Table of Contents
Introduction
We reached out to a leading retail manufacturer in the cosmetic industry—known for its strong presence across Asia and Africa and a consistently high turnover, with revenues reported at approximately ₹958–966 crore for FY2024. The company, established in 1989, specializes in developing and retailing a wide portfolio of skincare, haircare, and wellness products, supplying these through an extensive network of retail outlets, salons, and clinics.
Key details include:
- Employees: Over 3,000 skilled professionals, including medical doctors, nutritionists, physiotherapists, cosmetologists, and wellness counsellors.
- Retail Stores and Locations: Operating approximately 310+ locations spread across 139 cities.
- Countries of Operation: Presence in 11 to 14 countries across Asia and Africa, including India, Sri Lanka, Bangladesh, Nepal, Singapore, Thailand, UAE, Oman, Bahrain, Qatar, Kuwait, and Kenya.
This broad footprint and skilled workforce enable the company to maintain a robust distribution network and deliver trusted, science-backed beauty and wellness solutions to millions of customers annually.
Problem Statements
Problem Statement 1
Problem Statement for the PO’s, SKU Discrepancies
Despite its leadership and broad product portfolio in the cosmetic industry, this retail manufacturer is encountering significant operational challenges that impact both efficiency and its relationships with major retail chains:
- Purchase Order (PO) Discrepancies: The business frequently faces issues with mismatches or errors in purchase orders received from large retail chains such as Reliance and Dmart. These discrepancies disrupt order fulfilment, delay shipments, and require additional back-and-forth communication to resolve, hampering workflow efficiency.
- SKU ID Mismatches: There is a recurring problem with SKU (Stock Keeping Unit) identifier mismatches between the manufacturer and these retail chains. These inconsistencies lead to confusion over product codes during ordering, billing, and inventory tracking—often resulting in incorrect deliveries or inventory shrinkage.
- Manual Data Interpretation and Entry: The company’s dependence on manual methods for interpreting and entering transaction and order data increases the risk of human error. This manual process prolongs cycle times, reduces productivity, and introduces inconsistencies that can cascade into downstream operational problems.
- Pricing Mismatches: Discrepancies in pricing communicated to or agreed upon with big retail chains pose frequent challenges. These can result in billing disputes, delays in receivables, and erode trust, all of which negatively affect both profitability and long-term partnerships.
Collectively, these issues impede the seamless flow of information and products between the manufacturer and major retail chains. Addressing these pain points is crucial for the company to maintain its market-leading reputation and deliver a consistent, high-quality experience to its partners and end customers.
Problem statement 2
Additional Problem Statement – Quantity & Proof of Delivery Mismatch
The retail manufacturer dispatches orders to major retail chains through third-party freight agents. However, there are recurring discrepancies between the quantity or product shipped and the quantity recorded in the Proof of Delivery (POD) acknowledged by the retail chains.
For example, while the manufacturer may ship 1,000 units, the retail chain’s received quantity as per POD may indicate only 900 units. Such mismatches create disputes during payment processing, as the retail chain issues payments only for the quantity recorded in their system, rather than the quantity mentioned in the manufacturer’s invoice.
This results in:
- Revenue Leakage: The manufacturer receives lower payments than billed.
- Dispute Resolution Delays: Significant time and resources are spent reconciling differences with retail chains and freight agents.
- Operational Strain: Additional investigations, documentation, and liaison add complexity to logistics and finance.
- Financial Losses: The cumulative effect of underpayments across multiple deliveries causes sizeable revenue and margin erosion.
Resolving this challenge is critical for the manufacturer to protect operational profitability, improve cash flow, and ensure trust is maintained with large retail partners.
Proposed Solution
After evaluating all the challenges faced by the retail manufacturer, we proposed our product “Snoh Fusion”.
Snoh Fusion is an advanced intelligent document processing (IDP) solution that utilizes artificial intelligence technologies such as OCR (Optical Character Recognition), natural language processing (NLP), and machine learning to automate the extraction, classification, and validation of data from a wide range of document formats including PDFs, scanned documents, invoices, and contracts. It significantly reduces manual data entry and errors, streamlines document-centric workflows, and boosts operational efficiency by enabling fast and highly accurate processing of complex documents.
Snoh Fusion continuously learns and adapts to evolving business document requirements, integrating seamlessly with enterprise systems like ERP and CRM. It is designed to help businesses transform manual, error-prone paperwork into automated, scalable, and precise processes that enhance decision-making and productivity.
