September 2022

5 Key applications of supply chain optimization

Supply chain optimization is no longer a buzzword; it has attracted too many eyeballs following the disruptive COVID pandemic. An organization’s supply chain functions like the human nervous system; any disruption in the network can have a lasting impact on the business anatomy. That can vary from the unavailability of adequate supplies for meeting the inventory crunch to clogged distribution channels resulting in delayed delivery of finished items.

The worldwide network of supply chains is susceptible to frequent changes connected with the global market, be it a pandemic or the present geo-political disruptions. Effectively managing a supply chain is hard, especially when it is still in the recovery phase. Being vulnerable to external factors, the supply chain needs the power of AI and Automation to continue functioning smoothly. Automation, AI, and other advanced technologies have the potential to dramatically enhance supply chain performance, especially for consumer-perishable goods across industries. The term supply chain optimization is born out of the strategic implementation of such technologies to improve its overall efficiency.

Due to the pandemic, nearly 38.8% of small businesses in the US alone faced supply chain delays. This calls for improved visibility in the supply chain, yet, 69% of companies surveyed in a recent study didn’t have total visibility. And that culminated in $1.14 trillion worth of out-of-stock items in 2020¹.

Powered by new-age technologies, supply chain management provides enterprises with a competitive edge in the market, as cited by 57% of companies². 70% believed that it is the critical driver for quality customer service³. Opting for a tech-based software solution for managing the supplier-distributor network is the first step toward optimization.

That being said, supply chain optimization does the following tasks to help an enterprise achieve process efficiency and improve the performance of various operations.

Supply chain optimization for CPG sector: A key to end-to-end planning

The entire sector dealing with consumer perishable items fared well despite the unprecedented challenges of the COVID pandemic by neutralizing cost challenges. Demand for packaged goods suddenly increased as customers hoarded more such items during the lockdown. Yet, the worst time is far from being over. External factors like uncertain demand, shifting tastes, changing standards and regulations, and heavy reliance on the global supplier network are to be blamed.

Even though a massive section of the industry shows reluctance to adopt data analytics and AI for optimization, most companies dealing with perishable items have started shifting towards the same. They have adopted autonomous end-to-end planning to gain a strategic approach to managing supply chains in volatile conditions. The outcome was noteworthy as most witnessed an increase in revenue, a reduction in inventory, and a decrease in supply chain costs.

What is supply chain optimization?

Supply chain optimization covers all the processes involved with the optimal placement of inventories within the network, minimizing various operational costs and adjusting other influencing factors. With the help of modern technology, the network is optimized to ensure it is at its peak efficiency. This quantitative approach identifies the best combination of facilities, warehouses, resource allocation, and other elements.

Benefits of supply chain optimization: Create an optimal supply chain design to make it reliable, cost-efficient, and market-ready

Top 5 use cases of supply chain optimization

Demand sensing: Accurate planning to meet upcoming demand can make all the difference as it empowers managers to gain a complete overview of demand fluctuations and stay prepared. Further, excess purchases of less popular goods can be avoided with the correct information and analytics, eventually freeing up valuable warehouse space. And most importantly, supply chain optimization does help in cutting overhead costs arising from poor demand planning and excess spent on resources to catch up and reduce lead times.

Inventory control: Supply chain optimization is necessary for managing inventories, primarily for companies selling perishable items. Their products usually have shorter shelf lives. Hence, the chances of wastage and inventory loss are always high. To prevent an over-stock situation, AI tools are needed along with accurate data to calculate safety stock, assess inventory reduction opportunities, and guide optimal inventory levels. Demand variations, supplier lead times, and batch sizes are a few areas considered while planning inventories.

Order fulfillment: Order fulfillment on time is key to keeping customers satisfied and engaged. And this is directly connected with inventory management. Simply put, if adequate inventories are available during order placement, it is easier to ship the same to customers as per their time and quality requirements. Supply chain optimization tools increase on-time orders, order accuracy, and the number of happy customers. Again, meeting orders is just as important as producing them in the first place. End-to-end supply chain optimization caters to those requirements, i.e., optimizes order delivery on behalf of the organization.

