Transform Manufacturing Operations with Intelligent Document Processing – A CXO Perspective

Key takeaways 

• Intelligent Document Processing eases the workloads and bottlenecks created due to manual document processing.
• The technology intelligently extracts information from paper documents and makes it available for further processing and insight generation.
• It eliminates process inefficiencies and bottlenecks and allows businesses to be agile, flexible, and scalable. It proves to be a strategic business investment.

Manufacturing businesses work with thousands of suppliers for manufacturing each of their product categories, which generates heavy paperwork in the form of purchase orders, invoices, quality inspection reports, bills of lading, bills of material, shipping documents, maintenance documents, etc. Matching the documents manually across suppliers and customers and their payment and receivable is often a mammoth and cumbersome task. In addition, siloed processes, disconnected operations, and manual efforts create an environment conducive to human error. Intelligent Document Processing is the answer to such process inefficiencies. 

What are the shortcomings of manual processes in Manufacturing?

• Error proliferation: Errors in manual data transcription and order processing can lead to defective output in the production stage. Further, repetitive tasks, such as transcribing data and matching data across paper documents and PDFs introduce errors in the workflow.
• Difficult reconciliations: Reconciling data for different orders is a cumbersome manual task and significantly affects productivity. Errors due to manual processing at this stage can result in significant rework and loss.
• Lack of visibility: As information stays buried in scattered paperwork, it affects decision-making and results in opportunity loss. Decision-makers and auditors struggle to piece across the jigsaw to see the bigger picture. It results in a lack of proactive problem-solving.
• Supply delays: Interactions across the Manufacturing value chain depend on data exchanges in paper format. Errors in data transcription lead to delays and added operational costs. Procurement delays in geopolitically volatile environments can cause major losses.
• Changing regulatory environment: The Manufacturing landscape is governed by strict regulatory compliance. Paper documents are difficult to store and retrieve, which hampers the quick traceability for audits and adds to the cost of records management and storage.
• Gaps in QC & QA: The QC reports, non-conformance reports, deviation reports, etc., require fast action for analyzing root causes and trends and initiating corrective and preventive actions. Slow QA redressal leads to product recalls and loss of customer trust.
• Lack of scalability: As the Manufacturing operations increase, the influx of documents increases, thus creating bottlenecks for manual processing. As a result, the operations framework cannot scale as required. It caps off the growth and revenue.
• Rising operational costs: To accommodate increasing operations, businesses have to increase headcount and manual efforts. Similarly, the efforts involved in error rectification, rework, document storage, and retrieval also increase, and hence the operational costs.

What are the previous generations of Document Processing? What is the significance of Intelligent Document Processing in Manufacturing Operations? 

Intelligent Document Processing is a powerful productivity boosting tool. It unlocks the information hidden under mountains of data, sequentially puts it in the Manufacturing ERP systems in real-time, and improves visibility, thus allowing the leadership and CXOs to make well-informed and timely decisions.

The technology has evolved over multiple generations –

• Manual document processing (earlier than the 1990s): In this generation, processes were entirely handled through paper forms and manual data entry. The data transcription, typing, document classification, and data routing were done manually by human resources. This slow and error-prone method was both labor-intensive and time-consuming. It resulted in procedural bottlenecks and data latencies during decision-making that significantly impacted revenues.
• Optical Character Recognition (OCR) (earlier than 2000s): This generation of document processing technology saw a significant shift from manual processing to digital document processing. It facilitated the conversion of typed or printed text into machine-readable information. Though this technology allowed digital storage and archival of data, which was keyword searchable, it had many limitations. The degree of data extraction accuracy was low due to the varying fonts and legibility of the copy. It had a better data extraction accuracy with a structured format of documents, but only with significant manual intervention. 
• Advanced OCR (earlier than 2010s): This technology was powered by rule-based engines and templates. It was configurable with rules for extracting data from each field based on its location in typical document categories. It eased the data extraction through structured documents, such as purchase orders and invoices. Technology variations such as Optical Mark Recognition (OMR) for reading checkboxes evolved in parallel. Workflows routed the extracted data to the downstream systems. However, the technology was totally reliant on templates and rules requiring significant manual intervention, as it would succumb to the document layout changes. It lacked cognitive extraction and assimilation of data.
• AI/ML-driven Intelligent Document Processing (IDP) (2010 till date): It leveraged AI/ML algorithms and brought in cognitive data extraction and continuous self-learning into the picture. It went beyond rules and templates to identify content, structure, and layout and extracted the data with accuracy even when the typical positioning of the fields in the document changed. Learning from exception handling led to better confidence levels of extraction with each batch processing. However, the technology still required training and optimization with limited capability of understanding the context or the meaning of the text.
• IDP today and future roadmap (2020s and future): The technology is still evolving with a holistic use of advanced models built on deep learning, natural language processing (NLP), and computer vision. It is moving towards a completely autonomous and context-aware processing of highly unstructured data.
  Intelligent Document Processing leverages AI/ML and NLP to transform the text from unstructured, semi-structured, and handwritten documents into structured information that integrates with the downstream systems within fraction of the time required in manual processing.

The AI algorithm-driven technology churns heavy loads of data and generates tangible outcomes for the annual reports of the Manufacturing businesses in the form of cost savings, accelerated processes, revenue creation, and greater opportunity generation by using the existing resources.

