Baca artikel ini dalam Bahasa Indonesia: Demand Planning: Mengapa Ini Penting untuk Bisnis Anda
Demand planning is the process of forecasting future customer demand and then aligning purchasing, production, inventory and cash with that forecast. Without it, businesses tend to swing between two problems: running out of the items that sell, or a warehouse full of stock that does not.
Why demand planning matters
- Better-balanced inventory. Enough stock to fill orders without locking capital in the warehouse.
- Cash flow under control. Large purchases can be planned and funded well ahead, not at the last minute.
- Better customer service. Orders are filled on time, so customers do not switch to competitors.
- Stronger supplier negotiations. Volume forecasts give you a basis to ask for better prices and terms.
The basic steps
- Gather historical sales data by product, ideally 12 to 24 months, so seasonal patterns show.
- Clean the data of one-off events, such as an unusually large single order or periods when stock ran out.
- Build a baseline forecast from trends and seasonality, for example the run-up to Lebaran or year end.
- Add market information: planned promotions, new products, new customers and input from the sales team.
- Turn it into a purchasing and cash plan, then compare forecast with actual every month to improve accuracy.
Common mistakes
Relying on gut feel, looking only at last month, not separating fast and slow movers, and never reviewing the forecast. Inaccurate sales and inventory data in the accounting system also throws the forecast off from the start.
Start with clean data
Good demand planning starts with accurate records of sales, purchases and inventory. For inventory in Xero, see inventory management with Xero. Logiframe's accounting outsourcing team helps make sure inventory values and cost of goods sold are recorded correctly every month.
Frequently asked questions
What is demand planning?
The process of forecasting future customer demand and aligning purchasing, production, inventory and cash with that forecast.
How much historical data is needed?
Ideally 12 to 24 months per product, so seasonal patterns show.