Logistics management: The Suez Canal crisis created a domino effect in the global supply chain, which was already struggling with the aftershocks of the pandemic. The incident strained containership availability, delayed shipments, and increased freight charges. Such incidents can prove devastating for companies selling perishable goods. Even though such incidents are rare, they can significantly impact the global supply chain. But with AI-powered supply chain network optimization, such incidents can be averted or provide time to implement contingency plans. Through AI, IoT, and predictive analytics, shipment companies can forecast vessel schedules and berth activities. They can reduce expenditure by optimizing schedules and ensuring goods flow effortlessly throughout the network.

Customer service: Lastly, supply chain optimization indirectly impacts customer service. This use case is directly connected with the other use cases mentioned above. In simple words, the higher the number of orders reaching users on time and per quantity and quality expectations, the happier customers are with the organization’s service. An optimized supply chain notifies customers if a shipment is delayed and the estimated time by which the order will eventually reach them. Further, supply chain optimization software solutions help enterprises identify customers receiving late orders to process adequate compensations, remediate unfortunate situations, and keep their business intact.

In a nutshell, supply chain optimization brings agility and resiliency to the network, breaks data silos, and allows prompt actions to avert any significant calamity. Sectors dealing with perishable goods or items with shorter shelf lives need to resort to AI solutions to mitigate risk or loss of stock, revenue, and customers.

Document AI for invoice processing: Key benefits to consider

Today, technologies like Automation and Artificial Intelligence are no longer a figment of imagination. Companies are finding ways to onboard similar intelligent solutions to expand operational efficiencies and employee productivity. Document AI is one example where machines are trained to simulate human review of documents using various AI capabilities like Machine Learning and Natural Language Processing.

These tech-based solutions are gradually infiltrating nearly every industry, every sector, every niche, and every operation with their wider applications, invoice processing being one of them. AI invoice processing can be a game-changer if incorporated on time. But the question remains – where are we with AI and Automation?

Document AI for invoice processing: A reality or a concept in the making?

Enterprises are turning to smart digital solutions to go paperless to meet ongoing preferences. Such moves are incredibly beneficial for accounts payable teams as they have to handle bulk paperwork and intricate approval procedures while processing company invoices. Moreover, most of the tasks are repetitive in nature, which can be automated with advanced technologies such as Document AI. It can be the ideal bet to eliminate inefficiencies or errors usually connected with manual work, while increasing accuracy and avoiding payment delays.

However, the reality is far from the truth.

Typically, the accounts payable department processes over 1000 invoices per month, most of which are received via fax. So much for the paperless office concept, which remains a myth for many enterprises even today. And one minor error while adding specifics on the invoice can easily cost them their jobs and their company’s reputation. The chances of human errors are always high with any manual method, exclusively for invoice processing, not to mention the loss of untapped opportunities for the company.

And there are greater risks to consider.

The proportions of financial fraud remain high – concentrated in four departments

According to reports, nearly half of reported thefts and embezzlement happen within four departments that handle invoices, payroll, financial statements, and sales forecasts. In most cases, employees conceal theft, with a staggering 32% attempting to alter physical documents while 39% create fraudulent physical documents.¹

The accounts payable department is the most vulnerable department in an organization, often targeted by scams. Embezzlement of cash, fraudulent payments, and financial statement manipulation are just a handful of examples. With paper-based invoices, the chances of such fraudulent activities remain high always. They can vary from false billing to over billing and overpayments.

Contrarily, an automated invoice processing software solution offers preventive measures to mitigate accounts payable fraud.

How does Document AI prevent fraudulent invoice processing?

Automated invoice review: The function of matching key documents generated across the procurement stages has a fair chance of preventing employee fraud. It covers comparing invoice line-item information with purchase order details and reviewing receipts to ensure goods and services have been delivered. This approach, however, can take days when executed manually. With Document AI, the entire cycle completes in just minutes.

Vendor match capability: At times, invoices are generated by fake vendors, which are difficult to track when human hands are involved. But detecting them at the initial stages can curb any possible damage to the company. AI invoice processing allows systems to scan multiple repositories, existing vendor databases, and various documents to detect fakes.

