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Integrating quality function deployment and benchmarking to achieve greater profitability

290

Ashok Kumar Grand Valley State University, Grand Rapids, Michigan, USA

Jiju Antony Caledonian Business School, Six Sigma Research Centre, Glasgow Caledonian University, Glasgow, Scotland, UK, and

Tej S. Dhakar Professor of Quantitative Studies, School of Business, Southern New Hampshire University, Manchester, New Hampshire, USA Abstract Purpose – In this paper, the aim is to propose a framework for utilizing quality function deployment (QFD) and benchmarking in combination to chalk out an improvement plan that redesigns or modifies existing processes to a point where they consume the least amount of resources while imparting the maximum value (in the sense of customer satisfaction) to the output. Design/methodology/approach – Using a real world case study, the paper demonstrates that the marriage of two tools – QFD and benchmarking – is synergistic in its import and vital to a company’s strategic and financial superiority. Findings – The product and process design was improved by using the combination of QFD and benchmarking techniques discussed in the paper. As a result, the company accomplished significant financial and strategic results. Research limitations/implications – The case study includes competitiveness analysis at the first house of quality (HOQ) but not at the subsequent HOQ due to lack of information from the competitors. However, the paper demonstrates the competitiveness analysis at the first HOQ which can be extended to all subsequent HOQ. Practical implications – The research would be useful to academicians and practitioners in developing their own integrated versions of QFD and benchmarking methodologies to improve their products and processes and gain strategic advantage. Originality/value – Despite the mutual dependence between a firm’s strategic and financial performance and the consequent dependence on market share and profitability, which can both be maximized using QFD and benchmarking, the research that employs both techniques is virtually non-existent. Keywords Quality function deployment, Customer satisfaction, Benchmarking, Competitive analysis, Product design, Quality improvement Paper type Research paper

Benchmarking: An International Journal Vol. 13 No. 3, 2006 pp. 290-310 q Emerald Group Publishing Limited 1463-5771 DOI 10.1108/14635770610668794

Introduction As the search for new tools, techniques, and strategic paradigms to meet and, more appropriately, exceed the ever-increasing customer expectations rages on in the new millennium, two methodologies – benchmarking and quality function deployment (QFD) – in conjunction with each other have the potential and the capability to help

accomplish this goal. The discerning and stringent customer of today is no more satisfied with low-cost products; s(he) demands high quality, high variety/more options, and rapid delivery of a product – all at the lowest possible cost. Since, all four of these objectives are mutually conflicting – i.e. each one drains resources at the cost of the other – there is a dire need for an optimizing tool that helps guide the planning and budgeting process with an eye on customer needs. By virtue of the way it is designed, the QFD methodology assures, with high a degree of confidence, that a company will design and develop its new products or product improvement programs exactly the way that maximizes customer satisfaction using the least resources. Indeed, using Knapsack type of modeling (Wasserman, 1993), it is possible to allocate resources optimally in a fashion that maximizes the fulfillment of customer needs (equivalently, increase in customer satisfaction) from a pre-specified budgetary constraint. Additionally, QFD is a tool that is simple in its approach, rigorous in its methodology, elegant in its content, and compulsive in its results. While QFD is a powerful tool to identify what needs to be done to the product to respond optimally to customers needs, benchmarking presents an opportunity to optimize processes for maximizing the output value while minimizing the resources used. Thus, if the goal of an organization is to maximize customer satisfaction for a given amount of resources (or budget), QFD is a wonderful tool to use. However, if the goal is to maximize customer satisfaction through the design of a new product or modification of an exiting product, benchmarking helps design or modify processes and/or practices that yield the most value while the resource usage is minimized. The word value used here is in the generic sense and refers to those attributes of the product that would improve customer satisfaction. There is ample empirical and theoretical evidence that customers are more satisfied with a product compared to its competitors’ if the product differentiates itself from others in the same class on quality, level of customization, and/or delivery speed. With the advent of web hosting services, during and after sales service have become additional variables in the determination of customer satisfaction. In summary, QFD methodology helps design/modify a product that maximizes customer satisfaction while benchmarking helps develop processes that will produce such a product with the least resources. It is a tautology, therefore, that a combination of benchmarking and QFD methodologies permits extraordinary opportunity for companies to generate tremendous strategic advantage. The burden of this paper is to illustrate how the service function of a manufacturing company, acting as an independent consultant, brought about necessary product and process innovations using QFD and benchmarking that resulted in significant improvement in market share and profitability of the company. We will provide a brief review of the overall concepts involved in the case study. In a math programming parlance, any organization’s strategic problem may therefore be formulated as follows. Assume that a company’s strategic position is a function of just two variables: its market share m and its profitability p. Let f(m, p) be a function that maps the strategic position of a company from the exact values of the two variables m, p. Let b be the total resources in dollars that a company is willing to invest to carve out a higher strategic position. Then, the strategic formulation is: Max fðm; pÞ subject to B # b;

