Demand sensing: A vital solution for effective supply chain management

In today’s competitive world, being & remaining agile is key. Brands must be adaptive enough to react quickly when any factor affects demand or supply. Otherwise, they run the real risk of halting the entire production process. Moreover, revenue-based dependencies can easily falter upon improper or apathetic monitoring of trends. As you’re aware, supply chain management can be very precarious to handle, as it entails many uncertainties & unexpected challenges. These can range from being impacted by shortages to more niche-related issues, such as contract disputes between two brands that supply & demand production.

This is where demand sensing steps in. So, let us briefly run through what it is & does?

Demand sensing — Definition and its importance in the supply chain

Demand sensing is a process of continually monitoring & reacting to varying trends in the supply chain. By deploying the power of AI (Artificial Intelligence) & it is empowering plus contributing counterparts of ML (Machine Learning), live data analysis can ensue. Such statistical extrapolation & predictive analytics will help forecast the future (especially when economic turmoil is concerned). This allows brands to better prepare themselves for the unknown as and when it may happen. It is a culmination of this which we refer to as the ‘demand sensing process.’.

Importance of demand sensing:

Demand sensing vs. Demand planning

Demand sensing operates on the principle of sorting through & evaluating voluminous data to identify the factor that has the largest impact on demand. Its software runs downstream data to alert stakeholders upon stock shortages, etc.

Demand planning, on the other hand, is more related to creating a strategy to deal with fluctuating demand. Sales forecasting is imperative to ensure that not too little or much is done to maintain a continual revenue stream without overly hindering issues. This cross-functional feature achieves a balance between customer satisfaction & their demand and supplier’s supply capacity to fulfill that demand. Hence, you will never be either over or understocked. Various statistical models can be used here, including linear regression, moving averages, seasonal trends, or following a sales forecast itself.

TradeEdge Demand Sensing —Strengthening supply chain efficiency

TradeEdge Demand Sensing solution provides “actionable insights by aggregating real-time data about product sales and inventory across multiple channels.”

TradeEdge Demand Sensing helps enterprises optimize functions across the entire spectrum of demand planning, helping them improve accuracy, efficiency, and manage unpredictability to drive business growth.

Let’s take a look at an example.

Our client, a global clothing retail company, was looking to strengthen its position as a leading omnichannel retailer and focus on consumer experience by emphasizing data-driven decisions. The need of the hour was to gain access to their wholesale network data. With TradeEdge, they enabled 99% first-time-right secondary sales data by harmonizing data from disparate sources.

Demand sensing has thus become an essential part of supply chain management due to its intuitiveness & live responsiveness. Hence, by applying demand sensing solutions, organizations can operate with greater efficiency, speed & scalability to adapt to any unexpected disruptions in the future supply chain.

Creating supply chain harmony with a connected cycle of information

Visibility and control are critical for businesses to derive total value from their supply chain processes. Without a doubt, great products and a firm brand name lay the foundation for optimal growth. Sadly, though, a broken supply chain and an opaque network of supplier-distributors-logistics can be significant hindrances. With the complexity of modern supply chains, tying them all together into
a coherent whole and building supply chain harmony increasingly require a technological edge and data.

Data is one of the missing links here, which, when adequately harmonized, can make the whole process, from suppliers to sales, as clear as crystal.

How can enterprises leverage technology to gain more visibility and utilize insights to control their operations effectively? Let’s look at how enterprises can harness the power of advanced technologies to gain insights, thus creating supply chain harmony.

Achieving end-to-end visibility in modern supply chains

Communication is vital to creating supply chain harmony and achieving end-to-end visibility. That is possible only when information on how each element in the supply chain works is available to everyone in the network. Big data plays a pivotal role here, but not data presented in an unstructured manner.

The data harmonization process considers all efforts to collect, collate, and combine data from multiple sources. Then, the same data is presented to users in a structured way to view what is expected and what is happening closely.

Today, most businesses have their footprints spread across the globe. Hence, it becomes imperative for them to coordinate with local vendors, retailers, and distributors to reach hyper-local customers. It’s no wonder that achieving end-to-end visibility is critical to developing supply chain management. And data is essential for enabling supply chain harmony. However, one of the most challenging aspects is to have visibility – adding a high degree of digitally-enabled visibility into the supply chain. This requires organizations to enhance oversight of internal processes and extend the reach of information flows upstream in the supply chain.

