## 7 Discussion and Conclusions  
Our results have shown that mass customization manufacturing needs a special approach from the point of using ERP systems and analytics. Appropriate focus on intermittent goods is necessary to provide interpretable and relevant predictions. We examined three companies that exhibit features of mass customization production. In each of these companies, there is a great need to perform accurate sales and production forecasts. However, there are no employees in the manufacturing companies who can deal only with making forecasts. This task belongs to planners who are not data scientists. Consequently, forecasting tools aimed at them should be \"user friendly\" with simple and interpretable  
Figure 6: The distribution of work in the new methodology. High-risk work is highlighted in purple. Compared to the execution times of the classic methodology, the execution times of stages 2 and 3 are longer, but stages 4 and 5 are shorter. In that case, migration of full sample of data take place in the second stage, so we do not have test data migration task in stage 3 and preparation of transaction data task in stage 4.  
selection of parameters. Moreover computation needs to be fast and not resource intensive, as ERP systems need them for different things, for example MRP calculations. Our results in this work show that even appropriate grouping (or in statistical sense pooling) of product data by intermittent goods allows much improved predictions, which can allow for the efficient use of those forecasts in planning. This addresses the needs indicated in the introduction. This approach requires further research and we can indicate the directions that we are interested in for our forthcoming work:  
* Creating direct predictions for intermittent goods directly, by incorporating usage statistics in final products and production lead time requirements. This would not necessitate additional data collection and could be realized within a normal implementation regime;
* Combining forecasts with the DDMRP (Demand Driven Material Requirements Planning) approach to calculate the inventory of subordinate items, assuming that for non-key level 1 intermediates, whose inventory cost is not large, we maintain a sufficiently large safety stock (buffer);
* Creation of hierarchical models and partial pooling to better capture the uncertainty characteristic for low volume productions. In our work we have also considered the process of forecasting module implementation. As it was shown in the previous section, we can significantly reduce time and risk factors. This addresses some of the gaps indicated in the introduction. This gives a large potential for future implementations in the mass customization industry. Conceptualization, K.G.-D., B.Z. ; methodology, K.G.-D.; validation, K.G.-D., E.K. ; formal analysis, K.G.-D.; investigation, K.G.-D.; resources, B.Z., K.G.-D. and A.D.; data curation, K.G.-D.; writing--original draft preparation, K.G.-D.; writing--review and editing, E.K., J.B.; visualization, K.G.-D.; supervision, J.B.; project administration, A.D.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript. The research was partially funded by the European Funds of the Regional Program of the Silesian Voivodeghip no. UDA-RPSL.01.02.00-24-08B2/17-00 and partially by AGH's Research University Excellence Initiative under the project \"Interpretable methods of process diagnosis using statistics and machine learning\". Not applicable. The anonymized data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality of InfoConsulting's customer data. The authors declare no conflict of interest. Samples of the compounds are available from the authors. The following abbreviations are used in this manuscript: