Despite the recognized importance of demand side management (DSM) for mitigating the impact of variable energy resources and reducing the system costs, the academic and industrial literature have taken divergent approaches to DSM implementation. The prequel to this work has demonstrated that the inflation of the net load baseline forecast, used by the industrial unit commitment formulation, leads to higher and costlier day-ahead scheduling of dispatchable resources compared to the academic method. Consequently, these baseline inflation errors have to be corrected in the downstream enterprise control activities at faster time scales, increasing the control efforts and reserve requirements for the real-time market dispatch and regulation service. This paper compares the two DSM approaches and quantifies the technical impact of industrial baseline errors in subsequent layers of control using an enterprise control methodology. The adopted enterprise control simulator encompasses three interconnected layers: a resource scheduling layer composed of a security-constrained unit commitment (SCUC), a balancing layer composed of a security-constrained economic dispatch (SCED), and a regulation layer. Baseline error is absent in the social welfare model. The simulations with the industrial model are run for different baseline error levels. The baseline inflation is assumed to have the same effects in the day-ahead and real-time market. The resulting implications of baseline errors on power grid imbalances and regulating reserve requirements are tracked. It is concluded that with the same regulating service, the introduction of baseline error leads to additional system imbalance compared to the social welfare model results, and the imbalance amplifies itself as the baseline error increases. As a result, more regulating reserves are required to achieve the same satisfactory system performance with higher baseline error. NOMENCLATURE i, j, l, k, t indices of dispatchable generators, dispatchable demand units, buses, lines, and time N GC Number of dispatchable generators N DC Number of dispatchable demand units N B Number of buses T ED real-time market time step ΔW t incremental social welfare at time t ΔU DCjt incremental utility of the j th dispatchable demand unit at time t ΔC DCjt incremental cost of the j th virtual generator at time t ΔC GCit incremental cost of the i th dispatchable generator at time t A DCj , B DCj quadratic and linear utility function coefficients of the j th dispatchable demand unit A DCj , B DCj quadratic and linear cost function coefficient j th virtual generation A GCi , B GCiquadratic and linear cost function coefficient of the i th dispatchable generator P DCjt , ΔP DCjt dispatched and incremental power consumption at the j th dispatchable demand unit at time t P DCjt − P DCjt dispatched power generation at the j th virtual generator at time t P GCit , ΔP GCit dispatched and incremantal power generation at the i th dispachable generator at time t ΔP lt , ΔD lt dispatchable generation and dispatcha...