Importance of Near Real-Time Data for Better Demand Planning and Visibility

Companies serving local and international markets often face challenges while working with a complex network of global demand-supply chains. Their challenges vary from inventory management inefficiencies to delayed responses to market indications.

Poor demand planning can be cited as one of the key reasons here. And demand planning relies heavily on data. Let’s explore the complex relationship in depth.

Importance of Data for End-to-end Supply Chain Visibility

Supply chain demand planning is crucial to help predict the demand for products in near real-time to ensure they are delivered when the customers need them.

Hence, the demand planning process balances supply/inventory and market demand for products and services. Sadly, the entire process will remain unrealized in the absence of data, giving birth to other subsidiary challenges.

Let’s take a look at how a global sports merchandiser increased their demand visibility by 60% with near real-time data harmonized from myriad sources.

The client, a global multibillion-dollar corporation, conducted its sales through a network of multiple intermediaries such as wholesalers, distributors, single-brand and multi-brand retail outlets. They were thus looking for a partner to enable their POS data acquisition. They faced many process and business challenges elucidated below:

Process Challenges

The difficulty of data collection: Too many human touchpoints in recording and data extraction for demand planning complicated the entire process and increased errors.

Non-standardized data capture: Each retailer / distributor / partner followed their own point-of-sale (POS) system. This obstructed the seamless flow of data to the parent organization. Most data captured was deemed asynchronous and useless.

Delays in data flow: Onboarding a partner often took 12-18 months. Further, data remained unavailable for weeks.

Business Challenges

Inefficiencies in inventory management: Since accurate and timely data was not available, sales and supply chain leaders failed to optimally manage the existing inventory of finished goods across markets.

Inability to perform demand planning: Without data, clear real-time visibility into sales performance across regions, products, and other dimensions was unachievable. Hence, planners worked on demand planning using sell-in data (no. of products sold to retailers) instead of the more precise sell-through data (no. of products sold to customers).

Low trust in data: Most data received from different elements in the demand-supply chain network was not streamlined and consolidated. Hence, analytics derived from such data during demand planning were considered untrustworthy or unreliable.

Sub-optimal response to market dynamics: Slow onboarding of partners (which took 12-18 months), delays in data availability (as much as weeks), and poor data quality meant that companies failed to respond to market indications appropriately or on time.

How can Global Data Acquisition and Harmonization Help in Better Demand Planning & Visibility?

Demand planning software solutions like TradeEdge can easily foresee global data acquisition and harmonization for better demand planning.

The systematic, process-oriented approach of TradeEdge is easily adaptable to the client’s ecosystem while working closely with the leadership team, studying their data acquisition process; interacting with multiple stakeholders like client vendors, client partners, business team, technical teams, and market leads; and involved in various stages of the product lifecycle.

Benefits of Data Acquisition and Harmonization:

With access to real-time harmonized data, the global sports merchandiser enabled end-to-end supply chain visibility across the ecosystem. Below are a few benefits:

The New Way Forward

Demand planning has become an integral part of businesses following massive disruptions and challenges during the pandemic. That was a wake-up call for all and highlighted the importance of global data acquisition and harmonization for better demand planning and visibility across the value chain.

Key to Successful Supply Chain Collaboration & End-to-End Visibility

A successful Supply Chain depends upon how each element works together, be it a supplier, shipper, or logistics provider. If one element fails to perform well, the entire process will fall apart. It doesn’t take much time for customers to notice that and spread the word, costing companies billions of dollars in poor PR.

Experts believe data and trust are critical factors keeping the whole fragile relationship intact. Also, having diverse suppliers who understand their local situation and provide the correct information on transportation networks can save businesses from disruptions.

Previously, Supply Chain Professionals followed a lean approach, focusing solely on the cost element. Immediately after the grueling reality of the pandemic hit hard, the vulnerabilities of the legacy supply chain models were exposed to the world. It demonstrated how unstable the demand-supply chain could be in the face of adverse and uncontrollable situations.

The Cost of Constrained Global Supply Chain Collaboration

The age-old practice of sharing just what is needed, when it is needed, has created a significant rift between customers and suppliers. As per experts, poor collaboration between customers, suppliers, vendors, and others in the demand-supply chain translates into decreased service levels to the customers and a massive loss of credibility. And the loss of credibility results in lesser revenues and lost opportunities.

A Fundamental Shift in Relationships

Companies need to reassess their relationship with different elements in the supply chain and focus more on multi-shoring to de-risk supply chains, not necessarily multi-sourcing. This will safeguard businesses from facing any disruptions in their procurement-supply channels if significant disruption hits specific geographies.

The importance of supply chain collaboration has been recognized only recently when businesses faced major roadblocks during the global crisis. To build a resilient supply chain network, longer-term relationships with every element are crucial alongside collaborative mechanisms to leverage joint resources and expertise. That’s the only way out to survive in the emerging new reality.

Increasing Supply Chain Visibility through Collaboration

So long, relationships between end-customers and tier-1 suppliers have been shallow and primarily transactional, pervading the visibility gap in the network. Supply chains for organizations don’t end with tier-1 suppliers; the suppliers’ suppliers are just as crucial. The absence of a proper understanding of what is happening at every stage can leave planners and organizations vulnerable to disruptions.

