- Client: Leading US clothing company
- Industry: Clothing
- Products or services: Jeans
- Areas of operations: North America (including Mexico and Canada), Europe, Asia, Middle East and Africa
- Annual revenue: ~$4.9 billion
The client faced multiple challenges that affected process efficiency in the following ways:
- Unstandardized Accounts Receivable (AR) under Finance and Order Management (OM) in customer service resulted into silo environment, driving customer dissatisfaction and high volumes of unapplied cash, leading to credit hold.
- Improvement required on time taken for book close (work day 5, which denotes that the financial books will close five days from the month’s close date. Improvement in this means that management receives financial statements quicker)
- No mechanism to track the status of month-end activities and perform root cause analysis for improvement.
- Item mismatch and erroneous reports resulted in longer rectification time
- For sales order management
- Inventory research and sorting of mismatched price consumed more than two hours per day per FTE. Mismatch of prices happens when a quotation and the order against it have different prices, this reduces the efficiencies within the team, which delays the orders being booked and shipped to the customer, also leading to customer dissatisfaction
- Average Turn Around Time (TAT) for an order was 4 to 5 hours.
- Accounts Payables
- Duplicate payments, as the name suggests means that the systems or team has a probability of making duplicate payments.
- Unpaid PO - There is no timely reporting at the moment to display unpaid PO. This leads to complexity in revenue prediction due to multiple variable factors including past and future probable events
- Inaccurate revenue predictions (~80% accuracy) - manual process and limited accuracy affected the company’s cash flow decision.
Wipro’s answer to the challenges came in the form of process improvements and the implementation of automation tools. Order management and AR was brought under one management with end-to-end accountability for streamlining user experience. Wipro also deployed descriptive analytics using Tableau and predictive analytics through R and SQL server. Process efficiency was enhanced with bots, and customers of the client on EDI orders were supported through customer portals enhancing the user experience. In addition to this, following were the solution components designed to address the different challenges:
- Leveraging Blackline and Base))) reports for book close monitoring and analysis, as well as, error analysis and identifying preventive actions
- Real-time access to MIS through Wipro Base)))TM Prism and Wipro Base)))TM Govern for quick insights and actions
- Implementation of accounting and reconciliation rules and policies
- Streamlined activity tracking through introduction of document control standards like version control
- Implementation of error proofing, error tracking and validation bots to streamline processes in sales order management
- Deployment of duplicate payment identification tool to identify and resolve duplicate payments
- Cockpit access for downloading unpaid PO report for automated identification of cases
- AI based revenue prediction for deduction of causal inferences with real-time recommendations and dashboards
- Feedback based learning enabled the model to mature into a prescriptive data modelling tool