Monitor and track employee actions: Usually, employees responsible for accounts payable frauds operate as individuals or small groups. But Document AI offers ML-based anomaly detection capability to track and monitor employee engagements in the Accounts Payable process in real-time and thwart malicious behavior.

Automated approval workflow: Automated approval workflows address the time-intensive factor inherent in manual approval processes. It ensures compliance and leaves a digital trail of the actions performed across each stage. This can certainly help mitigate the risk of malicious intent.

Enforced compliance: Digitally improving the entire invoice processing workflow can curb Accounts Payable fraud. It calibrates actions with time stamps and personas, later used for system audits. Systems similar to Document AI auto-generate reports, where employee activities are traced, and areas of improvement are identified. Further, real-time alerts provide empirical insights into process operations.

Other benefits of Document AI for invoice processing:

Key takeaways

Fraudulent acts surrounding accounts payable will continue to evolve; so will the solutions. However, most organizations are trying to embrace digital solutions and simplify the intricate system of invoice processing. Paper invoices still retain their premier positions, but they are gradually stepping aside for definite reasons, environmental, fraud, or otherwise.

And Document AI is filling the gap as the intelligent alternative to automate invoice generation and effectively deal with error, time, and labor-intensive factors. AI invoice processing is the best bet to prevent chances of strained relationships between partners from missed, delayed, or double payments.

How do companies avert contract risks and derive optimum value using Contract Analysis?

Contracts are a written constitution or a roadmap for conducting business between parties. Poor analysis of contractual terms and missed compliances can result in huge losses for both parties to the contract. Therefore, contract analysis should be continuous to avert any possible risk and drive revenue for both parties.

What is contract analysis, and why does it matter?

When two entities enter a contract, they are bound by its terms and conditions. A contract defines what is to be delivered, how, and when. Contract Analysis allows the parties to keep track of the information within the agreement, flags what’s already been achieved and what is yet to be delivered, and ensures the expected quality and compliance standards are aptly met. It continues all through the contract lifecycle.

Contract risk analysis is essential because it can add extra value to your business’s bottom line. The chances of errors or missed terms are high when agreements are drafted and evaluated manually. After all, a business contract is not an A4 sheet document; it is a thin booklet of papers filled with contractual terms, figures, and dos and don’ts. Inadequate evaluation of business agreements during the negotiation process can be cited as one of the major causes of such errors. Any missed detail or error will only sap profits from your business.

A few such common errors could be:

All the errors mentioned above and many more are visible only after multiple evaluations of contractual terms. However, multiple analyses might not be convenient due to time constraints. But, manually o analyzing agreements only delays the process and increases the chances of more errors being left behind unresolved.

Contracts are just like business documents. They are a goldmine of data presenting endless opportunities if extracted intelligently. Just like document processing, legal agreements need an intelligent solution. AI-powered contract analysis can very well fit the profile.

How do companies mitigate risk with contract analysis and drive revenue?

Studies show that an inadequate risk management strategy during the contracting process can result in a loss of value of 9% or more. That value can be in terms of revenue or business opportunities. Unfortunately, such risks are often overlooked by companies during the contracting process.

AI-powered contract review and analysis takes the labor, time, and error-intensive factors out of the process and accommodates adequate evaluation of terms and clauses to avert plausible risks.

Solutions like XtractEdge Contract Analysis leverage vision-based, semantics-based, and language-sequence-based ML techniques to review contractual terms and identify hidden risks. Such risks can be in the shape of the following:

Often, such nuances exist in granular details that are highly likely to get missed or overlooked when reviewing contracts manually. Poor visibility of contractual terms makes it even more difficult for legal advisors to identify key problem areas and address them. A manual approach could result in more losses and further delays. Hence, AI contracts analysis is the best solution for organizations.

The latter breaks down contractual clauses and provisions and identifies the true intent behind each term and abstract entity or entities mentioned in the contract. This approach enables ease of reading and interpretation.

A smart contract analysis usually does the following tasks while reviewing contracts:

AI contract analysis empowered by added capabilities improves contract visibility and proactively accommodates continuous risk identification and prevention. At the same time, it upholds the confidentiality of partner details and contractual terms. This effortless and errorless approach can quickly drive revenue for partners and safeguard them from any risk arising from misrepresented contracts.