Gain a deeper understanding of upstream elements

To end the vulnerability of key players, an increased supply chain visibility through collaboration with both upstream and downstream suppliers is crucial. Hence, deepening the direct data links upstream in supply chains and enabling information-sharing capabilities tier one, two, three suppliers, or beyond can print the apt picture of what is happening in their supply chains.

Trust is the key to successful supply chain collaboration

Today, environment-conscious customers like to have complete knowledge and control over the products they purchase; hence, information regarding traceability back to the origin is essential for them, especially from a sustainability perspective.

Sadly, most retailers are reticent about sharing inventory and sell-specific data with others for fear of losing negotiating power capabilities and data to cybercriminals. Companies should either move closer to suppliers and build a relationship of trust or incentivize partners to collaborate – whichever works for your business.

Supporting suppliers lead to success

Besides ensuring supply chain collaboration through relationship-building, direct financial or IT support can help with the successful functioning of modern supply chains.

There can be technological disparity existing among two or three-tier suppliers. Providing them with the necessary technology and solution will improve your supply chain network visibility.


Finding a win-win solution based on mutual trust, understanding, and collaboration within the supply chain can reduce vulnerabilities for companies to the shortages that have plagued so many industries during the pandemic.

But, waking up to the new reality will not happen overnight. Companies should start their homework from a granular level by bridging the gaps with different tier suppliers and fostering a mutually benefiting environment of shared technology, knowledge, data, and trust. And throwing Automation into the mix can address problematic scenarios and usher in new opportunities to manage more extensive supply chain networks.

Leveraging Document AI for Faster and Insightful Decision-Making

Document AI uses new-age technology like Artificial Intelligence, Machine Learning, and Natural Language Processing to train software to imitate human actions when reviewing documents. Unlike humans, AI-enabled solutions like XtractEdge are incapable of omitting granular data; hence, data extracted are relevant, purposeful, and without any human error or bias.

What is Document AI – From Customers’ Perspective

Customers prefer Intelligent Document Processing with minimal to negligible human touch in the automation process. The former desire meaningful real-time data to aid in faster and more insightful decision-making, available as and when needed.

Hence, their expectations revolve around high extraction accuracy, with models that facilitate document discovery from unstructured/semi-structured documents in multiple templates, file, and image formats.

Also, mere extraction of data doesn’t serve the purpose of customers. When customers consider Document AI , they expect the solution to seamlessly fit into their business landscape. That’s how Document AI can foster significant improvements in day-to-day operations.

Expected Features of Document AI

The main focus of how customers perceive Document AI should be when deploying a solution. Here is a list of expected features derived from customers:

Obviously, some of the above features will be revealed gradually post successful initial deployment of the Intelligent Document Processing solution. But, it is a given fact that customers’ expectations with Document AI have evolved with time. They look at Document AI not as a mere content extraction platform; instead, they consider it a one-stop solution capable of handling touchless end-to-end flow that hosts the features mentioned above and many others.

Challenges in Document Extraction

Enterprises handle a vast pool of data daily. And 90% of data is unstructured and locked in documents of various formats, which makes extracting the desired information cumbersome at any given time. Adding to this is the inability of legacy systems of data extraction to provide output data in a consumable format by downstream applications.

When discussing customer expectations, we hardly refer to solutions that breaks/dissects various documents and extracts content only. Instead, we indicate a platform capable of identifying and sharing ‘meaningful’ content in context for their operations.

Following are a few challenges faced by legacy approaches to data extraction:

Document complexity: As per experts, nearly 70% of organizations still depend on old, paper-based documents. Scanning each document lost in different folders and various email trails is both time and labor-intensive, also prone to mindless human errors.

Domain specificity: Every organization uses specific document types. For instance, a few companies prefer Google Doc Suite, while others use Microsoft Suite. Then, there are types and formats to maintain waybills, loan applications, tax forms, invoices, and so on. Hence, any document extraction tool should be able to identify such domain-specific context.

Bulk data: Enterprises deal with bulk data, which is not humanly possible to manage, maintain, and extract insights. Also, not all solutions can handle such volumes of data, which impedes the speed of data extraction, eventually nullifying the purpose of utilizing technology.

Disjointed approach: Many solutions are disjointed; hence, major enterprise document problems remain unsolved.


Based on customer expectations, large-scale Document AI implementation handled various business needs across the industry, including insurance, pharma, financial, or utilities.

The sole benefit of Document AI is delivering business benefits that align with customers’ organizational objectives. Of course, the points mentioned above are just the tip of the iceberg. There are scopes of improvements when they bring automation to document processing in their landscape. And, as customers gradually delve deeper into the discovery phase, they realize what all is capable of automation using Document AI extraction and processing platform like XtractEdge.

Demand Sensing: The First Step to Creating a Resilient and Future-Ready Demand-Supply Chain

Consumer Goods companies (CGs) are exploring different ways to improve demand sensing because legacy demand sensing techniques have proved ineffective in today’s increasing supply chain complexity and evolving consumer behavior.

