Supply Chain evolution with automation
The rise of automation isn’t hidden from enterprises, nor is the fact that unstructured data volumes are surging. Supply-chain enterprises can utilize these trends in the following sub-functions of task automation, strategic sourcing, and contract management and achieve enhanced efficiency.
Task automation with unstructured data in the supply chain uses technology to automate repetitive and manual tasks that involve processing unstructured data in the supply chain. Unstructured data includes information that cannot be easily organized into a table or database, such as images, videos, audio files, and free-text documents.
Examples of task automation with unstructured data in the supply chain include:
- Automated data extraction from invoices, purchase orders, and other unstructured documents.
- Automated categorization and classification of unstructured data, such as product descriptions or shipment details.
- Automated data entry into supply chain management systems, reducing the risk of errors and increasing efficiency.
- Automated image recognition and analysis, such as detecting damage to goods during shipment.
Strategic sourcing with unstructured data automation in the supply chain uses technology to automate the processing of unstructured data as part of the strategic sourcing process. Strategic sourcing is the procurement process of identifying, evaluating, and selecting suppliers based on criteria such as cost, quality, and delivery time.
Examples of strategic sourcing with unstructured data automation in the supply chain include:
- Automated data extraction from supplier proposals, contracts, and other unstructured documents, reducing the time and effort needed to evaluate suppliers.
- Automated categorization and analysis of unstructured data, such as supplier ratings and reviews, to support supplier selection decisions.
- Automated risk assessment and monitoring using unstructured data, such as news articles and financial reports, to assess the stability and reliability of suppliers.
- Automated contract management, including creating and tracking contracts, reducing the risk of errors, and improving the speed of contract execution.
- Automated image recognition and analysis of supplier facilities, allowing organizations to assess their operations and make informed sourcing decisions.
Contract management with unstructured data automation in the supply chain makes use of technology to automate the processing of unstructured data in the contract management process, helping organizations to manage contracts with suppliers more effectively.
Examples of how unstructured data automation can be used in contract management include the following:
- Automated data extraction from contracts, purchase orders, and other unstructured documents to streamline the contract management process.
- Automated classification of contract terms, such as delivery dates and payment terms, to ensure compliance and reduce the risk of disputes.
- Automated data entry into contract management systems, allowing organizations to track contract status, deadlines, and other important information.
- Automated image recognition and analysis of signed contracts, reducing the risk of errors and improving the accuracy of contract management.
Task automation, strategic sourcing, and contract management with unstructured data automation improve the efficiency, accuracy, and security of processes, leading to improved supplier relationships, reduced costs, and increased competitiveness in the market.
Financial data automation
Automating financial data involves intelligent document processing in terms of accounts payable and receivable, risk management, verification and fraud detection, process automation, smart claims, validation, and exception handling.
- Intelligent Document Processing: Automated data extraction from loan applications, insurance claims, and other unstructured documents to streamline application and claims processing.
- Accounts Payable and Receivable: Automated data entry and reconciliation of financial transactions, reducing the risk of errors and increasing efficiency.
- Risk Management: Automated data entry into risk management systems, allowing BFSI organizations to identify and manage risks more effectively.
- Verification and Fraud Detection: Automated image recognition and analysis of ID and financial documents, reducing the risk of fraud and improving security.
- Process Automation: Automated workflows and processes in areas such as customer onboarding, loan processing, and claims management.
- Smart Claims: Automated processing of insurance claims, including data extraction, classification, and decision-making.
- Validation and Exception Handling: Automated checks and balances to validate data accuracy, with automated exception handling to ensure that any errors are quickly identified and addressed.
Automation in the finance sector improves accuracy along with security, which positively impacts turnaround time.
Legal data automation
Legal and compliance, with the help of virtual legal Assistants, contract management, and document automation, aims to enhance legal drafting and client communications by automating routine tasks and providing more efficient support.
- Virtual Legal Assistants: AI-powered tools providing legal support and guidance, including research, document drafting, and contract management
- Contract Management: Automated tracking and management of legal contracts, including data extraction, classification, and entry into compliance systems. This help to reduce the risk of errors and ensures that contracts are managed in accordance with legal requirements.
- Document Automation for Enhanced Legal Drafting: Automated document generation and assembly, including data extraction and formatting, to streamline the legal drafting process and improve efficiency.
Virtual legal assistants, contract management, and document automation improve the efficiency and accuracy of legal support and compliance operations while reducing the workload of legal professionals. By automating routine tasks, legal professionals can focus on more complex and value-added activities, thus improving client communication and overall satisfaction.
Customer service automation
Automated customer service aims to enhance call-center interactions by providing customers with more personalized and efficient support via self-service, recommendations, and insights from past transactions.
- Self-Service: A customer-facing platform that allows customers to access information easily and solve common problems on their own, reducing the need for support from a call-center agent.
- Recommendations: Personalized suggestions for products, services, or solutions based on a customer's past transactions and interactions. This information helps customers quickly find what they need and reduces the time spent on the phone with a call-center agent.
- Insights from Past Transactions: Data analysis of a customer's past transactions and interactions to identify patterns, preferences, and potential issues. This information can be used to inform call-center interactions, improving the accuracy and efficiency of support provided.
The goal of improving customer experience by providing more personalized and efficient support is achieved by reducing the workload of call-center agents; these tools can also help to improve the efficiency and cost-effectiveness of customer service operations.
Marketing and sales data automation
Unstructured data automation in marketing and sales makes use of trending metrics to automate various marketing and sales activities to improve efficiency and personalization.
- Communication: Personalized messaging and interactions with customers, using data analysis to inform communication content and timing.
- Campaign Management: Automated planning, execution, and tracking of marketing campaigns, including targeting, segmentation, and measurement.
- Content Creation: Automated generation and optimization of marketing content, including product descriptions, social media posts, and email content.
- Forecasting: Predictive analysis of future sales and marketing performance based on historical data and market trends.
- Lead Generation and Prioritization: Automated identification and ranking of potential customers based on data analysis and lead scoring.
Improved customer experiences, increased sales, and better alignment of marketing and sales goals can be seen as a product of increased efficiency and personalization.
HR data automation
Automation of HR data focuses on resume mining and document automation for onboarding to reduce turnaround time.
Resume mining is the automated analysis and categorization of resumes to identify and extract key information, such as education, skills, and work experience. This information can be used to quickly identify suitable candidates for open positions.
Document automation for onboarding is the automated processing of onboarding documents, such as employment contracts, tax forms, and benefit enrollments. This includes data extraction, classification, and entry into HR management systems, reducing the risk of errors and increasing efficiency.
Resume mining and document automation for onboarding in HR management streamlines the hiring process, improves efficiency, and reduces the time and effort required to onboard new employees. This improves candidate experiences, reduces HR workload, and ensures more accurate and timely employee data management.
In conclusion, the automation of unstructured data is a valuable tool for organizations looking to gain a competitive advantage and improve operations. By automating the processing and analysis of unstructured data, organizations can uncover insights, streamline operations, and improve overall efficiency. This technology can be applied in a variety of industries, including finance, healthcare, and retail, among others. By utilizing unstructured data automation, enterprises can improve customer experiences, reduce costs, and increase their bottom line. As technology continues to evolve, we can expect to see even more innovative applications and use cases for unstructured data automation in the future. The automation of unstructured data is a powerful tool for organizations looking to harness the value of their data and drive business success.