Manufacturing flexibility operations management

Manufacturing flexibility operations management

Manufacturing flexibility: defining and analyzing relationships among competence, capability, and customer satisfaction

Qingyu Zhanga,1, Mark A Vonderembseb,∗, Jeen-Su Limc,2

aDepartment of Economics and Decision Sciences, Arkansas State University, State University,

Arkansas, AR 72467, USA

bDepartment of Management, The University of Toledo, Toledo, OH 43606, USA

cThe University of Toledo, Toledo, OH 43606, USA

Received 25 April 2001; accepted 1 April 2002

Abstract

Fast and dramatic changes in customer expectations, competition, and technology are creating an increasingly uncertain environment To respond, manufacturers are seeking to enhance flexibility across the value chain Manufacturing flexibility,

a critical dimension of value chain flexibility, is the ability to produce a variety of products in the quantities that customers demand while maintaining high performance It is strategically important for enhancing competitive position and winning customer orders

This research organizes literature on manufacturing flexibility and classifies it according to competence and capability theory It describes a framework to explore the relationships among flexible competence (machine, labor, material handling, and routing flexibilities), flexible capability (volume flexibility and mix flexibility), and customer satisfaction It develops valid and reliable instruments to measure the sub-dimensions of manufacturing flexibility, and it applies structural equation modeling to a large-scale sample (n = 273) The results indicate strong, positive, and direct relationships between flexible

manufacturing competence and volume flexibility and between flexible manufacturing competence and mix flexibility Volume flexibility and mix flexibility have strong, positive, and direct relationships with customer satisfaction

© 2002 Elsevier Science B.V All rights reserved

Keywords: Empirical research; Flexibility; Management of technology; Manufacturing; Operations strategy

1 Introduction

Manufacturers face an increasingly uncertain

ex-ternal environment as the rate of change in customer

∗Corresponding author Tel.:+1-419-530-4139;

fax: +1-419-530-7744.

E-mail addresses: qzhang@astate.edu (Q Zhang),

mark.vonderembse@utoledo.edu (M.A Vonderembse),

jlim@utnet.utoledo.edu (J.-S Lim).

1 Tel.: +1-870-972-3416.

2 Tel.: +1-419-530-2922.

expectations, global competition, and technology accelerates (Huber, 1984; Skinner, 1985; Jaikumar, 1986; Doll and Vonderembse, 1991; Germain et al.,

2001) Researchers and manufacturing managers con-tend that flexibility is a strategic imperative that en-ables firms to cope with uncertainty (Gerwin, 1987; Sethi and Sethi, 1990) Flexibility is the organization’s ability to meet an increasing variety of customer expectations without excessive costs, time, organi-zational disruptions, or performance losses Upton (1994, 1995)defines flexibility as increasing the range

0272-6963/02/$ – see front matter © 2002 Elsevier Science B.V All rights reserved.

PII: S 0 2 7 2 - 6 9 6 3 ( 0 2 ) 0 0 0 6 7 - 0

of products available, improving a firm’s ability to

re-spond quickly, and achieving good performance over

this wide range of products

To attain the type of flexibility that customers

want (i.e quick delivery of a variety of high-quality,

low-cost products), organizations seek value chain

flexibility (Zhang, 2001) Value chain flexibility is

broadly defined to include product development,

manufacturing, logistics, and spanning flexibilities

(Zhang, 2001; Day, 1994) It focuses primarily on

filling customer orders rather than on merely

improv-ing the efficiency and effectiveness of equipment and

processes Such a focus requires manufacturing firms

to develop cross-functional and cross-company efforts

that eliminate bottlenecks, increase responsiveness,

and create a level of performance that enables firms

to build competitive advantage (Blackburn, 1991;

Hamel and Prahalad, 1989)

Manufacturing flexibility, the focus of this study, is

the ability of the firm to manage production resources

and uncertainty to meet customer requests (Behrbohm,

1985; Gerwin, 1993; Kathuria and Partovi, 1999;

