Manufacturing System Design Meets Big Data Analytics for Continuous Improvement

Desired business results are the direct result of the system design. It is also theorized that the ‘thinking’ within an organization creates the organization's ‘structure’ or design, which then drives the system's ‘behavior.’ Achievement of enduring change in a system's performance must begin with a change in the thinking of all the people in the enterprise, but especially that of leadership. In the absence of such a change in the thinking, the needed structural changes within a system may result in short-lived, point solutions, resulting in localized optimization of sub-systems versus systemic improvement. Axiomatic design, applied to a manufacturing system, is a design methodology to best reflect, understand and control the inherent complexity of large-scale integrated systems. System stability, and ultimately cost and span-time reduction, are the desired objectives of system design. This paper provides an overview of the manufacturing system design decomposition, and discusses the integrated use of data analytics to identify bottlenecks for system-improvement and use of the manufacturing system design decomposition to cost-justify resource allocation decisions for improvement.