Dynamic Innovation Portfolio Management: A Model Predictive Control Approach for Product-Process Innovation Trade-Offs

The proposed model derives closed-form solutions for investment efforts in both product development and its associated production processes while balancing economic profit, product quality, and marginal production costs. The system dynamics, including state and control variables, as well as the relevant constraints, are explicitly formulated. To demonstrate the practical utility of the framework, a numerical example is investigated. Simulation results illustrate adaptive strategies that anticipate future market conditions, manage product–process innovation trade-offs, and respond effectively to changing innovation returns. In the baseline numerical scenario, the model achieves a cumulative profit of 7850 with an average period profit of 196. Finally, a sensitivity analysis is conducted to examine the impact of key model parameters on system performance.

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Publication Details

Journal
Processes
Published
2026-02-05
DOI
https://doi.org/10.3390/pr14030557
Primary Topic
Capital Investment and Risk Analysis
Type
article
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article

Dynamic Innovation Portfolio Management: A Model Predictive Control Approach for Product-Process Innovation Trade-Offs

Lotfi Tadj, Sobhi Mejjaouli
Processes
Capital Investment and Risk Analysis
article

Dynamic Innovation Portfolio Management: A Model Predictive Control Approach for Product-Process Innovation Trade-Offs

Lotfi Tadj, Sobhi Mejjaouli
article en

Abstract

The proposed model derives closed-form solutions for investment efforts in both product development and its associated production processes while balancing economic profit, product quality, and marginal production costs. The system dynamics, including state and control variables, as well as the relevant constraints, are explicitly formulated. To demonstrate the practical utility of the framework, a numerical example is investigated. Simulation results illustrate adaptive strategies that anticipate future market conditions, manage product–process innovation trade-offs, and respond effectively to changing innovation returns. In the baseline numerical scenario, the model achieves a cumulative profit of 7850 with an average period profit of 196. Finally, a sensitivity analysis is conducted to examine the impact of key model parameters on system performance.

ProcessesVol. 14(3)
Alfaisal University (SA)
Industry, innovation and infrastructure
Openalex Percentile: Top 67%
Capital Investment and Risk Analysis
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Dynamic Innovation Portfolio Management: A Model Predictive Control Approach for Product-Process Innovation Trade-Offs — Lotfi Tadj, Sobhi Mejjaouli · Processes (2026) | TGRS Research Map | TGRS