6 ways online retailers are using AI for improving their account payables process and decision-making
In today’s digital era, e-commerce and online distributors are constantly seeking innovative solutions to enhance their operational efficiency and decision-making processes. One area where AI technology has shown significant promise is in Account Payables (AP) management. By harnessing the power of AI, online retailers can optimize their financial processes, make data-driven decisions, mitigate risks, optimize cash flow, and strengthen vendor relationships. In this blog, we will explore the reasons why AI is transforming AP management and highlight how some well-known e-commerce companies have successfully implemented AI-powered systems.
These are the 6 ways online retailers are streamlining their accounts payable’s process with artificial intelligence:
1️⃣ Supplier Performance Analytics
2️⃣ Risk Assessment
3️⃣ Supplier Segmentation
4️⃣ Payment Forecasting and Optimization
5️⃣ Intelligent Sourcing and Recommendations
6️⃣ Invoice Processing Automation
Supplier Performance Analytics
AI algorithms can analyze supplier data and generate performance metrics, such as on-time delivery rates, quality scores, pricing trends, and performance benchmarks. By evaluating these metrics, you can assess the performance of your suppliers and make data-driven decisions regarding supplier selection, contract renewals, and negotiations. More specifically:
AI algorithms can analyze historical data and track the on-time delivery rates of suppliers. By evaluating the consistency and reliability of suppliers in meeting delivery deadlines, e-commerce companies can identify high-performing suppliers and establish stronger relationships with them. This analysis also helps identify suppliers who may be struggling to meet delivery commitments, enabling proactive measures to address the issue.
AI can assess the quality of products or services provided by suppliers. By analyzing customer feedback, product reviews, and return rates, AI algorithms can generate quality scores for each supplier. This information allows e-commerce companies to assess the reliability and consistency of suppliers in delivering high-quality products or services. By aligning with suppliers who consistently meet quality standards, retailers can ensure customer satisfaction and maintain their brand reputation.
AI algorithms can monitor and analyze pricing trends for products or services offered by suppliers. By tracking market fluctuations, competitive pricing, and supplier pricing strategies, e-commerce companies can identify opportunities to negotiate better pricing terms with suppliers. AI-powered pricing analytics enable retailers to optimize their procurement processes, minimize costs, and maximize profit margins.
AI can establish performance benchmarks based on industry standards, historical data, and key performance indicators (KPIs). By comparing supplier performance against these benchmarks, e-commerce companies can evaluate suppliers objectively and identify areas for improvement. This analysis facilitates constructive discussions during supplier negotiations, ensuring mutually beneficial agreements and continuous improvement.
Risk Assessment
AI can help you identify potential risks associated with your suppliers. By analyzing supplier data, external data sources, and market indicators, AI algorithms can flag indicators of financial instability, compliance issues, or operational risks. This information enables you to evaluate supplier risk profiles and take proactive measures to mitigate any potential risks.
Let’s consider an e-commerce company that sources products from multiple suppliers to fulfill customer orders. The company has implemented an AI-powered supplier risk assessment system that analyzes various data sources, including supplier financial records, market indicators, and news articles. The AI algorithms are trained to identify potential financial instability among suppliers.
In this case, the AI-powered system detects a risk associated with Supplier X. The system analyzes Supplier X’s financial statements and identifies a significant increase in their debt-to-equity ratio over a short period. This raises concerns about the financial health and stability of Supplier X.
The AI algorithms further analyze external data sources and news articles related to Supplier X. They find reports of a recent legal dispute involving Supplier X, which may result in potential financial liabilities for the supplier. Additionally, the algorithms detect negative sentiment in customer reviews and online discussions about the quality and reliability of Supplier X’s products.
Based on these AI-driven insights, the e-commerce company becomes aware of the following risks associated with Supplier X:
The increase in debt-to-equity ratio indicates potential financial challenges for Supplier X, raising concerns about their ability to fulfill orders and maintain a stable business operation.
The ongoing legal dispute suggests potential legal and financial liabilities that could impact Supplier X’s financial stability and ability to meet contractual obligations.
The negative sentiment in customer reviews and online discussions raises concerns about the quality and reliability of Supplier X’s products, which could lead to customer dissatisfaction and damage the e-commerce company’s reputation.
A specific type of risk is fraud. AI can also help your business in fraud detection: artificial intelligence algorithms can analyze historical transaction data and supplier information to identify patterns indicative of fraudulent activities. By flagging suspicious transactions or suppliers, e-commerce companies can proactively prevent financial losses and maintain the integrity of their AP processes.
