## 6 Development Data-Driven Implementation Methodology
### Impact of New Methodology for Implementation in the Mass Customization Companies  
The proposed methodology has been evaluated in terms of the application of the successive stages of the forecasting methodology in the mass customization industry. Those stages of the methodology that will be specific to this industry and require additional attention are described below.  
1. Formulation of the forecasting problem--in this phase, two elements are most relevant to the mass customization industry: * Establishing the role of traders in the forecasting process--individually won contracts can be so large and non-cyclical that they result in high forecast unveriability. Hence, traders should be able to manually adjust the forecast with the knowledge they have from the market; * To determine the product groups that will be forecast together.
2. Analyse the forecasting rationale--if the problem is formulated well enough in the previous step, the rationale analysis should be classic and not contain too many unexpected difficulties;
3. Determining input data requirements--an important step for this type of industry is precisely the appropriate establishment of the input data requirements. Therefore, it is necessary to properly define proxy indices and put them together into a forecast;
4. Data collection--the collection of data is from previous companies' systems or indicates external sources;
5. Data analysis--classical, but in this industry, this stage can be time-consuming;
6. Data processing--as the industry is characterized by high variability, there will be outlier observations in the data sets, for which a difficult decision has to be made as to whether, or how to, include them in the modelling stage or not;
7. Modelling stage--in this industry, in addition to properly modelling the grouping of items, it is often necessary to take into account data from traders. That is, to develop an appropriate information flow over manual adjustments to forecasts created by predictive models;
8. Evaluation--due to the manual revisions of forecasts, evaluation is a more time-consuming stage due to the inability to rely only on statistical measures of forecast quality.  
With the proposed implementation, we have reduced the implementation time. The Figure 5 shows the distribution of work in the classic IFS methodology. The Figure 6 shows the distribution of work in the new methodology.  
The reduction in time is due to a reduction in the risk of prolonged realization in the later stages of the project and a reduction in user support time in the final phase of implementation. By working on full data analysis and modelling practically from the beginning of the project, project evolution is also achieved faster. The new methodology increases the time intensity of the second and third stages of work, but reduces the risk of extension of the final stages. The process mapping and re-engineering in Step 2 takes less time because steps have been added to support these tasks such as establishing data requirements and data analysis. In stage 3, the training time of mapped processes is reduced because specific tasks for forecasting related to data processing, modelling, and evaluation of results have been added. In the previous approach, increased testing of the forecasting models and support for users in learning how to forecast was left for the last stage, due to the previous lack of all actual data. As a result, the evaluation of the solution takes place much sooner. End users do not need support spread over, for example, the next three months. A two-day support during the first month of the production launch of the system is sufficient.  
Figure 5: The distribution of work in the classic IFS methodology. High-risk works for the demand forecasting module are highlighted in purple. Especially in second stage process re-engineering and process mapping task without data analysis are high risk because they may be based on faulty assumptions.