

With an intention to expand their market reach, organizations are exploring promising growth prospects in low penetration emerging markets. But one of the most significant barriers in these economies is the ‘complicated and diversified’ nature of the supply chain distribution setup. In such challenging situations, a company can only achieve the desired efficiencies if it gets timely insights into consumers’ ever-evolving demand preferences in a specific region and time.

Nevertheless, these preferences can be handled successfully, provided the supply chain distributors receive the demand signals on time. This is where the role of a collaborative supply chain distributor system comes into play.

What is Supply Chain Collaboration?

Supply chain collaboration is coordinating with various internal departments and affiliates to maintain an optimized supply chain flow to meet demand and ensure timely delivery. It is the practice of creating real-time collective visibility with the supply chain associates to enable the detection and resolution of problems. The supplier collaboration system includes the detailed scope of supply chain utilities, involving purchase order processes, estimating, capacity planning, and quality management.

Earlier, the supply chains were vertically oriented, with a single organization managing the processes of manufacturing, raw materials, distribution and sales, and marketing. It is not the scenario currently. Most supply chains are decentralized with multi-firms involved, all playing distinct roles in the cycle. With no single company having comprehensive control of all the involved processes, supply chain collaboration is the only way out for unification.

However, the idea of collaboration in its nascent stage with most of them still comprises a mix of organizations. Some firms collaborate, while others follow the traditional supplier/buyer norm.

Challenges of Supply Chain Collaboration

How to enhance Supply Chain Collaboration?

The collaboration can be improved in emerging markets by:

 When leveraging supply chain collaboration, the following features are vital for enabling a seamless process flow.

Real-time data sharing: All the parties involved should have access to the latest data for planning, implementation and exception management decisions.

End-to-end visibility: Visibility of the whole supply chain is central for understanding the happenings at any given time regarding its current inventory status, concerns the suppliers are encountering, and any possible discrepancies in capacity or forecasting. Visibility facilitates a company’s capability to recognize potential problems and respond accordingly.

Configurable workflows: Aligning compatible systems and workflows enables supply chain authorities to synchronize with partners more efficiently.

Advanced AI: AI and ML in supply chain collaboration solutions go beyond data analytics. AI enables faster identification of security threats that provide recommendations on what to do about them, allowing operators to make improved, quicker decisions in harmonization with supply chain partners.

In-context messaging: In-context messaging supports collaboration by assisting the users in connecting seamlessly in real-time without switching between systems.

Benefits of Supply Chain Collaboration:

Supply Chain Collaboration – The way ahead

An integrated network allows businesses to ascertain and connect to their trading partners through a single portal, then collaborate with them using common, near-real-time data and streamlined workflows.

Without a unified business network, the global supply chains will be handicapped with isolated, siloed efforts to connect an assortment of producers, suppliers, distributors, and service providers. By exploiting the power of connected networks, companies will be able to take clues from the past, better forecast the future, and drive the desired business consequences together with a global community of associates.

The implementation of TradeEdge Business Network platform provides many benefits, including “automated and efficient inventory tracking, better control over product pricing, promotion optimization, better visibility to customer needs and demands, a consistent view into product and category performance, and access to harmonized data.” 1

Document AI solutions for insurance firms

Insurance companies face challenges while handling vast volumes of paper documents and form-based processes. Thus, due to this overspill of regular back-office operations, heavy dependence on human labor, and antiquated techniques and legacy systems, the insurance industry has turned its engine of functional processes towards the journey of an end-to-end connected digital transformation through document AI solutions.

Going beyond the traditional approach

The insurance carriers have shifted their approach by giving away the traditional business models and applying new paradigms like usage-based premiums, connected Insurance, and intelligent underwriting. Insurance customers are opting for on-demand and personalized experiences. To meet the ever-changing demands, insurance firms are implementing a wide array of technologies such as automation, blockchain, Artificial Intelligence (AI), Big Data and Analytics, Cloud, Optical character recognition (OCR), and the Internet of Things (IoT).

One such efficient solution is the XtractEdge Platform, which uses Machine Learning and Deep Learning based techniques to manage intricate multi-document data, making it actionable and ready to unlock the latent business value.

