Quality is the key to economic success of an enterprise, because it increases productivity at little cost and is vital for business growth and enhanced competitive position. Quality is achieved by reducing variability (the Six Sigma principle) and eliminating waste due to defects, waiting, inventory, unnecessary motion, transportation, overproduction, and over-processing (the so-called Lean principle). Cost of fixing quality problems in the field increases exponentially, as evidenced by the recent Boeing 787’s Li-ion battery fire-hazard problems and high recall rates by automotive manufacturers, medical equipment makers, and laptop producers. As faulty design and manufacturing are behind most such problems, early failure analysis and mitigation can substantially reduce warranty costs caused by recalls as well as consequent loss in reputation.
What is Lean: Lean focuses on response time (cycle time) reduction, where cycle time is the value-added and non-value added time in manufacturing a product or in providing services. Lean identifies sources of waste to reduce the non-value added elements. The sources of waste can be classified into the following seven categories:
- Waiting: Any non-productive time waiting for parts, tools, supplies, personnel, failed systems to be brought on-line,…
- Transportation: wasted effort due to unproductive service calls, transport of materials, parts or finished goods to storage or between processes;
- Correction: Repair or rework or revisits;
- Inventory: Maintaining excess inventory of parts, raw materials or finished goods.
- Motion: Any wasted motion to pick up or stack parts or due to walking;
- Overproduction: Manufacturing more than needed before it is needed;
- Processing: Doing more work than is necessary.
Little’s theorem links work-in-process (WIP or queue length) with cycle time and throughput. Indeed, larger WIP implies larger cycle times and saturated throughputs (overworked personnel and systems). Lean improves throughput by creating and optimizing smooth operational flows by level loading, reducing setups, linking suppliers, and reducing time and waste.
What is Six Sigma: Six Sigma focuses on reducing variability, thereby improving product/process quality. Variability stems from failures, setups, long infrequent disruptions, synchronization requirements, and many others. Variability causes congestion (i.e., WIP/cycle time inflation), propagates through the system and inflates the seven sources of waste. Reduced variability leads to highly capable processes, guaranteed lead times and high levels of service.
TEAMS Toolset: A number of quality and inflated cycle time problems can be alleviated by verifying design attributes related to fault detectability and diagnosibilty, and system reliability, availability and life cycle cost. Design for Testability (DFT) facilitates such verification capability, and thereby reduces unexpected downtime, maintenance, warranty and logistic costs – leading to a higher degree of customer satisfaction. Diagnostic modeling and analysis capabilities of QSI’s TEAMS toolset enable the designers to perform DFT optimization for remedying deficiencies in the design phase, and service engineers to arrive at rapid operational fixes for deployed systems.
QSI’s TEAMS Toolset features a common model-based ‘systems engineering’ methodology, as well as off-line design and on-line implementation processes, to build highly reliable, dependable, and serviceable systems. The highly acclaimed integrated diagnostic modeling methodology embedded in the TEAMS toolset helps design engineers to
- Identify the potential failure modes in complex systems and characterize nominal and faulty behaviors under various modes of operation. This lack of knowledge often leads to long duration disruptions.
- Design tests to detect anomalies under varying usage, environmental and operating conditions; lack of detection and diagnosis capability is a major source of variability and throughput reduction.
- Perform model-based testability analysis and Failure Modes, Effects and Criticality Analysis (FMECA) a priori during design rather than be surprised by field failures,
- Design optimal sensor allocation strategies to maximize fault diagnosability and minimize maintenance and parts inventory costs,
- Provide on-line/off-line diagnosis and prognosis schemes that are robust and adaptive to different system configurations and operating conditions for proactive and predictive maintenance lading to high system availability,
- Sequence tests to minimize setups and mean time to isolate and repair; these are two major contributors of variability in a production or service system,’
- “Diagnose before dispatch” capabilities reduce the unproductive service calls,
- First-time fix rates reduce rework and revisits.
Use of the same models and algorithms throughout a system life-cycle ensure consistent specification and analysis of system requirements, rigorous evaluation of design for service trade-offs of system architectures, selection and implementation of the best design, easy verification of design implementation, and post-implementation assessment of how well the product meets the specified requirements. This “build a model once and use it many times” approach enhances communication and coordination among complex system stakeholders, and reduces development risk (cost and schedule) by improving productivity and quality.