## 3 Demand Forecasting in Mass Customization Production  
Demand forecasting is a fundamental part of many industrial problems in the area of inventory management and production planning [23]. Accurate demand forecasts influence quality S&OP (Sales and Operations Planning), which contributes significantly to the overall management of the company. This makes it possible to maintain adequate inventory levels and thus save on warehouse maintenance costs. In [10], [PERSON] et al. concluded that generating demand forecasts is undoubtedly the most important part of the mass customization industry. They show why mass customization is required in smart manufacturing, and they identify suitable demand forecasting techniques by comparing the traditional time series analysis method (ARIMA model) with a nonlinear neural network model. Despite this, [PERSON] et al. [11] pointed out that forecasting demand for products from the apparel industry is still a difficult task for both academia and the industry. In Ref. [12] [PERSON] et al. present a specific forecasting problem in the mass customization industry through the issue of car production. They note that car manufacturers produce various types of final cars, which makes it impossible to forecast the demand for individual configurations. The contribution rate of each of the options to total sales is what companies are able to forecast. However, the current forecasting approach does not take into account the principles of customer choice of configurations, so that the forecasted contribution coefficients are often not feasible under actual conditions, leading to overstocking, shortages of final products and consequently to customer dissatisfaction. The problem is formulated as finding a point in the convex cone of feasible configurations that has the minimum Euclidean distance from the predicted input coefficients. The authors present an approach similar to a variant of the Frank-Wolfe method that sequentially constructs a feasible region and stops when it finds the best feasible contribution coefficients. Demand forecasting in the mass customization industry is characterized by high volatility. In addition, the market is currently experiencing long delivery times for raw materials needed for production. This is due to globalization and deepening specialization. Long replenishment times, in turn, increase the need for long-term forecasting. A major difficulty in planning replenishment in this industry is also the high complexity of BOM graphs. An additional difficulty is the short product life cycle. Without sufficiently long time series describing sales, it is impossible to make long-term forecasts using classical techniques. Indeed, using a traditional sales forecasting solution means that detailed forecasts are needed for numerous products over a long time horizon. In the scientific literature, as noted above, we can find many papers on the advanced methods of demand forecasting in the mass customization industry. However, there is a big gap between research and business, because business is short of the ability to use complex forecasting methods and usually simple solutions are used. This gap was noted, among others, in the paper [24], where the authors analysed the gap between research and practice in the field of demand forecasting: obstacles to the adoption of advanced techniques and methods, and analysing the empirical findings. In addition, the commerce of decision support systems does not have complex solutions in this area. Most technologically advanced companies use ERP (Enterprise Resource Planning) and APS (Advances Planning and Scheduling) systems to manage planning and scheduling. The largest IT systems of this type, such as SAP, IFS, SYSPRO, Oracle, Microsoft, Asprova, provide only basic forecasting methods, such as [4; 5; 6; 25]:  
* Naive methods;
* Moving average;
* Holt-Winters exponential smoothing models;
* Seasonal Box-Jenkins model;
* Croston model;
* Linear regression;* and, some of them, ARIMA.