Demand-supply chain is constantly subjected to rapid change by various external forces, like global emergencies, weather trends, economic trends, and so on. These key drivers of change shape and reshape demand based on current market needs, just as we saw during the pandemic lockdown.

What is Demand Sensing, and Why do CGs Need a Robust Solution?

Demand sensing captures the short-term trends using predictive analytics to help CGs predict and deliver exactly what customers will want; when they want.

During the COVID pandemic, Consumer Goods companies were under pressure to balance supply with demand since the demand for their products increased manifold, primarily due to consumer hoarding behavior, as per 48% of CGs. Nearly 51% of Consumer Good companies listed business disruptions affecting operations during the lockdown, among other reasons.

The conditions in the supply chain didn’t improve post-pandemic. Instead, companies witnessed a 10xincrease in the volume of new products hitting the market compared to what had been the situation earlier.

These factors accentuated the need for a robust demand sensing solution to boost profits and stay competitive in the volatile market.

Challenges of Legacy Systems of Forecasting Demand

The primary difference lies in the type of data used for understanding future demands. Demand forecasting relies heavily on historical data, such as past sales, to predict future demand, which is sufficient when planning for long-term sales; however, not ideal for short-term planning.

On the other hand, demand sensing uses new-age technology to forecast near-future demands. In a customer-centric market, the near future can either refer to the future in hours or days based on how dynamic the demand-supply chain is to cater to ‘what the customers want’ and ‘when they want.’

Traditional demand planning and forecasting is inherently handicapped to aptly forecast short-term goals, especially in the absence of the correct data availability at the right time. Unavailability of data is the biggest challenge in forecasting demand-supply aptly.

Here are four key challenges when it comes to demand planning data needs:

The Future of Demand Sensing: Matching Expectations with Technology

According to experts, Automation and other technology solutions like Advanced Analytics, Artificial Intelligence, or Sensors are critical to offering the best-in-class supply chain, guaranteeing a seamless digital transformation journey for CGs and others.

Hence, companies that optimally integrate people, processes, and technology will outperform those that do not.

According to an analyst report, an ideal demand sensing technology needs a few key ingredients to match the demands of the dynamic market, including Demand Signal, Demand Visibility, Demand Frequency, Supply Reliability, Supply Agility, Right Strategy, and Tech Investments. All of these sum up to improve the end-to-end capability of CGs.

But the most foundational competency of a demand sensing solution should be to offer granular visibility into network-wide sales and inventory that help CG companies know what is selling, where and at what speed.

This information optimizes functions across the entire spectrum of demand planning, right from product development, category management, trade promotion planning, etc. A good demand sensing solution aims to help companies make the right decisions and be more responsive.

Hence, demand sensing solution should provide:

Benefits of Demand Sensing

So long CG companies have relied on syndicated data providers. Courtesy of disruptive forces like the pandemic, more and more retailers collaborate directly with the suppliers to capture near real-time demand signals. They are also engaging with customers or end-users to access those demand signals and bring back relevant insights.

These insights, when fed into intelligent solution, can garner the following benefits:

Demand Sensing: The Dawn of a New Resilient Supply Chain

Shorter forecasting lag periods are the prerequisite of an agile supply chain response. The realization dawned after CGs experienced a hard time trying to meet supply with changing demands, addressing inventory shortages or unused inventories during the pandemic.

The COVID pandemic was a catalyst for change. What could have been a change some years later happened now. And, demand sensing enabled by Automation and AI technology evolved as the perfect antidote building a more resilient supply chain for all.

Document AI: Unlocking Real-Time Intelligent Information from Unstructured Documents for Improved Decision-Making

Data is the lifeblood for enterprises today, especially when it comes to making strategic decisions in a complex business environment. But, the majority of data is unstructured and present in various formats and types, from documents and emails to images, making it difficult to categorize, segregate, and analyze.

Businesses have realized that having data just for its sake is no longer sufficient. Extracting insights and making them accessible for decision-making can help enterprises unlock hidden business value at scale. Hence, AI-based data extracting solutions like Document AI is becoming the need of the hour.

What is Document AI?

Document AI or Document Intelligence uses AI technology to collate unstructured data from various documents, structure them into readily consumable information, and generate data for analysis as and when needed.

Unlike humans, AI Document Processing can easily extract granular information or capture subtle nuances in the sources often overlooked by humans. Since the entire process of data extraction and processing completes automatically in nanoseconds, it is possible to gain real-time insight into existing processes without disrupting the workflow.

Understanding the Importance of Document AI

Enterprises deal with many company documents, PDFs, printouts, emails, messages, invoices, and others daily. Each of these documents is a powerhouse of data, carrying valuable information. But, extracting insights from them is both time and labor-intensive.

There are four key challenges enterprises face when extracting and processing data from documents:

Huge volume of documents to be processed: Document volume is posing a significant challenge for businesses. Especially when unprocessed documents hide valuable insights, the pressure to extract them from millions of documents builds upon human employees when done manually. Hence, most information extracted is often contaminated by biased judgments or incomplete because granular data remain hidden.