Hill, 1994; D’Souza and Williams, 2000; Koste and

Malhotra, 1999).Sethi and Sethi (1990)contend that

manufacturing flexibility is a hard-to-capture concept,

andUpton (1995)believes that confusion and

ambigu-ity about this concept inhibit its effective management

Slack (1983, 1987)distinguishes resource

flexibil-ity (e.g., machine flexibilflexibil-ity) from systems flexibilflexibil-ity

(e.g., mix flexibility) Correa and Slack (1996)

de-fine the attributes of systems flexibility (range and

response) and types of systems flexibility (e.g

prod-uct mix and prodprod-uction volume) Different descriptors

for manufacturing flexibility overlap; as an example,

process flexibility intersects with operational

flexibil-ity Some descriptors are aggregates of others; process

flexibility includes routing flexibility, machine

flexi-bility, and material handling flexibility The concept

of manufacturing flexibility is confounded because the

attributes of flexibility (i.e range, mobility, and

unifor-mity) and the components of flexibility (e.g machine

flexibility and volume flexibility) are often mingled

(Barad, 1992; Gupta, 1993; Benjaafar, 1994) This

im-precise language makes it difficult to develop valid

and reliable measures of manufacturing flexibility and

to improve theory development

Clear definitions and accurate measures are needed

to construct and test theory related to manufacturing

flexibility The literature on this important subject

is accumulating including case studies (Maffei and Meredith, 1995), industry specific studies (Suarez

et al., 1996), and mathematical models (Kumar, 1987; Benjaafar and Ramakrishnan, 1996; Gupta, 1993; Jordan and Graves, 1995; Byrne and Chutima, 1997)

Upton (1995, 1997) provides a measure of process range based on a small sample survey (54 plants)

Suarez et al (1995, 1996)offer a measure of flexi-bility on the printed circuit board industry.Gupta and Somers (1992) develop measures of manufacturing flexibility based on a large-scale survey, but they do not clearly describe the dimensions underlying each type of manufacturing flexibility

Some researchers emphasize manufacturing flexi-bility as an internal resource, a competence (Carter, 1986; Das and Nagendra, 1993) They highlight task sequencing or dispatching disciplines, and they de-velop flexible machining systems with totally auto-mated functions to cope with uncertainty But flexible systems that focus on creating internal competen-cies (e.g routing flexibility and machine flexibility) may not enhance customer satisfaction Satisfaction increases as the firm builds capabilities (e.g mix flexibility) that provide value to customers To under-stand manufacturing flexibility, the internal compe-tencies and external capabilities of flexibility should

be clarified, and relationships between them should

be examined

This paper contributes to the manufacturing litera-ture by: (1) delineating manufacturing flexibility into dimensions of flexible manufacturing competence (machine, labor, material handling, and routing flex-ibilities) and flexible manufacturing capability (vol-ume flexibility and mix flexibility), (2) proposing a research framework, including hypotheses, that relates competence to capability and capability to customer satisfaction, (3) developing valid and reliable mea-sures for the dimensions of competence and capability, and (4) testing the hypotheses described in the frame-work using structural equations modeling The results and implications of our findings are also discussed

2 Theory development

Kickert (1985)believes that “flexibility can be con-sidered as a form of meta-control aimed at increasing

control capacity by means of an increase in variety,

speed, and amount of responses as a reaction to

uncer-tain future environmental development” (p 24) From

this perspective, the breadth and intensity of flexibility

needed to cope with changing customer requirements

cannot be provided by one department or function It

requires a company-wide effort to increase

responsive-ness and eliminate bottlenecks across the value chain

(Blackburn, 1991; Hamel and Prahalad, 1989; Yusuf

et al., 1999)

