Who is the Company

A multinational corporation that develops and markets footwear, apparel, equipment, accessories, and services.

The Challenge

The company employed SAP e-business Apparel and Footwear order workflow system (SAP AFS) to manage their contracts. This was done via a series of Excel spreadsheets used to reserve inventory. The Excel-based system was a time-consuming manual process riddled with potential human errors.

Every action taken by a user or any customer order updates, such as rejections, cancellations, value-added services modifications, or quantity changes, were recorded in spreadsheets after being received in the SAP system. This process took a lot of time and effort, prompting the company to seek an outside solution.

The existing system was underperforming—the lack of clarity on inventory availability created uncertainty in the number of materials available per size. The system was also highly error-prone because of the manual process of entering information on materials, colors, and sizes.

In brief, the company was looking for:

  • A clear and accurate picture of inventory: The old process could not provide an accurate and timely picture of company inventory, making it difficult for company planning teams to identify minimum viable products.
  • Order error reduction: The former manual process introduced errors that produced unreliable information on materials needed for different product colors and sizes.
  • Integration with their current infrastructure: The company needed a solution designed to work with their existing SAP infrastructure, avoiding the costs and downtime associated with a rip-and-replace strategy.

    The Solution

    To simplify the time-consuming Microsoft Excel-based process, our engineers introduced Supply Protection (SUP) flagging, a system that tracks alterations in product attributes via a flagging mechanism and relays this information to downstream systems.

    Supply protection is a feature of the Advanced Available-to-Promise business function in SAP S/4HANA. The transition to SUP flagging diminishes the likelihood of data inaccuracies and eliminates the technical challenges associated with manual updates.

    The company’s inventory planning group will use the SUP Flag information to filter eligible products from Demand Consolidation and Disaggregation (DCD) to Protect, Chase, and Cancel outputs, creating and maintaining SUP buckets in SAP/S4HANA.

    A third-party partner supplies SUP flagging-related data as files stored in AWS S3 buckets. Based on the product information provided, data is then transferred to DCD. This becomes a source of truth for further downstream systems like inventory planning and product dimensional data systems to safeguard SUP-flagged products.

    Our engineers created Directed Acyclic Graphs (DAGs), a data structure that models dependencies in complex planning processes. The DAGs allow for efficient computation and analysis by ensuring tasks are executed in a specific order without circular dependencies. They enable the system to self-trigger whenever a new file or data is uploaded.

    Here are a few key takeaways from the solution:

    • Automatic tracking reduces time and errors: Using the SAP Support Protection flagging allows our solution to automatically track alterations in product attributes, saving time and reducing errors.
    • Single source of truth for SUP flagging: In the new system architecture, DCD is the single source for SUP flagging across all teams.
    • Separation of tasks improves efficiency: Since the DAGs for triggering the code and making the changes are provided to the team, they can be self-driven by the team with no dependency on the development team.
    • Test data prototypes save time: Since getting data from the source is usually tedious, prototypes with test data for all possible EDGE cases were written and validated in advance.

    Business Impact

    • Scalability increases ROI: Our team designed the new system to be reusable for the company in other parts of the world. They don’t have to spend more money to roll the system out to other regions.
    • Reduces manual effort and errors: The more orders processed, the greater the benefit with no manual error going unchecked.
    • Real-time access to accurate data: The company can now plan the release of new products with realistic expectations of inventory availability, allowing it to adapt more rapidly to changing market conditions.
    • Aligns with the business strategy: As our solution works with the current infrastructure, the customer doesn’t have to rip and replace, saving them a ton of money.

    Technologies Used

    Databricks: A unified data analytics platform leveraging Apache Spark's processing capabilities
    Amazon S3: An object storage service from Amazon Web Services
    Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows, enabling the organization and management of data pipelines
    Snowflake: A cloud-based data warehousing platform for data storage, processing, and analytics
    Pyspark: The Python library for Apache Spark
    O9: AI-driven integrated business planning platform

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