ð1Þ

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where B is the actual dollars spent. Here, one could reasonably argue that m is the value function of customer satisfaction. One can further postulate, based on manufacturing literature (Swamidass, 1986; Hayes and Pisano, 1996) that:

m ¼ gðP; Q; C; DÞ

292

ð2Þ

where P is the price of the product, Q is the quality level, C is the degree of customization or number of features, and D is the speed at which product is delivered. g is an appropriate non-linear function. In turn, each of these (P, Q, C, and D) is a function of resources spent and the efficiency with which the resources are managed to build value in the product. If r is the resources spent in dollars, h represents the efficiency and smartness with which resources are transformed into the product, then one can reasonably construct the following model for profitability p:

p ¼ Kðm; r; hÞ

ð3Þ

where K is an appropriate function. The above equations represent a somewhat circular argument, but it is evident that to improve the bottom line, resources must be used efficiently and smartly, the latter implying that resource usage must be dedicated to customer satisfaction. Irrespective of the path one takes to reach the conjectures just stated, no one would deny the fact that if one accepts the logic of (1) through (3), estimating the shape and relationships embedded in f,g, and K are not easy to define or establish. This is where QFD and benchmarking come to the rescue of an organization. QFD would help optimize g (although it may not be explicit – just the product design would establish the levels of quality, customization, etc.), whereas benchmarking would help optimize K. Despite mutual dependence between a firm’s strategic and financial performance and the consequent dependence on market share and profitability which both can be maximized using QFD and benchmarking, the research that employs both techniques is virtually non-existent. Literature survey on QFD and benchmarking In this section, we review relevant literature in QFD first and then review few works that combine benchmarking and QFD in their research. We found only nine such papers on Proquest – ABI Inform, and after study, only five were found useful to review here. Others had pursued benchmarking and QFD as independent techniques in their work. Quality function deployment QFD has been around for over three decades but it has begun to realize its full potential as a powerful tool to improve companies’ strategic position only since 1990s. Businesses that build their strategies around customer satisfaction are gradually realizing that QFD, quite often in conjunction with Kano model (Shen et al., 2000a, b; Tan and Shen, 2000) presents an excellent opportunity to converge on the exact product that customers would love to buy. The power of QFD lies in the fact that, when implemented properly, QFD yields a plan, a product, and/or a detailed budget that would maximize customer satisfaction for a given number of dollars. Originally developed by a Japanese shipbuilding firm in the early 1970s, QFD was imported by the US auto industry in the 1980s and has now achieved widespread use

throughout the world – both in manufacturing products as well as in services. The Japanese first introduced QFD in a shipyard in 1967. QFD is translated from six Japanese Kanji characters Hin Shitsu Ki No Ten Kai whose meaning is shown in Figure 1 (ASI, 1992). An important fact about the QFD is that the methodology of development of House of Quality (HOQ) is so generic that it can be employed in a large variety of situations. For instance, each HOQ has a set of customer needs (left room) and a set of product requirements (top) that satisfy the customer needs. Thus, HOQ can be used in any situation with an internal customer-supplier relationship. In other words, in any situation of the type X needs Y, X can be an internal customer (equivalent to customer needs) and Y can be the top of the HOQ. This fact is exploited in listening to the customers’ voice through a four-step HOQ process that starts from customer needs and ends at process controls (see QFD methodology section). By virtue of the fact that the top of HOQ (product specifications) is designed so as to deliver the left (customer requirements), QFD has been used extensively in a variety of applications. First and the foremost, use of QFD is in new product or service design and development or modification of existing products or services, especially when there is limited budget allocation for such activities. For instance, QFD is used in designing service offerings in healthcare sector (Dijkstra and van der Bij, 2002), in software industry (Barnett and Raja, 1995), in construction industry (Abdul-Rahman et al., 1999), in education or pedagogical applications (Houston and Lawrence, 1998; Hwarng and Teo, 2000; Lam and Zhao, 1998; Motwani et al., 1996; Pitman et al., 1996), in Airline industry (Ghobadian and Terry, 1995), IT-information modeling (Omar et al., 1999); just to name a few. QFD has been employed very extensively in manufacturing – clothing manufacture (Chan et al., 2002), OEM (Parkin et al., 2002). In terms of product design, QFD has not only been used to identify critical product specifications commensurate with customer needs but also to prioritize the specifications in terms of their importance. Franceschini and Rossetto (2002) develop an interactive algorithm using QFD to prioritize the technical characteristics of any product design. Such prioritization is absolutely necessary in order to bring about maximum customer satisfaction with limited resources available. Jussel (2000) show how QFD can be used to improve product design and thereby improve business performance for innovative businesses competing on product customization. Lochamy and Khurana use QFD to develop new products for Chrysler Motors. Fung et al. (1999) in the only study of its

HIN

SHITSU

KI

NO

TEN

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KAI

FUNCTION QUALITY DEPLOYMENT FEATURES MECHANIZATION DIFFUSION ATTRIBUTES DEVELOPMENT QUALITIES EVOLUTION

Figure 1.

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kind (to our knowledge) demonstrate how the target levels of product attributes can be determined when there is positive or negative interaction between the attributes (see roof of HOQ description in next subsection). They employ fuzzy inference to arrive at optimal product attributes. Over the last decade, the theory of QFD has been significantly enriched by employing various existing techniques in conjunction with QFD to simplify, prioritize and optimize the product specifications that maximize customer satisfaction. Kwong and Bai (2002) use AHP (analytical hierarchical process) and fuzzy sets theory for determination of the importance of customer requirements. Bouchereau and Rowlands (2000) use fuzzy logic, artificial neural networks, and the Taguchi methods to design an automobile product with optimum specifications. Partovi (2001) uses a double-matrix concept for quantifying Heskett’s strategic service vision model. Delano et al. (2000) combine decision theory with QFD to get the best product specifications for a cargo/passenger aircraft. Ginn et al. (1998) combine FMEA and QFD methodologies to develop a superior quality product for Ford. Finally, Wasserman (1993) use the concept of Knapsack type solutions to maximize the customer satisfaction based on the attribute selection. Owing to the flexibility of QFD and the scope of its application, QFD has become a significant planning and strategic tool in its own right (Partovi, 2001; Lee and Ko, 2000; Lee et al., 2000; Lu and Kuei, 1995; Han et al., 2001; Tan and Shen, 2000, and others). Benchmarking and QFD Benchmarking has been variously defined as the process of identifying, understanding, and adapting outstanding practices from organizations anywhere in the world to help your organization improve its performance. It is an activity that looks outward to find best practice and high performance and then measures actual business operations against those goals. According to a premier benchmarking web site (www. benchnet.com/), the ten best organizations that are considered role model for benchmarking are: Bank of America, Xerox, TRW, Dana, US Army, Saudi Aramco, US Department of Veteran Affairs, DynMcDermot, NASA, and Social Security Administration. Also the ten top business processes that are currently benchmarked are: information systems technology, benchmarking, human resources, customer service satisfaction, employee development training, call centers help desks, document control records management, process improvement management, accounting, and internal and external communications. Companies that have won Malcolm Baldrige National Quality Award (please see www.nist.gov) are generally good candidates for benchmarking. Also, companies that have won other quality awards, such as EQFM award, Australian Quality Award, Deming or Dr Juran Awardees are also good candidates for benchmarking. There are very few studies that combine QFD and benchmarking. Shen et al. (2000a, b) suggest that: “. . . customer satisfaction benchmarking can help decision makers identify areas for improvement, make strategic decisions, and set targets on desired satisfaction performance” and propose the use of hierarchical benchmarks for strategic competitor selection and decision making. The term customer satisfaction benchmarking refers to comparing your own product on each customer need (left side of HOQ) with that of your competitors’ and initiating corrective action wherever your product falls short. Partovi (2001) presents an