Value chain flexibility includes product

develop-ment, manufacturing, logistics, and spanning

flexibil-ities (Day, 1994; Zhang et al., 2002) It enables firms

to introduce new products quickly, support rapid

prod-uct customization, shorten manufacturing lead times

and costs for customized products, improve supplier

performance, reduce inventory levels, and deliver

products in a timely manner (Zhang et al., 2002)

Integration, coordination, and communication across

the value chain are essential for success regardless

of how many different firms are involved and which

firms own the assets (Day, 1994)

Product development flexibility enables firms to

respond with product modifications and new product

commercialization (Sobek et al., 1999; Srinivasan

et al., 1997) Such flexible design and development

capabilities can increase manufacturability by

sim-plifying product structure and standardizing

compo-nent parts (Clark and Fujimoto, 1991; Gerwin, 1987;

Griffin, 1993; Sethi and Sethi, 1990) This can make

manufacturing faster and easier Manufacturing

flexi-bility enables firms to produce the needed quantity of

high-quality products quickly and efficiently through

set-up time reduction, cellular manufacturing layouts,

preventive maintenance, quality improvement efforts,

and dependable suppliers These are predicated on

machining, labor, material handling, and routing

flex-ibilities (Boyer and Leong, 1996; Chen et al., 1992;

Hyun and Ahn, 1992; Ramasesh and Jayakumar, 1991;

Sethi and Sethi, 1990) Logistics flexibility enables

the smooth flow of materials, which facilitates the

production and deliveries of high-quality, value-added

products (Porter and Millar, 1985) Flexibility in

physical supply, purchasing, physical distribution, and

demand management are key components of logistics

flexibility (Lambert and Stock, 1993; Porter, 1985)

Spanning flexibility insures that different departments

or groups (inside and outside of the organization)

can coordinate product design, production, and deliv-ery in ways that add value to customers (Day, 1994; Cooper and Zmud, 1990; Hayes and Pisano, 1994)

It is within this context of value chain flexibility that manufacturing flexibility is discussed and a research framework is developed and tested

2.1 Framework for manufacturing flexibility

Manufacturing flexibility is a complex, multidimen-sional concept that has evolved over the years (Sethi and Sethi, 1990) Early in its development,Leaver and Brown (1946)propose a series of small, functionally oriented machines that can be plugged together in dif-ferent sequences to make difdif-ferent products.Diebold (1952)recognizes manufacturing flexibility as essen-tial for producing discrete parts effectively and effi-ciently.Abernathy (1978)andHayes and Wheelwright (1984) view manufacturing flexibility as a tradeoff between efficiency in production and dependability in the marketplace Achieving flexibility in large-volume production without sacrificing efficiency begins with the development of manufacturing cells and flexible manufacturing systems Efficiency and flexibility are achieved by reducing set-up time and cost, shifting

to product-oriented layouts, increasing equipment reliability, and enhancing quality (Monden, 1983; Schonberger, 1986)

Manufacturing flexibility is the ability of the or-ganization to manage production resources and un-certainty to meet various customer requests Hayes and Wheelwright (1984)consider manufacturing flex-ibility to be a strategic element of business, along with price (cost), quality, and dependability Priorities assigned to each of these factors determine how an organization positions itself relative to it competitors

Sethi and Sethi (1990) consider manufacturing flexi-bility as a set of elements that are integrally designed and carefully linked to facilitate the adaptation of processes and equipment to a variety of production tasks.Upton (1995) identifies attributes of flexibility including potential flexibility versus demonstrated flexibility and robustness (maintaining a status quo despite a change) versus agility (instigating change rather than reacting to it) Upton (1995) also de-scribes internal flexibility as what the firm can do (competencies) and external flexibility as what the customer sees (capabilities) This distinction is central

Fig 1 Impact of flexible manufacturing competence on capability and customer satisfaction.

