Case Study

Why it Pays off to Streamline your Forecasting Practices with Dedicated AI

Sam Verdonck
February 6, 2024
January 16, 2024
min read

In a recent meeting with one of the world's largest fashion retailers, I was astounded to uncover the intricacies of their forecasting practices. Despite their industry leadership, the organization faced a significant challenge in managing the multitude of forecasts crucial for their operations. This revelation prompted a deeper exploration into the issue, revealing a patchwork of forecasts scattered across different business units. Let’s talk about why it makes sense to streamline your forecasting practices, creating a reliable baseline that fosters standardization, agility, and maintainability.

Streamline your Forecasting Practices with Dedicated AI

The Current Forecasting Landscape at a Typical Enterprise

Like so many other companies, the fashion retailer relies on hundreds of forecasts to plan and optimize various aspects such as sales, inventory, and utilities. However, managing this extensive forecasting portfolio is no small feat. The forecasts are currently divided among a few dozen teams, each employing their unique techniques, standards, and forecasting leads. This diversity gives rise to a range of issues:

  • Performance discrepancies: Some forecasts are highly accurate and instill confidence, while others fall short of delivering reliable predictions.
  • Maintenance challenges: Most forecasts require regular manual tuning and upkeep, leading to inefficiencies in the overall forecasting process.
  • Transparency concerns: The lack of a unified solution results in some forecasts being explainable, while others remain a complete black box.

The absence of a centralized product owner further intensifies these challenges, hindering scalability and preventing oversight of efficiency, performance, and adoption.

The Path to Business Agility in Forecasting

Recognizing the crucial role forecasting plays in their operations, the global fashion retailer is now poised to enhance business agility. The proposed strategy involves three key steps:

  1. Harmonizing Models: Adopt a standardized AI approach as the baseline for all forecasts across different domains and departments. This harmonization aims to improve visibility, manageability, and performance by establishing a common framework for forecasting.
  1. Automation for Maintainability: Automate processes wherever possible to enhance maintainability and provide greater flexibility in a constantly evolving business landscape. Automation not only reduces manual effort but also ensures that forecasting practices can adapt swiftly to changes in data, operations, and market dynamics.
  1. Leveraging Harmonized Automation for Scaling: Use the harmonized automation as a foundation for scaling and forecasting more extensively. By unlocking the predictive potential of unused data, organizations can generate new insights, fostering greater data-driven decision-making.

‍Fostering Enterprise Agility: Unifying Forecasting Practices for Transformative Impact

The call for enterprise agility in the realm of forecasting is answered through the harmonization of models, strategic automation, and leveraging predictive insights at scale. As our customer prepares to roll out these transformative measures, the potential impact on efficiency, performance, and decision-making is eagerly anticipated.  

This move towards standardization promises not only to streamline forecasting practices but also to create a robust foundation for future business agility across various domains and departments. The journey towards a unified forecasting approach is poised to revolutionize the landscape for enterprises aiming for greater standardization, agility, and maintainability.

Article by Sam Verdonck, Chief Growth Officer at Tangent Works

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