The study addresses the need to coordinate an industrial enterprise’s technological development with scarce resources and commitments to previously approved projects in resilience conditions. Individually attractive projects may form an infeasible portfolio, while expanding the most heavily utilized resource does not necessarily improve the overall outcome. The purpose is to develop a portfolio management method linking the joint selection and scheduling of technology projects to the identification of minimally sufficient changes in resource provision in resilience conditions. The research employs integer programming, exhaustive enumeration, nondominated solution analysis, and deterministic computational experiments. Its information base comprises numerical project attributes from two previously published test examples (10 projects requiring three resources and 16 projects with time-phased costs). No project attributes were generated for this study. The first experiment covers 17576 combinations of resource capacities, whereas the second examines 71 annual budget levels over a 5-year planning horizon. A threshold-based resource diagnosis procedure is proposed. For a specified improvement in portfolio value, it constructs a set of minimal resource packages that achieve the target while respecting restrictions on revising approved decisions. A necessary and sufficient condition for package effectiveness is established within the finite deterministic model. The contribution is the integration of this diagnosis into a managerial choice between resource expansion, calendar budget reallocation, and portfolio reconfiguration. Across the tested capacity grid, normalized project ranking falls short of the exact optimum by an average of 4.73%. Jointly increasing two resources raises portfolio value by 10 benchmark units, compared with 5 units obtained by adding the separate expansion effects. A commitment to retain a previously approved project eliminates this improvement. In the scheduling experiment, the loss from sequential selection followed by optimal schedule repair reaches 20.77% relative to the integrated optimum. The practical purpose is to provide auditable evidence for portfolio committee decisions rather than replace managerial judgment with an optimization output. The numerical effects describe the benchmark formulations and do not estimate implementation benefits for Russian industrial enterprises. Further research should test the method against corporate data, incorporate uncertain technological outcomes, and quantify the organizational costs of portfolio revision.