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PurposeThis paper presents a novel multi-objective optimization model aimed at enhancing the success rate of resource planning (RP) implementation. The model optimization is developed based on the organizational structure types, fit-gap contingency analysis reports, uncertainty optimization problems on implementation schedule time and relative time and budget constraints.Design/methodology/approachTwo pivotal strategies are employed: RP tools redesign through customization and organizational redesign. The synergistic integration of these strategies is essential, recognizing that RP tools implementation success hinges not only on technical aspects but also on aligning the system with organizational structure, culture and practices. In the analysis phase, a committee of experts identifies the initial gaps, which are evaluated through three conflicting objective functions: cost, time and penalty and running by the e-constraint method. In case of uncertainty nature time of RP tools implementation, the Activity-on-Arrow (A-O-A) method has been utilized.FindingsThe e-constraint method is utilized to derive the Pareto-optimal front, representing solutions effectively addressing identified gaps. A compromised solution is then proposed using the LP-metric method to strike a balance between conflicting objectives, ultimately improving RP tool implementation by reducing misfits.Originality/valueTo demonstrate and validate the model, a controlled case study is initially presented, illustrating its effectiveness. Subsequently, a real industry case study is provided, further validating the model’s applicability and practical relevance. This comprehensive approach offers valuable insights to optimize RP tool implementation outcomes, a critical concern for organizations undergoing technological transitions.
PurposeThis paper presents a novel multi-objective optimization model aimed at enhancing the success rate of resource planning (RP) implementation. The model optimization is developed based on the organizational structure types, fit-gap contingency analysis reports, uncertainty optimization problems on implementation schedule time and relative time and budget constraints.Design/methodology/approachTwo pivotal strategies are employed: RP tools redesign through customization and organizational redesign. The synergistic integration of these strategies is essential, recognizing that RP tools implementation success hinges not only on technical aspects but also on aligning the system with organizational structure, culture and practices. In the analysis phase, a committee of experts identifies the initial gaps, which are evaluated through three conflicting objective functions: cost, time and penalty and running by the e-constraint method. In case of uncertainty nature time of RP tools implementation, the Activity-on-Arrow (A-O-A) method has been utilized.FindingsThe e-constraint method is utilized to derive the Pareto-optimal front, representing solutions effectively addressing identified gaps. A compromised solution is then proposed using the LP-metric method to strike a balance between conflicting objectives, ultimately improving RP tool implementation by reducing misfits.Originality/valueTo demonstrate and validate the model, a controlled case study is initially presented, illustrating its effectiveness. Subsequently, a real industry case study is provided, further validating the model’s applicability and practical relevance. This comprehensive approach offers valuable insights to optimize RP tool implementation outcomes, a critical concern for organizations undergoing technological transitions.
PurposeThe contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.Design/methodology/approachThis paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.FindingsThis study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.Originality/valueThe model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.
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