By Joseph S. Bobinis

This series explores the relationships of methodology for robust engineering and the possible effects of that method in order to accelerate the evolution of systems. The goals are to determine how systems evolve or progress; the principles involved in that progression and; whether robust engineering methods can influence those engineering principles toward evolution and progress. The following is Part Three of a three part series. Part One discussed the nature of engineering progress known as teleology and Part Two explored the causes of system evolution. In the final section of this three part series the author defines conclusions and applications to robust product design.

Applications

Robust product design focuses on ensuring the right- the target- system is built. It is the process of prioritizing system functions to customer requirements and needs (source of system purpose). By performing this critical objectification (purpose to system) of customer needs associated with cost and quality the likelihood of designing the wrong product (failure) is greatly reduced, which means design, product and support rework is reduced. Through robust design, system evolution is provided an accelerant.

Robust design performs this activity through the creation of experiments which simulate critical causal relationships that create the divergence of system purpose from system behavior. It is a method that allows the engineering enterprise to perform this activity during design and manufacturing. In this way a product or system is evolved before it is ever fielded. During the creation of experiments robust design does this through isolation of a subset of system functions and environmental influences. It tinkers with the subset of possible causes of variation during design including the holistic perspective of all factors that could be contributors to system failure.

Many of the examples given for robust engineering applications have occurred during the manufacturing process or later in the life cycle (operational failures). Acceleration of system evolution would be better served by applying these methods during integration and testing. This is consistent with the developer of the Taguchi methods, Genichi Taguchi’s strategic objective to accelerate the failure modes of any system while it is still affordable to design countermeasures to solve them.

Reflection for the Sustainment Engineering Professional

Does total ownership cost analysis provide a measure of robust design?

In dealing with the aspect of cost, an individual must address total ownership cost (TOC). In the past, total system cost has not been visible to the acquisition community, particularly those costs associated with system operation and support. “Under the ‘must cost’ approach to buying aircraft, the airframe integrator first conducts market research to determine potential customer requirements for a new airplane, collecting information about the price per plane as well as other performance and operating cost-related objectives desired by the airlines.”¹

Robust design is measured using two critical factors:

1. Cost: How well the functions of a product perform in relationship to their intended use. These include all costs associated with TOC: design to cost (cost to design), unit production cost (cost to produce) and life cycle cost (cost to support). The measurement of cost is holistic and accounts for a product’s complete life cycle and the cost of the functional attributes of a system over time.
2. Quality: Measured as variance of target function over time (on-target engineering). As a measurement function in robust design, it is not measureable separate from cost (quality loss costs). Any deviation of functional targets is considered a loss of quality. An individual, therefore, must quantify functional attributes of a system against stakeholder needs and iterate those requirements to a set of measurable parameters.

When addressing total cost, experience has shown that a major portion of the projected TOC for a given system or product stems from the design decisions made during the early phases of program planning and system conceptual design. The greatest proportion of the TOC impacts are realized downstream in the system operation and support. For example, Boeing’s approach to their Boeing 777 operation and sustainment program operated under the tenet that the greatest opportunity for influencing these costs is realized during the early phases of a program. This approach is consistent with Taguchi on two fronts:

1. Target design
2. Quality of function and loss function as a measure of loss of economic value

These factors should be measurable from the perspective of TOC. The inclusion of the integrated vehicle health management system as part of the original design of the 777’s aircraft integrated management system is a prime example of this understanding. “After labor and fuel costs, maintenance costs represent the third largest expense item for both regional and national carriers with maintenance costs commonly comprising 15 percent to 18 percent of the operational expenses. The cargo mission contract (CMC) helped air carriers achieve maintenance cost reductions at 50 percent to 80 percent.”²

All of these initiatives were implemented through their development of design build teams where concurrent engineering occurred within cross-discipline teams who were represented by all the major stakeholders including operations (pilots) and sustainment (maintenance engineers) professionals. Why was this approach such an important aspect of the Boeing 777 design and how does this integrated design team affect robustness during the design?

1.The analytic or decomposition methodology of classical engineering decomposition at times makes some limiting assumptions:

• That even complex system behaviors can be deduced as a priority. That most use cases as well as external system influences (noise) can be accounted for during initial design.
• Empirical (robust) engineering methods assume that system complexity entails, that an individual cannot decompose a system completely and account for all system behaviors through the decomposition method. It requires that an individual place measurement and reflexive functions in the system design, production and support processes and even in the system functions. It assumes that functions will evolve and change over time. The system may be subjected to environments, users and contexts that it was not designed for unanticipated noise factors. The requirement for a method to measure and countermeasure unknown and/or emergent noise factors.
• There is a process for discovery and adaptation in this engineering design method and it is more akin to the scientific method than geometrical problem solving. It acknowledges the “variation in nature,” and the divergence of methodology required to sufficiently design physical/functional attributes from operational/support attributes of a system. This principle is at the heart of robust engineering experimentation and measurement.

Some possible outcomes when both methods (deductive and inductive) are not used:

• Cost to evolve system functions over time is unfeasible.
• Cost to support systems makes the product untenable.
• Users cannot efficiently produce the functions the system was intended to do.
• Systems cannot operate or function with other systems.
• Cost to manufacture systems prices them out of the market.
• Systems are not adaptable to an evolving user and customer needs.
• Systems cannot be fielded to schedule and/or time to market constraints.

Since operations and support seems to be major cost drivers of the system over time, they also represent the areas of robust engineering experimentation with the highest potential trade space for quality loss analysis.

References

1. Mark Lorell. Cheaper, Faster, Better? Commercial Approaches to Weapons Acquisition; RAND Corp. Santa Monica, CA. USAF Contract #F49642-96-C-001, pg. 98; 2000.
2. Gorinevsky, Gordon, Beard, Kumar, Chang. Design of Integrated SHM System for Commercial Aircraft Applications; 5th International Workshop on Structural Health Monitoring, pg. 1; September, 2005.

About the Author:

Joseph S. Bobinis is a project management professional and has written several papers on product development, systems engineering and logisitics. He holds a B.A. in philosophy from Ithaca College. He currently specializes in Sustainment Architecture, IT architecture and engineering processes. Contact Joseph S. Bobinis at joseph.bobinis (at) lmco.com.