to the notion of internal competencies and external or

customer-facing capabilities

Day (1994)claims that organizations achieve

cus-tomer satisfaction by building capabilities on a set of

competencies.Fig 1provides an overview of the

rela-tionships among flexible manufacturing competence,

mix flexibility, volume flexibility, and customer

satis-faction Flexible manufacturing competencies, which

include machine, labor, material handling, and routing

flexibilities, have a direct and positive impact on

vol-ume flexibility and mix flexibility Volvol-ume flexibility

and mix flexibility are external elements of

com-petition (capabilities) that should lead to increased

customer satisfaction This framework is useful for

studying manufacturing flexibility at the resource and

organizational level and for developing and testing

structural relationships among the constructs

2.1.1 Competence and capability

Prahalad and Hamel (1990)contend that an

organi-zation should focus on developing core competencies

that help it to create enduring customer satisfaction

Teece et al (1997) extend this discussion of core

competencies to include capabilities They argue that

firms should not be viewed as a portfolio of assets

(internal competencies) but as a set of mechanisms

by which customer-pleasing capabilities are selected

and built Stalk et al (1992) claim that competence

emphasizes technological and production expertise

at specific points along the value chain while

capa-bilities are broadly based and encompass the entire

value chain In this respect, capabilities are visible

to the consumer while the internal competencies that support those capabilities rarely are

Competence and capability correspond to sec-ondary flexibility and primary flexibility as described

by Watts’ et al (1993) This perspective can assist managers in identifying which capabilities are critical

to their customers and which competencies support those capabilities Externally focused flexible capa-bility can be viewed as a linkage among corporate, marketing, and manufacturing strategy (Watts et al., 1993; Kathuria and Partovi, 1999) Internally focused flexible competence provides the processes and in-frastructure that enable the firm to achieve the desired levels of flexible capability

Hyun and Ahn’s (1992) cone model suggests that flexible manufacturing competence including ma-chine, labor, material handling, and routing flexibili-ties support volume flexibility and mix flexibility A firm’s ability to change machining operations, labor activities, material handling modes, and routes should

be useable for different production volumes and prod-uct mixes (Kathuria and Partovi, 1999; D’Souza and Williams, 2000) The definition and literature support for these sub-dimensions of manufacturing flexibility are given inTable 1and are discussed in the following sections

Each sub-dimension has three distinct attributes: range/variety, mobility/responsiveness, and unifor-mity (Upton, 1995; Koste and Malhotra, 1999) Range

is the firm’s ability to make a large or small number

Table 1

The definitions of sub-constructs of manufacturing flexibility

Manufacturing flexibility The ability of the organization to manage

production resource and uncertainty to meet various customer requests

Chen et al (1992) , Leong et al (1990)

Machine flexibility The ability of a piece of equipment to

perform different operations economically and effectively

Gupta (1993) , Hyun and Ahn (1992) ,

Chen et al (1992) , Sethi and Sethi (1990)

Labor flexibility The ability of the workforce to perform a

broad range of manufacturing tasks economically and effectively

Upton (1994) , Hyun and Ahn (1992) ,

Ramasesh and Jayakumar (1991)

Material handling flexibility The ability to transport different work pieces

between various processing centers over multiple paths economically and effectively

Hutchinson (1991) , Sethi and Sethi (1990) , Coyle et al (1992)

Routing flexibility The ability to process a given set of part

types using multiple routes economically and effectively

Upton (1995) , Gerwin (1993) , Sethi and Sethi (1990)

Volume flexibility The ability of the organization to operate

at various batch sizes and/or at different production output levels economically and effectively

Carlsson (1989) , Gerwin (1993) , Sethi and Sethi (1990)

Mix flexibility The ability of the organization to produce

different combinations of products economically and effectively given certain capacity

Boyer and Leong (1996) , Sethi and Sethi (1990) , Gupta and Somers (1992)

of different products and to make very similar or very

different products The greatest range is when a large

number of very different products can be produced

Mobility is the ability to change from one product to

another, quickly High mobility minimizes the need

for long production runs Uniformity is the ability to

maintain performance standards as a firm switches

among products High uniformity indicates the ability

to maintain quality as the product is changed (Leeuw

and Volberda, 1996; Sethi and Sethi, 1990; Upton,

1995)