How does Document AI address key data challenges in banking?

The banking sector generates a massive amount of data, which is growing at a rapid pace due the adoption of digital technologies. Moreover, following the online transition of banking activities, customers leave behind their digital footprints or valuable customer data, which presents endless opportunities for enterprises to capitalize on. The legacy approach to processing data presents unprecedented challenges, which an intelligent solution like Document AI can readily address.

Document AI uses various AI capabilities like Natural Language Processing, Computer Vision, OCR, and others to simulate human review of documents. For instance, EdgeVerve’s Document AI platform, XtractEdge, helps scale up and process millions of documents across the length and breadth of your enterprise.

The banking sector is a data-intensive industry handling customer documents in bulk every day. However, manually processing and extracting insights from each record is no longer feasible. By introducing AI in banking, the sector can easily override numerous challenges it currently faces pertaining to managing and optimizing data productively.

Key data challenges in the banking sector

It is not only about handling bulk data but also securing it in a centralized system easily accessible by authorized users. Hence, automating the processing of documents and extracting data is just one of the many benefits of Document AI.

The following are a few common challenges the sector is currently facing:

The challenge of keeping up: The banking industry has always been the last to embrace and adopt new technology and innovation. As a result, most legacy systems are inadept at handling the increasing workload. When they attempt to collect, store, and analyze the required amounts of data using outdated infrastructures, the overall system’s stability can fall like a house of cards. This is one of the very reasons why Fintech adoption is so high.

The bigger the volume, the greater the risk: Banking providers upload sensitive customers’ or corporate data into their legacy systems. Data leakage is a given fact in the absence of robust security infrastructure. Banking providers must ensure that data collected by them is secured at all times. But, the numerous outbreaks of data breaches in the banking sector attest otherwise.

Lower levels of data maturity: Data maturity refers to how banking and other financial institutions are utilizing the data available to derive the maximum value out of them. An increased data maturity means improved predictive analytics capability, resulting in more data-driven decisions. However, the knowledge and technology gap in the process is responsible for most banks still relying on legacy approaches to extracting insights from raw data. And the outcome is often shrouded in biases and errors.

How to address data challenges with Document AI?

As the name suggests, Document AI leverages various new-age technologies like Artificial Intelligence and Machine Learning not only to address the document processing problem; it does a lot more.

Just like the example mentioned earlier, AI-powered document processing tools can not only orchestrate the processing of millions of documents without human intervention at record speed, but it also ensures complete security of the data extracted. In addition, such solutions offer robust security features and other elements that quickly address the challenges the banking sector currently faces.

Benefits of AI in banking

AI works best in the presence of data. The banking sector is a data goldmine, which, when extracted intelligently using AI capabilities, can provide banks and even other organizations a real chance to unlock value from their idle data resources. The benefits of Document AI in banks and financial institutions are immense; a few of which are mentioned below:

In a nutshell, Document AI is altering the traditional way we do banking. It not only enhances customers’ user experience but also revolutionizes how banks operate. Undoubtedly, the current challenges, especially with data, raise concerns for bankers. But, with the timely implementation of the right tech-based solutions like Document AI, data challenges will soon become history.

How does Computer Vision address data extraction challenges?

Managing unstructured data has emerged as the biggest hurdle for enterprises. Due to various structural data discrepancies, advanced technologies like Intelligent Automation and AI have become at the top of the agenda for enterprises as it enhances quality and reduces response times with fewer resources. Through analytics-based insights, it identifies the bottlenecks, traces the improvement areas, and creates bots to enable companies to offer an end-to-end business solution to the customers. However, this process can only work efficiently if the data is available in an actionable manner.

Against this backdrop, the need for digitization to capture, extract and process data seamlessly has grown exponentially. To tackle these document data challenges and unlock business value, enterprises have begun implementing some of the latest digitization technologies; one such technology is Computer Vision.

Computer Vision is modeled on human capabilities to identify, verify, and examine images and videos. Computer Vision applications play a crucial role in converting unstructured data into actionable data.

Why should enterprises go beyond OCR?

