Experience how Avercast can help you to Balance Demand & Supply for your business.
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Unfulfilled orders, lost revenue, missed opportunities, and dissatisfied customers – these supply chain-related discrepancies can be easily alleviated. But not when demand planners are buried in spreadsheets, pulling data, and correcting someone else’s errors. But there are forecasting methods that can streamline your future predictions, inventory planning, and overall supply chain performance.
This blog post will describe various forecasting techniques and their importance to your business.
There are four best practices for the quantitative forecasting approach:
Qualitative methods also have a fourfold approach to efficient forecasting.
Naïve forecasting is an easy-to-implement approach that relies on your business’s historical data. This method utilizes your past year’s actual data as current period forecasting data. This way, you can quickly predict your future strategy based on your previous data. Due to its simplicity, it has various benefits such as being easy to implement, needing limited data, not being tricky for system integration, being an ideal technique for steady demand, and being appropriate for small businesses. Although this method is crucial for many organizations, it has its own limitations. For instance, it does not provide real-time data, lacks accuracy, is challenging to predict seasonal changes, and gives a more reactive approach than proactive decision-making.
This method is one of the most accessible practices for supply chain forecasting. It evaluates data points by creating an average series of subsets for complete data. The average is used to develop a prediction for the coming period and then reevaluated each month, quarter, or year.
For example, if you begin your commercial activities at the start of Q1 and want to predict sales for Q4, you can pull the sales average of the past three quarters combined to calculate the next quarter’s sales projections. The moving average method does not consider that recent data may be a better future benchmark and should be given more weight. It also does not reflect seasonality or major trends shifts. As a result, this forecasting method is best suited for inventory with low order volume.
This technique works by separating the time series into several components. Exponential smoothing is a knowledgeable approach to supply chain management. This process uses weighted averages, assuming that past trends and events mirror the imminent future.
When it comes to comparing this method with other quantitative methods, it makes it easier to come up with data-driven predictions without analyzing multiple data sets. With the appropriate tools and expertise, this method can be easy to apply and ideal for short-term forecasting.
Avercast, A TransImpact company, provides state-of-the-art demand forecasting software that gives you complete visibility into your future demand and optimal inventory control. Our demand planning and inventory management solutions deliver accurate reports, so you keep just the right amount of inventory at all times. Schedule a demo or connect with our experts to learn more about our solutions.