Elementos Arquitectónicos

This chapter was dedicated to reviewing the literature on Lean Manufacturing Systems (the context within which RPD is applied) and two inter related concepts statistical thinking and CI. Through the review of the literature on Lean Manufacturing Systems (section 2.2), the researcher found that although the primary aim of Lean is CI of operational performance to add value to the customer and the organisation, it is possible to build additional value (see Figure 2.2) through Lean at the design stage of the product, through superior product quality (e.g. Hines et al., 2004). This will be one of the primary elements that will be carried forward to the next chapter in framing the research questions. Another primary element of Lean that will be carried forward to the next chapter is the emphasis on problem solving (section 2.2.3), which is also common in Taguchi’s RPD approach.

The researcher also reviewed the subject areas Six Sigma (section 2.3.5) and LSS (section 2.3.6) for two reasons. Firstly, reducing variation remains a central objective in these approaches, which is also common in Taguchi’s RPD (see next chapter) although the latter is a product design concept. The researcher showed that Six Sigma and LSS typically originate from quality by control activities (e.g. SPC) and hence the notion that quality needs to be built into the project at the design stage is not subsumed these methods. Secondly, the fieldwork the researcher conducted (Chapter 5) to reduce variation at the Lean apparel plant was a LSS project in every respect but the name.

The literature related to the study is continued to next chapter (Chapter 3), which primarily reviews the literature on robust parameter design approaches. The next chapter also shows the research questions based on the knowledge gaps identified through the literature review.

CHAPTER 3

QUALITY AND ROBUST PARAMETER DESIGN

―A Sinhalese Proverb INTRODUCTION

3.1.

This chapter continues the literature review that initiated from the previous chapter. Since the overall aim of this study is to understand the applicability of Taguchi’s RPD approach in a mature Lean apparel manufacturing environment, a considerable portion of this chapter was dedicated to the review of literature on RPD approaches. The knowledge gaps identified from the literature review presented in the previous chapter and this chapter and as well as the research questions are presented in this chapter. The subsections of this chapter are organised as follows.

Section 3.2 introduces, in the order of maturity, the three primary ways in which quality control and quality assurance is accomplished in a manufacturing organisation: quality by inspection (QbI), quality by control (QbC), and quality by design (QbD). RPD is an important subdomain of QbD. Section 3.3 provides a synopsis of the traditional/classical DoE approach, as a precursor to the literature on RPD, which is presented in the next section (section 3.4). Section 3.4 on RPD covers the literature on the theoretical foundations of Taguchi’s approach to RPD (section 3.4.1), the key debates on this approach (section 3.4.2), and the alternative RPD methods relevant to the study (section 3.4.3). Section 3.5.1 provides a synopsis of Design for Six Sigma (DFSS) for the purpose of comparing DFSS with integration of Taguchi’s RPD approach within a Lean context. Section 3.5.2 provides a rundown of alternative robust design approaches for the purpose of comparing these with Taguchi’s RPD approach. Section 3.6 begins by highlighting the knowledge gaps (section 3.6.1) that emerge from the literature to pose the four research questions (section 3.6.2) of the study. Finally, section 3.7 concludes the chapter by providing a summary of what was learnt from the literature.

48 PRODUCT QUALITY ASSURANCE 3.2.

Traditionally quality is controlled either by inspection (inspecting the incoming goods or finished goods to weed out the nonconforming ones) or by control: continually monitoring the process and taking corrective action when necessary (Dale et al., 2013; Rahman, 1995; Summers, 2010). These approaches provide little incentive for a manufacturer to embed quality into the design stage of the product. On the other hand, QbD aims to build quality at the design stage of the product (Antony, 2014; Rahman, 1995; Roy, 2010; Samson & Sohal, 1990).

Quality by Inspection 3.2.1.

Quality by inspection is the oldest known ‘scientific’ method used to control the quality of incoming goods into a production process, or the finished goods produced by the process. Inspection, which can range from 100% inspection to a sample-based inspection (which can be based on a simple sampling plan or a very elaborate sampling plan), has two main disadvantages. Firstly, the inspection approach fails to acknowledge the fact that all processes show variation (which can come from causes inherent to the system or causes external to the system) and that understanding the root causes of variation is the key to reduce nonconformities. Secondly, inspection does not involve problem solving as such (e.g. root cause analysis) and consequently, inspection fails to use the knowledge of the operations staff either in quality control or quality improvement (Dale et al., 2013; Deming, 2000; Rahman, 1995).

