A SEQUENTIAL MULTI-CRITERIA DECISION MODEL FOR SMEs OPERATIONS "STAIRCASE STRATEGY"

Abstract

This paper focuses on the gap in strategic execution in small and medium-sized enterprises, which results from structural resource constraints, limited capital, and cognitive overload. Traditional strategic management frameworks often advocate for concurrent, parallel investments of resources across multiple strategic dimensions, which typically induces severe operational distress, rapid cash-burn rates, and potential bankruptcy in smaller firms because a small business is not simply a smaller version of a large corporation. To resolve this execution bottleneck, this paper presents a formalization of the conceptual staircase strategy as a Sequential Multi-Criteria Resource Allocation Model. The methodology integrates Multi-Attribute Utility Theory with zero-one dynamic integer programming and deterministic state-space transitions, segmenting the strategic execution process into discrete temporal phases that we call them as Stop Stations. The model also limits the number of active projects per stage to decrease project fatigue among small management teams and enforces dynamic state-transition equations in which yields as outputs from previous stage support subsequent stages. The methodology was validated using a simulated case study of a precision engineering firm, which evaluated three competing projects: a cloud customer relationship management and automated billing system, an advanced computer numerical control milling unit, and an operator cross-training program. The initial working capital was fifteen thousand dollars, and the technical staff capacity was eighty hours. The simulation results confirm that, while the high-utility machinery upgrade was initially financially infeasible because its cost of eighteen thousand dollars exceeded the starting capital, the optimization model proposed here successfully identified an optimal, self-funding sequence. Specifically, in the analysed case the model prioritized the low-cost customer relationship management system, which cost eight thousand dollars and consumed twenty staff hours, because its execution could generate enough overhead labor reductions and capital yields that dynamically expanded the firm's resources, making the machinery acquisition feasible in the subsequent planning period. We find that this scientific mathematical approach helps avoid poor strategic decisions and resource spreading by setting clear focus limits and updating resources as needed. This puts the challenge-reaction-learning process of entrepreneurial resilience into practice and supports dynamic capabilities when facing deep uncertainty. We suggest that operation’s managers of small firms stop using fixed, multi-year strategic plans. Instead, they should try ninety-day cycles that use feedback and visual tools like the Business Strategy Canvas to guide their decisions. This research offers a practical and efficient way to connect operational knapsack optimization with long-term strategic planning in fast-changing, resource-limited business settings. It provides a strong base for developing future decision support tools.