Supplier Segmentation
AI algorithms can segment suppliers based on various criteria, such as product categories, geographic locations, or performance levels. Segmenting suppliers offers several benefits for e-commerce companies. Here are some key advantages of supplier segmentation:
Supplier segmentation allows companies to customize their approach and strategies based on the specific characteristics and needs of different supplier groups. By understanding the unique requirements of each segment, companies can develop.
Supplier segmentation helps allocate resources effectively by identifying suppliers that are critical to the company’s operations or have the potential for strategic partnerships. By focusing resources on key suppliers, companies can optimize their efforts, streamline processes, and allocate resources where they will have the most significant impact.
Supplier segmentation empowers companies with a better understanding of their supplier landscape and performance levels. This knowledge strengthens their negotiation position during contract renewals, pricing discussions, and other supplier interactions.
Supplier segmentation aids in optimizing the supply chain network by identifying alternative suppliers within each segment. This diversification enhances supply chain resilience, reduces dependency on a single supplier, and mitigates the risk of disruptions.
Payment Forecasting and Optimization
AI can analyze historical data, market trends, and supplier patterns to forecast payment timelines accurately. By optimizing payment schedules, e-commerce companies can manage their cash flow effectively, take advantage of early payment discounts, and ensure timely payments while maintaining healthy relationships with suppliers.
Intelligent Sourcing and Recommendations
By analyzing supplier data, market trends, and other relevant factors artificial intelligence provides intelligent sourcing recommendations. These recommendations can help you identify alternative suppliers, evaluate potential cost savings, and optimize your supply chain network.
Let’s consider an e-commerce company that operates in the fashion industry. They have implemented an AI-driven intelligent sourcing and recommendations system to optimize their supply chain and improve profitability. This system is able of:
- Analyzing the industry by monitoring market trends, consumer preferences, and competitor activities.
- Assessing the performance and capabilities of existing and potential suppliers.
- Forecasting demand: The AI algorithms leverage historical sales data and market trends and accurate demand forecasts for different fashion products, enabling the company to anticipate demand fluctuations
So, based on market analysis, supplier evaluations, and demand forecasts, the AI system generates intelligent sourcing recommendations. These recommendations identify alternative suppliers, sourcing regions, or product categories that offer cost savings, improved quality, or better availability. For example, the system might recommend exploring new suppliers in a specific region known for producing high-quality fabrics at competitive prices. It can also suggest diversifying the product range by sourcing trendy accessories that are in high demand among target customers. These recommendations are data-driven and aligned with the company’s sourcing objectives, profitability goals, and customer preferences.
This is just a simple example displaying how, by leveraging AI algorithms to analyze data, evaluate suppliers, forecast demand, and generate intelligent recommendations, an online business can optimize its supply chain.
Invoice Processing Automation
Last but not least, AI can play a crucial role in automating invoice processing, streamlining traditionally manual and time-consuming tasks. In particular:
- AI-powered tools enable the extraction of data from invoices, eliminating the need for manual data entry.
- Machine learning algorithms can accurately interpret and categorize invoice information, reducing errors, improving processing speed, and freeing up valuable employee time.
Dost leverages AI technology in its invoice and delivery-notes-processing capabilities. Similar to human perception, when we receive a document like an invoice, we recognize the presence of text and are able to extract and categorize the information we read. Dost follows a similar approach by employing the following steps:
- Computer Vision: Using computer vision techniques, Dost’s software identifies the areas within the document where text is located. It effectively recognizes the textual elements and distinguishes them based on the type of information they represent. This includes differentiating vendor information, client details, addresses, invoice IDs, total amounts, and more.
- Natural Language Processing: Once the text has been identified and labeled, Dost’s NLP capabilities come into play. The platform employs advanced NLP algorithms to understand the extracted text. It applies techniques such as NER (Named Entity Recognition) to identify and classify specific entities within the text, such as names of people, organizations, locations, and other relevant information.
By combining computer vision and NLP technologies, Dost enhances the processing of invoices and delivery notes. It accurately extracts the relevant information from these documents and comprehends the text using NLP techniques like NER. This enables efficient data extraction, classification, and analysis, facilitating streamlined invoice management and improved business processes.
With the help of Dost’s AI-powered NLP capabilities, organizations can automate the extraction and understanding of crucial data from invoices and delivery notes. This automation not only saves time and effort but also reduces errors and enhances overall operational efficiency.
In conclusion, by harnessing the power of AI in AP management, e-commerce companies can achieve greater efficiency, accuracy, and transparency in their financial processes. These AI-driven capabilities enable businesses to make data-driven decisions, optimize cash flow, mitigate risks, and strengthen supplier relationships, ultimately driving growth and success in the highly competitive e-commerce landscape.
