## 5 Conclusions and Future Work  
Companies consider the flexibility in terms of rapid part-on-demand production of additively manufactured DSP as a realistic driver for the future adoption of the DSP concept. In this regard, companies applying 3D-printed parts within their supply chain networks consider digital storage as the main opportunity and demand 3D printing specialists. For very large companies, it seems to be easier to acquire monetary and human resources to set up the infrastructure necessary to realize this concept.  
To reap the benefits from the presented opportunities, several technical barriers need to be overcome. The low number of usable materials, insufficient accuracy levels, tolerances, and the limited build chamber volumes were perceived as the major technical barriers. We conclude that post-processing constitutes mandatory production steps to fulfill minimum surface roughness levels (required by 13 out of 14 cases) and larger build envelopes (locally 47% and best-in-class 76% of part sizes 3D printable) must be offered to lay the foundation of efficient DSP distribution scenarios. The low number of commercially available 3D printing materials cannot be confirmed as the main technical barrier directly since 70% of investigated materials are industrially 3D printable; however, repeatable mechanical and surface roughness properties of parts remain challenging issues for numerous materials. Larger companies are legitimately worried by inadequate build envelopes, especially in the case of distributed manufacturing. Summarized, mentioned aspects lead to the conclusion that the DSP concept for AM parts demands for further development to be considered a comprehensive alternative. For the future, a more detailed study regarding additional factors which prevent companies from implementing industrial 3D printing capabilities in their organization is desirable. In this regard, real case studies from different countries could support this approach.  
N.K. and [PERSON] wrote this manuscript and conceived the overall study design; [PERSON] and [PERSON] were validating, reviewing, and editing this study.  
This research was funded by Tekes (Finnish funding agency for technology and innovation) and a consortium of companies within the scope of DIVA (Digital Spare Parts).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Opportunities of Digital** & **Mean** & **CI** & **Median** & **SD** & **SW GD** & **SW (Number of Employees)** & \(\chi^{2}\) **QD** & \(\chi^{2}\) **(Number of Employees)** \\ \hline Part-on-demand production & 4.18 & 3.91–4.45 & 4 & 0.98 & 0.75 (\(p=0.000\)) & 0.75 (\(p=0.000\)) & 2.89 (\(p=0.58\)) & 15.10 (\(p=0.52\)) \\ \hline Faster production with DSP & & & & & & & & \\ compared to conventional & 3.84 & 3.51–4.17 & 4 & 1.20 & 0.82 (\(p=0.000\)) & 0.82 (\(p=0.000\)) & 3.54 (\(p=0.62\)) & 16.30 (\(p=0.70\)) \\ spare parts production & & & & & & & & \\ \hline Digital storage & 3.55 & 3.14–3.95 & 4 & 1.47 & 0.85 (\(p=0.000\)) & 0.85 (\(p=0.000\)) & 6.31 (\(p=0.28\)) & 31.69 (\(p=0.05\)) \\ \hline Topology optimization & & & & & & & & \\ (Enhancing function) & 3.30 & 2.95–3.65 & 4 & 1.27 & 0.88 (\(p=0.000\)) & 0.88 (\(p=0.000\)) & 8.25 (\(p=0.14\)) & 21.39 (\(p=0.38\)) \\ \hline Topology optimization & & & & & & & & \\ (Saving material) & 3.24 & 2.85–3.63 & 3.5 & 1.39 & 0.90 (\(p=0.001\)) & 0.90 (\(p=0.001\)) & 7.18 (\(p=0.21\)) & 25.99 (\(p=0.17\)) \\ \hline Hybrid materials & 2.76 & 2.37–3.14 & 3 & 1.36 & 0.94 (\(p=0.010\)) & 0.94 (\(p=0.010\)) & 12.52 (\(p=0.03\)) & 15.53 (\(p=0.75\)) \\ \hline Outsourcing, crowdsourcing & 2.71 & 2.31–3.12 & 3 & 1.46 & 0.92 (\(p=0.002\)) & 0.92 (\(p=0.002\)) & 3.13 (\(p=0.68\)) & 17.14 (\(p=0.64\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ranked opportunities of DSP, N = 50; CI = confidence interval, SD = standard deviation, SW = Shapiro Wilk, \(\chi^{2}\) = Chi-squared test.