Variety of data formats: Adding to the volume is the variety of formats used in sharing company information. When volume can be easily handled with technology, extracting data from multiple formats becomes challenging.

The veracity of data in documents: As mentioned earlier, unstructured documents are mostly error-prone. Data entered wrongfully, valuable data skipped during manual entries, and even unreadable sections showing up in the extracted information can render the insights undependable. Such errors in certain documents like contracts can result in legal or compliance issues. Manual verifying every document is not feasible in terms of time, hindering on-time data availability.

The velocity at which documents need processing: Companies need to access real-time data during decision-making instantly. However, mapping and processing large volumes of documents in a short span is challenging for the human resources involved. Businesses need faster time to value, but the processing speed of existing disjointed solutions doesn’t solve this challenge.

How can Document AI Help Overcome the Challenges of Volume, Variety, Veracity, and Velocity?

Businesses need document processing automation to successfully unlock business value from enterprise documents when needed, irrespective of document complexity or domain specificity. Only Document AI-powered by NLP, Computer Vision, Deep Learning, and Machine Learning can easily overcome the challenges mentioned above and convert unstructured data into a structured format using content classification, entity extraction, and advanced searching.

Here are a few use cases of Document AI:

Document volumes in the financial services industry

KYC is an integral part of every banking service, which involves manually extracting data from documents in multiple formats and originating from multiple sources. This process is highly effort-intensive when we consider millions of documents processed at about the same time. Hence, the cycle time for onboarding new account owners increases. With its supervised and unsupervised learning capabilities, Document AI can help banks streamline the document and effort-intensive KYC process. Further, this technology helps banks to realize faster turnaround times and increased compliance.

Document variety for healthcare payers

Healthcare insurers receive thousands of claims requests every day. Requests are usually in different documents, images, or PDFs. And images are usually of poor and inconsistent quality. Processing these claim requests involve too much labor. It is both time and resource-intensive and often leaves behind a trail of human error. Document AI digitizes claim request documents by extracting required data and processing images for quality enhancement using vision capabilities.

Document veracity for auditing firms

Similarly, accounting firms handle bulk financial documents to assess the financial soundness of companies. It is a part of due diligence. However, the documents are processed manually, and extracted content is circled, validated against sources of truth, and audited/unaudited documents. Document AI uses computer vision, NLP, and intent-based entity extraction capabilities to automate the extraction process and highlight discrepancies between submitted documents and sources of truth. Automation accelerates activities such as generating comfort letters for companies under scrutiny.

Velocity in document processing for procurement teams

Procurement contract management is resource-intensive, especially for large organizations. With thousands of new contracts added every month, a historic load of over thousands of contracts takes time to process. Unfortunately, globally executed contracts like LIBOR come in multiple types and formats and include critical terms and clauses related to local regulations and third-party suppliers. Extracting such critical clauses manually and comparing the language with standard templates is an inefficient approach translating into more risks. Document AI’s unsupervised clustering extract terms and clauses and determine the potential risks of their contracts and suppliers quickly and accurately. Hence, contract cycle time is highly reduced, and negotiation of minimal operational risk is improved.

The Future of Document AI

Companies are increasingly looking to unlock hidden value from documents and have intelligent information at their fingertips in near real-time. However, the legacy system of manual document processing can never reflect an accurate picture of the processes, not to mention the errors that remain behind. But, with the help of Document AI, such needs are easily met, irrespective of ever-evolving document types and volumes.

However, adopting new technology should not mean upending your existing systems from the ground up every time. The focus should be more on finding solutions that can seamlessly plug and play into existing enterprise systems.

EdgeVerve’s Document AI solutions XtractEdge Platform and XtractEdge Contract Analysis, help businesses unlock the value hidden in complex documents and streamline decision-making.

Connected Automation: The Next-Level of Automation Connecting Technology, Processes, and Humans Together

Robotic Process Automation or RPA is the harbinger of all the new changes taking place in the business realm, following the immediate need for enterprise-wide digital transformation. As RPA matured, adoption started spreading, but with a more limited scope than we hoped for.

Enterprises today are looking for ways to be more strategic with automation. Technologies are evolving to bring more business use cases under Automation; coupled with the right capabilities and best expertise, the scope for RPA has broadened over the years to Intelligent Automation, often referred to as Connected Automation.

Connected Automation Overview and Its Relevancy in the Current Business Scenario

The concept of Connected Automation was born out of a need for companies to solve specific automation challenges or opportunities emerging in certain parts of a large operation. As a result, businesses are bringing intelligence to automate processes to foster connected services, enabling end-to-end Automation.

In order to meet the urgency for rapid digital transformation, businesses are shifting Intelligent Automation to core company metrics like Sales and Customer Experience. Such a transition in organizations’ approach to accepting Automation enterprise-wide stemmed from the never-ending tech potential of digitally processing work in a more general sense.

Today, RPA has evolved into Intelligent Automation. Comprehensive models for complete end-to-end processes are being developed to provide a treasure map for automating almost any process running in a large enterprise. This enables customers to measure the business value from various business use-cases in real-time.