But, the modern supply chain has a complicated web of data, and in specific instances, sufficient information is not available due to various silos. In order for businesses to gain a complete picture, it’s essential to centralize, catalog, and manage this constant data flow.

This is where Control towers play a vital role in tying a supply chain into a coherent whole that has end-to-end visibility. These centralized decision-making and data dashboards bring together the information critical for that supply chain and allow for rapid analysis and action. Businesses are focused on achieving total customer satisfaction. In order to reach the stage, the former needs to bring together all the data available in the market, from the supplier’s end, from the customers that are buying from other tier-one suppliers, and from the distributor’s end, into a centralized system for easy accessibility, which is the harmonization of data

Feeding market demand into supply chains

Data collating is not the end of the journey; instead, it is just a new beginning. The primary task commences post when the consolidated data is processed and analyzed to extract actionable insights.

Further, feeding demand data into supply chains increases efficiency in understanding demand patterns, opening up demand-side information to the broader supply chain, and improving supply chain collaboration and resiliency.

Enterprises today are looking to make demand signal flow all the way to the uppermost point in the value chain, which is the raw material supplier, thus making the entire process seamless and optimizing the networks to reduce friction. Thus, getting the market signals as quickly as possible and then responding quickly will help improve supply chain collaboration and inventory worries. Data transparency smooths out the nervousness in the chain, leading to better functioning of logistics and forecasting. And, data harmonization lays down the foundation for a collaborative supply chain.

Being agile by using Automation

Leveraging Automation can help businesses free logistics professionals from a host of background tasks and address the huge number of potential variables that affect a moderately sized supply chain. In developed markets, PoS information can run into several gigabytes of data every day, depending on the granularity of such information. The frequency of sharing that information and the granularity of sharing that information cause data explosion.

Analytical tools powered by AI and ML capabilities process data presented harmoniously, extract granular insights and feed the supply chain with the correct information in real-time. This allows a planner to see what inventory was held up even when in transit, understand the business-critical items, and immediately move to a mitigation strategy and potentially charter airfreight for essential items within hours.

In a nutshell, timely and consistent data that flows back to the suppliers is critical as it provides them with an open platform to input their stock or open up inventory availability directly to suppliers and distributors.

Thus, taking the improved data generated by better collaboration and turning that into automated insights and actions helps improve supply chain visibility and immunes businesses from unforeseen disruptions. Availability of data can help create supply chain harmony.

Fostering connected automation with end-to-end automation platforms

Process automation was born out of the need to solve specific challenges in certain parts of a large operation. Robotic Process Automation (RPA) rapidly became a household name at the turn of the millennium. Businesses across industries started deploying RPA bots to automate recurring and repetitive tasks to achieve process efficiency, reduce cost, and eliminate manual efforts.

Even though RPA matured with time and its adoption spread far and wide, this technology had limited scope for expansion. Enterprises are now wrestling with how to be more strategic with automation. The ability to automate more of the end-to-end processes is currently a top priority. Nearly 50% of respondents in a survey see the value of an end-to-end automation platform as a meaningful way to achieve process optimization.

Evaluating the importance of Process Optimization

Process optimization enables businesses to optimally utilize existing resources while eliminating the cost and time-intensive factors and errors left behind with too much human involvement in each process. In order for enterprises to optimize and fast-track each business process, RPA tools are employed in recurring tasks prone to human errors.

Initially, tactical automation for optimizing business processes was restricted to finite use cases, with a maximum of 100 RPA bots deployed. Besides the inherent shortcomings of RPA bots, owners faced other bottlenecks when scaling automation to other key business use cases. The use cases get too hard to automate at important points in the process since most of these points involve interactions with unstructured data or documents or human-human interactions; the final configuration of the automation is built to stop and start again, with workflow passing back and forth to a human operator(s).

And there are other internal barriers to scaling automation as well, including:

Lack of IT readiness: Nearly 37% of respondents in a survey agreed that the absence of IT readiness is one of the top-most challenges faced while scaling automation to other key processes. The existing IT infrastructure is not adept at handling the various complexities arising out of seamless automation implementation.

Lack of skills: 31% of respondents cited the lack of skills and understanding as the second-most barrier to implementing end-to-end business process automation.

Identifying the right processes to automate: Manual efforts fail to discover the right process candidates for automation. Manual process mapping and discovery can omit granular variations and discrepancies existing in each task; hence, the outcome is not good enough to ensure the success of automation implementation. Nearly 30% of respondents agreed that this was a major roadblock.