analytical method for quantifying Heskett’s Strategic service vision for a service organization. Starting with two matrices in series that connect market segments, service processes, and QFD elements, patrovi uses Analytic Hierarchy Process and Analytic Network Process techniques in a creative way to determine the synergistic effects of the column variables. Benchmarking is then used to identify areas of breakthroughs in service performance. Pfohl et al. (1999) employs benchmarking for the spare parts logistics in a German mechanical industry. While benchmarking is used to identify areas of improvement, total functional deployment (similar to QFD) is then employed to develop target levels consistent with the projections from benchmarking. Ettlie (1993) states that benchmarking had a significant impact on the process and organizational methods by which new designs are formulated as well as on the QFD process that was used to develop new products. In a three-phase plan development by AT&T to improve the cycle time of their new products (Sansone and Singer, 1993), AT&T found using benchmarking that they take anywhere between 133 and 250 per cent of the time taken by their Japanese competitor. They used statistical process control and QFD to bring their cycle time in line with that of their competitors. QFD and benchmarking methodologies What is QFD? QFD is an interdisciplinary team process that aids in planning for new or improved designs and processes such that: . focus is on customer requirements; . competitive environment and market/customers are factored into all decisions; . the inter-functional teamwork is strengthened; . customer requirements are translated into measurable goals for each department; and . the involvement of all employees is garnered towards “listening to the voice of customer”. The heart of the QFD methodology is what is called a HOQ which is shown in Figure 2. Note specifically the interaction boxes which are part of advanced QFD methodology. Primary purpose of these is to explain the interactions of the adjacent boxes that are shown connected through shadowed arrows. Readers are referred to Prasad (1998) for detailed explanation of advanced QFD. The QFD methodology The QFD methodology works through the concatenation of four (generally speaking) houses of quality. Please see Figure 3. We provide synoptic details here. Readers are advised to look up other works (ASI, 1992) for greater details. It is useful to understand the role of an HOQ as a relationship between a customer and a supplier where each of these terms is used in a generic sense. The box on the left of the product features/characteristics relationship matrix (PFCRM) of the first HOQ represents the needs of the customers which must be met to satisfy the customer. The box at the top of the PFCRM box contains a list of product characteristics or features that would satisfy those customer needs. The degree or level of satisfaction of customer

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Figure 2. House of quality with advanced interactions

Features Inter-relationships Matrix (Roof) Advanced Interactions

Product Features (HOWS)

Advanced Interactions

Customer Needs (Whats)

Customer Needs Product Characteristics/ Features Relationship Matrix

Competitiveness Analysis Matrix

Advanced Interactions

Target Determination Matrix (HowMuchs)

Product Planning Matrix

HO Q - I

Figure 3. Sequence of HOQs

Part Deployment Matrix

HO Q - II

Production Control Matrix

Process Planning Matrix

HO Q - IV

HO Q - III

Advanced Interactions

needs depends on the levels at which the product characteristics exist in the present design of the product. Using the competitive analysis box – the one on the right of the PFCRM box – one determines the target levels of each of the product characteristic/feature so as to maximize customer satisfaction for a given amount of resources (usually dollars). Interestingly, the competitiveness analysis matrix includes a composite impact of customer needs, existing competitors’ products, and the sales/revenue potential of each need’s marginal improvement. In other words, the first HOQ has in-built mechanism for external benchmarking. The second HOQ, called the part deployment HOQ, helps assign the parts to each feature/component of the product so as to meet the level of targets set in the first HOQ. The analytical process is essentially the same although customers or competitors are no more involved in the process or resultant action of this HOQ. This is pretty much the internal matter of the company although some level of internal benchmarking can be usefully employed here provided the company can peek into a competitors’ product parts and features. The third HOQ, called the process planning matrix, concentrates on development of the best processes and practices that would deliver the target levels of specifications set for each part or feature of the product. Once again, benchmarking can play a major role here since we are talking about “best” practices/processes. Indeed, this HOQ in conjunction with the first one determines the level of quality, customization, and agility of delivery and of course the cost of the product. In other words, the quality of resource management plays a vital role in constructing and managing the resources for maximum customer satisfaction. The last HOQ, called the production control matrix, makes sure that the processes do exactly what they were expected or intended to do. This is the HOQ that makes sure that the customer need is indeed fulfilled, that is, the customer voice is in reality heard. The controls are so designed that they guarantee that the best processes and practices projected in the third HOQ are indeed employed (e.g. through benchmarking). The best practices and processes are so designed through the use of innovative engineering, management or through benchmarking, that they maximize the accomplishment of target levels of product features or characteristics set forth in the second HOQ. Finally, since the product characteristics were developed using an analytical process that guarantees maximum satisfaction of customer needs (embedded in the competitiveness matrix), the reverse chain of HOQs ensures the fulfillment of customer needs or what is alternatively described as “listening to the customers”. Application of QFD and benchmarking: a real world study We now describe how a Michigan-based company accomplished significant financial and strategic results using a combination of QFD and benchmarking techniques. To protect company’s identity as requested, we would call the company – The RST Inc. The RST is an aspiring new entrant with only five years of longevity in the furniture industry. A singular distinction that this company has is its hierarchical structure. Please see Figure 4. The service and staff functions, such as finance, marketing, human resource and maintenance fall within the purview of the service VP. Furthermore, VP (service) is also responsible for customer satisfaction. The role of service function in the sense of customer satisfaction is all encompassing. For this reason, all activities involving product innovation, product improvement, value analysis, process

