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Demand Forecasting Methods

Using Machine Learning and Predictive Analytics to See the Future of Sales

What is the biggest "pain point" for executives in companies?

Gartner, the world's leading IT research firm, gives a clear answer: demand volatility.

To many factors - starting with weather variability and ending with posts from social media influencers - impact buyers and cause them to change their minds frequently. Making the situation even worse, things that change shoppers' intentions happen quite unexpectedly.

Consider, for example, teenage climate activist Greta Thunberg. Her protest to stop flying for environmental reasons sparked the "Flight Shame" movement.

There is no magic wand to predict scenarios like the "Thunberg effect." But there are technologies to enhance the accuracy of demand forecasting. Frankly, it will never be 100 percent accurate, but they are accurate enough to help you as a business achieve your business goals.

In this blog post, we will outline the capabilities of current forecasting methods. Unfortunately, the pictures are in German, but please ask us for English ones. 😏

The importance and significance of forecasts in demand and supply planning

Demand forecasting is the process of estimating the likely future demand for a product or service. The phrase is often used used interchangeably with demand planning, but the latter is a more broad process that begins with, but is not limited to, forecasting.

According to the Institute of Business Forecasting and Planning (IBF), demand planning "uses forecasts and experience to estimate demand for various items at different points in the supply chain." In addition to estimates, demand planners participate in inventory optimization, ensuring the availability of needed products and monitoring the gap between forecasts and actual sales.

Demand planning is the launch pad for many other activities, such as warehousing, distribution, price forecasting, and especially supply planning, which aims to meet demand and requires data on what customers are likely to want.

Here we return to forecasting. Being as close to reality as possible is key to improving efficiency throughout the supply chain. But how do you achieve the highest possible accuracy? The answer depends on the type of company, the available resources and the objectives.

So let's compare the available options: traditional statistical forecasting, machine learning algorithms, predictive analytics that combines both approaches, and demand sensing as a supporting tool.

Classic statistical forecasts:
suitable for stable markets, but not for changes.

Traditional statistical methods (TSM) have been around for a long time and are still an integral part of forecasting processes in production and retail.

The only difference to earlier calculations is that they are now performed automatically by software solutions. For example, time series forecasts for sales and trends can be created in Excel.

To forecast the future, statistics uses data from the past. This is why statistical forecasts are often referred to as "historical analysis." Traditional forecasts are still the most popular method of predicting sales. Typically, demand planning solutions based on statistical techniques can be executed in Excel and existing enterprise resource planning (ERP) systems seamlessly, without the need for additional technical expertise. The most advanced systems may consider seasonality and market trends, as well as apply numerous methods to fine-tune results.

It should be noted that an important requirement for statistical forecast accuracy is the stability of the market. The analysis assumes that history repeats itself: situations that occurred two or three years ago will repeat themselves.

Demand patterns that can be measured well statistically:

However, this is far from the reality. Statistical methods fail to predict illogical changes in customer preferences or to predict when market saturation will happen. In summary, automated statistical forecasting provides a sufficient level of accuracy for:

- medium to long term planning,
- well established items that have a stable demand, and
- Forecasting overall demand
- Is not suitable for sales planning of individual stock keeping units (SKUs).

So, does it possibly make sense to invest in more sophisticated technologies, such as machine learning or artificial intelligence, after all?

Machine Learning for Demand Planning:
Enhanced Accuracy at the Cost of Added Complexity.

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Vergleich der Methoden, Chart, green, grün, orange

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