Resistance to change: Often, the existing human workforce is not open to change and considers automation a direct threat to their position in the organization. This mindset translates into their reluctance to share valuable inputs during process discovery and eventually resist the automation implementation efforts. 22% of respondents agree this was another barrier to building an
end-to-end automation platform.

Besides the aforementioned barriers, others are added to the list, such as implementation cost, lack of clear vision, lack of holistic approach to integrating RPA into broader transformation, and unrealistic vendor promises.

Robotic process automation can be considered a harbinger of major changes gradually emerging in the technology space. And, despite its shortcomings and obvious challenges, other tech-based capabilities are coming together with RPA to ensure a far-reaching automation implementation. Assembling the right capabilities with the best expertise has broadened the space from RPA to what is often called Intelligent Automation (IA), also sometimes called connected automation or hyper-automation.

Even though technology providers have yet to standardize the flavors of additional capabilities being integrated into their offerings, a fair mix of the same is already available.

Current capabilities offered by automation platforms that enable end-to-end automation

The available automation platforms offer the following capabilities to power end-to-end process automation:

Low/no-code capability: Nearly 21% of the respondents in a survey agreed that low code/no-code capability is one of the prime factors to consider in the available automation platforms. LCNC tools enable rapid design and deployment of human-RPA bots interactions. It no longer requires a custom web app each time a human is needed in the loop.

Process orchestrator: It is considered the heart of the automation platform sequencing work by applying business rules or engaging predictive ML models that move the work down the correct path. In order for the business rules or data to reveal the rules that define how work is getting done, insights need to be embedded into the design process and the process orchestrator. And that can be achieved by delving deeper into the wiring of the orchestrator. According to the same survey, nearly 18% of respondents cited this element as one of the key capabilities powering end-to-end process automation.

Complex document processing: A deeper understanding of how processes get performed has helped illuminate IA’s solvable challenges. And to do that, full document processing with natural language understanding is already available, taking the shape of another key enabler, as cited by 3% of the respondents from the same survey.

Other capabilities include advanced automation analytics, automation script tools, machine learning decision models,  and customizable web-based dashboards.

In a nutshell, an end-to-end automation platform connecting people, processes, and data is the basic foundation for intelligent automation. With the dawning of this new realization, enterprises are working towards finding an automated solution that can seamlessly combine multiple capabilities to extend the use-case and value of comprehensive automation solutions.

Data and analytics: Unlocking business opportunities for e-commerce businesses

Data is paramount for every business, especially when making important decisions or formulating new strategies. In e-commerce, a colossal amount of data lies untapped across its operations, supply-distribution networks, and customer services. In their granular form, such data can create awareness regarding market trends, shifts in consumer buying behaviors and competitors, and disruptions occurring deep down in the demand-supply-value chain.

With the help of e-commerce data, such crucial information is collected from different touchpoints and processed to make data-driven decisions.

What is e-commerce data, and how does it help businesses?

As per statistics, nearly 49% of business leaders agree that analytics help with better decision-making, and 10% of the other respondents stated that such analytics improve relationships with end customers and other partners.

But, navigating through the massive datasets and numbers is overwhelming and manually extracting and analyzing such data often paint half-truths due to granular data omission or erring when extracting data from bulk documents.

However, with the help of the right tools and technology, such hurdles can be easily overcome.

E-commerce data is drawn from multiple sources such as:

Once the available data is harnessed, it can be put to practical usage in several areas, such as:

Sales trend and share analysis: It is crucial to understand the sales trend and market share analysis of one’s own business and competitors. With e-commerce data, information about the above categories is easily tracked versus competitors across key categories and brands.

Media performance analysis: Big e-commerce businesses spend millions of dollars in marketing procurement and execution to outreach a broad customer base, both hyper-local and those living beyond the national borders. Analyzing media performance is crucial for multiple reasons, especially during budget allocation. Consolidating media performance metrics, including ROAS, ACOS, CPM, CTR, and Impressions, and measuring them weekly, quarterly, monthly, or annually is one of the critical functions of optimizing media spending.

Organic search performance: Identifying leading organic search terms and focusing on SEO strategies to rank them helps e-commerce companies identify areas where their search strategy is winning or losing.

Profitability analysis: Making use of e-retailer PoS data (Profit Per Unit and Cost of Goods Sold) to determine optimum product margin vs. thresholds. The output paints a clear picture of whether the business is generating enough profit or is simply hitting a blank wall.

