## 6 Results  
The results are depicted for three companies, each of them compared for the three planning methodologies, standard \"one fits all\" planning, static planning based on historical Do-Check, and dynamic Plan-Act planning based on current and historical Do-Check. All three companies in all different methodologies have the same target goals with two priority goals and several other second priority goals. These goals are a minimum of 280,000 of real demand satisfied, quality rate suppliers with a minimum of 95%, an absenteeism rate lower than 12%, an investment level lower than USD 1000 million, a profits level lower than USD 700 million, and an ROI higher than 10%. From these goals, the absenteeism rate and profit indicators are the first targets to be met, followed by the second priority goals. Moreover, the \"one fits all\" planning model derives the 40% modularization rate based on non-goal-oriented planning, i.e., a push-innovation strategy. On the other hand, static planning is already a pull-innovation strategy oriented to target goals based on the analysis of databases of the three companies that tried to introduce similar modularization strategies. Based on statistical analysis, 80% of modularization is derived and maintained throughout the innovation life cycle. Later, the dynamic Plan-Act planning modifies the original 80% modularization level depending on the results obtained along the innovation life cycle towards achieving the target goals at their two priority levels. For that purpose, in Tables 1-3, it can be observed how the dynamic model changes the percentage of modularization in each PDCA cycle. All companies present three PDCA cycles. The first is based on historical data from other companies at the initial time (t = 0 weeks) and remains until week 120. Then, a Plan-Act is performed based on the Do-Check evolution of the company, as well as the similar evolution of the other three companies. Based on this analysis, the modularization rate is reviewed, and a new value is provided and maintained until week 260. Then, the same process is followed, adjusting the modularization rate until week 500, the end of the innovation life cycle. The results for the first company, as presented in Table 1, show how the best results in terms of production are obtained for the static historical Do-Check model, while the dynamic Plan-Act model is closer to the absenteeism rate of 12% as well as to the profit level of 700 million USD. Although optimization can be observed from the \"one fits all\" planning and the static planning, the dynamic Plan-Act model is not able to meet the priority goals at the same time with three iterative PDCA improvements. For this company, it needs to be assessed whether the introduction of the innovation makes sense considering the impacts on the overall indicators. On the one hand, the absenteeism rate improves slightly; however, profits decrease, and the ROI of the innovation is negative. Here, it is where the DIIS gains relevance and significance as it can predict these results, enabling any manager and decision-maker to assess the improvements if they are worth the investment and the negative results in some indicators. The results for the second company, as presented in Table 2, show how the best results in terms of real demand are obtained for the dynamic Plan-Act model with almost 20,000 units more than the \"One fits all\" model. Moreover, the absenteeism rate is 9.5%, being lower than the target goal and 3% lower than the \"One fits all\" model. Furthermore, the profits level is above the goal of USD 700 million for all three planning methods while securing more than USD 150 million more for the Plan-Act model.