Demand forecasts help make the right business decisions by predicting future demands for products and services. Demand forecasts cover finely detailed data, historical sales data, surveys and more. You can improve these forecasts by combining real-time information, advanced analytics, machine learning, and data science.
Accurate demand forecasts help you with inventory management, capacity planning, product demand, and resource allocation. Accurate estimates allow you to organize appropriate SKUs (inventory holding unit), stock up on an adequate amount of products, and set reasonable prices to meet customer demands without encountering supply bottlenecks.
In this content you can learn a general description of the different demand forecasting techniques and the advantages and disadvantages of each method.
There are three main types of demand forecasting processes:
1. Qualitative and Quantitative
2. Time Series Analysis/Forecasts
3. Causal Models
They each reinforce different data and have varying demand prediction accuracy and error rates.
Qualitative forecasting is a type of demand forecasting based on subjective factors such as customer opinions and market trends. Often, you use it when there is very little or no historical data.
Qualitative prediction is less accurate than quantitative estimation but is often faster and cheaper to implement. Qualitative estimates combine or aggregate as much data and information as possible to produce unbiased estimates.
Pros:
• Can be used for new products.
• Focuses on customer feelings and perceived needs.
• It is fast to develop and produce results.
Cons:
• Being subjective can lead to false conclusions.
• Difficult to compare with historical data or other estimation methods.
• Collecting data from customers takes time.
The Delphi method is a structured technique used to obtain expert opinions or estimates. It is mainly used in predictions and has been shown to give more accurate results than individual estimates, especially in cases where there are serious disagreements among experts. The basic principle of the Delphi method is to avoid biased predictions about probability and value, gathering the judgments of a group of experts.
Market research uses a formal approach to estimate market demand. It is used to test hypotheses about future demands. Therefore, it is useful for emerging and new markets. These estimates often use case studies, research, focus groups, customer experience reports, and more.
Panel consensus is a process used to gather data and ideas from a group of experts. You can use it to generate ideas, identify trends and make decisions. Panelists are selected based on their knowledge and expertise on the topic. The panel typically convenes to hold a discussion and then votes on the topic. The resulting consensus is then used to make a decision or advise.
The predictive forecasting technique is used to generate ideas about the future. This technique involves a group of experts who share their predictions about the future. These predictions are then analyzed and compiled into a report. This report is used to make decisions about the future.
This technique involves looking at past events and trends to identify patterns that may emerge in the future.
Quantitative estimation is often more accurate than qualitative estimation, but it can be more expensive and time-consuming to implement. It uses historical data, statistics, and other objective metrics. This type of forecasting is often used for inventory planning, short-term and long-term sales forecasting, and supply chain management optimization.
Pros:
• It is more objective and neutral.
• Can detect patterns and trends.
• Better for long-term demand forecasting.
Cons:
• Handmade operations are expensive to create when using them.
• It is difficult to take into account external factors and newer information.
• Can miss big picture ideas and trends.
Pure forecasting methods use historical data to predict future demands. Therefore, you cannot account for new demand effects such as trends and events.
Seasonal forecasting uses historical seasonal data, such as actual sales, to estimate future seasonal demands. Seasonal forecasts can cover events on a monthly, quarterly and more period. Sometimes, data can be distorted by major economic factors such as distressed period, increased customer spending, or SKU availability.
Earnings trends predict year-end results, such as estimating profit margins, but can be inaccurate during periods of price volatility. These estimates are based on past performance and do not include current trends, patterns, or other economic fluctuations.
Past growth rates help you anticipate the growth rate in a specific metric by measuring its past data. These data can help you predict future growth if you expect performance to be consistent. Price volatility and trends can affect the accuracy of the forecast.
Linear regression is a detailed process that uses a set of data points to determine a cause-effect relationship. It does this by comparing how a particular variable affects an outcome. When you establish a relationship between data points, you can use this to predict expected outcomes. But for this method you need to use a variable that seriously affects the result. As they say, correlation does not prove causation.
Time series analyses use data from many years to estimate potential demand. But the data should reveal precise, reliable, and stable relationships and trends.
Trends gathered from historical data are used to assess whether current trends in consumer demand are in the same place as where you expect them to be, behind or ahead of where you expect them to be.
Other factors, including economic deterioration and seasonal changes, can affect the accuracy of time series analyses. But you can use additional statistical techniques to account for these variations in data and analysis.
Pros:
• It relies on historical data to determine the trend.
• Detect long-term relationships in data.
• It is usually cheaper than other methods.
Cons:
• Data must be of high quality.
• Data should follow clean and consistent patterns.
• It only works when the future is very similar to the past.
The moving average technique helps you detect trends in the data. The process involves taking a series of data points and calculating the average, then showing it on the graph. The direction of the moving average determines the trend.
The exponential smoothing technique uses a similar operation with the moving average but assumes that the most recent data point is the best estimate of the current trend. This technique assigns exponentially decreasing weights to data points as they age. The weight assigned to a particular data point depends on the value of a parameter. You can use exponential smoothing with seasonality or without seasonality.
The Box-Jenkins technique uses a process similar to exponential smoothing but assigns errors or an error rate to older data points.
The X-11 technique uses exponential smoothing and a similar process to Box-Jenkins but uses multiple multiple parameters including seasonality, trends, and irregularity. This technique is very useful in medium-range predictions.
This trend projection technique determines future trends by inferring from past data in a given dataset. Trend projection can help predict future demands for a product or service, future sales figures, or other numerical data.
Causal models are the most sophisticated of prediction tools. They are the most suitable models for long-term forecasts. Causal models require you to rigorously analyze past data until you identify clear relationships between two data points or factors.
Causal models further emphasize causality relationships within data and can incorporate information such as other analyses and location data. Therefore, it is likely that you will continue to update causal models as new information is received.
Pluses:
• Can be used for long-term forecasts.
• Can define relationships between data points.
• It is usually more accurate than other methods.
Cons:
• Data must be of high quality.
• Data analysis can take a long time.
• Requires access to historical data.
Regression is a powerful statistical technique that you can use to predict future values of a response variable based on known values of predictive variables. In regression analysis, the relationships between variables are quantified using the optimal truism (or regression line), which captures linear relationships between predictor and response variables.
The econometric model technique is a statistical approach to modeling economic data. You can use this technique to predict future economic activity, measure the impact of economic policy, and understand the relationships between different variables in the economy. Econometric models are usually estimated using historical data.
Purchasing intention surveys are a fundamental tool used to measure consumer behavior. By using historical sales information, you can make more informed marketing and make product decisions by measuring consumers' purchasing intentions. You can also use such surveys to identify potential problems or areas of opportunity within the company.
The input-output model is a quantitative economic technique used to predict the fluctuating effects of changes in an economic sector on other sectors. The input-output model is based on transactions between firms within an economy, as captured by an input-output table.
Life cycle analysis is a tool used to assess the environmental impact of a product or service. Life cycle analysis looks at the entire life cycle of a product or service, from the raw materials used to produce it, to the waste generated after disposal. Life cycle analysis can be used to determine the most environmentally friendly way to produce a product or service.
Hadoop is a distributed data management platform or open source recovery framework used to store and process big data. Sometimes it can also be described as a reduced distributed operating system.
Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records.
Data architecture is a set of rules, policies, standards, and models that govern and determine the type of data collected, and show how this data is used, stored, managed, and integrated within an enterprise and database systems.
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