Inventory planning: To optimally utilize existing inventories, e-commerce data analytics software leveraging AI/ML capabilities determines how much stock is needed to meet the market demand. This enables e-commerce owners to have direct control over their inventories and plan their supply accordingly.

Lost buy box analysis: Data analytics in e-commerce determines the factors driving Lost Buy Box by using data from the PDP (Product Detail Page).

Ratings and review analysis: Storefront insights extracted from user ratings and reviews help e-commerce owners identify underperforming SKUs compared to the competition. Businesses then work on the weak areas to address the common issues based on user complaints. They connect with them to listen to their inputs and address them to enhance customer satisfaction.

Share of search analysis: With the help of e-commerce data, analysis of paid/organic search performance can help businesses draw insights from their campaign execution and take measures for underperforming.

Product page content compliance: Product content and integrity analysis compare the content with internal company content to check for any compliance issues and address them immediately.

Mining for e-commerce gold with technology – The best way to win with e-commerce

E-commerce companies are gold mines of data. With the TradeEdge platform, businesses can gain valuable insights. Such e-commerce data analytics can paint a clear picture of where their business stands in the market vs. competition.

TradeEdge is a cloud-based solution that provides insights across the demand value chain, similar to the points above, for accelerating profitable growth and getting one step closer to an autonomous supply chain.

Hence, e-commerce businesses benefit immensely from gaining maximum channel visibility, adding new channel partners, improving retail execution, and faster reaching new markets.

Optimizing supply chain execution with AI and advanced analytics

Supply chain is an integral part of executing business success, from procurement sources to logistics and end-users – all are finely woven into its intricate network web, each playing a critical role. The execution of the supply chain attracts significant attention because this area is ripe for cost savings if things are done right. Harnessing advanced analytics can provide the most-wanted insights into how each element functions independently and together as a unit to ensure the steady flow of goods, even in the face of unprecedented challenges.

Today, supply chains are becoming increasingly complex, with several partners getting added to the network to cater to the needs of overseas customers. Hence, optimizing supply chain execution with analytics is more important than ever.

What is supply chain execution?

Supply chain execution encompasses the myriad tasks making up the supply chain and their steady flow, from procurement, sourcing, order fulfillment, and warehousing to transportation of end goods to customers. Every step in the supply chain leaves behind an array of information.

With the help of advanced analytics, such insights are extracted in real-time and shared with concerned partners to optimize the process accordingly and make it immune to any sudden disruptions, predictable or unpredictable.

For example, the supply chain network of Consumer Goods companies is ridden with many challenges, impeding their growth. These challenges include:

Evaluating the importance of supply chain analytics

Supply chain analytics is the analysis of information drawn from the various elements connected to the company’s supply chain. One miscalculation or error in either of the processes creates a domino effect, rippling through the network and impacting every element along the way. This will leave the customers heavily unsatisfied. Hence, timely availability of supply chain analytics can save the business millions of dollars and its reputation.

The following are the benefits of optimizing supply chain execution with analytics for businesses:

Improved visibility: Businesses need to get a comprehensive eagle-eye view of their supply chain. Data and analytics shared on a single cloud-native platform help businesses and other concerned parties spot patterns and anomalies in a particular location, analyze the data and take actions accordingly.

Better understanding and mitigating risks: Again, identifying market patterns from historical data and present disruptions equips businesses with the knowledge and foresight to promptly create fail-safe plans to mitigate timely.

Supply chain planning enhanced: Supply chain planning is based on market demand for goods and services. Real-time insights provide such data based on which future planning is carried out effectively.

More customers satisfied: Better planning and timely address of disruptions improve customer satisfaction significantly. With the help of supply chain analytics, businesses can match the delivery promise to customers and reduce the loss of business to competitors.

Higher profitability: The whole purpose of supply chain analytics is to ensure businesses save as much cost from the network and identify opportunities to scale efficiency and profitability.

Driving insights to action with TradeEdge

TradeEdge Platform captures insights across the demand value chain and makes them available to every person responsible for decision-making in the supply chain. Such insights accelerate profitable growth and foster an autonomous supply chain.

This cloud-based solution enables brands to gain maximum channel visibility, add new channel partners, improve retail execution and reach new markets faster.