2.2 Defining flexible manufacturing competencies

2.2.1 Machine flexibility

It is the ability of a piece of equipment to perform

different operations economically and effectively It

is a key variable in shop floor scheduling and the dual

resource constrained job shop The range element of

machine flexibility can be assessed by the number

of different operations a machine can perform and

the speeds at which it operates (Gupta, 1993; Hyun

and Ahn, 1992; Ramasesh and Jayakumar, 1991)

Mobility is high when operations can be switched with short changeover time and near zero set-up cost

If quality and efficiency are consistent across differ-ent operations and differdiffer-ent operating speeds, then the machine has uniformity As machine flexibility increases, higher levels of volume flexibility and mix flexibility can be achieved

2.2.2 Labor flexibility

It is the ability of the workforce to perform a broad range of manufacturing tasks economically and effectively It is an important element in the dual re-source constrained literature, but the conceptual and empirical literature tends to emphasize equipment flexibility and to neglect the potential impact of labor The workforce, however, plays a vital role in most production processes Flexible workers can handle uncertainty in the production process, such as absent workers, or they can respond to changes in demand

by shifting the workforce as needed The number of tasks that workers can perform, their speed of execu-tion, and their ability to learn quickly are measures

of the range element of labor flexibility The ability

of the workforce to recognize the need for a change

in work deployment and to execute the change can

measure the mobility attribute The ability of the

worker to maintain quality and efficiency across a

variety of jobs can measure the uniformity attribute

of labor flexibility As a result, workforce flexibility

is a major factor in determining the extent of volume

and mix flexibility (Hyun and Ahn, 1992; Ramasesh

and Jayakumar, 1991; Upton, 1994)

2.2.3 Material handling flexibility

It is the ability to transport different work pieces

be-tween various processing centers over multiple paths

economically and effectively.Hutchinson (1991)notes

that insufficient consideration of the material handling

subsystem can constrain the benefits of a flexible

manufacturing system in terms of product mix and

production volume Paths can act as bottlenecks that

starve downstream stations if processing (movement)

times are too long Changing over material handling

equipment to accommodate different products can

cause delays and increase costs From this

perspec-tive, material handling activities are like machines in

the manufacturing systems with range, mobility, and

uniformity attributes The number of paths between

work centers and the types of materials transported

capture the range attribute of material handling

flex-ibility Mobility can be examined using the time or

cost associated with adding a path Material

trans-fer time, cost, and quality issues, such as in-transit

damage, can measure uniformity (Sethi and Sethi,

1990)

2.2.4 Routing flexibility

It is the ability to process a given set of part types

using multiple routes economically and effectively

Routing flexibility is widely studied in the flexible

manufacturing system literature because it allows

firms to find alternate processing centers in case of

machine breakdowns or system overloads These

alternate routes increase the options available to

man-agement, thereby enhancing mix flexibility These

alternate routes also provide the opportunity to apply

underutilized equipment to expand volume flexibility

Range can be evaluated by the number of alternative

routes and the extent to which a route can be varied

Mobility can be evaluated by time and cost expended

to make a change, and uniformity can be measured

by differences in processing time and quality when alternative routes are used (Sethi and Sethi, 1990; Upton, 1995)

2.3 Volume flexibility

Volume flexibility is the ability of the organization

to operate at various batch sizes and/or at different production output levels economically and effectively

It demonstrates the competitive potential of the firm

to increase production volume to meet rising demand and to keep inventory low as demand falls (Gerwin, 1993; Sethi and Sethi, 1990) It is widely discussed

in economics literature and assessed by the cost curve (Carlsson, 1989) If a cost curve is U-shaped with a long flat bottom, it is viewed as flexible because there

is a wide range of production volumes with little dif-ference in costs The level of aggregate output over which the firm sustains profitability under normal con-ditions indicates the range element of volume flexibil-ity In this case, range is the number of output levels where the average cost curve is flat The time required

to change output level captures the mobility element while production costs and quality levels provide a measure of uniformity

