August 2022

How does demand collaboration in supply chain help businesses?

With the dynamics of consumers’ demand shifting frequently, one of the biggest challenges for the Fast-Moving Consumer Goods (FMCG) manufacturers presently is order fulfillment. Subsequently, to meet the demands of this ever-shifting ecosystem, the FMCG, also known as Consumer-Packaged Goods (CPG) manufacturers, should shift their focus from the retailers and distributors to the consumers’ requirements and behavior.

It means that the current focus should be consumer-centric rather than supplier-oriented. To realize this, consumer goods manufacturers should revamp their distribution networks by implementing a more collaborative approach, a multi-network system. This is how the role of demand collaboration in supply chain comes into play.

Going beyond the traditional value chain

Due to the emergence of various new products and the increasing prominence of online commerce (e-commerce) channels, the traditional value chain has almost lost its relevance in the modern-day context, giving space to the multi-source collaborative demand planning approach. Also, the onset of the COVID-19 pandemic has paced up the transformation needs and behavior of the customers, pressing the urgent requirement of a demand collaboration in the supply chain management model.

Hence, it is an indication that this new purchasing behavior will exist and continue for some time now with the customers getting used to the comfort of the home delivery of their ordered products on the same day.

Significant reasons for the failure of the traditional value chain:

The need for an effective demand collaboration approach for FMCG enterprises

An adequate supply chain collaboration platform enables the companies to build up an end-to-end and real-time visibility within the supply and demand network throughout the logistics collaboration supply chain.

That kind of visibility facilitates the retailers and CPGs to nullify the demand distortion impact and avert an array of problems resulting from unanticipated fluctuations to supply and demand signals. Without a collaborative association between all the parties involved, a minute change in demand can twist the communication in the whole supply chain network. However, these problems can be efficiently managed by applying a multi-enterprise, over-the-top supply chain collaboration platform such as TradeEdge Network.

Also, supply chain collaboration helps the companies to better adjust to various pre-COVID-19 trends that have come across since 2020. These include the management of demand volatility and product deficiencies in the supply chain.

How does demand fulfillment approach enhance your business?

Demand collaboration in supply chain enables companies:

TradeEdge Business Network – Connect and collaborate

TradeEdge Network, a proven multi-enterprise, over-the-top platform, enables the FMCG manufacturers to connect and build an ‘all-through’ network chain with the distributors and their partners. This not only maximizes the order fulfillment but also assists in bridging the supply-demand communication gaps in real-time, thus enhancing the visibility within the supply chain. In addition, TradeEdge also helps companies to find a multi-channel supply value network.

Additionally, it enables the involved partners to manage the inventory level strategically in the supply chain after the demand signal is identified. The core objective of this value chain is to form a connected network to check further sales loss.

A use case of a collaborative approach with the TradeEdge platform

By implementing the TradeEdge Business Network solution, one of the premier beverage manufacturers witnessed a sales growth of 2% during the pandemic and thus could rapidly meet consumers’ demands. However, due to panic-purchasing, it could not fulfill the orders on time. Therefore, it employed the TradeEdge platform, connected with the retailers and distributors, improved visibility, and could meet the demands in less than two days.

The future scope of demand collaboration in supply chain

Considering the efficiency of a collaborative demand planning model in the supply chain, the organizations should strategize and design a more connected network of supply chains by leveraging the right demand sensing solution in the coming times. This will not only enhance the visibility in the supply and distribution network chain, but it will also be able to quickly meet the customers’ demand.

Five key benefits of AI-powered document processing

In the wake of digital transformation where company paper-based documents have transitioned into digital files, emails, PDFs, and PNGs, extracting, processing, and entering facts and figures need extra pairs of human hands. The whole process is laborious, monotonous, and error-prone. Artificial Intelligence can easily recognize the content semantics and automatically acquire knowledge with minimal human intervention. Hence, AI-powered document processing emerged as the intelligent approach to amplify the whole data extracting process with higher accuracy.

What do we understand by AI-powered document processing?

Intelligent Document Processing leverages the power of AI and Automation to extract data from unstructured and semi-structured documents and convert them into structured and consumable information. Captured data from records, regardless of size and format, are further validated and filed under specific sections.

How does AI document processing work?

Even without realizing it, companies often discard valuable company documents as trash without evaluating them for critical insights present in granular form. Digital or paper documents are data goldmines, which, if extracted intelligently, can help with game-changing decision-making. However, realizing the importance of such data is not enough in the absence of adequate and supportive technology.

In order for businesses to fully optimize such granular data without losing sight of them during manual data extraction, they need new-age technology solutions. AI-powered document processing is what many enterprises believe is a blessing in disguise.