W. Edwards Deming, one of the principal quality gurus of the 20th century, was one of the earliest to discuss the fallacies of inspection, both at organisational level and societal level. Deming once mentioned that “the right quality and uniformity are foundations of commerce, prosperity and peace” of a society (cited in Howard, 1992). He argued that cost to a company for losing customers due to poor quality is infinite (Gunter, 1987). To him, poor quality is caused due to the deviation of functional characteristics of the product from their specified values (or attributes), due to variation. Deming asserted that “quality by inspection” is an obstacle towards efficiency and effectiveness. He argued that quality by inspection is late, ineffective and that quality cannot be improved by inspection (Deming, 1982; Walton, 1989). Quality by control or (more specifically)

quality by statistical process control of the processes at different stages of production was the alternative proposed by Deming.

Quality by Control 3.2.2.

Quality by control acknowledges the fact that variation is inherent in any process and that this variation can be due to either causes inherent in the process (“common causes”) or causes external to the process (“special causes”). Deming showed (e.g. Deming, 2000) that the action required to reduce the two types of variations are totally different from one another and that wrong actions add cost to the company as well as to the end user. Deming asserted that understanding the type of variation without ambiguity and controlling the process (statistically) continuously (to bring the process under statistical control) are the key to process improvement (Deming, 2000; Montgomery & Runger, 2010).

Two unique tools are invariably associated with quality by control: Shewhart’s control charts and the Plan (P)-Do (D)-Check (C)-Act (A) continuous cycle of quality improvement (Deming, 2000; Rahman, 1995; Summers, 2010). A Shewhart control chart is a chart that shows time-ordered data (which can be physical measurements from the product or process or specific attributes of the process such a defects rates) graphically; it is essentially a statistical tool that is used to verify whether or not a process is stable at any given point in time (Dale et al., 2013; Deming, 2000; Summers, 2010). A stable process is defined as a process that is subjected to common cause variation only. Deming asserted that understanding whether or not the system/process is stable is absolutely essential before deciding whether it is necessary to improve the process, so as to reduce variation. The PDCA problem solving cycle on the other hand is a sequential, on-going approach of problem solving (e.g. variation reduction) that involves organisation-wide involvement (Dale et al., 2013; Summers, 2010).

Another important concept articulated by Deming, in connection with quality by control, is “supplier development” (Anderson et al., 1994; Dale et al., 2013). Deming proposed that management has to collaborate with the suppliers to improve the quality of the raw material and/or components that are sourced from the suppliers. Deming argued that switching from one supplier source to another on random variation does not reduce but increase the occurrence of special cause variation.

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In quality by control, once the process designers are able to eliminate the occurrence of special cause variation, action can be taken to reduce common cause variation; that is, reducing the inherent variation of the process. This can be accomplished by redesigning the manufacturing process through a major change or by making the process (or the product that the process manufactures) robust or insensitive to factors that cause the common cause variation (noise), by setting the process input parameters (control factors) at specific predetermined levels (Allen, 2010; Joseph, 2007). Redesigning the process is a costly option as it requires capital (e.g. new machinery, equipment, staff training). Unfortunately, quality by control defaults to the premise that reducing common cause variation (inherent process variation) requires a major process redesign, which in turn requires top management support (Does & Roes, 1996; Rahman, 1995; Young & Karr, 2011). Therefore, it can be argued that while quality by control provides a scientific approach towards reducing variation whilst in production, because reducing inherent variation of a process it deemed costly in this approach (Deming’s propositions are partly attributable for this), quality by control does not provide a major incentive to minimise variation in a stable system; instead it encourages limiting variation to specified limits (tolerances).

Quality by Design 3.2.3.

Quality by design refers to building quality into the product (and the processes that produce the product), meaning providing a superior product to the customer in the first place (Dale et al., 2013). Building products that stand robustly against the variability of uncontrollable variables that exist out there (e.g. in a factory, outside the factory when the product is put into use by the customer) in an efficient manner is one important product design and development aspect. This brings to the concept of quality by robust parameter design (RPD), which is now a well-accepted quality by design approach (Montgomery, 2013; Roy, 2010; Taguchi & Clausing, 1990).

Setting the process input parameters to achieve robustness is an off-line approach (i.e. a procedure undertaken at the product development stage rather than at the manufacturing stage) based on carefully designed industrial experiments. While conducting industrial experiments to set the optimal operating parameters of a manufacturing process is by no means inexpensive, compared to alternative approaches of achieving robustness, it is an

economic option as it does not usually require financial capital investment in the form of new machinery and equipment. In the literature, making a product or process robust against the noise (variability of uncontrollable factors), is known as the robust engineering (RE) or the robust design (RD) (Allen, 2010; Montgomery, 2013; Roy, 2010). RPD, a term often used interchangeably with RD, relies on adjusting the process parameters rather than investing in excessive capital such as designing and acquiring new machinery and equipment (in part, this equates to tightening the tolerances of the machinery, equipment, and other major components of production) to achieve robustness—that is invariance of product’s functional characteristics (Montgomery, 2013; Ross, 1996; Roy, 2010).

THE TRADITIONAL DOE APPROACH