Intelligent Document Processing Use Cases in Manufacturing

• Purchase Order processing: Receive hundreds of purchase orders through different channels in different formats, route them to a centralized base, classify them, and extract the data from the required fields. Validate the extracted data with the information base, and integrate it with the core business systems for faster order fulfillment. Improve stakeholder relationships with accelerated processes.
• Invoice processing: Capture data from the required fields, perform 2-way/3-way matching, flag exceptions for human-in-the-loop intervention, reduce reconciliation, accelerate closures, and avail early payment discounts.
• Shipping document processing: Auto-extract required fields from bills of lading, packaging details, custom declarations, delivery receipts, etc. Quickly process these documents and streamline the shipping process across multi-modal customs offices. Track the shipments with real-time visibility, minimize delays, and save costs.
• Contract Management: Extract main clauses, terms, contract renewal dates, pricing structures, etc., from contracts. Raise alerts for critical milestones and compliance requirements and ensure cost savings.
• Work Order processing: Automate work order data extraction across key fields, such as customer details, product specifications, due dates, etc. Integrate the extracted data into the core business systems. Optimize scheduling and speed up processing.
• Bill of Material processing: Accurately extract the details from BoMs received from different channels. Ensure that the specifications are accurately captured and consistently used across the different teams, such as procurement, engineering, and production, eliminating rework.
• Maintenance log processing: Expedite logs processing by extracting critical data points, such as equipment identification number, service date, service type, engineer notes, parts replaced, etc. Automate predictive maintenance by tracking service history.
• Expense report processing: Quickly extract data points from expense forms and receipts to automate further processing and expedite reimbursement.
• Employee details processing: Automate the data extraction from employee onboarding documents and forms, timesheets, etc., creating time for HR executives to focus more on important work areas.

More Intelligent Document Processing Use Cases for Manufacturing >>

Prominent Benefits of Intelligent Document Processing

• High data accuracy and integrity: Automating unstructured data extraction from paper documents eliminates errors resulting from human transcription with a high degree of confidence and accuracy. The integration of this high-integrity data results in real-time visibility of inventories, production, finances, etc., and faster data-driven decision-making.
• High efficiency gain: The high accuracy gain in data extraction eliminates rework. It improves the process efficiency by almost 10x. Document-driven processes that took days now take minutes. Faster order-to-cash processes improve cash flow. Faster procurement and payment cycles improve supplier trust.
• Lower operational costs: Automation of the manual data transcription and extraction processes results in highly reliable data. It reduces the costs involved in correcting errors and rework. It also reduces the requirement for in-house physical document storage and management. It also allows FTEs to focus on multiple high-value tasks while overseeing the document management.
• Improved responsiveness in dynamic markets: Intelligent Document Processing enables real-time access to information across different functions and operations that previously lay buried in paper documents. It allows Manufacturing businesses to act swiftly in case of quality issues and supply chain reorientations in highly dynamic and volatile markets.
• Risk management in paper-driven processes: The technology creates detailed audit trails, which enable adherence to statutory requirements. It allows quick access to the information in QC and compliance documents across departments. It allows taking quick corrective and preventive actions and eliminates collateral damage in the process-intensive scenarios.
• Better-equipped employees: Intelligent Document Processing frees up the critical knowledge workers from monotonous and repetitive tasks, allowing them to focus on strategic and analytical activities. It improves the business throughput and brings in employee satisfaction, which is so much responsible for employee stickiness. 
• Scalability: The technology is Cloud-based and inherently scalable without requiring a corresponding increase in physical infrastructure or manual realignment. It easily accommodates the seasonal increase and decrease in document-driven processes and the corresponding changes in the number of documents.

Championing the Intelligent Document Processing Implementation – A roadmap

Intelligent Document Processing is an AI/ML-driven, self-learning data capture technology. However, its implementation requires strategic planning. Some of the important aspects to be included in the implementation roadmap include – 

• Start small: Identify the departments and functions that will be involved in the scope of the automation at the inception of the automation journey.
• Define project goals: Shortlist the most document-intensive processes in the function with the highest potential for quick return-on-investment and create a Proof-of-Value (PoV).
• Perform due diligence: Analyze the existing document workflows, identify the different types of documents, identify their sources, and write down the business requirements for automation.
• Identify the solution partner: Identify the solutions that offer comprehensive AI/ML capabilities, higher data extraction confidence scores, highly accurate output, and seamless integration with downstream systems.
• Reduce exceptions: Identify solutions that pre-process the input documents to improve their quality. It increases the accuracy of the data extraction and reduces exceptions.
• Continuously optimize: Monitor the processing time and accuracy rate across different document types and continuously refine the underlying AI/ML models in addition to self-learning.

Intelligent Document Processing and its Strategic Perspective in Manufacturing

Staying ahead in a dynamic business landscape demands process agility and first-time rights. As a result, Intelligent Document Processing has become a business imperative and not just a “nice-to-have technology”. It is an important aspect of a vibrant market.

Manufacturers adopting the AI/ML-driven and template-free Intelligent Document Processing gain an optimal level of operational efficiency, improve first-time rights in document processing, and allow human resources to work on more important and strategic work areas.

Adhering to manual work practices exposes the business to the vagaries of the ever-changing business environment, hindering its agility, efficiency, and scalability. Newer technologies, such as Intelligent Document Processing, improve the business’s adaptability and responsiveness.

Simply put

Intelligent Document Processing is a strategic move for the future roadmap of the Manufacturing business landscape, which is fraught with numerous hurdles. This technology offers a solution for discovering the power hidden within the mountains of paper-process-driven data. Transforming the way of interacting with paper-based processes unlocks the hidden information in real-time, allowing it to be integrated with the downstream systems, for further processing as well as insights generation. Investing in Intelligent Document Processing thus proves to be a strategic move in the roadmap of the manufacturing business. It allows the business to move beyond the immediate hurdles presented by manual processes and stay ahead in the complex business landscape.

Next reading

• Jumpstart Automation using Intelligent Document Processing in Paper-intensive Environments
• Why Intelligent Document Processing?
• 9 RPA use cases & Keiretsu – The way forward for Manufacturing sector