STATA analysis on the respondents & conclusion

under the AM umbrella. The first question addressed to this group had the purpose to understand the reason why the firms have decided to continue to produce using the old paradigm. Nine firms over 16 has answered to an open question saying:

• "scarcity of the materials available and the excessive time in the manufacture of the devices";

• "longer production times";

• "production does not require the use of these technologies";

• "For the type of product we make (orthopedic insoles) there are still no tires with low hardness (around 30 shore A) and moreo er the time of realization is still too long",

• "The dimensional tolerances currently guaranteed by additive processes are not com-patible with the small size of our products";

• "financial reasons";

• "it does not fit with mass production";

• "we are in a partnership with an adopter";

• "do not fall within the business plan";

To summarize, 2 say no to AM for quality issues, 3 for not suitability with production, 2 for the production times, 1 for financial issues and anther for a strategic partnership.

The seventeenth question would like to understand if and when the non adopters are planning to invest in additive. As illustrated in figure 5.22 just the 6.2% of total has already planned to invest in additive manufacturing in the short term (within 1 year), the 25% in the medium term (within the next 5 year), instead the remaining 68.8% have not planned any investment.

Finally, the last question is the same of the one addressed to AM adopters about the critical issues (question 14). However the version for non adopters asks just to imagine if the adoption could mitigate or not the issues related to their supply chain. The results, depicted in figure 5.23 explain even why in the previous question there was an high per-centage of firms that have not any plan. Indeed just the 12.5% of the 16 respondents trust this new paradigm, while the 43.8% says that could partly solve the issues and another 43.8% that do not trust these new technologies at all.

Figure 5.22: Question 17: "Have you already planned future investments in additive technologies?"

Figure 5.23: Question 18: "Do you think that a production supported by additive technology could solve or at least mitigate the critical issues of its Supply Chain?"

the balance sheets of the companies belonging to the market segment corresponding with the ATECO code 325030) and from the questionnaire submitted to the sample.

For most of the variables of interest has been built a regression linear model. Generally speaking, a linear model for panel data has the characteristic of comprising differentiated intercepts (constants) for each observation belonging to the panel. These, interpreted as specific effects of single individuals, can be considered unknown but fixed, which are added to others to be estimated. This last model is called "fixed effects model" and it is the one we used for the variables above mentioned. In its simplest formulation, the model coincides with the estimation of a linear regression in which to the set of regressors are added as many dummy variables as the units of the panel. More specifically, the dummy we used are referred to years, in order to capture any time-related effects that are not

already in the model.

The first interesting finding derives from the dimension of the respondents. Indeed splitting in two half the sample of 100 firms (in figure 5.24 on the x axes, 1 if it is big, 0 for small) emerges that the 64.71% (22 over 34) of the bigger firms have taken part to the investigation, against the 35.29% (12) of small size enterprises. The other eleven observation depicted in figure 5.24 refers to the ones who just give some information by phone, as explained before. The other result that cross the dimensions and the adoption, on who actually filled the questionnaire (34 firms), says that there is no association between these two variables, in fact the number of big companies represent the majority (around 56%) for both adopters and proponents of the older paradigm, figure 5.25.

Subsequently has been performed an analysis to understand the association between the adoption and the localization of the ownership. Looking at figure 5.26 we can state that the number of foreigner are 5 over the 34 respondents and that, half of both Italian and foreign companies have adopted, while the other half have not. Then even for these two variables we can appreciate a low degree of association.

Figure 5.24: Firms size and response rate [4]

Let now move to more interesting testes on how the relevant variables has changed before and after the adoption on productivity indexes. In particular a linear regression with fixed effects has been built on the following variable:

• labour productivity (sales/number of employees);

• return on sales, determined by the ratio between operating profit and net sales, it is useful to understand the firm operational efficiency;

• value added per employee;

• market shares;

It is important before to start to premise the all the testes have been performed looking at the evolution, over the time frame available, year by year.