AI for document processing caters to document extraction and processing. It also stitches crucial data together using various information retrieval technology and compiles them into a single data pipeline. This offers a simple unified experience across all documents. When digitized information extracted from documents is integrated into enterprise processes, workflows become more data-driven due to its easy availability and accessibility.

Further, the Intelligent Document Processing is backed by other capabilities like Computer Vision and Natural Language Processing to scan text similar to how humans do, with a basic contextual understanding of font size and spacing. By using Sentiment Analysis, this AI solution reads the text in different languages and even captures the hidden sentiments behind those words. Its multi-modal image extraction capability combines OCR and Computer Vision to identify pertinent information and extract and convert it into structured data.

AI has not only redefined but reinvented how organizations optimize digital documents today. Let’s explore a few novel ways AI-powered document processing improves different business practices to benefit owners and employees alike.

Benefits of AI-powered document processing

In order to obtain a holistic interpretation of texts and images from massive volumes of documents, the Intelligent Document Processing is the best alternative to average human capabilities without downtime or error. The following are key benefits of AI document processing:

Predicting customer behavior accurately: AI has the power to unlock the full potential of an organization’s customer-centric data shared through emails and reviews. The sentiment behind each email or review is aptly captured with the right tool integrated, such as Semantic Analysis. IDP connects various departments like sales, marketing, and customer support. Here the extracted data uploaded are easily shared with concerned partners. When executives have the advantage of customer-related datasets at their disposal, they can easily predict buyer behavior to augment their buying experiences.

Predicting possible issues and anomalies: AI identifies similarities between extracted data and classifies them into specific datasets or groups based on predefined criteria, intent, and meaning. This allows pertinent information extraction in a structured manner. With the power of automation, enterprises can move faster with their decision-making by evaluating information in real-time and detecting anomalies proactively.

Saving productive hours for employees: Processing bulk documents daily is no easy task and involves a lot of time and human resources. The sheer amount of productive hours invested in the whole process reduces the business’s overall productivity. As a result, workflows are delayed, mindless errors occur, and decision-making becomes incompetent and unrealistic. AI-enabled document processing takes over the cumbersome task and eliminates the time and cost-intensive factors of the process.

Utilizing in-house skills optimally: When employees are relieved from the needle in the haystack search, they can focus better on more value-added roles for which they were hired initially. In addition, with more bandwidth, employees can re-engage in their work with their morale boosted and their productivity increased.

Improving document security: Since company documents in the cloud carry sensitive information, data breaches are a breathing reality. Hence, security has become the topmost priority for companies today. AI-powered document processing extensively scans documents for sensitive information and automatically redacts it as and when required. Further, this software solution keeps a tab on any unusual activity and immediately warns of a possible data breach before it happens.

In a nutshell, AI document processing helps unlock unlimited possibilities, enabling the growth of end-to-end automation of document-centric businesses. Since the number of unstructured documents is likely to double in quantity, now is the time to invest in IDP solutions like XtractEdge.

Evaluating the correlation between demand sensing and product traceability in supply chain

Traditional demand planning techniques have been rendered useless in the face of increasing supply chain complexity and evolving customer behavior. The pandemic had proven the inefficiency of legacy methods of planning and forecasting future sales. Existing data silos add to the pressing issues, which often give rise to chances of multiple products recalls. This results in millions of dollars in loss for businesses resulting in reputational, legal, and regulatory issues. In order for companies to strike a perfect balance between surplus and adequate inventories, they need a more ingenious solution.

Demand sensing is the answer to increasing transparency of the supply chain network, reducing the cost of product recalls, controlling quality, and preventing life loss.

What is demand sensing, and how does it help businesses?

Demand sensing powered by AI technology can easily pick up short-term demand trends from the downstream supply chain. As a result, it helps businesses stay future-ready with surplus stocks to meet sudden changes in customer needs adequately.

Today, external forces like global emergencies, weather trends, economic trends, and competitor strategies easily shape and reshape demand. Traditional forecasting methods fail to capture these subtle factors influencing market demand shortly. It prevents companies from out-of-stock situations or over-abundance of inventories, eventually leading to loss of items for want of demand.

This is especially true for perishable food items or goods with shorter shelf lives.

Hence, demand sensing solutions aid companies in planning their inventories in sync with the current demand to avoid using existing stocks that have crossed their shelf lives. Strategic planning of future sales to manufacturing matching stock of items safeguards businesses from making wrong decisions that implicate customers’ lives and ruin their reputation in the market.