Data Digitization: Harnessing digital solutions to make data readily available

Data offers businesses a much-needed edge over competitors. A large amount of data is hidden among piles of documents waiting to be extracted for making game-changing decisions. RPA has helped organizations transform digitally and enhance process efficiencies by automating repetitive manual tasks. Unfortunately, businesses hit a roadblock with non-digital data in documents such as invoices, scanned paper forms, statements, claims, and receipts.

How can organizations extract insights, especially from data that is locked away in scanned documents? This is where data digitization comes to the rescue.

Introduction to data digitization

Digitization is the process of turning analog information – such as images, written text, etc, into digital signals/components. Digitization is when data is turned into digital formats such as digital images, signals or rather into the binary. This is done to help process data digitally. It helps enable computer processing and helps carry out complex digital processes efficiently. Digitization is turning analog information into a numerical or binary format.

Digitization improves the very scope of digital processing and its transmission as it helps carry out processes much more efficiently in half the time. It helps data to be accessed by multiple points without any loss and to be converted into the desired formats.

Digital data can also be stored much more easily. Tons of paper data can be compressed into simple files with the help of digitization helping it to be preserved and functional for a long period of time.

What is data digitization, and how does it help businesses?

Whether big or small, every organization has to deal with bulk documents daily. Each of these documents is a gold mine of valuable company insights. However, most documents are non-digitized; hence, Extracting Data requires manual effort to scan and enter inputs into the enterprise IT system. Digitization helps businesses to take a leap forward by taking away paper processing and instead, enabling automation. Digitized data is not only faster but also increases transparency within the organization.

By using something like XtractEdge, businesses can provide specific solutions as per their client requirements and provide a packaged solution which is in tune with the customer’s needs. A tool like this would increase business value by making documents from the organization ready for integration, processing, and eventually consumption.

How does the Data digitization solution work?

Digitization of data seems like the best bet for businesses to end the age-old approach to handling documents internally. The emergence of deep learning techniques in computer vision and NLP has enabled a new breed of Data Digitization Solutions.

Manual data extraction has its set of challenges, such as:

Difference between digitization and digitalization and digital transformation

Digitization and digitalization may sound very similar however, there is quite a distinction. For instance, digitalization can only happen after digitization. Digitization is the process of converting data which would otherwise be on paper, into the digital space. Digitalization refers to the phenomenon of digitizing data in a particular space – a business or industry. It is a strategy that is implemented to help carry out digitized processes which will innately speed up processes within any company or industry.

Now unlike both these terms, digital transformation is a much broader term which focuses on the people. It is the adaptation of technology and digital data by the masses. It needs change to happen within an organization so that all their data is now digital. It requires them to be able to access this data and process it. Digital transformation is this process wherein people of the company start to work with the digitized data and interact with it to create meaningful impact. Digital tools like AI back up the process of digital transformation.

Why is data digitization important?

For businesses to thrive in today’s world, it is important to have insights based on analytics and a processing network in place to run through the major chunk of the data. This can only be done through Digitalization of Businesses. It’s simply too slow to do this scope of work manually. To make use of the growing technology that’s been developed constantly, it is crucial to have all company documents digitized.

As data is constantly in demand, digitization has tremendous scope and opportunity. Almost every sector needs data; hence, it is safe to say data digitization has one foot in every industry, be it manufacturing to retail, logistics to consumer goods, or banking to insurance.

Data Digitization use cases

A few businesses use cases are mentioned below:

Form digitization: Multi-page paper-based forms involving a mix of typed text, handwriting, checkboxes, and other fields and tables will be better handled when digitized.

Dynamic extraction or touch-free zero template extraction: Helps deal with non-standard, non-structured input documents that contain the same information in varying layouts. Content classification and extraction from mixed-type documents: Digitization solutions cater to extracting data and digitizing them from documents of varying types and formats.

Information consistency checking: Every business has a specific complex use case requiring mature products, which address all the previous use cases and support the definition of consistency verification rules that enforce Domain-Specific Rules for information consistency.

6 Advantage and disadvantages of Data Digitization


The flip side:


Data Digitization is the future of all industries and quite rightfully so to help businesses achieve process efficiencies and address bottlenecks arising from processing paper-based documents for extracting unstructured data. The above use cases and applications aptly point out why data digitization solutions have become the need of the hour.

How to break organization silos to improve supply chain resilience

Greater logistics effectiveness and efficiency are not just gained by focusing on goods production and transit. Supply chain resilience also depends upon a smooth operation inside a company focusing resources on critical problems, reacting flexibly to a wide range of scenarios, and bringing in stakeholders from across the business to weigh up the options and make the right decisions.

