Working Capital Analytics

Improve your cash flow by accurate receivables payment scoring or better inventory management.

The team

Michal Ambler

Michal Ambler

EY Czech Republic, Manager in the Advanced Analytics team

Natalia Kudinova

Natalia Kudinova

EY Czech Republic, senior

The typical first steps in working capital areas

  • Receivables – payment terms analysis, payment profiles by customers, DSOs… 
  • Payables – payment terms analysis, payment profiles by suppliers, DPOs…
  • Inventory – product segmentation analysis, SLOB

Following up on prospective areas with advanced analytics use cases

Receivables

Developing a machine learning model for receivables payments to identify likely payment date and receivables at risk of being paid late or never, along with insights into main risk factors

Inventory

Develop an analytical model to identify for each SKU the equilibrium (threshold) point between potentially lost sales (and / or lost margin) and cost of holding inventory, taking into account production lead time and uncertainty around demand predictions

Use available data with state-of-the-art machine learning methods to improve forecasting accuracy in order to optimize inventory levels and replenishment policies

 

  • Create an agile and responsive finance function

    New technologies and market entrants, changing customer needs and behaviors, and exponentially growing data volumes create a need for change. Yet many finance functions continue to struggle with cumbersome processes, nonintegrated systems and a heterogeneous structure.

    Analytics can help you to:

    • Monitor performance management
    • Reduce the costs of finance
    • Speed up financial systems
    • Align people capabilities
    • Anticipate financial risks

  • Manage your risks to secure future success

    While analytics has created substantial benefits for businesses and consumers alike, there are corresponding risks that go along with it. The need to secure sensitive data, to protect private information and to manage data quality exists whether data sets are big or small. However, the specific properties of big data create new types of risks that necessitate a comprehensive strategy that allows a company to utilize analytics while avoiding the pitfalls.

    Analytics can help you to:

    • Improve risk and internal audit processes and enable the business to deliver even better products and services
    • Enable the risk and internal audit functions to provide business insights and act as a strategic advisor while reducing costs
    • Simplify regulatory processes and improve quality
    • Detect the next threat

  • Unlock the value of your customers

    Technology has empowered consumers to be smarter, better informed and more demanding than ever. They are adept at filtering information and move effortlessly between channels, and they expect value and quality in return for their money as well as personalized offers that match up with their needs. Whatever the sector, businesses face stiff competition. By analyzing sales data at a customer level and integrating it with competitor information and market events, machine-learning algorithms can unearth hidden insights about customer behavioral patterns that allow you to reposition, restructure and better focus your efforts on profitability and growth.

    Analytics can help you to:

    • Attract and retain the right customers
    • Unlock the full value of existing customers
    • Optimize your channel mix

  • Create value in human resources

    The usage of advanced modeling and reporting tools will help you make optimized, data-driven decisions on allocating human resources and to forecast future service demand. Analytics helps you to optimize resource planning as well as better understand your employees, which in turn will support you in achieving superior performance while reducing costs.

    Analytics can help you to:

    • Gain employee insight
    • Optimize resource allocation

  • Create an agile and responsive supply chain

    Traditional supply chain models are shifting and require you to adapt to the new realities, requiring sophisticated, connected tools to drive optimal performance; enable better operational decision-making; monitor risks; predict disruptions; and support rapid recovery as part of an overall supply chain strategy. This requires companies to increase their adoption of advanced tools grounded in analytics and visualization to help improve operational efficiency across their end-to-end value chain and meet customer demand.

    Analytics can help you to:

    • Achieve operational excellence
    • Optimize productivity
    • Reduce costs

  • Outsource your analytics: analytics-as-a-service

    The EY Client Technology Platform combines market-leading proprietary technologies with advanced analytics capabilities and deep industry sector knowledge. The solutions available on our platform are compatible with leading industry standards and meet strict security and regulatory requirements. The data is securely hosted in the cloud and is aligned with data privacy policies. Our platform provides scalable infrastructure to meet client needs in traditional and nontraditional data functions, from operations to customer experience, talent and HR, marketing and more.

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