Connected factory: real time data analysis for manufacturing efficiency

Abstract

Pratt and Whitney is expecting an increase in demand for new engines and for parts supportive of aftermarket service, maintenance, and repair. To avoid expensive capital investments in additional production capacity, Pratt is taking several approaches to better utilize existing capacity. In a business where historically margins have been high, demand was flat, and in some years decreasing, and staffing had relatively low turnover, conditions were not forcing leaders to focus on identifying ways to eliminate waste or adapt cutting edge manufacturing analytics. With the introduction of new and innovative products, Pratt & Whitney is quickly approaching conditions where demand will outpace capacity. Additionally, demographics of the employee base has started to hit a point where many key and tenured employees have started to and will continue to retire leaving a knowledge gap behind.

To attack this growing problem, Pratt is taking several approaches to win more efficiency and effectiveness out of existing capacity. These include lean initiatives supported by connected and real time manufacturing technologies. Sensors and monitors are primarily used to gather data about machine condition and performance which is fed back to calculate Overall Equipment Effectiveness (OEE), a lean metric used to identify waste in the manufacturing process. The production team in Columbus has done a lot over the past few years to increase production, but as utilization rates increase, they are looking for new ways to expand capacity. The problem faced by management is identifying and reacting to losses as they occur, rather than retroactively, which is caused, in part, by inadequate access to the data.

This problem of reacting timely to losses is exacerbated by attrition of experienced workers who had tribal knowledge of the processes and how to react, whereas newer employees have not developed those reactionary instincts yet. Pratt & Whitney in Columbus has been collecting and storing data from their forge presses for years; accessing and analyzing that data in real time and integrating decision making based off that data has not been a part of their process. Using machine state tags, that is logic based off Programable Logic Controllers (PLCs) to tell users if the machine is in a run state, going through a changeover, or sitting idle, management can view the state of machines anywhere they can access the Pratt network. This data has also been used to calculate production efficiencies by part number by asset by calculating actual cycle times and comparing them to the engineering design time per part.

This is fed as an input to the new scheduling tool developed over the past few months which is meant to capture the intricacies of how different materials perform on different presses and optimize total production time by maximizing tool life among the presses. I have identified key inputs and business analytics processes to evaluate suboptimal efficiencies in the production process. This has affected the manner in which Pratt & Whitney in Columbus conducts business and permeated throughout the management structure to be included in events from daily production meetings all the way up to weekly executive report outs. Initial results show scheduling efficiency would improve output up to 8%, and the data has been utilized to uncover other areas for efficiency gains amounting to a 25% go get by the end of the year.

This research has shown that a data rich environment can present you with a vast array of opportunities if the data can be aggregated and interpreted timely enough to feed the decision-making process of production and if the organization has a culture to embrace it.