Question 1 of 6: Quality Philosophy, Cost of Quality, Quality Planning and Designed Experiments

Question 1: Quality Philosophy, Cost of Quality, Quality Planning and Designed Experiments (20 marks)

Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.

(a) Deming's quality philosophy

W. Edwards Deming's philosophy holds that quality is primarily a management responsibility, not a worker responsibility: roughly 85–94% of quality problems originate in the system (processes, materials, equipment, policy) that only management controls, and workers cannot inspect or exhort their way past a badly designed system. He formalized this into the 14 Points for Management, which management teams are expected to internalize as a package rather than pick individual items from. The core themes are: (1) create constancy of purpose toward improvement of product and service, planning for the long term rather than quarterly results; (2) adopt the new philosophy — refuse to accept commonly tolerated defects, delays and mistakes as a cost of doing business; (3) cease dependence on mass inspection to achieve quality — build quality in at the source instead, since inspection is after-the-fact and does not improve the process; (4) end the practice of awarding business on price alone — move toward a single supplier per item in a long-term relationship of loyalty and trust, since lowest-bid purchasing pushes cost and variability downstream; (5) improve constantly and forever the system of production and service, to improve quality and productivity and thus constantly decrease cost; (6) institute training on the job; (7) institute leadership — supervision should help people do a better job, not merely enforce quotas; (8) drive out fear, so everyone may work effectively for the company; (9) break down barriers between departments so people from different functions work as a team; (10) eliminate slogans, exhortations and targets for the workforce that ask for new levels of productivity without providing the methods; (11) eliminate numerical quotas and management by objective — substitute leadership; (12) remove barriers that rob employees of pride of workmanship; (13) institute a vigorous program of education and self-improvement; (14) put everybody in the company to work to accomplish the transformation, since it is everyone's job.

Underlying the 14 points is Deming's System of Profound Knowledge: appreciation for a system (understanding that optimizing a component in isolation can worsen the system as a whole), knowledge of variation (distinguishing common-cause variation, which is inherent to the system and only management can reduce, from special-cause variation, which is assignable and correctable at the local level — tampering with a system that shows only common-cause variation makes performance worse, the well-known funnel experiment), theory of knowledge (prediction requires a theory, not just data), and psychology (people are motivated intrinsically; fear and extrinsic ranking schemes such as merit pay and forced ranking destroy that motivation). His message for management is therefore that sustainable quality improvement is a leadership and system-design problem, achieved by continuously reducing variation and removing fear, not by inspecting defects out or by pushing workers harder.

(b) Productivity/quality/profit and the cost of quality

Deming's chain reaction links these directly: improving quality reduces costs (because there is less rework, fewer mistakes, fewer delays and snags, and better use of machine time and materials), which improves productivity (more good output per unit of input); improved productivity and lower cost allow the firm to capture the market with better quality and lower price, which sustains the business and creates more jobs. This directly contradicts the older assumption that quality and productivity trade off against each other — in a poorly controlled process, chasing quality by adding inspection or slowing the line does trade off against output, but reducing the root causes of defects (Deming's actual prescription) increases both simultaneously, since less scrap and rework means more of the same input converts into shippable, first-pass-good product. Quality also drives profit on the revenue side: higher conformance and higher perceived quality increase market share, allow premium pricing, and lower warranty/liability exposure, while poor quality drives customers away and imposes visible and hidden costs on the firm.

The cost of quality (COQ) is traditionally split into four categories. Prevention costs are incurred to keep defects from occurring in the first place — quality planning, process design, training, supplier qualification, designed experiments. Appraisal costs are incurred to determine the degree of conformance to requirements — incoming inspection, in-process inspection, final test, calibration of measurement equipment. Internal failure costs arise when nonconformances are found before the product reaches the customer — scrap, rework, re-inspection, downtime, downgrading. External failure costs arise when nonconformances are found after delivery — warranty claims, complaint handling, returns, product liability, and the much harder to quantify loss of customer goodwill and future sales. A key managerial insight (the "1:10:100 rule") is that the cost of a defect grows roughly by an order of magnitude at each stage it goes undetected: a dollar spent on prevention is far cheaper than a dollar of appraisal, which is in turn far cheaper than the internal or, worse, external failure cost of the same defect reaching a customer — this is the economic argument for investing in prevention (SPC, robust design, poka-yoke) rather than relying on inspection and after-the-fact rework.

