AI Forecasting

Escape the Excel Labyrinth: Unleash the Power of AI-Driven Demand Forecasting

In the ever-evolving world of supply chain management, accurate demand forecasting is the cornerstone of success. However, for many supply chain analysts, the go-to tool remains Excel, a familiar yet fundamentally flawed solution. While Excel offers a surface-level ease of use, its limitations become increasingly problematic as data sets grow and supply chains become more complex. Let’s face it: Excel is a spreadsheet program, not a crystal ball. Here’s why it’s time to graduate from Excel and embrace the power of AI-powered forecasting for your supply chain.

The Struggles of Spreadsheeting Your Way to Success

While Excel might seem user-friendly, its rigidity and limited capabilities make it a poor fit for sophisticated demand forecasting. Here’s how Excel trips up supply chain teams:

  • Inflexible and Time-Consuming: Excel’s static formulas and limited data handling capabilities become cumbersome as data sets grow. Imagine hundreds of rows and columns dedicated to historical sales figures, promotions, and external factors. Manual data entry becomes a time-consuming black hole, leaving less time for analysis and strategic planning. Copy-paste errors and inconsistent formatting further complicate matters, casting doubt on the accuracy of your forecasts.
  • Limited Accuracy: Excel relies on historical data and static forecasting models, often failing to account for seasonality, promotions, unexpected events, or external factors like economic shifts or social media trends. A missed variable can throw your entire forecast off, leading to stockouts that frustrate customers or excess inventory that ties up valuable capital. 
  • Lack of Visibility: Excel offers limited real-time data integration. Your forecasts are only as good as the latest manual data entry. If there is a critical supplier disruption between your last data update and your next forecast, Excel won’t flag this potential disruption, leaving your team blindsided by unexpected stock shortages.

The Excel Hacks: Band-Aids on a Broken System

Despite its limitations, many supply chain teams resort to Excel hacks to wrangle some semblance of accuracy from their forecasts. Here are a few common (and ultimately unreliable) tactics:

  • Macros and VBA Scripting: Macros and VBA scripts can automate some data manipulation tasks for Excel-savvy users. However, creating and maintaining these scripts requires specialized knowledge and can introduce new complexity and potential errors. If someone leaves the company with the key to your forecasting script, you’re back to square one.
  • Scenario Planning: Creating multiple “what-if” scenarios in separate spreadsheets allows you to account for different possibilities. Managing dozens of interconnected spreadsheets quickly becomes unwieldy and error-prone, and tracking a single product across multiple scenarios becomes a logistical nightmare.
  • Tribal Knowledge and Intuition: Seasoned supply chain professionals often rely on experience and gut feeling to adjust Excel-generated forecasts. While valuable, institutional knowledge can be subjective and prone to bias. Imagine two experienced analysts disagreeing on the impact of a new marketing campaign. Excel can’t resolve such subjective judgments.

The Trust Factor: Can You Believe Your Spreadsheets? 

The biggest challenge with Excel-based forecasting is the inherent need for more trust in the results. With limited data visibility, static models, and a high potential for human error, it isn’t easy to have confidence in the accuracy of your forecasts. Are you overstocked because your forecast overestimated demand? Or are you facing stockouts because your forecast missed a hidden trend? With Excel, the answer is often a frustrating “maybe.”