Operating data collected and stored in historian must be managed to extract their full potential. Part I of this paper proposed a structured way to process industrial data. This framework considers the analysis scope definition, data management steps and operating regimes detection and identification. The value of this proposed framework is demonstrated in Part II through the use of cost accounting for operational problem-solving. With the use of operations-driven cost modeling (contingent on activity-based costing concepts) and processed data corresponding to steady-state operation, incremental cost reduction can be assigned to each operating regime in order to identify the most cost-efficient one. The overall objective of this paper is to convert processed industrial steady-state data and cost information into knowledge that can be used to optimize the washing department of a chemical pulp mill. More specifically, different operating regimes are assessed, and the most suitable operating strategy is defined.