(c) Key steps in quality planning and quality function deployment

Quality planning (in the sense of Juran's quality trilogy — planning, control, improvement) is the structured process of identifying customers, discovering their needs, and designing products/processes able to meet those needs at a competitive cost. Its key steps are: (1) identify the customers, both external (end users, purchasers) and internal (the next department/process downstream); (2) determine customer needs, translating what customers say they want into measurable, technical product/service requirements; (3) develop product/service features that respond to those needs, optimizing the design against both customer requirements and the needs of the firm (cost, manufacturability); (4) develop the process that can produce those features under operating conditions, at minimum cost; and (5) transfer the resulting plans to operations, including verification that the process is capable of meeting the design intent under normal production conditions.

Quality function deployment (QFD) is the primary tool used to carry the "voice of the customer" through this planning sequence without losing information at each translation step. QFD organizes the translation using a matrix format widely known as the House of Quality: customer requirements ("whats") are listed down the rows, ranked by importance and benchmarked against competitors; engineering/technical characteristics ("hows") are listed across the columns; the relationship matrix in the body records how strongly each technical characteristic affects each customer requirement; a correlation "roof" records how the technical characteristics interact with (support or conflict with) each other; and target values for each technical characteristic are set at the bottom, informed by the weighted customer importance and the competitive benchmarking. The output of one House of Quality (customer requirements → product characteristics) becomes the input row of a second matrix (product characteristics → part characteristics), and so on through process planning and production planning — a cascade of four linked matrices that keeps the original voice of the customer traceable all the way to the shop-floor control plan, rather than being reinterpreted informally (and eroded) at each functional handoff.

(d) Designed experiments, EVOP, and Taguchi's three design steps

Designed experiments (DOE) play a central role in off-line process/product improvement because they let an engineer change several factors simultaneously in a structured, statistically efficient way and separate the effect of each factor (and their interactions) from background noise — something a one-factor-at-a-time approach cannot do reliably and cannot do at all for interactions. Factorial and fractional-factorial designs identify which process/design variables actually drive the response and by how much, allowing the process to be centered on its optimum and, critically, allowing the variance of the response to be reduced (not just the mean shifted) by finding factor settings at which the response is insensitive to noise — this is what separates DOE-driven improvement from simple trial-and-error adjustment.

EVOP (Evolutionary Operation) is a technique for running small, deliberately conservative designed experiments (typically a 2² factorial with a center point) directly on a full-scale production process while it continues to manufacture saleable product, rather than in a separate pilot plant. Because a single cycle's factor perturbations are kept small (within the process's normal operating tolerance) the effect of any one cycle is usually too small to detect against noise, so EVOP accumulates and averages the results of several repeated cycles, moving the operating point a small step toward the estimated optimum each "phase," and giving management continuously updated information (an "information board") on which effects are becoming significant. It trades the speed of an aggressive, off-line DOE for the ability to improve the process without ever stopping production or scrapping conforming output.

Taguchi's three steps in product/process design are: (1) system design — using engineering and scientific knowledge to develop a basic functional prototype design, choosing the technology, materials and nominal architecture that can meet the functional requirement; (2) parameter (tolerance-free) design — using designed experiments (Taguchi's own orthogonal-array methodology, closely related to fractional factorials) to determine the best-performing levels of the CONTROLLABLE design parameters, chosen specifically to make performance robust against (insensitive to) the uncontrollable NOISE factors (environmental variation, manufacturing variation, degradation), at no extra material cost — this is the step where most of Taguchi's cost-free quality improvement is captured; and (3) tolerance design — only if parameter design alone cannot meet the requirement, tightening tolerances on the most sensitive parameters (identified via the parameter-design experiments) as a last resort, since tightening a tolerance generally does cost money (better materials, tighter process control, more precise equipment). The strategic point of the sequence is that expensive tolerance tightening is deferred until the cheap lever — robust parameter design — has been fully exploited.