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innovation and improvement, and process mapping are all within the purview of service function. Manufacturing has mostly a “compliant” role although they are encouraged to submit suggestions for product and process improvements. VP (service) is also the chief of planning and strategy committee. The role of manufacturing on the strategy committee is to provide information as requested and implement committee’s decisions in performing manufacturing tasks. RST has some very worthy world class competitors, e.g. Steelcase, Haworth, Herman Miller, as well as a couple of relatively new competitors in south Michigan. However, RST prides in the innovative capability of its service staff and hence its CEO has set forth a very ambitious program of innovating its entire line of furniture products. Initial results from SWOT analysis have shown that the marginal utility of an invested dollar is the highest in office furniture industry. In addition, RST has gone against the economic vein and hired (a better word would be stole) two premier strategists from its world-class peers. Their goal is to exploit the huge “hole” in the competitive terrain for customization. We will skip the details of formation of steering committee and other procedural steps taken to finally decide to go the way of QFD and benchmarking. Suffice it to say that VP (service), a very dynamic individual, has determined, based on a feasibility study that these two methodologies coupled with brainstorming and Pareto analysis where needed are likely to yield the fastest results. It was envisioned that the QFD will be used to identify the customers’ unsatisfied and unspoken needs and benchmarking will be used to optimize the production and marketing processes. Separate sub-committees, each headed by a high level manager, were formed to pursue the QFD and benchmarking effort. It was also realized that these are mostly sequential efforts so that a mobile team that would initially coordinate the efforts of QFD team but would then move to the benchmarking committee was the best way to move forward. The efforts and work of both committees were subject to review by CEO once every month. Project statement For the QFD team, the following scope was defined for existing products that were doing well in the market: Broad QFD scope statement. To modify or redesign the popular existing office furniture products so as to maximize customer satisfaction through maximum customization and faster time performance.

President & CEO

VP Service

VP Manufacturing

Task Task

Figure 4. Partial organization chart

Product Improvement and Customer Satisfaction

Human Resource

Finance & Accounting

Marketing

Maintenance

Manufacturing

Similar statement (not included here) was chalked out for new products in home furniture as well. As is well known, the QFD processes, if done to their fullest potential, snowball in size very quickly. Even with reasonable elimination of choices at every stage, the final QFDs have a large number of process attributes to control. For this reason, we will report only partial results of a QFD/benchmarking project that was undertaken by the company. The first HOQ presented here is truncated by up to 80 per cent in customer needs and a corresponding number of production specifications. Similar levels of truncations have been applied to other stages of HOQs. One of the products that RST sold well due to its powerful ergonomic design was a high-end office chair. The 178 incline of the backrest was a result of a year long study and there were numerous other features that provided increased physical comfort and physical wellness (a term quite loosely thrown around by the CEO of RST). A 178 incline was not only comfortable but also was optimal for keeping blood pressure to a minimum under prolonged usage of the office chair (wellness). A more appropriate statement for the truncated QFD would be:

Integrating QFD and benchmarking 299

To develop a new height-adjustment mechanism or to modify the existing one that optimizes the physical wellness of the user, while maintaining the cost within the specified range conducive to the marketability of the office chair.

Benchmarking Based on the market conditions, customer preferences, and competitive distinctive competencies, the following benchmarks were set after a feasibility study that lasted four weeks and involved sales personnel, customers, external consultants and, of course, all the expertise and inside information that the world class companies’ strategy provided (Table I). Having set the benchmarks, the process of developing HOQ’s began, which we describe next. Implementation of quality function deployment The implementation process for the given project goal and its accomplishment through QFD began by identifying customer needs and developing the first HOQ. The two senior level employees hired from the world class companies also proved very helpful because of the wealth of information they brought from their respective companies. As we stated earlier, the first HOQ is limited to height-adjustment mechanism and within that further truncation has occurred.

Item/factor Development cost including prototyping Development time (months) Total cost of chair ($) Total market share

Average world class Our performance before competitor QFD exercise

Our performance after QFD exercise

$70,000

$72,000

15 percent lower

18 2,500 30 percent £ 3

16 2,800 7 percent

10 2,200 14 percent

Table I.