Silos are insular workgroups functioning as individual entities inside the organization. These entities maintain negligible communication with other teams and the outside world.

These silos can affect the supply chain and your organization’s overall performance.

What is supply chain resilience, and how are silos affecting it negatively?

A resilient supply chain refers to its capacity to resist and recover from any disruption caused by predictable and unpredictable factors. The term also defines the ability of the value chain to bounce back quickly, adopt new policies and find new ways to mitigate risks in the demand-supply network. When organizations employ multiple sourcing channels, ensuring the unhindered supply of raw materials and others from numerous routes can achieve supply chain resilience.

Organization silos can be detrimental to building supply chain resiliency. But who is responsible for these silos? The lack of the flow of information and a collaborative framework indirectly feed silos in the organization. Each department sets up goals that eventually conflict with the purposes of the other departments.

Then, there are data silos to consider. When crucial insights are collected and recorded by one department but not shared with others, silos appear, which impacts the company’s decision-making as a whole.

How can organizations break silos to build a resilient supply chain?

To break the shackles of silos and achieve supply chain resiliency, businesses should follow specific approaches; a few of which are described below:

Creating internal clarity

The sales and marketing functions may not be 100% aligned and connected to many supply chain functions. This reduces visibility and creates internal friction as supply chain teams struggle with new product launches or demand fluctuations, while sales and marketing lack awareness of what is possible from a logistics perspective.

Hence, it is critical to look closely at how the business aligns across functions and how information is disseminated, utilized, and acted upon.

The break in internal silos helps increase the clarity of its overall objectives and how it plans to achieve them, allowing the company’s top leaders to manage operations timely and efficiently. Sharing information from the lowest plant level up to the head of the organization results in prompt and updated situational awareness about any crisis and initiating actions accordingly. Any minor disruption at any point in the supply chain can rapidly cascade downwards, meaning if one business unit has been affected immediately, other business units get affected very soon.

Therefore, businesses should encourage cross-organizational coordination and sharing of information to build a resilient supply chain. This allows suppliers to proactively share information with the purchasing department to facilitate the collation of the best data on a particular day of a certain week in order to make the best decision for the subsequent production forecasts.

Prioritizing disruptions

Frequent internal meetings between departments not only break the existing silos but also work together to minimize the potential for significant disruption in the supply chain. By bringing more than two heads together, they quickly focus on key areas or particular moments of disorder. “What are the most critical materials and sub-components? Where are the transportation bottlenecks?”

These are the questions that supply chain planners need to ask in order to prioritize the correct route in their planning as soon as an adverse event occurs and then cascade that through their organization and straight to key suppliers. They should focus on what they need to be monitoring and then which levers to pull, so they can prioritize that in the moment and not waste time figuring out what is going on and what direction to take.

Keeping on top of changing scenarios

Creating an outlook and a plan for the complete unknown can cause severe headaches for supply chain professionals. Usually, the disruptive circumstances are unprecedented, for instance, the COVID pandemic. Outside of global conflict, there hasn’t been a comparable point where different international trade and production elements have been affected severely.

Hence, one can conclude that the forecasts are unreliable. Increasing the frequency of planning meetings and building close coordination across organizations is essential. Equally crucial is to have shared knowledge about the most functionally critical and vulnerable elements existing within the supply chain. Experts believe scenario planning and utilizing digital twins in a sandbox environment is the best way for businesses to build resilient supply chains. These can take a real-world structure and allow planners to change critical variables to understand how a scenario will play out.

Once scenarios are planned out, it is vital to share these with business-critical suppliers then, so that when an issue strikes, every key player in the supplier chain follows the same rule book.


Before drawing any inferences, businesses should focus their attention internally and understand how silos within the four walls create bottlenecks in the entire value chain. Supply chain resilience is achievable only when the walls are broken down and information is shared unhindered downwards to the lowest levels.

How Advanced AI pulls data from visually-rich documents

It’s no surprise that enterprises today are sitting on a goldmine of data. Artificial Intelligence has been garnering media hype recently, and it’s no longer in the experimental phase as more and more businesses are adopting it as essential to their business.

As per statistics, annual AI software revenue will touch the $100 billion mark by 2025. Hence, organizations are expanding their spending plans to leverage what AI has to offer optimally.

