DEMAND FORECASTING WITH MACHINE LEARNING

Read advice about demand forecasting with automl, learn with B2Metric blogs

BMS - Demand Forecast
2021-01-31
5 Minute Read
B2metric
Stock Optimization
AutoML
demand-forecast
supply-chain-optimization
Machine Learning
Automated Machine Learning

As B2Metric, we enable companies to forecast demand with higher accuracy. For this, in addition to traditional statistical methods, we use automated machine learning pipeline that detect the effect of external factors affecting demand through machine learning. To learn more about these solutions, you can visit our page.

 

                                  Demand Forecasting

 

Fluctuations in demands constitute is one of the biggest problems of company managers. Many factors, from the weather to social media posts, cause the consumer to change their buying behavior frequently. Moreover, these changes are not experienced in a long period, but in a very short period of time. For all these reasons, it becomes more and more difficult to anticipate the demands of consumers and to follow a path accordingly in production.

With businesses in almost every sector facing increasing demand fluctuations and rapidly developing market conditions, advanced demand forecasting methods are more important than ever. Without demand forecasting, businesses run the risk of making poor decisions about their products and target markets. Wrong decisions can have far-reaching adverse effects on stock holding costs, customer satisfaction, supply chain management, and profitability. In summary, you need to make demand planning and sales forecasting to create an effective production process. There are different demand forecasting methods you can apply for this.

 

 

 

 

 

What Are the Various Demand Forecasting Methods?

Qualified Estimation Method

Qualified forecasting techniques are one of the demand forecasting methods used when there is not much data to work on, like new businesses, or when a new product is launched. In this case, various techniques such as expert opinion, market research, and comparative analysis are used to generate quantitative predictions about consumer demand. This method is often used in industries such as technology where customer demand is difficult to measure in advance.

Time Series Analysis

When historical data and trends for a product or product line are clear, businesses tend to use the time series analysis approach for demand forecasting. Time series analysis is effective for determining seasonal fluctuations in demand, cyclical patterns, and key sales trends. Time series analysis is mostly used by well-established businesses with several years of data to work best and relatively stable trend patterns.

Causal Model Method

The causal model is the most sophisticated demand forecasting tool for businesses. Because this method requires specific information such as the relationships between variables that affect the demand in the market, competitors, economic forces, and other socioeconomic factors. As with time series analysis, historical data is key to creating a causal model prediction. For example, an ice cream business can create a causal model forecast by looking at factors such as past sales data, marketing budget, promotional activities, new ice cream shops in their region, competitors' prices, weather, and general demand.

Machine Learning Assisted Forecasting

We ensure that you reach the highest accuracy prediction results by choosing the best model for each component among machine learning, deep learning, and statistical methods. Different scenario types are created according to changing conditions and the most accurate strategy is created by analyzing their effects on the estimation results. Higher accuracy estimation results are obtained by formulating internal and external factors that may affect the demands.

AI & ML for Retailers

If we look at what artificial intelligence does in terms of demand forecasting, evaluations, and predictions made with artificial intelligence change the way a company evaluates the data in its possession. In addition, it saves time by performing processes that take a lot of time when done manually, such as data extraction, in much shorter times.

Artificial intelligence algorithms evaluate the data obtained from customers while making sales forecasts. Assuming that sales are made over the internet, the algorithm written; the number of page views, the number of views on the search engine (Google, Mozilla, Internet Explorer, etc.), the number of views of the members, sales prices, and demand changes according to price changes.

 

Demand Forecasting for Retail

Weekdays, seasonality, and other recurring demand patterns

With machine learning, it's possible to capture the effect when multiple factors are combined—for example, weather and day of the week. Demand for barbecue products increases when sunny and warm weather coincides with a weekend.

Weather, local events, and other external factors impacting sales 

External factors such as competitor price changes, local concerts, and weather can have an important effect on demand but are baffling to take into account in forecasts without a system that automates a big part of the work. At a high level, the effect can be quite insightful. On a warm day, you’ll likely see an increase in ice cream demand, whereas the rainy season will have increased sales for umbrellas, and so on.

Unknown factors impacting demand 

In brick-and-mortar retail, local occurrences—such as a direct competitor opening or closing a nearby shop—may cause a shift in demand. Sadly, data on the influence causing this shift may not be logged in to any system. At times, retailers’ internal arrangements also go unrecorded, such as including a product in a special off-shelf display area in a store. Favorably, machine learning can help in these cases. Machine learning algorithms can conditionally place a “changepoint” in the forecasting model, then track the following data to either disprove or validate the hypothesis. This allows forecasts to adapt rapidly and automatically to new demand levels.

Some examples of the unknown factors impacting demand: 

-Lost Deals Correction: Oversees and reports missed deals and stock-outs; merges this information into demand forecasting for more accurate evaluations of future demand.

-Product Segmentation: Segment your products by volume, sale, and lifecycle standing and establish forecasting strategies adapted for each segment.

-Store Clusters: Classify stores that are similar to each other depending on well-known store components and observed demand review.

 

As B2Metric, we enable companies to forecast demand with higher accuracy. For this, in addition to traditional statistical methods, we use artificial intelligence algorithms that detect the effect of external factors affecting demand through machine learning. To learn more about these solutions, you can visit our page.

 

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