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The first HOQ: production planning matrix The customer needs. The first HOQ (Figure 5) begins with identification of the customer needs. In this case, the customer needs related to the height adjustment mechanism were extracted from the parent QFD that was developed for the entire office chair. The needs were established by involving a focused group of customers (senior/high level officials from 23 companies), sales and marketing personnel, two aforementioned employees that migrated from other companies, and an external consultant who acted as a facilitator and elicitor of ideas. Fishbone, Pareto, and Vicinity diagrams were used to generate and extract the customer needs shown in the left column of the first HOQ. This group also rated the importance of each need on a scale of 1-5 that is shown in the third column from left of this HOQ. Engineering personnel, sales and marketing personnel, and the external consultant were used to identify and extract the product requirements from the parent QFD that were believed to influence or deliver the customer needs. Customer-need – product-requirement relationship matrix. The same group was used to evaluate and assign the strength of relationship between each pair of product requirement and customer need (9 £ 7). A three-point scale was used in assigning the numbers in each cell: 1 indicates a weak relationship, three represents a medium strength relationship, and nine represents a strong relationship. Moving further to the right of the customer-need – product-requirement relationship matrix, the level at which a need is satisfied by our product was adjudged on a scale of 1-5 (by the same group) and placed in the column captioned “our company”. Competitiveness analysis. Three world-class companies (Haworth, Herman Miller, and Steelcase) were also evaluated (identities are mixed-up for reasons of potential controversy) for the level at which their respective product satisfied each need. These evaluations are placed in next three columns – WCC1, WCC2, and WCC3. Based on the competitors’ weaknesses in theirs products, reflected in below five ratings (lower the worse), and the importance rating of each need (Column 3), the future targets for our performance on each need was determined. For instance, on the second need, speed of adjustment, our competitors were either two or three, which means there is a lot to be gained by doing better than our competitors on this need which customers perceive as very important (4 on a scale of 5). Furthermore, little improvements here would guarantee large returns since competitors are not doing so great on this need. Hence, a target of five was chosen to maximize customer satisfaction. The next column indicates the ratio of improvement, which is computed as the ratio of where the company wants to be versus where it is now in terms of the satisfaction of each need by the customer. For the second need, therefore, this ratio is computed as 5=2 ¼ 2:50: The next column, sales points, reflects the marginal profit potential of each need 1.5 for the second need thus indicates the judgment of the group that for every unit improvement in the satisfaction of this need (on a scale of 1-5), the profit improvement potential is 50 per cent. The next column is simply the overall weight of each need calculated as the product of the three important ratings – importance of the need, sales point, and the ratio of improvement. For the second need, this number 4 £ 2:5 £ 1:5 ¼ 15: Finally, the weight for each need is normalized on a scale of 1-100. This is done by dividing each need’s weight by the total weight of all the needs. All rows corresponding to the needs were then sorted based on the total normalized weight.

5 4 3

LTB LTB LTB

Safe

Comfortable

Interchangeable

9

9

2 2 4 5

WCC1 WCC2 WCC3

Our company in the future 5

5

4

2

2

3

* Ranked after the competitive analysis was completed.

Target 26-42"

4

Our Company Now

Units

13.2%

4.43

3

1

9

9

LTB

Effort to adjust

STB

N STB

3.92

9

9

9

3.09

3

3

9

1

4.42

1

3

9

9

1

3.67

9

3

3

2

4

3

2

2

2

20"

5

3

2

3

2

3

5

4

2

3

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yes

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infinite

3

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15"

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9.2% " within yes/no count arms reach"

3.67

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3

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9

LTB

N LTB

P

LTB

N

7.3% 11.7% 9.2% 13.2% 10.9% 10.9%

2.47

1

1

9

1

3

Knee Space

Product Requirements

STB

N

Number of adjustments

# of Inches seconds force inches count steps

14.3%

4

LTB

Customizable

1

Percentage

4

LTB

Easy Adjustment

3

9

Adjustment Range 4.81

4

LTB

STB

LTB

N

N

Complexity

SN

Time to adjust

Weight

5

LTB

Speed of adjustment

Customer Needs

Adjustment range

Importance

N

Meeting federal regulations

SN

Our Company Now 3

4

4

3

3

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3

WCC1 3

4

4

2

2

2

4

WCC2 3

4

4

2

2

2

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P

= Positive

STB = Smaller the Better

LTB = Larger the Better

SN = Strong Negative

N = Negative

4

4

3

3

4

3

3

WCC3

N

Adjustment increments

Legend for Roof

4

4

4

5

5

5

5

Our company in the future

N

Location of adjustment

SP = Strong Positive

1.33

1.00

1.00

1.67

1.67

2.50

1.67

Ratio of Improvement

N

Total

1.1

1.1

1.1

1.5

1.5

1.5

1.5

Sales Points

N

61.8

4.4

4.4

5.5

10.0

10.0

15.0

12.5

Weight

P

Percentage* 100.0%

7.1%

7.1%

8.9%

16.2%

16.2%

24.3%

20.2%

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Figure 5. Product planning house of quality (HOQ – 1)