2.4 Mix flexibility

Mix flexibility is the ability of the organization to produce different combinations of products economi-cally and effectively given certain capacity It enables

a firm to enhance customer satisfaction by providing the kinds of products that customers request in a timely manner Mix flexibility must be evaluated within the current production system configuration without con-sidering major facility modifications This implies that the production system can respond to changes in de-mand without impacting volume and capacity, which

is part of volume flexibility Without this condition,

an organization could simply acquire additional re-sources to manufacture different sets of products The number of different products manufactured by the firm

as well as the degree of differentiation of those prod-ucts captures the range attribute of mix flexibility The time and cost incurred for changing product mix mea-sure mobility/responsiveness The organization’s abil-ity to maintain product qualabil-ity and system productivabil-ity while manufacturing a variety of products measures

uniformity (Boyer and Leong, 1996; Dixon, 1992;

Gupta and Somers, 1992; Sethi and Sethi, 1990)

2.5 Customer satisfaction

Customer satisfaction is the degree to which

cus-tomers perceive that they received products and

services that are worth more than the price they

paid (Tracey, 1996) White’s (1996) meta-analysis

of manufacturing performance defines a set of

vari-ables that influence customer satisfaction including

quality, delivery speed, delivery dependability, cost,

flexibility, and innovation Schroeder et al (1986)

report similar measures of performance Koufteros

(1995) provides measure of competitive capabilities

that include cost, competitive pricing, premium

pric-ing, value-to-customer quality, product mix flexibility,

product innovation, and customer service Tracey

et al (1999) provide a similar set of measures: price

offered, quality of products, product line breadth,

or-der fill rate, and frequency of delivery As advocated

by Slack (1987)andSwamidass and Newell (1987),

this study proposes measures that are based on the

perception of experienced managers to assess

cus-tomer satisfaction These measures include retention,

ratio of price to value, quality, product reputation, and

customer loyalty

3 Hypotheses and research methods

Hypotheses are developed that relate flexible

com-petence to flexible capability and flexible capability to

customer satisfaction Research methods include the

item generation, pre-test, and pilot study methods used

for instrument development as well as large-scale

sur-vey methods

3.1 Relationships between flexible competence and

flexible capability

Flexible competence, including machine, labor,

material handling, and routing flexibilities, must be

planned and managed to achieve customer-desired

capabilities like volume flexibility and mix flexibility

(Koufteros et al., 1997) Volume flexibility is increased

as set-up time reductions for machinery and

mate-rial handling equipment allow more production time,

flexible workers learn to perform tasks faster and bet-ter, material handling systems operate at a faster rate, and alternate routes are created to engage underuti-lized equipment Mix flexibility is enhanced as set-up cost reductions for machinery and material handling equipment permit the production of a greater number

of highly differentiated products, flexible workers in-crease their level of skill and learn to produce more products, and new routes are established and used easily Thus, the following hypotheses are proposed

Hypothesis 1a Flexible manufacturing competence

has a significant positive impact on volume flexibility

Hypothesis 1b Flexible manufacturing competence

has a significant positive impact on mix flexibility

3.2 Relationships between flexible capability and customer satisfaction

Volume flexibility and mix flexibility are important organizational capabilities that must be planned and managed effectively to achieve customer satisfaction (Behrbohm, 1985) Firms achieve high levels of sat-isfaction by providing high value to their customers High value results in loyal customers who are more likely to repurchase and, thus, promotes long-term prosperity through the creation of a base of steady clients (Innis and LaLonde, 1994; McKee et al., 1989; Narver and Slater, 1995; Venkatraman and Ramanujan, 1986) Managers seek to build enduring customer satisfaction, which involves the develop-ment, accumulation, combination, and protection of unique skills and capabilities (Teece et al., 1997)

