Forecasting in Supply Chains: Challenges and Smarter Solutions
Improving forecast accuracy is a universal challenge for supply chains, but are businesses asking the right questions before investing in expensive tools?
1️⃣ Over-complication with technology: Many businesses invest heavily in advanced forecasting tools like AI-driven platforms, often without fully understanding the outcomes they want. Forecasting every SKU down to minute details isn’t always necessary.
2️⃣ Focus on decisions, not data: Instead of trying to predict every product’s demand, businesses should first ask:
Simplifying forecasts to key attributes (e.g., product groups or manufacturing requirements) can lead to better outcomes with less complexity.
3️⃣ Data readiness is key: Investing in systems before ensuring data quality is like building a house on sand. Without clean, consistent data, tools become inefficient and costly to maintain.
4️⃣ Iterative and scalable solutions: Not every business needs a big-name solution. Tailored, simpler tools—like custom-built forecasting systems—can deliver value quickly and at a fraction of the cost.
5️⃣ Avoid the "selection trap": Technology selection often starts with “safe” choices from well-known providers. But before committing to large-scale tools, businesses should evaluate their specific needs and explore smaller-scale, pilot solutions to test viability.
The goal isn’t just forecast accuracy but also agility in execution—building a supply chain that can adapt and thrive in today’s dynamic environment.