India, counted as the 5th largest retail hub globally is witnessing exponential growth in organized retail trade buoyed by changing consumer behavior, high adoption of online commerce, and rapid urbanization. While traditional retail is still a larger pie compared to organized retail, the latter is going up in the revenue mix for companies across different sectors in India. For example, in the FMGC sector, modern trade has grown from 25 percent to 30 percent approximately in the last year alone.
Even as organized retail has brought the advantage of accessibility and scalability to corporates sellers by leveraging the power of the digital marketplace to reach end-consumers, it has also manifested operational complexities, a lot of which is thanks to the traditional and manual of order to cash (O2C) cycle processes. This is a big issue when the corporates’ customers are large retail chains, e-commerce companies, Government institutions, other OEMs, etc.
Can you imagine the time and effort spent by an FMCG brand just to track all of the multi-product orders from a popular eCommerce platform and corresponding account receivables? Now imagine the challenge of doing this across many ecommerce platforms and large retail chains! The work is tedious, error-prone and cumbersome. Any inefficiency impacts working capital, customer relationships, and core financial metrics. Resultant write-offs, delayed/unapplied cash etc. have deeper implications on the financial health of the company.
Given the surge of demand from these channels, streamlining O2C processes in organized retail is important for both the sellers (accounts receivable) and buyers (accounts payable). This need is pronounced across industries such as FMCG, consumer durables, pharma, textile, auto among others. Companies are therefore looking to intelligently automate their AR management, Cash Application, Reconciliation and Vendor payment processes.
Under the organized retail segment, the seller organizations have limited say on the processes of their large customers such as e-Commerce companies, large retail chains like Reliance and DMart, OEMs like Maruti, and Government organizations). For instance, organized retail buyers usually make weekly bulk payments to their vendors. They may make one single payment against multiple invoices across stores, appropriating deductions (e.g. for short supply or defects or credit notes or schemes), and may subsequently send a “remittance or payment” advice via email. The challenge for the seller corporate is to reconcile these complex payments and adjustments against their customer outstanding ledger in their Enterprise Resource Planning (ERP) systems. This is a time-consuming process and disputes, if any, have to be resolved quickly. Otherwise, this can cause significant backlogs eventually leading to write-offs and dissatisfaction for both buyers and sellers.
There are further upstream and downstream processes that can benefit from automation as well. Purchase Order (PO) exchanges are an example wherein the buyer sends a PO that needs to be ‘read’ and reconciled to buyers’ source data, and accepted within the ERP systems based on SKU/price validity, order conditions, etc. Another mandatory step is reconciliation against Goods Received Notes (GRN) which are often physically handed over at depots or warehouses. Handling of returns that are triggered at a later point due to expiry, product issues, etc. adds further complexities. All these processes are interlinked and are candidates for automation.
Tech to Organise the ‘Unorganised’
Some of the possible solutions to optimize the above processes include Artificial Intelligence (AI) / Machine Learning (ML) based document data extraction and reconciliation, Electronic Data Interchange (EDI), BOTs/ adaptors to enable integration to ERPs, etc.
EDI is possibly a long-term potential solution. It could take a few years to gain adoption and will need buyer and seller organizations in the industry to agree to the standards and common technology hub creation.
Meanwhile, context-driven AI / ML solutions with deep domain capabilities could be an ideal immediate solution for both buyers and sellers to automate their AR and AP, respectively. For example, for cash application reconciliation, ML-based machines can read and interpret data sources like AR ledger, GRN, claims, payment information, remittance advices, etc., and then apply AI to do multi-point auto reconciliation.
Smart AI/ML and deep domain reconciliation platforms can bring significant benefits to buyer and seller organizations in the AR/AP processes – they are faster, far less expensive and highly accurate. These solutions help accelerate revenue recognition, lower write-offs, provide complete audit trails and assist in dispute resolution improving stakeholder satisfaction in the ecosystem.
To conclude, India’s B2B landscape is fast evolving and digital transformation in many traditional sectors has been fast-tracked by the global pandemic. Ecommerce and modern trade, in that sense, is driven largely by technology on the consumer front. However, on the B2B side, it is still shackled in manual and time-consuming processes or caught up in fragmented technology systems. Companies can reap significant benefits by unlocking their working capital if they leverage the power of intelligent automation, not just as a piecemeal solution, but across the entire O2Ccycle.
By Narayan ‘Naru’ Ramamoorthy, chief revenue officer, Global PayEX