Finance Operations Automation: A Global Luxury Apparel Manufacturer Case Study

Transforming a 5-day manual process into 4 hours with intelligent reconciliation automation

Executive Summary

A global luxury apparel manufacturer transformed manual finance operations across its European network by combining Intelligent Document Processing (IDP) with Robotic Process Automation (RPA) and ERP integration. The automation addressed high-volume finance activities, including material ledger closing, AP/AR management, GL reconciliation, and financial reporting.
Key results at a glance
Metric
Result
The implementation gave finance teams a more standardized, scalable operating model while reducing repetitive work and allowing skilled finance professionals to focus on analysis rather than transaction execution.

The Challenge

Manual Finance Operations Across a Global Brand

A global luxury apparel manufacturer was managing finance operations across a complex European network involving multiple entities, company codes, plants, and business operations. With finance activities spread across a large operating footprint, maintaining consistent and timely processing was becoming increasingly difficult.

One of the most demanding activities was Material Ledger Closing (MLC). The monthly process required finance teams to manually execute a sequence of SAP transactions, validate statuses, perform price determinations, execute costing steps, and close periods across multiple plants and company codes.

The process stretched across several days and required skilled SAP finance personnel to repeatedly perform the same sequence of activities. The existing workflow required checks before the close, execution of multiple costing steps, monitoring of system statuses, and final period-lock validation.

According to the case study, the MLC process previously took approximately five days, consuming around 160 effort hours per period. The process covered 71 EU plants across 20 company codes, making consistency and sequencing particularly important.

MLC was not the only challenge.

The broader finance operation also involved manual accounts payable and accounts receivable management, including invoice processing, validation, payment-related activities, and receivables follow-up. General ledger reconciliation required teams to extract and compare financial information, identify discrepancies, and perform necessary adjustments.

Financial reporting added another layer of manual effort. Finance teams needed to collect, validate, consolidate, and analyze information from the ERP environment to produce timely and accurate reports.

These processes created several recurring problems:

The challenge was therefore broader than automating a single finance task. The organization needed a way to standardize and automate repetitive activities across its finance operations while maintaining appropriate controls and human oversight.

The Solution

IDP + RPA for Finance Automation

The organization implemented an integrated finance automation approach combining Intelligent Document Processing (IDP), Robotic Process Automation (RPA), and ERP integration. The objective was not to replace the existing finance technology environment. Instead, automation was introduced around the existing ERP processes to remove repetitive manual work while allowing finance systems to remain the core source of financial information.

RPA for repetitive finance workflows

UiPath was used as the core RPA technology to automate repetitive, rule-based finance activities. For Material Ledger Closing, the automation replicated the sequence that finance employees previously performed manually in SAP. The bot could execute required transactions, check system statuses, perform costing activities, validate results, and complete period-lock activities.

The automation also supported exception handling. If an issue occurred, the workflow could raise an incident for human intervention instead of restarting the entire process.

The MLC automation covered the full lifecycle, from prerequisite checks through costing to final period closure. The source case study describes automation across 71 EU plants and 20 company codes, with six sequential costing steps executed through the automated workflow.

IDP for document-heavy finance processes

While RPA is effective when information is structured and processes follow predictable rules, finance operations also contain documents that require information extraction and validation.

This is where Intelligent Document Processing was introduced. IDP can capture information from invoices and other financial documents, extract relevant fields, and prepare structured information for downstream processing. This reduces the need for finance employees to manually read documents and enter the same information into ERP systems.

For AP and AR management, IDP supported invoice-related processing by extracting and validating key information, while RPA handled subsequent system interactions and transaction posting.

Automating AP and AR management

The AP/AR workflow combined document intelligence with process automation. For accounts payable, invoice information could be captured and validated before being passed into the ERP workflow. RPA bots could then handle repetitive system activities such as posting and processing transactions. For accounts receivable, automation supported activities such as receivables tracking and customer follow-up for overdue accounts. This combination created a division of responsibilities:

GL reconciliation and financial reporting

The solution also automated General Ledger reconciliation. Instead of requiring finance teams to manually extract and compare records, RPA could retrieve relevant information, perform comparison activities, identify discrepancies, and support the adjustment process.

Financial reporting was similarly streamlined through automated extraction, consolidation, validation, and report generation.

The overall architecture therefore connected several previously manual activities into a more standardized finance workflow.

Rather than viewing intelligent enterprise automation as a collection of isolated bots, the organization used IDP and RPA as complementary technologies across multiple finance processes.

The result was a broader automation model covering document processing, ERP transactions, reconciliation, closing activities, and reporting.

Results

The automation produced measurable improvements in processing time, manual effort, accuracy, and scalability.
Metric
Before
After / Result

The most visible transformation occurred in Material Ledger Closing. The process moved from a five-day manual cycle to approximately four hours, dramatically reducing the amount of time finance teams needed to spend executing the monthly workflow. The implementation eliminated approximately 160 effort hours per period, equivalent to around 1,920 hours annually.

The automation also standardized processing across 71 EU plants and 20 company codes, reducing the risk of plant omissions, sequencing errors, and inconsistent execution.

Accuracy reached 95%+ following automation, while the latest case-study reporting identifies a 73% reduction in monthly close processing time.