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Specification targets. To keep the size of the QFD from snowballing, the top four needs ranked by their normalized weights (shadowed cells) were selected for further analysis and to develop targets for the specifications that contribute to the needs just identified. To accomplish this, a search for the specifications that scored a nine in the product requirements-customer needs matrix corresponding to each need (with shadowed normalized weights) was conducted. Please see all shadowed cells in the matrix. It turned out that all specifications were involved in satisfying at least one of the needs identified for enhancement. Therefore, for all specification a weight was computed that reflected a weighted sum of the elements in the column corresponding to a specification multiplied by the normalized weight in that row. For instance, for the first specification, the specification weight was ¼ 9*20.2 per cent þ 3*24.3 per cent þ 1*16.2 per cent þ 16.2 per cent þ 9*7.1 per cent ¼ 4.806 per cent. Weights for all specifications were computed likewise. At this point, evaluations were made on a scale of 1-5 for our competitors with respect to where they fared in each specification. Moving parallel to what we did in the competitive analysis, a target score on a scale of 1-5 was assigned (see our company in the future row). These target scores were then converted to actual values of the specifications. For instance, the specification in the adjustment mechanism that would determine the time of adjustment would hit a maximum target of five provided the adjustment process could be completed in 5 seconds. The second HOQ: part deployment matrix Six of the most salient product specification requirements were then selected for further expansion into second HOQ (Figure 6). These were, therefore, placed in the left room of the HOQ so as to allow their role as customer needs now. The top of this matrix represented the part characteristics of the mechanism and was now divided into three segments: adjustment mechanism, base understructure, and work surface. Each of these was further segmented into more specific part characteristics. The weights were then assigned to each cell at the intersection of a part characteristic and product requirement. Once again a three-point scale (1, 3, 9) was used to assign the weights commensurate with the strength of relationship. There was no competitiveness analysis room in this HOQ since there are no information/data pertaining to the competitors available here. The weights and targets for each part characteristic were determined exactly the way it was done in the case of the first HOQ. The third HOQ: process planning matrix Once again to keep the exposure within reasonable size, six part characteristics were chosen for further reporting. These part characteristics are shown in the customer need compartment of the third HOQ (Figure 7). Their internal suppliers are processes that would deliver on these part characteristic targets that were set at the stage of second HOQ. A partial list of the processes that are relevant to the part characteristics constitutes the top of the central room of the HOQ. Roof and the right most room are omitted as they are redundant to the analysis. The numbers on a three-point (1, 3, 9) scale are assigned exactly as we did in previous two HOQs. Also, the targets were computed and determined in an identical fashion to that of the two previous HOQs. Details are omitted.

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Figure 6. Part deployment house of quality (HOQ – II)

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The fourth HOQ: production control This HOQ represents (Figure 8) the final control mechanisms that are employed by quality assurance (through inspection, quality control, and other testing methods) to ensure that the processes indeed function as stipulated at the 3rd HOQ stage. As before, a sample of processes identified at third HOQ were used as the “customer needs” in the fourth HOQ. Some control measures are shown at the top of the fourth HOQ. As in the second and third HOQ, the roof and the competitiveness analysis are redundant for the fourth HOQ as well and therefore, not reported. The exact specifications of process as also the product characteristics are suppressed as it serves no purpose to report them but might expose the company’s design details of the mechanism.

Benchmarking effort It is worth pointing out that at each of the second, third, and fourth HOQs, internal benchmarking could have been used with great effect from the parsimony and innovation standpoint. The comparison of the world class company’s internal processes and controls could have revealed a lot of weak/inefficient spots in the way RST works. However, we had no access to that information. This also resulted in elimination of competitiveness analysis in each HOQ (other than the first). This, in turn, may have rendered substandard processes and process controls. However, since RST prided on their own innovative capabilities and had access to some of the inside information through their new hires, it is possible that the world class companies also may be suffering from imperfect processes and in turn from poor competitiveness. This seems to neutralize their advantage which could have accrued to RST through internal benchmarking.

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Figure 8. Process control house of quality (HOQ – IV)

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Conclusion In this paper, we theorized that there is a synergistic effect in integrating benchmarking with QFD methodologies for companies that seek higher levels of financial and strategic performance through product improvement. We used a real world example of a furniture company to show how QFD and benchmarking methodologies could be effectively combined to improve the design of a product for greater customer satisfaction. To the extent that greater customer satisfaction leads to higher market share and greater profitability; we have shown that QFD and benchmarking are, in effect, strategic tools whose integration has a synergistic effect. A singular limitation of our case study was that we lacked competitors’ data at micro level. This fact limited our demonstration of the prowess of internal benchmarking in optimizing internal processes of a company. However, we provided enough clues and discussions that would shed light on how internal benchmarking can be usefully employed in improving internal processes and controls within a company. Also, in our illustration, we used a truncated version of the original project that the case company used due to space paucity. However, in doing so, we were careful enough to include enough information so as to retain the integrity and completeness of our arguments. Our objective was to develop all four HOQs starting with benchmarks that were laid in advance and we accomplished that. Micro level benchmarks can be used in a parallel fashion. In terms of the success in achieving benchmarks laid out at the beginning of the case study, this story is still unfolding. Many of the items stipulated in the partial HOQ presented here are still in the process of being implemented. However, according to the latest quarterly report, and the CEO’s estimation, the company is already ahead of their goals that the strategy committee had set for profitability and market share. Once the implementation is complete, we will provide a sequel to this work outlining the lessons learnt, results obtained, and the product modifications that worked and those that did not. Based on the results thus far, we would like to conclude that the answer to the question we posed in the title of this paper is in the affirmative. Indeed, there is a need to integrate benchmarking and QFD methodologies to obtain strategic and financial synergy. QFD would help get the best product from the customer standpoint leading to the maximum possible customer satisfaction, whereas benchmarking would help develop the best and the most efficient processes at the expense of the least amount of resources. In conjunction, the effect would be that of a strategically superior manufacturing/service system that employs the integrated version of these two methodologies. We hope that this work would be useful for academicians and practitioners in developing their own integrated version of QFD and benchmarking methodologies to improve their products and processes and gain a sustained strategic advantage. References Abdul-Rahman, H., Kwan, C.L. and Woods, P.C. (1999), “Quality function deployment in construction design: application in low-cost housing design”, The International Journal of Quality & Reliability Management, Vol. 16 No. 6, pp. 591-9. American Supplier Institute (1992), Quality Function Deployment: Executive Briefing, ASI, Alan Park, MI.