Unfortunately, most business use-cases are neither logically connected nor make up a significant end-to-end process. Yet, the ability to automate more of the e-e process remains the top priority for customers. Hence, it is no big surprise that many customers are still focusing on deploying Automation to a single department with finite use cases.

In the absence of a clear path to becoming a strategic enabler, the energy behind the program can begin to drop.

Top Barriers to Scaling Intelligent Automation

Below are a few barriers to scaling automation:

Lack of IT readiness: Choosing the right process to automate is a top challenge in scaling automation. And the lack of IT readiness is cited as the number one reason by 37% of organizations surveyed. The legacy approach to identifying new processes for automation is time and labor-intensive and often leaves behind a trail of human errors. Also, granular variations in how individuals handle each process remain invisible to the naked eye. Adding to that is the attitude of managers, leaders, and other team members who consider automation a direct threat to their positions. They steer clear of any new change or cooperate half-heartedly. They lack the skills and expertise needed to understand automation. They are not IT-ready, which disrupts the automation adoption at scale.

Absence of desired skills-sets: There’s a considerable talent gap in enterprises today, hindering a full-scale deployment of enterprise automation. But, to accommodate the same, companies need not shell out the existing human capital. Instead, with the right amount of training, companies can pivot people in ways to develop and leverage existing human capital for Automation.

Biased approach to change: Change is often not seen in a good light. There’s bound to be resistance in employees who mostly fear the loss of jobs to Automation. Also, a tussle exists between 21st-century ways vs. 20th-century methods. Hence, the best way forward is to revolutionize the way organizations are led/managed as part of the transformation.

Absence of structured data for analytics: This stems from the lack of knowledge about Automation and a biased approach to accepting the change in the existing processes. And in the absence of data, meaningful use of data and analytics is not possible. Without data, understanding the nuances and roadblocks existing in old legacy systems is tricky, and human-manned data analytics fail to capture the accurate picture. So, the focus should be on getting leaders to ask the “right questions” vs. using data to support the wrong decisions.

Lack of clear vision: Surprisingly, 17% of organizations surveyed cited the lack of clear vision as one of the barriers to scaling automation, and quite rightfully so. The desire to change doesn’t align with their vision, especially when companies have no accurate data to work on and implement changes. Most are unaware of how processes work in their organizations. Hence, they are left feeling perplexed when faced with the urgency to bring automation to their organization.

Download Whitepaper – Powering Connected Automation for Large Enterprises

Scaling the Automation Program: Importance and Elements

Connected Automation is the foundation for future Intelligent Automation. When smart automation resources are united in an integrated platform that brings them together with a core of robust executional capabilities, the size and complexity of the use-cases grow two to ten times what RPA is capable of alone.

How can enterprises automate more of the end-to-end processes? How can enterprises scale their automation program?

This is where the need for an end-to-end automation strategy that connects People, Processes, and Data together comes into play.

Process: Discovery, Complexity and Exception Paths

In the legacy approach to process discovery, business rules and judgments made along the way determined the precise flow of a piece of work to conclude. To properly route the work when automated, it is critical to know the business rules. Unfortunately, business rules come from Compliance requirements, the design of the systems of record, requirements from other departments, and suppliers & customers, among others.

Even though manual process discovery is still the most prevalent approach, new technology solutions like Task and Process Mining are emerging to address the complexities of manual process discovery.

With the help of process mining capabilities, the complete process map with all its exception paths is laid down with rules that should be used to guide the flow of work. Process discovery tools also provide the data to create Next Best Action Machine Learning (NBAML) models for making dynamic decisions regarding real-time workflows. When connecting task mining to machine learning models, the transaction data moving through the process determines the best route for the work to take.

People: Voice and Human Machine Workflow

A variety of technologies across several related fields are developed to deal with a voice as a formal input into enterprise processes, such as:

Data: Documents, Unstructured and Semi-structured Data

Unstructured or semi-structured documents in volumes enter the enterprise system every day. Creating an omnichannel for input sources regardless of channel is important. But the main challenge faced by IA lies in digitizing the inputs needed for optimal automation.

With technology advancements, automation platforms have OCR tools as a web service, and by using capabilities similar to NLU, the unstructured and semi-structured are reduced to their essence and fed to the automation system. This determines the correct workflow that should follow the initial interaction.

Today’s platforms bring the core of task automation along with machine vision, NLU, and human-bot interfaces.

Powering Connected Automation for Enterprises with AssistEdge 19.0

Intelligent Automation, or as we commonly name it, Connected Automation, is gradually becoming core to enterprises’ business strategies.

Many bottlenecks keep enterprises from adopting automation at scale realizing the full potential of their automation initiatives. AssistEdge 19.0 is a cohesive automation platform that empowers enterprises to deliver a Connected Automation experience. It addresses disconnects by forging deeper connections between Processes, Data, and People.


The automation journey for most enterprises began as a mere tool to robotize basic, manual processes without involving humans. Enterprises now realize the need for platform solutions that can seamlessly combine multiple capabilities and pool their heads together to optimize the benefits of Connected Automation to its full power.

