Demand forecasters do the impossible — predict what products and services customers want in the future. Their forecasts inform decision-making about production and inventory levels, pricing, budgeting, hiring and more.
"While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts."
The goal is to produce exactly the amount of product to meet demand. No more. No less. Demand forecasting is used to anticipate the demand with enough time to manufacture the right stock to get as close to this reality as possible. The cost is high if you don’t get it right. Your customers will go to your competitors if you don’t have what they need.
Unfortunately, capacity, demand and cost aren’t always known parameters. Variations in demand, supplies, transportation, lead times and more create uncertainties. Ultimately demand uncertainties greatly influence supply chain performance with widespread effects on production scheduling, inventory planning and transportation.
On the heels of the global pandemic, supply chain disruptions and a pending economic downturn, many demand forecasters wish for a crystal ball. While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts.
What is demand forecasting?
Companies are becoming more and more data-driven by the day and use different forms of forecasting to optimize their operations. Forecasting is an art and a science. Getting it right is critical for supply chain operations, but it’s not easy.
Companies and executives use both sales forecasting and demand forecasting to help them cope with seasonality, changes in demand, competition and more.
Sales forecasting attempts to predict actual sales for a specific period and relies on real numbers compared to experience and intuition.
Demand forecasting tries to predict future demand for a product or service in a supply chain by relying on economic and consumer conditions in the marketplace. It’s the first step in production planning and a key approach to addressing uncertainties in supply chains. By looking at historical data, market trends, current sales, missed sales, economic conditions, statistical models, competitors’ actions, expert judgment and a variety of other factors, demand forecasting attempts to anticipate future customer behavior.
Benefits of demand forecasting
Predicting demands and trends is no longer a luxury to companies but a necessity.
Demand forecasting is the basis for many strategic and planning decisions in a supply chain. It can help businesses make more informed decisions about production, inventory and staffing. This can help a company avoid shortages or overages, which can be costly and damaging to the business.
Additionally, demand forecasting can help businesses identify trends and make long-term plans to capitalize on them. For example, if a business is seeing an increase in demand for a certain product, it can increase production to meet that demand.
Even with a simple forecasting method, safety stock can be reduced by half compared to calculating safety stock needs without using a forecasting method. Accuracy in forecasting significantly reduces operating costs and makes businesses more efficient and profitable.
Common methods and approaches to demand forecasting
There are two categories of demand forecasting methods: qualitative and quantitative.
Qualitative forecasting methods allow the demand planner to add internal and external expert opinions into the mix and are less reliant on past trends.
Quantitative forecasting relies on historical data about customer demand, supply chain, seasonal demand and other data-driven metrics. Most of the quantitative methods are machine learning models.
Qualitative approach
Qualitative forecasting methods depend less on data and tend to include more human inputs. A qualitative demand forecasting solution leverages the knowledge base within your company, as well as that of outside experts. Some of the most common qualitative forecasting methods include:
- Sales force composite
Sales team members have the most interaction with customers. They can often spot sales trends before other market information sources. - Market research
This forecasting method uses data about market trends and opportunities to create a demand forecast. - The Delphi method
The Delphi method, or Delphi technique, leverages expert opinions on your market forecast. This method requires engaging outside experts and a skilled facilitator.
Quantitative approach and machine learning methods
Quantitative techniques use machine learning models to understand current performance and predict future demand. Machine learning models are data-based statistical and mathematical processes. These algorithms run through a dataset, look at data features and pick up any underlying relationship and patterns that impact demand to give forecasters insights to predict future demand.
Machine learning models increase demand forecasting accuracy and free the demand planners’ time to provide insight and advise on forecasts.
Machine learning in demand forecasting makes it possible to avoid traditional challenges associated with planning, such as long delivery lead times, high transport costs, high inventory and waste levels, and incorrect decision-making due to inaccurate forecasts.
Some of the most common quantitative/machine learning forecasting methods include:
1. Time series approach
Time series forecasting occurs when you make scientific predictions based on historical time-stamped data. It involves building statistical models through historical analysis and using them to make observations and drive future strategic decision-making. These statistical techniques are used when several years’ data for a product or product line are available and when relationships and trends are clear and relatively stable.
In time series analysis, analysts record data points at consistent intervals over a set period rather than just recording the data points intermittently or randomly. Time series analysis helps to identify and explain different hidden patterns of raw data.
Time series can be decomposed into four components, each expressing a particular aspect of the movement of the values of the time series. These four components are:
- Secular trend: Describes the movement along the term.