Barnett, W.D. and Raja, M.K. (1995), “Application of QFD to the software development process”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 24-42. Bouchereau, V. and Rowlands, H. (2000), “Methods and techniques to help quality function deployment (QFD)”, Benchmarking, Vol. 7 No. 1, pp. 8-19. Chan, K., Chan, S.F. and Chan, C. (2002), “Applying QFD in the clothing manufacturing sector: a case study on improving a distance-learning program in Hong Kong”, Managerial Auditing Journal, Vol. 17 Nos 1/2, pp. 86-91. Delano, G., Parnell, G.S., Smith, C. and Vance, M. (2000), “Quality function deployment and decision analysis: a R&D case study”, International Journal of Operations, Vol. 20 No. 5, p. 591. Dijkstra, L. and van der Bij, H. (2002), “Quality function deployment in healthcare: methods for meeting customer requirements in redesign and renewal”, The International Journal of Quality & Reliability Management, Vol. 19 No. 1, pp. 67-89. Ettlie, J.E.E. (1993), “Revisiting the ‘house of quality’ foundations”, Production, Vol. 105 No. 4, p. 26. Franceschini, F. and Rossetto, S. (2002), “QFD: an interactive algorithm for the prioritization of product’s technical design characteristics”, Integrated Manufacturing Systems, Vol. 13 No. 1, pp. 69-75. Fung, R.Y.K., Law, D.S.T. and Ip, W.H. (1999), “Design targets determination for inter-dependent product attributes in QFD using fuzzy inference”, Integrated Manufacturing Systems, Vol. 10 No. 6, p. 376. Ghobadian, A. and Terry, A.J. (1995), “How Alitalia improves service quality through quality function deployment”, Managing Service Quality, Vol. 5 No. 5, pp. 25-30. Ginn, D.M., Jones, D.V., Rahnejat, H. and Zairi, M. (1998), “QFD/FMEA interface”, European Journal of Innovation Management, Vol. 1 No. 1, pp. 7-24. Han, S.B., Chen, S.K., Ebrahimpour, M. and Sodhi, M.S. (2001), “A conceptual QFD planning model”, The International Journal of Quality & Reliability Management, Vol. 18 Nos 8/9, pp. 796-812. Hayes, R. and Pisano, G. (1996), “Manufacturing strategy: at the intersection of two paradigm shifts”, Production and Operations Management, Vol. 5 No. 1, pp. 25-41. Houston, D. and Lawrence, K.A. (1998), “QFD in a university quality management course”, Annual Quality Congress Proceedings of ASQ, pp. 555-60. Hwarng, H.B. and Teo, C. (2000), “Applying QFD in higher education”, Quality Congress, ASQ’s . . . Annual Quality Congress Proceedings, pp. 255-63. Jussel, R.M.A. (2000), “How QFD improves product development across sites”, Measuring Business Excellence, Vol. 4 No. 1, pp. 28-33. Kwong, C.K. and Bai, H. (2002), “A Fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment”, Journal of Intelligent Manufacturing, Vol. 13 No. 5, p. 367. Lam, K. and Zhao, X. (1998), “An application of quality function deployment to improve the quality of teaching”, International Journal of Quality and Reliability Management, Vol. 15 No. 4, pp. 389-413. Lee, S.F. and Ko, A.S.O. (2000), “Building balanced scorecard with SWOT analysis, and implementing ‘Sun Tzu’s the art of business management Strategies’ on QFD methodology”, Managerial Auditing Journal, Vol. 15 Nos 1/2, p. 68.

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Lee, S.F., Lo, K.K., Leung, R.F. and Ko, A.S.O. (2000), “Strategy formulation framework for vocational education: integrating SWOT analysis, balanced scorecard, QFD methodology and MBNQA education criteria”, Managerial Auditing Journal, Vol. 15 No. 8, p. 407. Lu, M.H. and Kuei, C.H. (1995), “Integrating QFD, AHP and benchmarking in strategic marketing”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 85-96. Motwani, J., Kumar, A. and Zairi, M. (1996), “Implementing QFD for improving quality in education: an example”, Services Marketing Quarterly, Vol. 14 No. 2, pp. 149-59. Omar, A.R., Harding, J.A. and Popplewell, K. (1999), “Design for customer satisfaction: an information modelling approach”, Integrated Manufacturing Systems, Vol. 10 No. 4, p. 199. Parkin, N., Linsley, M.L., Chan, J.F.L. and Stewardson, D.J. (2002), “The introduction of QFD in a UK original equipment manufacturer”, Managerial Auditing Journal, Vol. 17 Nos 1/2, pp. 43-54. Partovi, F.Y. (2001), “An analytic model to quantify strategic service vision”, International Journal of Service Industry Management, Vol. 12 No. 5, pp. 476-99. Pfohl, H., Cullmann, O. and Sto¨lzle, W. (1999), “Inventory management with statistical process control: simulation and evaluation”, Journal of Business Logistics, Vol. 20 No. 1, pp. 101-20. Pitman, G., Motwani, J., Kumar, A. and Cheng, C.H. (1996), “QFD application in an educational setting a pilot field study”, The International Journal of Quality & Reliability Management, Vol. 13 No. 4, pp. 99-107. Prasad, B. (1998), “Review of QFD and related deployment techniques”, Journal of Manufacturing Systems, Vol. 17 No. 3, pp. 221-34. Sansone, F.P. and Singer, H.M. (1993), “AT&T’s 3-phase plan rings in results”, Appliance Manufacture, Vol. 41 No. 2, pp. 71-4. Shen, X.X., Tan, K.C. and Xie, M. (2000a), “An integrated approach to innovative product development using Kano’s model and QFD”, European Journal of Innovation Management, Vol. 3 No. 2, p. 91. Shen, X.X., Tan, K.C. and Xie, M. (2000b), “Benchmarking in QFD for quality improvement”, Benchmarking, Vol. 7 No. 4, pp. 282-91. Swamidass, P.M. (1986), “Manufacturing strategy: its assessment and practice”, Journal of Operations Management, pp. 471-84. Tan, K.C. and Shen, X.X. (2000), “Integrating Kano’s model in the planning matrix of quality function deployment”, Total Quality Management & Business Excellence, Vol. 11 No. 8, pp. 1141-51. Wasserman, G.S. (1993), “On how to prioritize design requirements during the QFD planning process”, IIE Transactions, Vol. 25 No. 3, pp. 59-65.