How is Data Unlocking Business Opportunities for CPGs

E-commerce was well-established in many categories, but it was gradual for Consumer-Packaged Goods (CPG) companies until the COVID pandemic. The pandemic gave the customers a more convenient way of meeting their needs. What was just 13% of shoppers purchasing CPGs before the COVID soon jumped to 31% after March 2020.

The CPG market changed overnight; however, the experts believe the change was for good. The pandemic presented a massive scope of opportunities and triggered daunting challenges. As for CPG companies, ensuring a seamless supply chain execution became a significant cause of concern.

Supply Chain Execution Challenges for CPGs

Shifting the stores online meant outreaching more buyers than before. This translated into increased sales and more business for CPGs. Maybe not! Soon, they struggled with meeting the high demand for CPG products. In the absence of real-time data, supply chain execution fell apart with missed demands and shipments, lost sales, and penalties.

CPGs realized that to drive more sales and ensure visibility over the competition, they need a comprehensive data and analytics foundation to serve as a feedback loop. This is where Autonomous Planning & Execution comes into play. Supply chain planning and execution helps CPG companies capture real-time data and analytics to make agile, data-driven decisions. Failure to do so would result in them losing ‘seller authority’ – and the market share passes swiftly over to competitors them losing ‘seller authority’ – and the market share passes swiftly over to competitors.

Data Challenges in Supply Chain Execution

Even though a lack of on-time data is an issue for CPGs, unstructured data presents a real challenge.

Reliable, accurate, and real-time data can address numerous challenges currently faced by CPGs following the rapid digitalization of their legacy stores. With real-time data, CPG companies can forecast demand and ensure their brand visibility in online retail environments. The challenge — data is generated and drawn from multiple sources such as:

These sources contain useful, valuable, and reliable data but in varied formats and stored in different sources. None of these data is deemed helpful for CPGs unless consolidated and harmonized.

What can CPG companies do with e-commerce data?

For more information, read the whitepaper on how data helps unlock the opportunity for CPG companies.

Autonomous Supply Chain Planning & Execution — How does it Help CPGs?

A platform like TradeEdge captures granular, unstructured data from various sources and in varied formats and presents them in a that is convenient to CPG decision-makers.

It uses ML/AI algorithms to provide actionable insights in order to provide a clear picture of how sales, supply chain, marketing, and finance teams are performing, driving more effective e-commerce operations. For instance, the TradeEdge Platform helped one of the leading CPG clients improve its case fill rate by 6% and achieve 4.5% lower inventory, fewer out-of-stock penalties, and up to 1.4% of category revenue.

A supply chain execution platform like the TradeEdge creates a supportive infrastructure for e-commerce clients, dividing the demand-supply execution into four steps:


Creating an infrastructure for a more effective e-commerce set-up is precisely what the CPGs need to handle bulk online orders and manage offline sales. To realize such an ambitious objective, CPGs should ensure seamless execution of their demand-supply chain. That’s not possible in the absence of data.

This is where an Autonomous Supply Chain Planning & Execution Platform like TradeEdge can be a real game-changer.

Process Discovery: The First Step in your Automation Journey

Enterprises leverage RPA to automate rule-based, high-volume repetitive tasks; not well-equipped for non-standardized processes requiring human intervention. Though RPA has numerous benefits, many organizations fail to realize its value.

Businesses today need to identify the right processes to automate, including exceptions and deviations. This is where Process Discovery comes in — helping organizations to choose the right processes to automate and enabling RPA scalability.

In order to implement automation enterprise-wide, owners need to have a microscopic view of how the existing processes work, the nuances involving human and system interaction, and the outcomes post-successful deployment of automation in bite-sizes.

Manual process discovery is divided into Process Mapping and Process Mining. The traditional, human approach to Process Mapping and Mining fails to accurately capture how business processes work and interact.

Manual Process Mapping

Manually mapping the business processes is subjected to biased judgments, which often overlook subtle nuances existing in the processes. Also, capturing granular data remains mainly invisible to human eyes. Moreover, once mapped, processes remain in the shape of static documents. However, business processes tend to change over time. Unfortunately, the documents carrying insights post mapping are never updated to keep up with the change. So, these documents rarely carry real-time insights; hence, any measures taken on these data are often flawed and short-sighted.

Other roadblocks include:

Further, manual process mapping documents do not include data about process performance, process scalability, and business exception/failure statistics.

If the automation implementation strategy is based on such data, the outcome will remain largely ineffective, reflecting poorly on ROI.

Process Mining

Process mining is a better version of process mapping, which uses various automated tools to record system event logs and apply sophisticated algorithms to automatically identify and map business processes. This is by far the most reliable method of identifying proper business process candidates for automation than traditional process mapping catered to by stakeholders.

However, process mining has its share of challenges. For example, process mining tools can capture events only, not user and activity tasks. Hence, various nuances remain primarily under the wrap due to increasing human and system interactions. Also, process users lack the required expertise to correctly interpret data captured by the tools or identify the changes in the business processes. Here, experts have to step in to interpret the generated information, which is often colored by biased opinions of the former.