AI in document management sounds like a great possibility for extracting data from complex and visually rich documents. AI technologies such as Document Digitization, Computer Vision, Natural Language Processing, and Intelligent Search provide a host of benefits when applied to document management and help businesses succeed in today’s volatile market conditions.

AI-based document management offers the below benefits:

Data digitization: There are tons of paper documents, unstructured data, and unprocessed data lying there that enterprises cannot use. Different elements and visual objects are present in the data, such as graphs, charts and tables, logos, and text. Hence the first step is the digitization of data.

Data extraction: AI document processing solutions dig deeper to bring out subtle variations and granular differences invisible to the naked eye. And the process of fetching data from a library of documents is done in nanoseconds.

The next step is to make sense of the data and get a better sense of the information. Then enterprises try to understand the context and also conduct document classification – for instance, if someone uploads a bunch of documents, which is a collection of checks, invoices, purchases, or sales orders, Document AI automatically separates them.

Data analytics: AI has immense potential when it comes to data analytics. AI document analysis collates and extracts data in massive quantities and derives meaningful insights using predictive analytics and data visualization. Such actionable insights improve decision-making and optimize processes significantly.

How AI in Document Management pulls data from media

Technology has come a long way from CAPTCHA boxes asking for random words to be typed to prove you are a human. Today, it is digitizing books, decoding, and interpreting various media types, which are no longer restricted to plain text.

Broadly, documents are categorized into the following types:

So far, AI in document management has proved worthy of extracting information from text-based documents. But how can enterprises capture insights from visually-rich media?

For AI to extract the data helpfully, it not only has to understand contextual clues from the text itself but also be primed to handle and interpret elements such as images, logos, symbols, charts, and captions, among others.

Here, Optical Character Recognition might not be cut out because it requires consistency in document formats. Advanced AI technologies such as computer vision and NLP models are ready to compensate for OCR’s disadvantages and help enterprises decode millions of documents accurately.


Hence, document understanding is much more than OCR or handwriting recognition and requires the capability to detect & recognize various structural elements in enterprise documents. With computer vision and NLP models combined, AI can make document extraction, processing, and comprehension a breeze.

Process Discovery and Mining: The new paradigm of Process Excellence

Most automation initiatives have failed to live up to their expectations. Researchers believe the problem lies in the approach, especially when enterprises rush into automation projects, repeatedly picking the wrong process candidate or the wrong way to automate the fitting process.

To answer the questions mentioned above, enterprises need granular and broad intelligence-driven insights to make accurate decisions. In order to scale RPA success, businesses must understand two things: First, why they need Automation. Secondly, which process improvement projects should be taken as the first candidates.

A combination of both  Process Discovery and Mining can offer just that – helping conceptualize, execute, and scale automation and process improvement needs.

What is Process Discovery, and how does it help enterprises in their Automation journey?

Process Discovery is tasked with recording primary data from user keystrokes for delivering accurate process execution representation. On the other hand, Process Mining reduces the gap between this extracted data and actionable insights using insights encapsulating a broader view of processes concerning enterprise strategy.

A combination of both addresses the varying levels of granularity existing in enterprise processes. Further, Process Mining focuses on L1-L3 levels of granularity to analyze event commits and application logs to power discovery, monitoring, and process improvement based on current organizational information. Analytics derived from the data help enterprises review organization-wide process maps supported by a comprehensive understanding of process structure.

By tracking human-system interactions at the keystroke level, Process Discovery adds on-ground intelligence, eliminating any subjectivity from the automation planning process.

Both Process Discovery and Mining have proved effective in ensuring automation success. A combination of both helps enterprises with the following capabilities, such as:

How do Process Discovery and Mining drive results?

Process Discovery and Process Mining address automation requirements in critical areas, such as:

Process efficiency

The first step to scaling processes’ real efficiency is detecting bottlenecks and their reasons. It is done by evaluating the number of variations and exceptions, the time spent on non-essential activities, and the nature of application delays and inefficiencies. With the help of Process Mining, enterprises conduct root cause analyses to estimate the cost impact of process inefficiencies accurately. Then by applying Process Discovery tools, enterprises can drill deep into understanding process execution at a granular level.

With both strategies, enterprises understand their performance baseline, underpinning automation initiatives and setting the foundation for sustained and continuous improvement.

Process automation

Process intelligence requires a two-fold intervention:

However, Automation success is much more than choosing the proper process candidates. It requires diligence in the approach to Automation and accurate forecasting of the initiatives’ potential impact.