Demand sensing and product traceability: Evaluating the correlation between the two

Tracking a product throughout its lifecycle is initiated at the pre-production stage, following the procurement of raw materials to the post-production and distribution stage until the item reaches its end customer. This is what defines product traceability. Product traceability is crucial because it prevents cost overruns for the company.

Demand sensing in the supply chain provides trustworthy demand information to enterprises and helps them reduce the latency period between demand origination and meeting the same with adequate supply. Furthermore, by providing them with enough bandwidth, demand sensing and product traceability solutions empower manufacturers and logistics to comply with quality assurance, hygiene standards, and specific regulations for each design, manufacturing, and distribution process.

Demand sensing solutions allow granular visibility into network-wide sales and inventory. Such insights are crucial for businesses, helping them to understand what is selling, where, and at what speed. When combined with a traceability tracker, enterprises can quickly identify items that need recalling and replacing, proliferate products and efficiently meet unmet demands.

The key challenges in product traceability

Poor visibility in the supply chain gives birth to a disjointed network that hinders the flow of information, impairing the ability of enterprises to understand what is happening upstream in the supply chain and communicate the same internally and externally. In addition, existing data siloes often hinder sharing of critical information with interested parties. Poor transparency in the supply chain can cause irreparable devastation.

Adding to that are some other common problems faced by enterprises, such as:

The challenges mentioned above make it impossible for companies to trace the origin of raw materials used in the manufacturing of goods or identify the non-compliance with regulations regarding sourcing and production.

Companies are left to fend for themselves when issues arise because of the existing data siloes.

Demand sensing in the supply chain: Boosting traceability with AI and ML

Supply chain managers have to operate in a fast-paced environment. Managing increasing customer demand, inventory, raw materials, external market conditions, and planning for inventory and product releases can save the day for all.

Demand sensing powered by AI and Machine Learning helps organizations with the fundamental change in demand earlier in the planning stage. Hence, one can approach suppliers on time to acquire raw materials, verify the origin of each raw material, speed up the manufacturing process, keep up with the quality and quantity expectations, distribute to warehouses at various centers, and ensure the safety and hygiene of stored inventories and oversee their timely distribution to end users.

Inventory management with TradeEdge Platform

TradeEdge, an intelligent supply chain management platform, enables “brands to gain maximum channel visibility, improve retail execution, reach new markets faster and get one step closer to being a resilient enterprise.” It’s enterprise-grade solutions, TradeEdge Demand Sensing and TradeEdge Product Traceability help unlock the true potential of supply chain for businesses.

A cloud-based and scalable digital solution powered by AI and ML algorithms keeps enterprises in complete control over their inventories, monitoring their movement in real-time. In addition, TradeEdge Product Traceability, solutions accommodate legacy and modern records systems across the supply chain and provide:

Demand Sensing and product traceability thus help companies stay ahead of the market by providing an end-to-end view of inventory movement. It also provides them enough bandwidth to effectively trace products throughout their lifecycle and accommodate item proliferation on time.

How Document AI helps enterprises in data extraction and end-to-end document processing

The sheer volume of daily documentation due to large volumes of unstructured data can delay the timely extraction of purposeful and relevant information. Also, mere extraction of massive data sets means nothing to users unless they are optimally analyzed and processed for further usage. In order for businesses to unlock value from documents, companies need intelligent automated solutions. Document AI can quickly fill the gaping need and proliferate meaningful data extraction efforts, regardless of format complexities.

Understanding Intelligent Document Processing

AI document processing can intelligently extract and convert unstructured and semi-structured data into meaningful and consumable information. Structured and usable insights power end-to-end automation for document-centric business processes. With the help of Document AI, businesses can leverage those valuable inputs to make data-driven decisions and scale their growth 8-10 times faster than the global economy.¹

What is Document AI?

Document AI is powered by AI, ML, and NLP capabilities that train computers to simulate human understanding and reviewing documents without direct intervention. This automated data extraction tool is integrated into a scalable cloud-based platform to help organizations scan, analyze, understand documents and extract granular insights effortlessly. It is an ideal solution, especially for enterprises handling millions of records of varying formats and processing 129 billion business emails daily.² Document AI takes humans out of the loop and works independently while employees can focus on more value-added roles. Key enterprise document extraction challenges and how Document AI solves them
Data-intensive enterprise landscapes bombarded with document type and format variations exhibit solid challenges for data extractors. Then, presenting the same output in a consumable format increases the complexity of the colossal task. The following are the common challenges often faced by enterprises when processing documents for data extraction:

EdgeVerve’s Document AI solution, XtractEdge Platform, effectively unlocks business value faster than human hands by optimizing the document extraction, processing, and comprehension pipeline. By automating the whole process, the solution not only curbs human error and associated losses but also saves enterprises time, effort, and budget. The latter can optimally utilize their in-house resources for their expertise by relieving them of repetitive and recurring workflows.