- Seasonal variations: Represent seasonal changes.
- Cyclical fluctuations: Correspond to periodical but not seasonal variations.
- Irregular variations: Random sources of variations of series.
Modelling time series data
There are several ways to model time series data. The three main time series models are moving average, exponential smoothing and ARIMA. The crucial thing is to choose the right forecasting method per the characteristics of the time series data.
- Moving average (MA) method is the simplest and most basic of all the time series forecasting models. This model is used for a univariate (one variable) time series. In a MA model, the output (or future) variable is assumed to have a linear dependence on the current and past values. Thus, the new series is created from the average of the past values. MA model is suitable for identifying and highlighting trends and trend cycles.
- Exponential smoothing (ES) method is a widely used forecasting model. Like the MA method, ES technique is also used for univariate series. Here, the new values are calculated from the weighted average of past values. The older a value, the lesser the weight assigned to it. As per the trends and seasonality of the variable, you may use the simple (single) ES method or the advanced (double or triple) ES time series model.
o Simple exponential smoothing method is used for a time series data with no trend or seasonality.
o Double exponential smoothing method carries out the smoothing process twice, due to the presence of a trend in data.
o Triple exponential technique, also known as Holt-Winters Exponential Smoothing, includes smoothing at three levels because of trend and seasonality in data.
o Exponential methods are generally applied for economic or financial entities. - Autoregressive integrated moving average (ARIMA) model is another widely used forecasting technique that combines two or more time series models. This model is suitable for multivariate non-stationary data. ARIMA method is based on the concepts of autoregression, autocorrelation and moving average. In the case of seasonal data, a variation of the model called SARIMA is applied.
o SARIMA (Seasonal ARIMA) is an extension of ARIMA that considers the seasonal element of the time series. While ARIMA can analyze data with a trend, SARIMA supports trend and seasonality data. Besides the three trend factors of autoregression, difference and moving average, SARIMA considers the three seasonal parameters for the same as well as a fourth factor for seasonal periods. The advantage of this model is that it can contain many parameters and their combinations.
There are numerous other methods we can use to forecast demand. These techniques can be applied, depending on the data requirements and business needs. Some of the most popular or frequently used techniques are GARCH (Generalized Autoregressive Conditional Heteroskedasticity), NNETAR (Neural Network Time Series ARmodel), Prophet, and LSTM (Long Short-Term Memory).
The choice of method or technique will be influenced by the characteristics of the product, the availability of data, and the unique needs of the company. Each type has advantages and disadvantages.
2. Casual approach
Causal forecasting is a strategy that attempts to forecast future events in the marketplace based on the range of variables likely to influence the future movement within that market. This type of prediction tries to determine what impact those anticipated variables will have on consumer demand.
A causal model is the most sophisticated kind of forecasting tool. It expresses the relevant causal relationships mathematically, including pipeline considerations (i.e., inventories) and market survey information. It may also directly incorporate the results of a time series analysis. Causal factors are key drivers/variables influencing demand. These variables include marketing initiatives, weather conditions, macroeconomics, channel inventory and more.
Casual methods for demand forecasting
There are several casual methods that can be used to forecast demand, including:
• Econometric models
• Regression models
• Diffusion index method
• Life-cycle analysis
Overall, the choice of causal method will depend on the nature of the product, the availability of data and the specific needs of the business.
Achieve forecasting accuracy today with machine learning
The growing need for customer behavior analysis and more accurate demand forecasting is driven by globalization, increasing market competition and the surge in supply chain digitization practices.
While it’s not a crystal ball, demand forecasting with the support of machine learning gives global supply chain leaders the accuracy they need.
When deciding on a demand prediction method to provide more accurate demand forecasting, it’s important to select an option in line with the data available from the business.
By implementing machine learning-based demand forecasting, businesses can significantly reduce forecasting errors. Companies that use artificial intelligence/machine learning-powered demand forecasting can reduce forecasting errors because it’s faster, more accurate and granular than other forecasting methods.
Machine learning is quickly becoming a ubiquitous technology, but it’s also an iterative technology. The sooner you deploy a solution, the better it becomes. The algorithms improve with data to analyze from new data sources and updated data from existing sources.
While it’s not a crystal ball, demand forecasting with the support of machine learning gives global supply chain leaders the accuracy they need. Amid demand uncertainties, using a machine learning model in demand forecasting has the greatest influence on supply chain performance with widespread effects on production scheduling, inventory planning and transportation.
