Uncertainty unveiled : decisions and operations in agile product lifecycle management

Abstract

In a fast-moving marketplace characterized by high uncertainty and frequent change, finding the balance between harvesting returns from core products and innovating in parallel is a tightrope act. A tense field of competitive forces and interests drives decision making on product roadmap priorities. We researched product lifecycle management (PLM) at the example of a software firm operating in a highly dynamic, global environment. Empirical evidence confirmed that making transparent and mutually accepted roadmap decisions, optimizing resource utilization throughout the complete product lifecycle, dealing with uncertainty and emergent information and balancing flexibility with pursuing long-term plans are key success factors. Despite the existence of numerous approaches towards product and innovation management, product strategy and operations, we identified the need for an integrative framework to aggregate relevant decision parameters and align decisions with underlying operations. We propose a PLM framework built around a ranking mechanism of projects on the roadmap as the focal point of decision making and operations. Based on different decision perspectives such as financial valuation, market needs, technologies or stakeholdersâEUR* interests, the ranking is established and continuously updated. Both a fast, semi-structured approach and a formal decision analysis method using the analytical hierarchy process (AHP) are proposed. The ranking is the basis to dynamically schedule activities based on resources and constraints. Principles of lean production are used to manage global workflows. Innovation controlling metrics are applied to monitor the execution and refine the decision process. The evaluation showed that ranking is a powerful tool to enforce, communicate and execute clearly defined priorities. The framework seamlessly aligns decisions and operations and combines plan-driven operations with agility for dealing with exogenous and endogenous uncertainty.