Can Data Entry from Purchase Orders Ever Be Fully Automated?
Managing Purchase Orders (POs) in supply chains is challenging due to the varying formats and SKU codes used by different customers. Traditional tools like OCR and EDI struggle with this diversity, often leading to manual data entry. AI is now providing a solution by automating the extraction and integration of data from these varied POs, enhancing both efficiency and accuracy.
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Can Data Entry from Purchase Orders Ever Be Fully Automated?
In supply chain management, handling Purchase Orders (POs) from various customers presents a significant challenge. The diversity in PO formats, including differing SKU codes and document structures, often leads to manual data entry, as traditional methods like Optical Character Recognition (OCR) and Electronic Data Interchange (EDI) are not fully equipped to manage this variability. This article explores the challenges in processing POs within supply chains and how Artificial Intelligence (AI) is eliminating manual data entry from POs.
Diversity in Purchase Orders
- Mismatched SKU Codes: Different SKU codes used by customers can complicate the matching process, consuming considerable time for manual mapping within ERP systems.
- Diverse Document Formats: Each customer's PO may have a unique format, which require case specific extractions that comes with high costs.
- Layered Order Processes: Supply chains often involve multiple layers of orders (manufacturer – wholesaler – reseller - end customer), adding further complexity.
- Limitations of Conventional Tools: Traditional tools like OCR and EDI can struggle in these diverse conditions. They require predefining fixed fields for each document variation, leading to partial automation at best. This tends to be more feasible for smaller customers who are more likely to comply with such standardization requests.
Manual Data Entry: A Tedious Reality in Supply Chains
Due to challenges related to PO management, it is usually too expensive to automate the process, so most companies still depend on manual data entry. This approach typically has the following downsides:
- Resource-Heavy: It requires significant manpower, which is costly.
- Prone to Errors: The likelihood of human error increases, affecting the accuracy of orders.
- Inefficient: It slows down the overall supply chain process.
- Reduced Job Satisfaction: Employees often find repetitive data entry tasks unsatisfying.
AI as a Solution for PO Processing
Latest developments in AI enable countless applications. Combining it with domain expertise can help eliminate manual data entry and overcome challenges in PO management automation. In contrast to traditional technologies (e.g., OCR), AI searches for data points by understanding the document, instead of relying on predefined fixed fields. This makes it agnostic to format, language, length, and changes. It requires minimal learning and is very easy to set up.
Setting Up AI
To extract and integrate data from incoming POs, only one template is required. This is done in three steps:
- Providing Examples: Users train the AI with a few examples to build and validate the template.
- Structuring template: Business users define data categories (e.g. SKU, quantities, price, discounts…), structured as instructed by the IT.
- Seamless ERP Integration: AI structures the extracted data for straightforward integration with ERP systems.
Here is an example of how AI is trained to extract data from incoming POs.
Advantages of AI in Purchase Order Management
- Easy to Use: Templates can be built by the business in minutes, in a format specified by IT.
- Language Agnostic: AI can process POs written in any language.
- Adaptability: AI can efficiently manage various PO formats and adapt as they evolve.
- Improved Data Quality: Enhanced data quality is basis for advanced analysis, reliable business planning, and accurate demand forecasting.
- Operational Efficiency: AI automation accelerates supply chain workflows.
- Employee Engagement: Less manual data entry means employees can focus on more strategic and fulfilling tasks.
Conclusion
Processing diverse and complex POs in supply chain management is a significant challenge, but AI technology offers a promising solution. By employing AI to automate the data entry process, businesses can achieve greater efficiency, accuracy, and employee satisfaction. Implementing AI in PO processing represents a significant step towards process automation in the supply chain, effectively accommodating the varying needs of customers.
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