Optical Character Recognition (OCR) plays a substantial role in automating business processes through its capability to replace manual labor with software-driven processing. Despite OCR being beneficial, it has its own shortcomings while handling complex processes. The key reason for this is that many documents are in inconsistent format, and OCR becomes more effective when documents are coherent data for comfortable digital accessibility. This is where Computer Vision steps in to resolve the problem.

Computer Vision utilizes an innovative and smarter way of scanning and detecting critical data in document form. When combined with automation and OCR, Computer Vision drives substantial performance improvement. Primarily, Computer Vision identifies those parts of interest in each document. This information then passes on to the OCR engine, where the information can be transformed into a structured format.

Once Computer Vision is implemented, it correctly identifies and demarcates the required object; OCR can then be deployed to extract and convert the right data with higher accuracy. In this way, Computer vision overcomes OCR’s limitations by acting as an in-between or preparatory step before text or data extraction.

What is Computer Vision?

Computer Vision is the practice of seeing, reading, and identifying objects or information in an unstructured setup by implementing Artificial Intelligence. Although OCR is a part of Computer Vision, it is not as effective as Computer Vision’s capability to outdo human accuracy in rapidly identifying and responding to visual inputs.

Computer Vision works by digesting massive quantities of data on related images to recognize specific characteristics and patterns. As it can enable the structuring of digital data, it enables OCR, which is still the most convenient way of converting data into digital forms.

How does Computer Vision work?

Computer Vision mimics the human brain and helps decode and make sense of visual data by using underlying patterns. For example, OCR is applied for seamless processing, but it becomes unproductive when used for images. However, AI and Computer Vision can use pattern recognition to identify documents and organize them together without human interference. Once recognized, Machine Vision and ML applications can be instructed to obtain data.

Some primary purposes of Computer Vision:

Need, significance, and benefits of Computer Vision

Applications are overcrowded with all categories of texts, videos, and photos. The same is the case with documents of enterprises in almost every industry. Hence, it has become mandatory for organizations to capture, analyze, and understand the requirements by identifying them from the available visual content and converting them into structured data. This is where the role of Computer Vision with AI and NLP becomes even more significant for ensuring moderation and examining the online visual content.

With the assistance of Computer Vision, the digital transformation journey is likely to succeed as most of the enterprise data is structured. Subsequently, businesses will need help to optimize digitization. Therefore, converting unstructured data into a digital format that can be identified, manipulated, and processed is crucial. Hence, the significance of computer vision in aiding enterprise digitization becomes even more prominent due to its advanced-level benefits.

Some of the benefits of Computer Vision are:

Assisting RPA: RPA relies on access to consistent data sources to run automated solutions. Hence, optimizing RPA depends on essentially translating the data into a digital format.

Diminishing risk: There are various risks associated with regard to data privacy management due to increasing cybercrimes. Hence, regulations are being made to safeguard certain forms of data. For instance, identifying digital customer data and its storage location is critical. However, this threat can be reduced by applying Computer Vision.

Drives innovation: The data obtained from scrutinizing the enterprise data will be lost if this data is not saved in a digitally identifiable and accessible format. After the data is identified, this uncovers the consumption trends, patterns, consumer preferences, challenges, and other factors that can help the management to drive enhanced decision-making. The sheer size of these data pools, or lakes, is tremendous. Hence, digital conversion is crucial.

Boosting customer experience: Assembling all the customer’s interaction or exposure data will only ensure that the experience is optimized and enhanced.

Increasing content extraction accuracy: Computer Vision expands output and reduces the number of transactions routed for exception management.

Conclusion

Computer Vision is fast emerging as the ‘go-after’ solution garnering much attention across all the major industries. Due to this, it will likely be able to operate on a broader ecosphere of content data in the coming times.

In the last few years, there has been an infusion of new products that demand the services of Computer Vision and AI, with companies applying Computer Vision for process optimization to identify processes to be automated with better precision and superior pace.

Additionally, with organizations increasingly embracing technologies like the Internet of things and AI across industries, enterprises have already begun to pay urgent attention to Computer Vision. Therefore, this momentum of companies leveraging various benefits from the adoption of Computer Vision Applications will surely continue in the times ahead.