Unless your supply chain and product line are very simple, spreadsheets simply can’t produce an accurate forecast—for two big reasons:
Dedicated supply chain planning systems are built for collaboration, and designed to provide everyone in the organization with a “single version of the truth.” Planners spend more time strategically improving inputs and much less on last-minute changes and fire fighting.
Because there is less manual effort required and no need to email multiple spreadsheets between people, it’s much easier to have timely information available. Cloud-based supply chain systems make it easy and secure to collect input from suppliers and channel partners. And systems that utilize artificial intelligence (AI) and machine learning (ML) technologies “learn” from past experience to produce more accurate forecasts over time.
One challenge in meeting high service level targets using spreadsheets is simply their nature: they are designed to do calculations, to give one result based on a set of values and mathematical operations. They aren’t designed for modeling or what-if scenarios, or managing uncertainty and probabilities. Planning is reduced to a “single number” forecast, which rarely matches reality.
They also can’t automatically adjust forecasts for fast versus slow-moving items or rapidly adapt to changing demand. The result is often shortages of the best-selling items combined with excess inventory of the slow movers. You get the worst of both worlds: high carrying costs and missed sales opportunities.
Cloud-based supply chain software specifically built around the concept of “service-level planning” is designed to accommodate the fact you may not want or need to target the same service level for every product in every location.
Look for systems that employ advanced algorithms and/or machine learning and probabilistic forecasting techniques. Instead of a singlenumber forecast, probabilistic forecasting identifies the range of possible outcomes, one of which is the most likely.
In addition, uncertainty modeling provides the ability to better handle slow-moving inventory and intermittent demand patterns which are becoming more common due to product line extensions, part proliferation and rapid replenishment cycles.