A Data-Driven Work Center Assignment and Pricing Strategy for a Job Shop

Abstract

Job shops with semi-autonomous work centers must understand their capacity utilization and financial state to maximize efficiency and profitability. Machine monitoring software allows managers to see the state of machines at any time and capture real-time capacity utilization. Job shops are positioned to maximize these work centers and must connect their manufacturing and operations strategy to the real-time shop data to maximize efficiency. This research is a case study in how a job shop can create a right-to-win strategy targeting jobs that are compatible and profitable for semiautonomous machines.

ADDMAN Precision Baltimore (APBAL), a precision machine shop in the aerospace and defense industry, is facing labor constraints and underutilized work centers. This research aims to develop a structured quoting strategy and strategic pricing model to optimize job allocation between APBAL’s two semi-autonomous machining centers: the Makino Machining Complex 2 (MMC) and the Fanuc Robodrill. By integrating qualitative observations, historical job data, and machine utilization metrics, this study identifies inefficiencies in current job assignment practices. Key findings indicate that aligning work center assignments with projected profitability and capacity utilization can improve overall efficiency. A decision-making framework and pricing matrix are proposed to enhance job quoting accuracy, optimize machine usage, and increase APBAL’s competitiveness in securing high-volume contracts. The results offer a scalable framework for APBAL and its parent company, ADDMAN Engineering, to deploy across other machining facilities, ultimately improving operational performance and financial outcomes.