Key challenges while handling insurance documents

There are numerous challenges for insurance companies while managing multi-documents, some of which are mentioned below:

Unstructured Data

The majority of Intelligent Document Processing (IDP) companies exclusively concentrate on semi-structured and unstructured data solutions. However, the solutions these firms offer are not entirely capable of processing documents that do not match the templates of their repositories and hence, do not provide the exact result.

The significant issues in this approach are inconsistency and obscurity of natural language within the documents. The range of words used is too broad to be entirely captured as it evolves constantly. Even the natural language processing (NLP) tools cannot process terms they haven’t seen during training. Another concern is the language vagueness which means the things can be mentioned differently. Also, NLP does not work as it cannot handle expressions with similar meanings but different words. This is where the XtractEdge document AI platform can step in and resolve the issues.

Claim settlements and documents processing

Settling claims requires plenty of paper works that delays the process.

With XtractEdge, insurance firms can reduce the amount of time it takes to settle claims by AI document analysis. Document AI solutions can help review and extract content from claims submission documents, including forms and unstructured documents.

Time consumed drafting the policies

New policy request processing requires insurers to collate large amounts of data from multiple documents and sources in addition to complying with proper guidelines, codes, and modifiers. Each application is reviewed for accuracy and accepted or declined. In most instances, there is a need to request additional or missing information as it prolongs the process. Drafting policies can be a complex and laborious task that has a direct impact on the customer experience. Hence, the insurance carrier can automate the policy process.

Retrieving records

Another challenge that surfaces when handling the policyholder records. As these records are hoarded in a common repository, it consumes time to find a specific file, even with an advanced filing system. However, the Document AI solution ensures that the system will extract the most recent data with the key connected to various data sources.

Collecting signatures

The customer must be present physically with paper documents to sign the document. Collecting these signatures consumes a lot of time, so approving the policies and claims gets delayed. Additionally, documents can be misplaced, which means that the document must be printed once again, which is not a cost-effective step for the insurance company. However, AI document processing can maintain the e-signatures that can be affixed at any time. Also, an alert system can notify them at every step of the signature collection process.

Document AI solution – Driving digital transformation in the insurance industry

A Document AI solution like XtractEdge has been successfully implemented in multiple enterprise clients across the globe. For instance, XtractEdge Platform enabled an American health insurer reduce claims processing time by automatically extracting the required information from claim request forms and supporting documents with more than 90% accuracy.

Insurance firms are reorganizing their business attitude and creating a customer-centric value approach, consequently positioning themselves as guardians more than payers. They are looking to innovate and contextualize products to meet changing customer requirements. In this way, they are modifying the insurance distribution tactic to adjust these new offerings to client demands and the digital technology-led disruption in the market.

Importance of a demand-driven approach to supply chain in retail

Embracing a demand-driven approach that navigates the complete supply chain to the customer demand will help gain better sense and react to customers’ purchasing preferences and trending behavior. However, specific challenges include demand and competitive dynamics, patchy supply chains, and complicated logistics driving this consumer-driven approach. These roadblocks apply to all industries, but in retail, these blockades become more prominent.

Reimagining a retail operation from a traditional, supply-driven venture to one that is customer-centric and demand-driven is an extremely tough task but not impossible. The primary build block to this makeover method is the development and application of a demand-driven supply chain that acts in response and foresees the customer demand instead of responding to supplier inventory. However, regardless of various subsidies, many retailers still shy away from incorporating the demand-driven supply chain (sometimes called demand-driven supply network) strategy.

What is a demand-driven supply chain?

A demand-driven supply chain identifies customer demand signals. Then, it transmits those replacement demand signals to retailers and suppliers in real-time so that the demand can be acted upon instantly. However, if this supply chain planning process is not implemented, firms tend to overplay by pacing up their production levels or slowing down. This can lead to varying inventory levels, with the impact being unreasonable and inefficient.

Benefits of demand-driven supply chain in retail

Demand-driven supply chain benefits all the entities involved in the supply chain, from suppliers to retailers, manufacturers, and the end consumer. Here are a few benefits mentioned below:

Effective ways of demand-driven supply chain management in retail

Let us consider some of the ways of demand-driven supply chain management in retail that help businesses to respond to consumer demand signals on time.