Wernerfelt (1984) argues that customer satisfaction analysis should expand its focus beyond product market positioning to include a set of resources and organizational skills, like volume flexibility and mix flexibility, that shape the firm’s long-term success Volume flexibility enables firms to satisfy customer requests by producing the exact amount of product ordered It enables firms to increase production vol-ume quickly in response to unanticipated needs and

to reduce volume quickly to avoid building inventory Volume flexibility reduces or eliminates waiting time for customers when demand levels fluctuate, and it re-duces costs/price by lowering inventory in the supply chain Mix flexibility enhances customer satisfaction

by producing the product with the features and

per-formance that customers want It enables firms to

produce a wide variety of products without excessive

time delays, premium prices, or declines in quality

Mix flexibility reduces the waiting time for special

order products that customers value highly (Kathuria,

2000; Schroeder et al., 1986; White, 1996) Thus, the

following hypotheses are proposed

Hypothesis 2a Volume flexibility has a significant

positive impact on customer satisfaction

Hypothesis 2b Mix flexibility has a significant

pos-itive impact on customer satisfaction

3.3 Instrument development methods: item

generation, pre-test, and pilot test

An extensive literature review was the basis for

developing an initial list of items to measure the

component of the manufacturing flexibility In

partic-ular, the following works were examined (Sethi and

Sethi, 1990; Upton, 1995; Gupta, 1993; Hyun and

Ahn, 1992; Gerwin, 1993; Gupta and Somers, 1992;

Kathuria and Partovi, 1999; Koste and Malhotra, 1999;

D’Souza and Williams, 2000) During structured

inter-views, the definitions of manufacturing flexibility and

its sub-dimensions were presented to four

manufactur-ing executives The interview results were analyzed,

and the research construct and measurement items

were revised These managers also participated in a

Q-sort to further enhance the content, convergent, and

discriminant validities Here, the practitioners acted

as judges and sorted the items into separate pools

The final scales for the manufacturing flexibility items

were five-point, Likert-type scales with 1= strongly

disagree, 2 = disagree, 3 = neutral, 4 = agree, and

5= strongly agree (The same five-point Likert-type

scales were used for customer satisfaction.)

For the pre-test, copies of the revised definitions

and measurement items were examined by 10 faculty

members from the same university They had

exper-tise in operations management, information systems,

and marketing They had the opportunity to suggest

changes in the definition as well as to “Keep”, “Drop”,

or “Modify” each item They were instructed to

sug-gest new items if they felt that existing ones did not

cover the domain of the construct

A pilot study targeted manufacturing management executives Corrected item–total correlation (CITC) was applied to 33 responses to purify the scales Factor analysis was performed on each scale to assess unidi-mensionality.Cronbach’s (1951)alpha was used to as-sess scale reliability During the pilot study, items with low CITC, factor loading, or reliability were deleted

or reworded In some cases, items were added to cover the domain of the construct The items that entered the large-scale survey for manufacturing flexibility are listed inAppendix A Detailed results of the pilot study are available from the authors

3.4 Large-scale survey methods

The large-scale survey was conducted using a mail-ing list provided by The Society of Manufacturmail-ing Engineers (SME) Five SIC codes were covered in the survey: 34 “fabricated metal products”; 35 “indus-trial and commercial machinery”; 36 “electronic and electrical equipment and components”; 37 “transporta-tion equipment”; 38 “instruments and measurements equipment” A second-wave mailing was conducted

2 weeks after the first mailing Out of 314 responses received (21 undeliverables, 11 blank returns, and 9 incomplete), 273 were usable, resulting in a response rate of 9.2% (i.e 273/(3000− 41))

The responses across SIC codes 34, 35, 36, 37, and 38 were 83, 65, 58, 38, and 29, respectively Firm size as measured by the number of employees was 100–249= 135; 250–499 = 63; 500–999 = 35;

and 1000 or more = 40 Job titles of respondents

were CEO/President = 70; Vice-president = 43;

Manager = 131; and Director = 29 Non-response

bias was examined using a Chi-square test; the non-significantχ2 test results indicate the represen-tativeness of the respondents for the sampling frame The results inTable 2show that there is no significant difference between the sample and respondents for SIC code (χ2= 7.6, d.f = 4, P > 0.10); number of

employees (χ2 = 8.6, d.f = 3, P > 0.01); and job

title (χ2= 5.3, d.f = 3, P > 0.10).

