Demand forecasting is the technique of estimating future customers' needs over a predetermined time frame. To provide the most accurate projections, demand forecasting typically considers historical information and other analytical information. Demand forecasting methods entail applying data analytics to understand and predict customer needs in order to analyse critical economic situations and enable decision-making to enhance firm profitability.
Data forecasting into the future is frequently done utilizing a global trend (rolling average) and seasonality (periodic index). The moving average and seasonal index, the two components, are based on previous historical trends. They combine to create a framework that can be used to predict the near future.
What is Seasonality?
The method of creating a seasonal index involves turning each time period's highs and lows into a number. This is accomplished by calculating the average for a whole set of data with the same number of matched periods and then subtracting the average for each period from the overall average. As a result, we are given an index whose sum corresponds to the number of phases in a complete cycle.
For instance, each cycle must have the same number of periods. There are three Januarys, three Februarys, etc. for a trailing 36-month period. This is done to ensure that no month is given more or less weight than another. We can get the mean for the whole period by dividing the mean for each month by the normal for the entirety of the period. This provides us with the yearly seasonality for each month. Revenue from all of January divided by Revenue from all other months is the January Index.
Combination of Seasonal Index and Moving Average together
Obtained by multiplying the moving average with the associated seasonal indicator for the projected month to obtain a projection for future dates. The outcome will serve as the predicted value moving forward for each month.
The term "seasonal demand" refers to a time series with recurring outcomes that are generally of demand brought on by seasonal occurrences. These recurring patterns can make it challenging for companies to predict future demand trends because they might occur across days, weeks, months, or quarters.
In the US, seasonal variations in demand for numerous products can be caused by religious holidays like Christmas or Ramadan, yearly celebrations like Valentine's Day or Halloween, and seasonal weather systems like snow in winter and scorching temperatures in summer.
Improve your ability to compete by predicting seasonal demand
Have you ever had stockouts at times of high demand, missed out on deals, or been forced to liquidate inventory during end-of-season reductions?
Both scenarios are typical for companies that incorrectly predict the seasonality of demand. But if you do it correctly, you'll have a competitive edge over others and benefit from the following things.
● Utilize periods of increased demand
By planning for seasonal variations, you can make sure you have enough stock on hand to capitalize on spikes in product demand throughout the busiest seasons of the year. If you primarily rely on the busy seasons to generate revenue, you must be at the forefront of your game and provide the best possible product availability throughout these times.
● Prevent having too many stocks
Additionally, it's crucial to avoid overestimating seasonal demand variations in your estimate. Overspending on inventories might result in cash flow issues and a weak balance sheet.
If you have extra inventory after the season, you must decide between selling it off at a loss and bearing the burden of high financing costs until demand increases.
● Orders must be timely placed, and suppliers notified
It's simple to overlook how crucial it is to prepare your suppliers ready for changes in seasonal demand. Smart companies will collaborate closely with their vendors by letting them know exactly what they need and giving them more than enough time to reply. They can help ensure delivery will satisfy client demand by organizing their orders in advance.
● Inventory management
You can optimize stock and make knowledgeable choices about safety inventory levels and renewal rules with precise demand projections that take seasonality into account. You can meet your service level goals and save up operating capital that is typically invested in surplus inventory by having the appropriate stock on hand. Both elements may significantly affect your competitiveness and profitability. In order to know the efficiency of artificial intelligence-based demand forecasting, check out our blog post on The Power of AI/ML in demand forecasting.
How to predict seasonally changing demand
Forecasting seasonal demand follows four fundamental guidelines:
● Determine the products that are impacted by seasonal demand.
It's vital to keep in mind that the term "seasonality" only applies to the percentage of demand variation that can be attributed to recurring patterns. As a result, you just need to find demand patterns that periodically reoccur predictably.
● Predict those peaks' respective sizes about average demand with accuracy
Before employing it in a prediction calculation, you should alter the demand if an item's seasonality is significant. To make the information clear and simple to use for predicting moving forward, it is best practice to keep consumer needs and other variable components distinct from your basic demand estimates.
There are numerous ways to increase forecast accuracy, including locating demand outliers and analyzing how they affect your projections. Additionally, calculating forecast error can assist you in determining the degree of mistake in your earlier demand projections. Then you can take this into account for subsequent ones and modify stock regulations, such as security stock levels or reorder points, by it. To do forecasting effectively log in to ThousenseLite.
What is Trend Forecasting?
Trend forecasting is indeed the technique of predicting future consumer preferences and purchasing behaviours using consumer and market research data. Product designers may benefit from trend forecasting expertise to assist them to create a product that appeals to and is purchased by their target market. This makes use of quantified and time-series data or numerical data components that show the frequency of trends over distinct periods. Forecasters investigate societal and environmental changes to ascertain the potential effects on consumer behaviour and priorities. In order to avoid retail forecasting mistakes follow ThouSenseLite.
The following are the top two categories of trend forecasting:
Long-term forecasting: often known as macro trends, examines general signs of the capability to adapt that could have an impact on consumers' lifestyles and daily routines. This kind of trend research makes predictions about how significant cultural shifts will affect the market as a whole and how they will affect customers' behaviour across multiple trend categories.
Short-term Forecasting: Seasonal forecasts, also known as short-term forecasts, concentrate on particular periods and often give an overview of clients' probable spending patterns for the following six to twelve months. Specific information regarding trends relating to particular product aspects may be provided by this predictive analysis.
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