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.
Challenges in Inventory Planning
– Quantitative Methods
There are four best practices for the quantitative forecasting approach:
- Naïve Forecasting:
In this method, you simply consider the previous sales year’s data and use it to forecast future sales; e.g., if you sold 100 smartphones last sales season, then this season, your ideal sales goal is 100 smartphones.
Moving Average Method:
Consider the average of past sales periods and apply it to forecast upcoming periods; e.g., if the average sales of the last four sales periods is 140, then the coming period will be in that range as well.
Exponential Smoothing Method:
Applies the weighted average method when looking at moving averages; e.g., if you are selling ice cream, you should weight January–March differently than July–September.
This method is all about predicting future trends based on market situations and your historical data. Companies with sufficient data on their past sales can efficiently utilize this method. For example, if you sell 200–300 stationery units during the new educational period, then you should maintain that inventory in your warehouses.
– Qualitative Methods
Qualitative methods also have a fourfold approach to efficient forecasting.
- Executive Opinion:
In this forecasting method, top-level executives come together to discuss the future of the company and its upcoming business period. For instance, the CEO, COO, and VPs of sales and marketing meet to discuss and decide where the company sales are headed.
Trustworthy advisors in the industry provide opinions about future movements, then another group of experts compiles and interprets the analysis to the decision-makers.
Sales executives are the ones who work on the ground level of any thriving market, which is why their opinions are the most essential. In this method, sales teams gather their data and experience to project future sales opportunities.
In this method, businesses ask customers about their experience and valuable feedback about products and services; e.g., customers are provided with a survey form on new or existing products to observe their behavior towards the products.
A more detailed overview of Naïve Forecasting, Moving Average, and Exponential Smoothing
- Naive 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.
- Moving Average
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.
- Exponential Smoothing
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.
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