Informatica

Policy-makers are often hesitant to invest in unproven solutions because of a lack of the decision-making framework for managing innovations as a portfolio of investments that balances risk and return, especially in the field of developing new technologies. This study provides a new portfolio matrix for decision making of policy-makers to identify IoT applications in the agriculture sector for future investment based on two dimensions of sustainable development as a return and IoT challenge as a risk using a novel MADM approach. To this end, the identified applications of IoT in the agriculture sector fall into eight areas using the meta-synthesis method. The authors extracted a set of criteria from the literature. Later, the fuzzy Delphi method helped finalise it. The authors extended the SWARA method with interval-valued triangular fuzzy numbers (IVTFN SWARA) and used it to the weighting of the characteristics. Then, the alternatives were rated using the Additive Ratio Assessment (ARAS) method based on interval-valued triangular fuzzy numbers (IVTFN ARAS). Finally, decision-makers evaluated the results of ratings based on two dimensions of sustainability and IoT challenge by developing a framework for decision-making. Results of this paper show that policy-makers can manage IOT innovations in a disciplined way that balances risk and return by a portfolio approach, simultaneously the proposed framework can be used to determine and prioritise the areas of IoT application in the agriculture sector.

This study introduces an approach in three phases to cover the disadvantages of the FMEA method including inability to assign different importance to risk factors and incomplete prioritization in uncertain environment. First, the values of Risk Priority Number (RPN) are set through the FMEA method. Then, the Step-wise Weight Assessment Ratio Analysis based on the Z-Number theory (Z-SWARA) method has been done to determine the weights of quintuplet factor. Finally, failures are prioritized using Multi-Objective Optimization by Ratio Analysis based on the Z-number theory (Z-MOORA). The results of implementation of the proposed approach by considering uncertainty and reliability represent a complete prioritization.