For broader finance automation programs, Auxiliobits also cites benchmark outcomes of approximately 40–60% cost reduction and 9,700+ hours recovered annually across relevant automation engagements. These are benchmark figures, not additional results attributed specifically to this luxury apparel manufacturer.

The operational impact extended beyond time savings. Finance professionals no longer needed to spend their working hours repeatedly navigating SAP transactions and checking routine statuses. Instead, automation handled predictable processing while teams could focus on exceptions, analysis, and financial decision-making.

The organization also gained a more scalable foundation for future growth. New plants and company codes could be incorporated through configuration rather than requiring the entire automation to be rebuilt, helping the process accommodate business expansion.

Lessons for Shared Services & GBS Leaders

For Shared Services and Global Business Services leaders, this implementation highlights an important principle: finance automation should be designed around the operating model, not just individual tasks.

Multi-entity finance environments often contain the same process repeated across different companies, countries, plants, and systems. When those processes are predictable, repetitive, and high-volume, they create strong opportunities for automation.

1. Start with processes that repeat across entities

Material Ledger Closing is a strong example. The same fundamental sequence had to be performed across dozens of plants and company codes. Instead of treating each entity as a separate process, automation can create a standardized workflow that operates across the network.

2. Use the right technology for the right work

RPA and IDP solve different problems. RPA is effective for structured, repetitive system interactions. IDP is useful when finance teams need to extract information from documents before a transaction can be processed. Combining the two creates a broader automation capability than relying on either technology alone.

3. Design exception handling from the beginning

Finance automation cannot assume that every transaction will succeed. The MLC implementation demonstrates the importance of structured exception handling. When something fails, the automation should identify the failed step, alert the appropriate team, and resume where possible rather than forcing employees to restart the entire workflow.

4. Measure capacity, not only cost

GBS leaders should measure more than headcount reduction. Metrics such as hours recovered, cycle-time reduction, accuracy, transaction volumes handled, and additional entities supported provide a more complete picture of automation value.

The broader finance shared services model increasingly depends on creating scalable processes that can support business growth without adding equivalent administrative complexity.

5.Build for scale

A multi-entity automation should make adding another entity or plant relatively straightforward. Configuration-driven automation, standardized workflows, and ERP integration can allow GBS organizations to expand automation without rebuilding every process from scratch. This creates an operating model where finance automation becomes a platform for continuous improvement rather than a collection of disconnected bots. 

How to Get Started

The first step is to identify where manual finance work is concentrated across your shared services or GBS organization.

Map the processes that consume the most employee time, particularly activities involving repetitive ERP transactions, document processing, reconciliations, reporting, and month-end close. Then measure transaction volumes, cycle times, manual effort, error rates, and exception volumes.

A structured assessment can help determine which processes are best suited for IDP, RPA, or a combination of both.

Our shared services automation approach focuses on integrating automation into existing ERP and finance environments rather than requiring a complete technology replacement.

You can also evaluate opportunities across intelligent enterprise automation, including finance, shared services, and other high-volume operational workflows.

For specific finance use cases, explore month-end close automation, reconciliation automation, and invoice automation.

The best starting point is usually one measurable process. Prove the value, establish the operating model, and then scale automation across related finance workflows.

Book a discovery call—see how your finance operations can benefit from IDP and RPA. Book a free discovery call. 

FAQ

What is intelligent document processing in finance?
Intelligent Document Processing, or IDP, uses AI and document-recognition technologies to extract, interpret, validate, and structure information from documents. In finance, this can include invoices, financial statements, forms, reports, and other documents that employees would otherwise need to read and enter manually.
RPA uses software robots to execute repetitive, rule-based tasks across business applications. In finance, an RPA bot can log into an ERP, retrieve information, enter data, perform calculations or validations, update records, generate reports, and trigger notifications.
Yes. RPA can automate many repetitive and rule-based activities within month-end close, although not every aspect of closing should necessarily be fully automated. Suitable activities can include data extraction, system status checks, reconciliations, journal preparation, report generation, validation steps, and ERP transactions that follow consistent rules.

A wide range of finance processes can be candidates for automation.

Common examples include:

  • Accounts payable and invoice processing
  • Accounts receivable management
  • General Ledger reconciliation
  • Material Ledger Closing
  • Month-end and year-end close activities
  • Financial reporting
  • Data extraction and validation
  • Payment processing
  • Asset accounting
  • Payroll-related workflows
  • Account reconciliation
  • ERP data entry and updates

 

IDP is particularly useful when the process involves extracting information from documents, while RPA is well suited to repetitive system interactions.

Implementation time depends on the complexity and scope of the process. A single, well-defined finance workflow with predictable rules can generally be implemented faster than a multi-entity transformation involving several ERP systems, complex integrations, and multiple exception scenarios. A typical implementation includes process discovery, automation design, development, ERP integration, testing, user acceptance, deployment, and monitoring.
Multi-entity organizations can benefit from finance automation in several ways. First, automation standardizes processes across entities and reduces variations in how routine work is performed. Second, it can reduce cycle times by allowing repetitive activities to run automatically rather than waiting for employees to complete each step manually. Third, automation can improve consistency and accuracy by executing predefined workflows the same way each time.

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