Optimal investment decision for industry 4.0 under uncertainties of capability and competence building for managing supply chain risks

This study analyses a risk-averse firm's decision to invest in Industry 4.0 (I4.0) in the presence of supply chain risks (SCR). Literature suggests that I4.0 assists the firm in mitigating SCRs by enhancing its capability and competence. However, due to SCRs associated with I4.0, building only capability and competence does not necessarily help the firm to improve its resilience from any uncertain fluctuations in ex-post net profit from the long-term return. Therefore, it is essential to investigate how a risk-averse firm makes an optimal investment decision in implementing I4.0 when SCRs impair capability and competence, where we determine a firm's optimal investment decision in terms of utility defined over the expected value and variance of its ultimate random profit from investing in I4.0. This study employs a mean-variance decision-theoretic model to examine I4.0 investment risk-return trade-offs under uncertainties stemming from two types of SCRs, namely system and operational risks, influencing the firm's capability and competence achievements, respectively. The model demonstrates that under certain sufficiency conditions on the relative risk-return trade-offs, higher variation in capability or competence, or greater congruency between the SCRs, may lead to a lower optimal investment decision in I4.0. However, the optimal investment may increase with higher expected capability or competence for a given level of these SCRs. Finally, we conduct a simulation of the theoretical model, using vignette-based experimental data as the benchmark parameter values, to quantify the predictive ability and analytical merits of our model by identifying risk-return trade-offs.