What is Process Discovery?

“It’s an AI-enabled method that helps organizations identify process variations and create detailed process maps to maximize the value of RPA.”

Benefits of Process Discovery

There are many benefits of Automated Process Discovery, a few of which are underlined below:

Improved quality and performance: When Process Discovery captures and interprets empirical data, the outcome presents a clear picture of tasks that need automation attention instead of what employees or consultants think to be done. Hence, the outcome is more accurate, reliable, and captures real-time, up-to-date process workflows minus historical, biased, or guesswork data.

Enhanced visibility: With automated process discovery, the enterprise’s visibility of specific process steps, ownership, and overall processes is guaranteed. The process discovery map becomes the blueprint for identifying new pathways and future automation opportunities.

Minimized risks: When information about the business process is shared with fewer users, the risk of data capture, loss, or corruption is minimized. Also, process maps created after process discovery will help owners understand if the suggested changes will add value to the organization or not.

Increased cost efficiency: Process discovery is a measurable way of implementing changes in the existing business processes. So, more tasks are brought under automation; hence, less need for extra human resources. In the absence of unnecessary repetitions, duplications, errors, and misjudgments, the business processes become more cost-efficient.

Improved scalability: Obviously, expanding RPA using manual data analysis is time-intensive. But, using automated process discovery, data captured is real-time, granular, and can help organizations make intelligent decisions on which processes to automate. This aims to unlock growth opportunities and enhance overall performances using minimal resources and time.

Maximized ROI: When time, cost, and resources are better optimized following business process automation enterprise-wide, the ROI from the automation program will precede the expected ROI figure.

Scaling the Benefits of Process Discovery with AssistEdge Discover

AssistEdge Discover is designed to streamline and automate the process discovery and mapping of business processes. This non-intrusive software leverages user keystrokes and sophisticated neural network algorithms for creating insightful process maps; hence, a powerful blueprint for scaling effective and continuous change management is realized.

Harnessing the Benefits of Supply Chain Execution Analytics to Increase Channel Visibility and Accelerate Profitable Growth

The global supply chain is flawed, ridden by many discrepancies, obstacles, and roadblocks. These hurdles translate into unimaginable challenges, which impede CGs’ success in the short and long term. Hence, leveraging trade execution analytics is the game-changer, enabling the global supply chain to become more streamlined and hurdle-free.

Exploring the Bottlenecks Stunting Supply Chain Execution

Significant execution challenges in the supply chain can impede a company’s growth. Before such hidden or unforeseen challenges derail the company’s growth entirely, it is better to identify them and act accordingly.

Below are a few bottlenecks common to CGs:

Demand fluctuations: The consumer market is never stable and is constantly subjected to changes influenced by multiple factors. As a result, short-term demand fluctuation can worsen, compounded by a lack of real-time demand signals. This causes high demand variability, stockpiling, and wastage.

Sales reps not productive: Static route planning pre-configured doesn’t allow deviations, often ignoring the shortest possible routes from Point A to Point B. Hence, more time spent on one customer results in two customers lost. This impeded the productivity of field sales representatives.

Too many manual touchpoints: Legacy systems have excessive manual touchpoints in the ordering process, resulting in increased order cycle time and costs.

Fill rates remain low: Lack of inventory visibility and delayed shipments and/or deliveries lead to lower fill rates.

Low market coverage: In brick and mortar, distance and store size leave large numbers of stores unserved or under-served.

With the help of supply chain management and execution solution, most of these roadblocks are quickly addressed.

Smart and tech-savvy CGs have recognized the importance of new-age technology to streamline the complex supply chain. As a result, they are reversing the tide in their favor. Let’s find out!

The Essential Ingredient for Supply Chain Success

Did you know that CGs adopting AI and advanced analytics experience more than 10% revenue growth?

Increased visibility of the supply chain can help address the bottlenecks mentioned above. However, data collected is usually present in different formats or scattered across multiple documents, which impedes data analysis. This is where supply chain execution and analytics can make a significant difference, driving efficient supply chain operations and positioning them for growth.

Benefits of Supply Chain Execution Analytics

Suggested Ordering

With the help of forecast data, channel inventory visibility, new product introductions, and promotional calendars, CGs are in a position to generate better-suggested orders for their customers. Suggested orders are based on product category, market basket analysis (to understand the category of SKUs purchased), Analysis of retailers’ buying patterns, and retailer warehouse capacity allotted to CG, among others.

CGs can experience a surge in sales opportunities with better-suggested orders. As a result, they can ensure retailers’ stock comprises the right mix of CGs products, strengthening relationships between trading partners, thus increasing current and future sales. Suggested ordering can provide CGs with various benefits — from leveraging channel partners’ working capital to getting more actionable product recommendations based on workflow steps.

Targeted Replenishment

This increases the visibility of retailers’ stock levels; hence, when replenishment occurs, the CGs are automatically notified of the same. Further, targeted replenishment simplifies the ordering process, eliminates costly emergency deliveries, and helps CGs to better estimate projected needs, thus maintaining optimal product quantities for each retailer.