Further reading Anonymous (1993), “Association for computing machinery”, Communications of the ACM, Vol. 36 No. 10, pp. 88-9. Ansari, A. and Modarress, B. (1994), “Quality function deployment: the role of suppliers”, Journal of Supply Chain Management, Vol. 30 No. 4, pp. 28-36. Armacost, R.L., Componation, P.J., Mullens, M.A. and Swart, W.W. (1994), “An AHP framework for prioritizing customer requirements in QFD: an industrialized housing application”, IIE Transactions, Vol. 26 No. 4, pp. 72-9.

Balthazard, P.A. and Gargeya, V.B. (1995), “Reinforcing QFD with group support systems”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 43-62. Chin, K., Pun, K., Leung, W.M. and Lau, H. (2001), “A quality function deployment approach for improving technical library and information services: a case study”, Library Management, Vol. 22 Nos 4/5, pp. 195-204. Cragg, P.B. (2002), “Benchmarking information technology practices in small firms”, European Journal of Information Systems, Vol. 11 No. 4, pp. 267-81. Dube, L., Johnson, M.D. and Renaghan, L.M. (1999), “Adapting the QFD approach to extended service transactions”, Production and Operations Management, Vol. 8 No. 3, pp. 301-17. Harding, J.A., Omar, A.R. and Popplewell, K. (1999), “Applications of QFD within a concurrent engineering environment”, International Journal of Agile Management Systems, Vol. 1 No. 2, p. 88. Huarng, F. and Chen, Y. (2002), “Relationships of TQM philosophy, methods and performance: a survey in Taiwan”, Industrial Management and Data Systems, Vol. 102 Nos 3/4, pp. 226-34. Hwarng, H.B. and Teo, C. (2001), “Translating customers’ voices into operations requirements – a QFD application in higher education”, The International Journal of Quality & Reliability Management, Vol. 18 No. 2, p. 195. Kathawala, Y. and Motwani, J. (1994), “Implementing quality function deployment – a systems approach”, TQM Magazine, Vol. 6 No. 6, pp. 31-5. Kauffmann, P., Ricks, W.R. and Shockcor, J. (1999), “Research portfolio analysis using extensions of quality function deployment”, Engineering Management Journal, Vol. 11 No. 2, pp. 3-9. Kumar, R. and Midha, P.S. (2001), “A QFD based methodology for evaluating a company’s PDM requirements for collaborative product development”, Industrial Management þ Data Systems, Vol. 101 Nos 3/4, pp. 126-31. Li, D., McKay, A., de Pennington, A. and Barnes, C. (2001), “A web-based tool and a heuristic method for cooperation of manufacturing supply chain decisions”, Journal of Intelligent Manufacturing, Vol. 12 Nos 5-6, p. 433. Lockamy, A. III and Khurana, A. (1995), “Quality function deployment: total quality management for new product design”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 73-84. Lu, M.H. and Kuei, C. (1995), “Strategic marketing planning: a quality function deployment approach”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 85-96. Lu, M.H., Madu, C.N., Kuei, C. and Winokur, D. (1994), “Integrating QFD, AHP and benchmarking in strategic marketing”, The Journal of Business & Industrial Marketing, Vol. 9 No. 1, pp. 41-50. Olhager, J. and West, B.M. (2002), “The house of flexibility: using the QFD approach to deploy manufacturing flexibility”, International Journal of Operations & Production Management, Vol. 22 No. 1, pp. 50-79. Philips, M., Sander, P. and Govers, C. (1994), “Policy formulation by use of QFD techniques: a case study”, The International Journal of Quality & Reliability Management, Vol. 11 No. 5, pp. 46-58. Pitman, G., Motwani, J., Kumar, A. and Cheng, C.H. (1995), “QFD application in an educational setting: a pilot field study”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 63-72. Pun, K.F., Chin, K.S. and Lau, H. (2000), “A QFD/hoshin approach for service quality deployment: a case study”, Managing Service Quality, Vol. 10 No. 3, p. 156.

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Saaty, T.L. (1980), The Analytic Hierarchy Process, McGraw-Hill, New York, NY. Shen, X.X., Tan, K.C. and Xie, M. (2001), “The implementation of quality function deployment based on linguistic data”, Journal of Intelligent Manufacturing, Vol. 12 No. 1, p. 65. Shin, J. and Kim, K. (2000), “Complexity reduction of a design problem in QFD using decomposition”, Journal of Intelligent Manufacturing, Vol. 11 No. 4, p. 339. Shin, J., Kim, J. and Chandra, M.J. (2002), “Consistency check of a house of quality chart”, The International Journal of Quality & Reliability Management, Vol. 19 No. 4, pp. 471-84. Shwe Sein Aye Ho, E., Lai, Y. and Chang, S.I. (1999), “An integrated group decision-making approach to quality function deployment”, IIE Transactions, Vol. 31 No. 6, pp. 553-67. Tan, K.C. and Pawitra, T.A. (2001), “Integrating SERVQUAL and Kano’s model into QFD for service excellence development”, Managing Service Quality, Vol. 6 No. 11, pp. 418-30. Zairi, M. and Youssef, M.A. (1995), “Quality function deployment”, The International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 9-23. Corresponding author Ashok Kumar can be contacted at: [email protected]

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