To assist in determining whether data collected

by SIC codes could be summed, the means for the SIC codes were compared for flexible manufacturing competence, volume flexibility, mix flexibility, and customer satisfaction MANOVA was completed to compare the means of these variables by SIC codes

Table 2

Comparisons of sample and respondents

SIC

Chi-square test (χ2= 7.6, d.f = 4, P > 0.10)

Number of employees

Chi-square test (χ2= 8.6, d.f = 3, P > 0.02)

Job title

Chi-square test (χ2= 5.3, d.f = 3, P > 0.10)

Note: (1) figures in parentheses are percentage; the calculation formulaχ2 =
(fe − fo) 2/fe (2) The sample (SME) list was cleaned up

by eliminating some names from the same company.

a 2979 = 3000 − 21, where 3000 is the sample size and 21 is the number of undeliverables.

The results indicate that there is no significant

dif-ference for each construct mean across the five SIC

codes This provides support for aggregating the data

4 Scale development and validation results

This research develops a set of valid and reliable

in-struments to measure the six sub-dimensions of

manu-facturing flexibility In this section, the purification and

factor analysis results are first reported To determine if

flexible manufacturing competence is a second-order

construct, a LISREL measurement model is run

4.1 Purification and factor analysis results

The 34 items inAppendix Awere purified by

ex-amining the CITC of each item as recommended by

Churchill (1979) The results, provided in Table 3,

show that the CITC scores for all items in the labor

flexibility, routing flexibility, volume flexibility, and

mix flexibility are >0.50 The CITC for one machine

flexibility (MA3) and one material handling flexibil-ity (MH5) are substantially<0.50 These items have

been deleted

After purification,Cronbach’s (1951)alpha was cal-culated to assess the reliability of each scale (see

Table 3) Alpha values >0.80 are very good for ba-sic research (Nunnally, 1978) As shown inTable 3, Cronbach’s alpha scores are 0.83, 0.91, 0.92, 0.92, 0.90, 0.92 for machine, labor, material handling, rout-ing, volume, and mix flexibilities, respectively The remaining items, 32 in total, were submitted to exploratory factor analysis to check for factor struc-tures among the various sub-dimensions As a general rule, the ratio of respondents to items should at least

be greater than 5 to 1 (Tinsley and Tinsley, 1987) The ratio of respondents to items in this research is

9 to 1, meeting the general guideline Factor loadings

>0.50 are considered significant and items with factor cross-loadings of 0.40 or above should be removed (Hair et al., 1995) To streamline the final results, factor cross-loadings<0.4 are not reported All of the

factor loadings were >0.50 with the smallest being

Table 3

Purification and factor loadings for manufacturing flexibility (large scale)

Sub-construct Coding CITC 1 CITC 2 α Factor loadings

0.59 Six factors emerge from the factor analysis with

most factor loadings >0.70 No items have factor

cross-loading of 0.40 or above, and all items load

on their respective factors The cumulative variance

explained by the six factors is 69.4%

The correlation matrix (Table 4) for the remaining

32 items was examined for evidence of convergent

and discriminant validity The smallest within-factor items correlation are: machine flexibility= 0.34,

la-bor flexibility= 0.54, material handling flexibility =

0.66, routing flexibility = 0.60, volume flexibility =

0.22, and mix flexibility = 0.51 These

correla-tions are significantly different from zero (P < 0.01).

This supports the claim that there is good convergent