From Retail Scheduling to Supply-Chain Living Wages: An Agent-Based Simulation to Quantify the Business Value of Improving Job Quality for All

Improving job quality for frontline workers is widely recognized as both a moral imperative and a potential source of competitive advantage, yet most organizations continue to underinvest in working conditions. A central reason is the absence of tools capable of translating job quality improvement into measurable and context-specific financial outcomes that decision-makers can act on. This paper reviews evidence linking job quality to firm-level financial performance and identifies limitations of conventional analytical tools that prevent organizations from making informed investment decisions. We argue that agent-based simulations can address these limitations by modeling heterogeneous agents whose behaviors and interactions allow organizational dynamics to emerge from the bottom up. We illustrate this approach through the Job-Quality Impact Explorer (JQIE), a proof-of-concept simulation focused on frontline workers in a retail environment that traces the causal chain from four job quality inputs (pay level, guaranteed minimum hours, advance scheduling notice and shift-swap ability) through worker satisfaction and behavior to company financial outcomes. Simulated results under the model’s current assumptions show that a company providing none of these benefits may earn as much as 35% less annual profit relative to an otherwise identical company providing all four benefits. Sensitivity analysis confirms that this pattern holds robustly across a range of assumptions about the underlying satisfaction effect. We discuss JQIE’s potential as a decision-support framework, its limitations, and ongoing efforts to expand this work to improve the working conditions of millions of workers in global supply chains.