  1. Real-time supply chain planning

When dealing with spreadsheets and ERP systems for planning, the organizations may rely on historical data, which lessens the adaptability factor. However, it is not at all a worry as one can follow the holistic method of supply chain management by using the TradeEdge platform as it is a real-time supply chain solution. Moreover, it offers accurate forecasts with its Demand Sensing solution.

  1. Unifying supply chain planning with enterprise planning

The next move is to link the traditionally siloed supply chain planning to operations, sales, and financial planning by orchestrating the short-term operational planning with the broader business planning activities so that one can make real-time updates to inventory estimates and supply. Connecting the planning processes allows business-wide collaboration.

  1. Anticipating the consumer demands

Most companies face the challenge of foreseeing what the customers want, when, and where they want? Again, this is where an efficient platform like the TradeEdge does the job with ease, as it allows end-to-end visibility across the supply chain by receiving the demand signals from the consumers. This enhances lead-time, profitability, and margins.

  1. Leveraging real-time data across supply chain

It is normal to see models become large and difficult to decipher, primarily because spreadsheets are the main planning tools. Thus, by integrating an efficient platform, TradeEdge can manage high volumes of real-time data. It helps in planning more accurately with minimum threats of over or under-stock inventory.

  1. Be accommodating enough to cope with change

Replacing old technology with the latest one has become the norm in the transformational world. Hence, to leverage investments, organizations should redesign the complete process to exploit all the new available prospects by embracing an effective demand-driven supply chain management platform like the TradeEdge.

Future scope of demand-driven supply chain management

Emerging markets have immense potential for consumer product companies. However, there are challenges in the retail execution due to scratchy supply chains and limited visibility. As a result, a well-laid down strategy to design a demand-driven supply network (DDSN) in the wake of evolving solutions with the correct application procedure can assist overcome all these obstacles. Furthermore, an efficient DDSN facilitates clear visibility into consumer demands and behavior, accurate demand forecasting, and efficient product life cycle management. Hence, it is fair to conclude that the retail supply chain will flourish in the future if a demand-driven supply chain solution is applied.

AI in document digitization transforming life insurance

Document and form-based work form the basis for daily business in most processes. This involves long hours of reviewing documents and manual work leading to longer waiting times for customers. Hence, insurance companies need a holistic solution that addresses the current challenges of handling a large set of information coming in physical form.

The insurance carriers need to perfectly paint a long-term roadmap that will enable experts to digitize documents by applying Artificial Intelligence (AI) solutions. AI in document digitization can help insurance companies address these problems and improve process efficiency down the road. First, however, before getting too deep, let us understand the basics of the digitization process.

What is the digitization process?

With the data accumulated and organized in repositories and databases in an unstructured manner, in most cases, across multiple digital forms, it becomes imperative for the organization to convert them into actionable insights by applying AI-powered digitization solutions. Hence, data digitization is transforming company information and documents from analog to digital form, making data readily available and consumable by machines and the human workforce.

With the insurance industry sensing potential of document digitization solutions, the domain has begun to implement these document digitization services. Consequently, the industry, particularly life insurance, has started to take transformational leaps at a commendable pace. But the point is, how?

Managing semi-structured and unstructured documents

Did you know that 80% of documents processed by insurance companies contain mostly unstructured content? Moreover, 85-90% need manual effort to extract, validate and convert the data into usable ones.1

Processing an over-abundance of semi-structured and unstructured documents is a daunting task. It needs a substantial manual force to transform them into actionable data. Hence, it becomes imperative for the insurance players to address this concern with an appropriate strategic approach.

However, the problem does not end here. Unstructured documents are amplifying by 55 to 65% every year.2 Therefore, the challenge of document storage and management is mounting every year for the insurance carriers. This is further making the document classification part more difficult due to handwritten notes and variable formats, hence becoming more susceptible to errors.

Data of customers is the most significant resource of the insurance industry, whether it is customer onboarding, underwriting, quoting, claims, policy servicing, and handling. However, with this plethora of documents in repositories, insurance firms find it difficult to manage large volumes of documents and form-based processes. Adding to it, the semi-structured and unstructured ones need substantial labor-intensive effort. Furthermore, the complicated, distributed, and legacy infrastructure also affects the operational efficiency and customer experience.