Implementation Process of Snoh Fusion in the Retail Manufacturing Company
- Assessment and Requirement Gathering
The process began with a comprehensive assessment of the company’s existing document workflows, focusing on purchase orders, invoices, proof of delivery records, and pricing documents received from major retail chains such as Reliance and Dmart. Since the company was already operating on SAP for its ERP and supply chain processes, the integration requirements for Snoh Fusion were mapped to ensure seamless data exchange between both systems. - Integration Planning and API Setup with SAP
A technical integration architecture was designed to connect Snoh Fusion directly to the company’s SAP system via secure APIs. This allowed Snoh Fusion to automatically retrieve relevant transaction documents and data from SAP for processing and, after validation, insert the cleaned and accurate data back into SAP. This eliminated the need for manual data downloads or uploads, ensuring real-time synchronization between both platforms. - Document Collection and AI Model Training
A wide set of transactional documents—purchase orders, invoices, delivery receipts, and proof of delivery forms—was gathered from the company’s retail chain partners and from SAP archives. These were used to train Snoh Fusion’s AI models to recognize multiple layouts, formats, and industry-specific terminology relevant to the retail manufacturing sector. - Automated Extraction, Validation, and SAP Sync
Using OCR, NLP, and AI-based rules, Snoh Fusion extracted essential data such as SKU IDs, quantities, prices, and PO numbers. Any detected discrepancies (e.g., SKU mismatches, quantity differences, or pricing conflicts) were flagged for human validation. Approved data was then automatically pushed back to SAP through the API, updating order, invoice, and delivery records instantly without manual keying. - Pilot Phase with SAP-Connected Workflows
The integration was tested with a small volume of real SAP-connected transactions to monitor extraction accuracy, API performance, and data round-tripping reliability. Feedback from the operations, finance, and IT teams helped optimize mapping rules, error handling, and automated triggers. - Full Rollout and User Enablement
After successful piloting, the system was rolled out across all relevant document workflows linked to SAP. Employees in logistics, finance, and supply chain operations were trained not only in Snoh Fusion’s features but also in managing SAP-integrated, API-driven document processes and exception cases. - Continuous Monitoring, SAP Integration Health, and AI Improvement
post-implementation, both data accuracy and SAP integration health were continuously monitored. Performance analytics tracked processing times, error resolution rates, and reduction in financial disputes. Snoh Fusion’s machine learning models were periodically updated with SAP transaction data to enhance prediction accuracy and adapt to new document types.
Results Achieved with Snoh Fusion
After the full-scale rollout of Snoh Fusion integrated with SAP, the retail manufacturer witnessed a remarkable transformation in its operational efficiency, financial health, and partner relationships.
- Data Accuracy Improvement – 85%+ Error Reduction
By automating the extraction and validation of critical fields from purchase orders, invoices, and proof of delivery documents, error rates dropped by more than 85%. This meant that mismatches in SKUs, pricing, and order quantities—previously a persistent cause of delays and disputes—were nearly eliminated. The result was cleaner, more consistent data directly flowing into SAP, removing the time-consuming need for manual corrections. - Operational Efficiency – 70% Faster Processing
What previously required hours of manual data entry and cross-checking could now be completed in minutes. Processing cycles for purchase orders and deliveries became 70% faster, allowing the company to respond to large retail chain orders with unprecedented speed. Administrative workload decreased significantly, freeing staff to focus on higher-value tasks rather than repetitive data handling. - Payment Dispute Reduction – 60% Fewer Conflicts
With accurate, real-time matching of invoices and proof of deliveries, the company saw a 60% drop in payment disputes with retail giants like Reliance and Dmart. The improved data transparency meant faster reconciliation, fewer back-and-forth communications, and more predictable cash flow. - Revenue Recovery – 5–7% Lost Revenue Recaptured
By eliminating quantity mismatches (e.g., shipping 1,000 units but POD reflecting only 900), Snoh Fusion enabled the company to recover an estimated 5–7% of revenue that would previously have been lost due to underpayments. This directly contributed to improved profit margins and financial stability. - Real-Time SAP Synchronization – 100% Automated Data Flow
Through API-enabled integration, data from processed documents was instantly updated in SAP—achieving 100% real-time synchronization. This removed the lag and inaccuracy risks associated with manual uploads/downloads, ensuring that the most current and accurate information was always available for decision-making. - Scalable Growth – 3x Higher Processing Capacity Without Extra Staffing
With automation handling the bulk of document-related workflows, the company tripled its capacity to process orders and supporting documentation without hiring additional staff. This scalability positioned the business to handle seasonal surges and expand into new markets confidently. - Strengthened Retail Chain Relationships – 50% Faster Dispute Resolution
The time to close a dispute or resolve a mismatch issue was cut in half. This speed and reliability reinforced trust with key retail partners, leading to better collaboration and smoother ongoing operations.
Expected ROI from Implementing Snoh Fusion
By integrating Snoh Fusion with its existing SAP environment, the retail manufacturer is expected to achieve a strong return on investment, driven by both operational efficiencies and revenue protection.
- Revenue Recovery Potential:
Improved accuracy in matching shipment quantities to proof of delivery records is expected to help the company recover around 5–7% of revenue that is typically lost due to underpayments or disputes with large retail chains. - Operational Cost Savings:
Automation of data extraction and validation could reduce manual processing time by 60–70%, lowering administrative costs and freeing staff for higher-value activities. - Faster Dispute Resolution and Improved Cash Flow:
With documentation errors expected to drop by more than 80%, payment disputes with retail partners should decrease significantly, leading to faster receivable cycles and better liquidity management. - Efficiency Through Real-Time SAP Integration:
The API-based integration ensures 100% real-time synchronization of processed data with SAP, improving inventory accuracy and decision-making speed. - Scalability for Growth:
The system is expected to enable a 2–3x increase in document and transaction handling capacity without adding workforce, supporting both seasonal peaks and future sales growth at minimal extra cost.
In essence, the investment in Snoh Fusion is anticipated to deliver a very high ROI within the first year, driven by a combination of recovered revenue, reduced operational costs, faster payment cycles, and enhanced scalability. We are also constantly connected with the manufacturer to ensure smooth system performance, address any evolving needs, and make continuous improvements for sustained ROI.
