Quick link: Dissertation - The Resource Consumption Model for Process Selection



Varieties of production economic models exist (e.g., return on investment analysis, break-even analysis, cost estimating, and design for manufacture) to aid production process design for product manufacture.  These models, however, fail to integrate sufficiently the concepts of cost, production cycle time, production capacity, and utilization.  The methodologies typically rely upon these factors being separately analyzed, but do not guarantee that they are.  Some methodologies use a narrow production volume range, or worse, one production volume in their calculations, which limits additional insight into economies of scale.

The resource consumption model for process design (RCM) is the result of several years of research into better models for the analysis and selection of process design alternatives.  RCM is a decision support methodology that provides greater understanding, fidelity and sensitivity analysis to process design than other techniques.  RCM’s foundational concept is that part production consumes resources that can be translated into cost, time, and utilization metrics.  RCM accounts for all resources, which can be equipment, labor, energy, material, tooling, and other consumables used by alternative process designs.  It characterizes resources generically and avoids the need for terms such as “fixed costs,” “variable costs,” overhead,” and so forth.  For each resource, RCM performs quantity-based, time-based, and system-based calculations for a production volume range and determines the controlling condition.  Resource calculations are accumulated to compare alternatives.  Results are shown in both tabular and graphical formats.  A computer model that uses several modern programming technologies was developed to integrate RCM concepts.

RCM concepts are applied to a manufacturing process design problem to demonstrate the method and the type of results and insights that RCM provides. A number of questions about the problem are addressed using RCM. A comprehensive modeling of process alternatives is very difficult, if not impossible, without RCM. RCM successfully demonstrates that new process design models can be developed utilizing mathematically intensive concepts and implemented using modern computational tools