Document digitization – Reshaping the life insurance industry

Despite a range of prospects from AI document digitization, the insurance players must avoid pitfalls and take significant steps in their strategic approach toward transformation. The life insurance carriers must prioritize and sequence their digital initiatives and implement a proactive way of working on designing the digital capabilities and quickly unveiling them. Therefore, it is high time that the industry increases its investments massively in the digitization plan and applies two-speed IT to make sufficient progress without getting slowed down by the replacement of legacy systems.

With the companies eyeing growth, it is now apparent that the reliance of the firms on emerging technologies is bound to increase for driving efficiency, productivity, and increasing the associated capabilities within the processes of the organization.

For instance, XtractEdge, Document AI platform by EdgeVerve, effectivelyhelps insurance companies to achieve efficient benefits from multiple insurance documents. Recognized ed as one of the top document AI solutions, XtractEdge promises quick-time delivery, swifter processing of claims, and efficient management of contracts for correct and timely decision making.

How do logistics operations benefit from automation?

Management of the logistics industry is central to the progress of the global commerce industry. However, the sector requires efficient management and a seamless operational structure for the timely deliverables of goods and services. Against this backdrop, global logistics companies across industries are implementing Robotic Process Automation (RPA) solutions to enhance operation, cost-efficiency, and rising cash flows.

Hence, it is evident that operational logistics efficiency plays a crucial role in maintaining an organization’s delivery timelines, profits, and success. This is where the significance of RPA in logistics becomes highly demanding.

RPA is a rule-based solution that is meant to accomplish pre-determined tasks automatically. As it is a software-based platform, it can perform manual, replicable, and high-volume data tasks without the intervention of any human force.

Potential of RPA in logistics

Business endeavors in the logistics industry rely highly on back-office operations such as scheduling a delivery, updating customer details on web portals with current load status and ETA, etc. By implementing RPA, primary business actions can become less tedious and repetitive, enhancing operational logistics efficiency and economies of scale. Hence, the logistics department can be effortlessly customized besides boosting efficacy.

RPA increases logistics and transportation industry operations by:

Ways RPA can enhance logistics efficiency

RPA can reshape the business processes by augmenting the logistics value chain efficacy. To illustrate this, let us consider some of the significant RPA use cases in logistics.

Order management: Order processing is inundated with high volumes of paperwork, which results in errors, delays in processing sales orders, and dissatisfaction among customers. Integration of RPA in logistics helps the company to transfigure the business with extremely coherent operations like order bidding, and scheduling to final approval.

Invoicing and payments: Automated billing operations help reduce operational costs and transform the entire process, from invoice creation and payment to reporting. The application of RPA enhances the cash flow, thus making sure of timely payments.

Shipment tracking: Labor and shipment costs are significantly reduced with the implementation of RPA. RPA helps automate manual tasks, provides accurate shipment details, tracks information, and helps streamline order fulfillment.

Demand and supply planning: Are you dealing with stock-outs and lack of visibility in your supply chain? Implementation of RPA in supply chain cuts down expenses with a cohesive inventory management system by assessing customer needs and providing real-time information on inventory levels.

AssistEdge 19.0 – An enterprise-grade logistics automation solution

AssistEdge, an enterprise-grade automation platform by EdgeVerve, works with global logistics enterprises to transform and upgrade the logistics value chain through supply chain automation.

For instance, a US-based logistics company observed that frequent delays in order delivery status updates led to higher Days-Sales-Outstanding and Days-to-Bill metrics.

With AssistEdge, they were able to streamline the order management process and significantly improve the overall cash flow. Read to know how AssistEdge helped the client reduce annual days sales outstanding by $2.5 million.

Future of logistics automation

Considering the numerous benefits of logistics automation, the global trade market will flourish with many growth avenues in the coming years. These future growth possibilities can be attributed to the increasing demand from the logistics industry to improve warehouse productivity and profitability and reduce operational costs. Besides this, the global logistics automation market is likely to witness massive prosperity due to other variables involving increased economy, consumer purchasing trends, and a significant rise in disposable incomes. However, although RPA in the logistics and transportation sector is boosting efficiencies and collaborations, it still requires some time before the advantages kick in fully.