Process compliance

Enterprises work in an increasingly complex regulatory environment, mostly fraught with challenges. Hence, compliance often becomes a hurdle.

Process Mining addresses the challenge by flagging cases and causes of non-compliance. Once the flagged instances are identified, Process Discovery takes over to capture non-compliant variations, application accesses, and resources expended on non-compliant activities. The result isn’t just a more compliant organization, but millions of dollars saved in potential fines.

Process training

The insights generated from Process Discovery and Mining develop functional responsibilities for employee roles’ clarity and specific process maps needed as training material for staff.

On the one hand, Process Mining identifies candidates for training alongside the training steps required. At the same time, Process Discovery generates BPMN-compatible business process maps and optimized process definition documents for enhancing process analysis.  A combination of process overview and execution analysis compares employee performance data across regions, departments, and teams.

Process Discovery and Mining: The road to Intelligent Automation

Intelligent Automation is a layered exercise, and cost reduction is one of its many benefits. Growth is the North Star, and Automation is essential to this end.

Automation initiatives can be impactful when they align with the business strategy and are backed by leadership buy-in to drive enterprise-wide transformation. But rushed pilots will translate into automation project failure and loss.

In order to drive progress in the face of a uniquely challenging environment, enterprises should prioritize  Process Discovery and Mining to scale the growth and success of automation projects. These stages are critical in driving enterprises on the road to successful Intelligent Automation.

Computer Vision: Extracting insights from visually rich complex documents

Artificial Intelligence has evolved to make existing enterprise operation models more efficient. But its real value lies in intelligently tackling more significant business complexities efficiently. Computer Vision is one of its most exciting capabilities tasked with automating processes usually catered to by the human visual system.

Machine Learning algorithms have already exceeded Computer Vision applications and their capacities beyond human visual computing power. Today, the same technology is used to mimic human capabilities in visual cues to extract insights from visually-rich complex documents.

What is Computer Vision, and how does it help enterprises?

Computer Vision is an Artificial Intelligence capability that gains a high-level understanding of digital images or videos and extracts information from images and other visual inputs.

Enterprises across industries and verticals process a staggering amount of information daily. Much of these data are scattered across various documents of different formats and originate from multiple sources. Document categories include contracts, invoices or even infographics. Documents are broadly segmented into the following:

Visually rich documents are heterogeneous, which makes processing and classification challenging endeavor. Unlike humans, the traditional OCR  falls short of interpreting data from such records since visually rich documents are anything but homogenous and consistent.

Computer Vision use cases

Structured textual documents rely mostly on templates and NLP-based analytics for information extraction. On the other hand, the semantic structure of visually rich documents is observed primarily by visual cues interpreted by the human brain.

Technologies that can help automate and augment visual-rich document recognition for intelligence can deliver a powerful impact. Here are the following use cases:

Ad tech companies: These organizations analyze various materials such as posters, pamphlets, catalogs, digital ads, and other content assets. Computer Vision technology is used to inform their conversion strategy, devise promotions, and focus their content creation approach.

Marketing: Marketing teams analyze marketing assets, competition communication, and even industry research materials to develop cogent approaches for marketing and sales. Most of these documents are rich in visuals containing important information, best extracted with Computer Vision.

Research: Large research organizations frequently sift through thousands of pages of information in different formats, creating inefficiency and running the risk of bias and inaccuracy from human processing. Hence, AI technology with Computer Vision capabilities can prove transformative.

Retail: The same technology extracts information from product labels.

How does Computer Vision work?

The actual benefit of AI Computer Vision lies in identifying meaning, not mere object recognition and classification. It follows the multimodal image extraction technique, which is a collective analysis of multiple information types in the same document for a coherent understanding of its content.

Here, the visual cues from the image are used to tie different document segments into a cohesive message. A visually-rich document is represented as a graph, with each node containing specific information. The edges of the graph connect the information logically. AI capabilities like Computer Vision and OCR are used in conjunction to extract data from each node. At the same time, the graph’s neighborhood knowledge ties up all the information together to build a consumable narrative.

Here’s an example of a multimodal approach.

XtractEdge Platform

XtractEdge Platform provides enterprises with the ability to identify information from images and scanned documents using object detectors, OCR, handwriting recognition, and signature tagging. It combines advanced Machine Learning, Computer Vision, and natural language processing to offer a robust intelligence layer, delivering on-demand services such as intelligent document processing, data enrichment, and contract analysis.