The four stages of Document AI:

Key benefits of Document AI for enterprises

Document AI takes the complexity out of business data clusters shared in varied formats and types with an automated data extraction approach. The following are key benefits of Document AI:

Solutions like Document AI are the burning need of the hour, especially when enterprises need data to formulate game-changing decisions to keep their businesses afloat in the competition. By providing a one-stop-technology solution, Document AI can unlock unlimited possibilities by effectively scaling up and processing millions of documents across the length and breadth of your enterprise. Moreover, it expertly handles the document complexities and quantities without leaving any error trail or omitting critical data from the sources.

Use cases and benefits of RPA in the banking industry

With the technologies advancing exponentially, automated solutions have forayed into all the major industries, including the banking domain. As a result, the demand for Robotic Process Automation (RPA) in banking has recently increased considerably, with the companies bound to implement the solution to gain an edge over the competition.

As the application of RPA in banking is receiving a lot of attention of late, it has taken a giant leap forward to alter the functional dynamics of the banking sector. The induction of RPA into the financial landscape can be attributed to RPA’s capabilities to lessen the cycle time and boost accuracy and productivity with better regulatory compliance.

However, before heading too far ahead, let us first delve into the fundamentals of RPA, its benefits, and other subtleties.

What is RPA?

Robotic Process Automation is a solution that helps enterprises automate manual business procedures efficiently so that employees can invest more time in value addition tasks rather than tedious ones. For example, RPA applications in banking allow banks to stay competitive and flourish in the financial landscape by permitting customers to automatically process payments, deposits, withdrawals, and other banking transactions without manual intervention.

Major benefits of RPA:

How can RPA be implemented successfully in banking?

Well-planned strategy: Suitable planning to implement RPA in banking business processes is one of the major deciding factors of growth as well-planned and followed strategies can help the industry focus on pre-RPA execution concerns.

Identifying processes that require automation: Identifying processes that can be automated is one of the essential steps for successful RPA execution. Hence, this becomes the perfect time for Process Discovery to pitch in, which is leveraged to define, map, and analyze a company’s business processes.

Build rational ROI prospects: The most challenging element of RPA implementation is identifying ROI and the time invested for its realization.

Training employees: Once the planning is done, the company must program the staff to easily withstand various challenges associated with automated solutions. Hence, tutoring and mentoring them with automation services will enable the bank and the staff to attain correctly assessed results.

Joining forces with relevant teams: As soon as the classification process is done, businesses must develop the required teams comprising individuals from the IT and business departments of the company to work in unison.

Use cases of RPA in banking

Regulatory Compliance: RPA implementation in banking helps them abide by their legal and financial regulations easily as RPA tools could substantially augment the use of the required compliance resources.

Accounts Payable confirmation: With accounts payable being a time-consuming process, it directs the employees to automate vendors’ invoices and then validate the data before dispensing it. Following it, IA can systematize these tedious processes by mechanically reading the invoices and crediting the payments after rectifying the errors and confirming data.

Customer Experience: Banks improve their customer experience via RPA in retail banking with the consumers reaching out to the staff to receive the solutions to many common problems. The tools can quickly examine the customers’ doubts and offer solutions to their inquiries.

Mortgage loan processing: For mortgage loans, banks must go through several steps, such as credit checks, employment verification, and inspection, before approving the loan. This process is easily overseen through RPA automation.

Fraud detection: It has become difficult for banks to check every transaction manually. As a result, the fraud cases are increasing with each passing day. However, RPA software can use algorithms to identify fraudulent transactions, identify them, and send them to the relevant subdivisions.

Know Your Customer (KYC) process: Banking institutions are also liable for amassing Know Your Customer (KYC). Hence, banks utilize hundreds of FTEs to corroborate the correctness of customer information.

Report automation: It is one of the primary use cases of RPA that helps the banks to prepare financial reports with accurate information.

Account Closure Process: Banks can send automated reminders through the RPA application if the consumers have not furnished the required proof for the account closure process.

Conclusion

With the COVID-19 pandemic affecting most financial institutions’ businesses adversely, it is now more imperative for the banking industry to implement RPA as urgently as possible. It will not only expedite the processes and generate higher revenues but also make up for the lost time and business during the pandemic.

The banking industry will skyrocket with the consumers now vying for infallible, secure, and faster solutions provided the right set of RPA applications such as AssistEdge is executed. Hence, this strategy is bound to empower the banking institutions to reshape the banking ecosystem’s growth trajectory.