On-Time, In-Full (OTIF)

This is considered one of the critical key performance indicators of CGs, arresting forecasting errors, manufacturing delays, and insufficient logistics and supply chain visibility. However, execution analytics can help CGs achieve better OTIF fulfillment by setting the right customer expectations during order-taking and providing real-time updates on shipment status.

Route Planning

Route planning aptly identifies which stores field reps should visit and in what sequence should replace conventional, calendar-based, static route planning. Dynamic route planning provides near real-time demand signals, ensuring sales reps are visiting the right stores at the right time, thus maximizing strike rate and visit productivity.

TradeEdge Platform: Driving CGs from Insights to Action

Partnering with the right solution provider can reduce more than half of the burden. As a result, CGs are increasingly searching for the best supply chain execution solution to help them overcome the challenges present in the current supply chain environment.

TradeEdge Execution Analytics empowers CGs with the right insights and increased visibility into the supply chain and converts those insights into actions, and implements them as and when needed.

Here are the following benefits of the TradeEdge Platform:

Hence, a comprehensive and user-friendly supply chain platform like TradeEdge helps provide real-time insights across the demand value chain. It also allows CGs to take actions on time, implement them, and accelerate profitable growth. Such platforms take CGs one step closer to operating in a fully autonomous supply chain, improving channel visibility and retail execution, and enabling them to reach new markets faster.

Powering Enterprises’ M&A Strategy with AI-Powered Smart Contract Analysis

Mergers and acquisitions (M&A) are emerging as a strategic tool for expanding business operations. And one of the core components of M&As is due diligence. In the event of contract analysis, the due diligence process plays a significant role in ensuring both parties’ safety against any unmentioned terms and conditions.

Evaluating the Importance of Contract Analysis

Due diligence is an integral part of contract analysis that involves investigating, auditing, and reviewing facts and details of a matter under consideration. And in the case of Mergers and Acquisitions, due diligence is performed on contracts/agreements signed between two or more parties. It involves examining the proposed parties’ financial and other track records before they enter into an agreement.

There’s just a significant difference between due diligence and contract analysis. The former is carried out at the beginning of signing a contract. The latter is an ongoing practice carried out throughout the contract lifecycle, ensuring all terms and conditions are met on time.

Contract risk analysis is crucial to every business agreement and is carried out by corporate lawyers. They measure the risks/ opportunities in these large volumes of customer and supplier contracts in a short period, sometimes in a few weeks. Efficient processing of these contracts depends on accurately understanding the terms and clauses.

The process is quite challenging, even for experienced lawyers, primarily when historical customer contracts are assessed manually to generate a representative assessment of potential risks/opportunities.

Challenges Involved with Manual Contract Analysis

Contracts existing in multiple silos: Historical customer contracts, reside in multiple silos. Therefore, bulk contract papers’ manual analysis requires massive human effort to assess the exposure to risks.

Involves huge paperwork: In the absence of legal contract analysis software, scanning each paper document for assessing their risk exposures is a humongous task. Moreover, historical paper documents are rarely structured; hence, analyzing each term and condition and assessing potential risks or opportunities hidden is a daunting task.

Contractual data is skewed or unreadable: Most of the data in these legacy contracts are skewed or deemed unreadable when using traditional extraction technology like OCR. As a result, such contract analysis tools don’t deliver the desired accuracy of the information, and they often struggle to process low-quality documents with issues.

AI-powered Contract Analysis: The Best Bet for Addressing Contract Analysis Challenges

Businesses need a contract analysis solution powered by AI and NLP. Advanced techniques like Natural Language Processing (NLP), Machine Learning, and Computer Vision capabilities offer the following benefits:

Introducing XtractEdge: The Future of Contract Analysis

XtractEdge is a comprehensive suite of Document AI and Contract Analysis powered by AI to extract actionable insights from various enterprise documents, contracts, and legal agreements.

This intelligent contract analysis tool uses human-in-the-loop to help businesses unlock opportunities and identify risks hidden in the terms and conditions of historical contracts. For instance, XtractEdge’s AI-powered contract analysis solution helped power a Hitech manufacturer’s M&A strategy. With its automated contract analysis capabilities, XtractEdge successfully processed the historical load of 30K+ customer contracts in just 1 week, extracting 50 intents and 125 entities from 4 different types of contracts with medium to high complexity.

Moreover, the contractual data extracted when fed into the Business Intelligence system can quickly generate accurate reports in real-time. Further, this best contract management software follows a robust security system to prevent unauthorized access to sensitive information. It also calculates risk based on differences in critical language and legal terms and performs risk-scoring on the entire portfolio of contracts.


Mapping a colossal volume of historical documents manually is not an intelligent way to contract analysis. Also, having too many human touchpoints can delay the process, resulting in lost business opportunities.

Contracts are written documents validating the relationship between two or more partners when entering into a Merger or Acquisition. But manually assessing the terms, conditions, hidden risks, and opportunities can result in many granular details being ignored.

Hence, an AI-powered smart contract analysis software like XtractEdge can relieve the legal teams of headaches and enable seamless contract abstraction, review, and risk scoring.