Figure 5.25: Firms size and AM adoption [4]

Figure 5.26: Foreign ownership and AM adoption [4]

For what concern the labour productivity, after preliminary analysis (a parametric t-test and a non-parametric t-test) is emerged that going forward with the years from the one of adoption, we reject the null hypothesis, on the equality of the expected values before and after the adoption (in favour of the latter ), with always greater degree of confidence. This last statement is partially confirmed by a linear regression test with fixed effects, indeed for the first year after the adoption (table 5.27) the adopters labour productivity was lower than the one of non-adopters and we strongly reject the null hypothesis. However the adopters labour productivity grows every year, becomes richly positive seven years after the adoption (but still lower than non-adopters) where we can not reject the null hypothesis in significant terms. At the end of the eighth year, finally the adopters productivity becomes better and we strongly reject the null hypothesis. These results are partially confirmed after a robust regression analysis (which considers the variables auto-correlation) and strongly confirmed for a regression analysis with fixed effects which introduces the year dummies, to capture any time-related effect that is not already in the model. However in

the robust version of the last model we reject with a lower degree of confidence.

Figure 5.27: Regression analysis with fixed effects from the first year after the adoption to the eighth [4]

The second variable of interest is constituted by the return on sales. For this variable we performed a linear regression analysis with fixed effects(figure 5.31) that partially confirms what said before for the labour productivity, indeed even if ROS value remains negative, during the years there is a decreasing gap between pre and post adoption. The same trend is observed for the robust analysis even if with a lower degree of confidence.

Subsequently we have performed the same analysis introducing the year dummies and the results confirms what said before, as depicted in figures 5.33 and 5.34, with the only difference that, in these two last analysis the ROS value becomes positive already in the fourth year after the adoption.

The next variable analysed is represented by the value added for each employee, which is a measure of employees’ effort usage. Performing the same 4 analysis used for the other two variables the positive trend for the adopters is confirmed, as shown in figures: 5.35, 5.36, 5.37 and 5.38. Focusing the attention on the last figure indeed, it emerges that after eight years we can reject the null hypothesis significantly.

Finally one of the most interesting variable examined is the market share evolution of the adopters. This variable confirms the previous graphs too, with a positive growth over time as shown in figures 5.39, 5.40, 5.41 and 5.42.

In conclusion, after the examination of these data it is possible to state that who invest in additive manufacturing technology actually increases its productivity and market share.

Figure 5.28: Robust regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Nevertheless the benefits are not immediate, meaning that it takes three or four years after the investment to start to appreciate all the advantages that these technologies could bring.

In the end, even if there are positive effects, the collection of new data from the balance sheets of the following years will allow more complete analysis, which could confirm or reject the truthfulness of the results exposed in this thesis.

Figure 5.29: Regression analysis with fixed effects and year dummies from the first year after the adoption to the eighth [4]

Figure 5.30: Robust regression analysis with fixed effects and year dummies from the first year after the adoption to the eighth [4]

Figure 5.31: ROS regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Figure 5.32: ROS robust regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Figure 5.33: ROS regression analysis with fixed effects and year dummies from the first year after the adoption to the eighth [4]

Figure 5.34: ROS robust regression analysis with fixed effects and year dummies from the first year after the adoption to the eighth [4]

Figure 5.35: Value added per employee regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Figure 5.36: Value added per employee, robust regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Figure 5.37: Value added per employee regression analysis with fixed effects and year dummies from the first year after the adoption to the eighth [4]

Figure 5.38: Value added per employee, robust regression analysis with fixed effects and year dummies from the first year after the adoption to the eighth [4]

Figure 5.39: Adopters’ market share, regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Figure 5.40: Adopters’ market share, robust regression analysis with fixed effects from the first year after the adoption to the eighth [4]

Figure 5.41: Adopters’ market share, regression analysis with fixed effects with year dummies from the first year after the adoption to the eighth[4]

Figure 5.42: Adopters’ market share, robust regression analysis with fixed effects with year dum-mies from the first year after the adoption to the eighth[4]