an information-processingmodel of maintenance management

Int. J. Production Economics 83 (2003) 45–64

An information-processing model of maintenance management

Laura Swanson*

Department of Management, Southern Illinois University at Edwardsville, Edwardsville, IL 62026-1100, USA

Received 1 September 2000; accepted 1 May 2002

Abstract

In the past two decades, changes in the production environment have made the task of making decisions about

allocating maintenance resources and scheduling maintenance work more difficult. More variables and consequences

must be considered requiring increased information-processing capacity. In this paper, Galbraith’s (Organization

Design, Addison-Wesley, Reading, MA) information-processing model is applied to study how the maintenance

function applies different strategies to cope with the environmental complexity. Based on data from a survey of plant

managers, the analysis shows that maintenance responds to the complexity of its environment with the use of

computerized maintenance management systems, preventive and predictive maintenance systems, coordination and

increased workforce size.

r 2002 Elsevier Science B.V. All rights reserved.

Keywords: Maintenance management; Computerized maintenance management systems; Advanced manufacturing technology

1. Introduction

The maintenance function is critical to a manufacturing organization’s ability to maintain itscompetitiveness. Without well-maintained equipment, a plant will be at a disadvantage in a market thatrequires low-cost products of high quality to be delivered quickly. Properly maintained equipment will havehigher availability and longer life. Poorly maintained equipment will fail frequently and need to be replacedsooner. Additionally, poorly maintained equipment is less likely to produce products of consistent quality.

In the past two decades, changes in the production environment have made the maintenance taskincreasingly complex. Higher levels of automation can make diagnosis and repair of equipment moredifficult (Robinson, 1987; Paz and Leigh, 1994). The high level of capital intensity associated withautomated equipment also places greater pressure on the maintenance function to rapidly repair equipmentand to prevent failures from occurring (Collins and Hull, 1986). Along with the intricacies associated withtechnologies, the maintenance function often has to cope with managing many different maintenance craftclassifications and increasingly complex organizational structures. All of this complexity makes thedecisions about allocating resources and scheduling work more difficult for maintenance. More variables

*Tel.: +1-618-650-2710; fax: +1-618-650-3979.

E-mail address: [email protected] (L. Swanson).

0925-5273/02/$ - see front matter r 2002 Elsevier Science B.V. All rights reserved.

PII: S 0 9 2 5 - 5 2 7 3 ( 0 2 ) 0 0 2 6 6 - 9

and consequences must be considered requiring increased organizational capacity for informationprocessing to support the ability to make quick and accurate decisions.

Recent studies have applied information-processing theory to study the information-processingalternatives used by manufacturing organizations to cope with the uncertainty associated with complexity.Stock and Tatikonda (2000) studied the technology transfer process using information-processing theoryand interdependence theory. In a series of case studies they found that technology transfer is mosteffectively managed when the level of technology uncertainty is matched with appropriate types ofinterorganizational interactions.

A study by Flynn and Flynn (1999) looked at the impact of complexity on manufacturing organizations.Their findings demonstrated that manufacturing organizations did indeed cope with complexities byemploying practices that reduced the need for information processing or increased the organization’scapacity for information processing.

In this study, the model used by Flynn and Flynn (1999) is applied more narrowly to the maintenancefunction. The model used in the Flynn study and this study draws on the information-processing modelintroduced by Galbraith (1977). Galbraith’s model proposes that organizations cope with complexitythrough different information-processing strategies. Reducing complexity to reduce information-processingrequirements is one strategy. Other strategies focus on ways of increasing the organization’s information-processing capacity either by information system investments or by organizational changes to facilitatedecision-making.

The objective of this research is to empirically study the relationship between the complexity of theproduction environment and the use of maintenance practices that assist in managing the information-processing requirements brought on by such complexity. Further, the research studies the relationshipbetween maintenance practices that support information processing and the performance of themaintenance organization.

Section 2 of this paper draws on Galbraith’s (1977) model to describe complexity and the factors thatcontribute to it. In Section 3, maintenance practices that contribute to the maintenance organization’sinformation-processing capacity are discussed in terms of the approaches suggested in Galbraith’s model.Section 4 provides details on data collection and measures used as well as the methodologies used to studythe hypotheses. Section 5 reports the results of the analysis, and Section 6 provides a discussion of theresults.

2. Complexity in the production environment

Organizational complexity can be directly linked to uncertainty within the organization. Galbraith (1977)defines uncertainty as the gap between the amount of information required to perform a task and theinformation already possessed by the organization. Complexity results in problems that are more difficultto understand or analyze, resulting in greater uncertainty (Perrow, 1967). Increased complexity has thepotential to affect the organization adversely resulting in reduced performance (Flynn and Flynn, 1999).

Galbraith (1977) proposed a model for describing the factors that contribute to an organization’suncertainty and mechanisms for coping with uncertainty. The model includes both internal and externalfactors that affect uncertainty levels. Goal diversity represents external factors such as the number ofproducts, markets and clients that the organization serves. In Galbraith’s model, each of the differentexternal constituents contributes to the amount of information needing to be gathered and considered tosupport decision-making processes. Labor diversity, or the number different types of workers within theorganization, primarily determines internal uncertainty.

Flynn and Flynn (1999) proposed an expanded set of factors that may contribute to internal uncertaintyin manufacturing organizations. These factors include manufacturing diversity and process diversity.

L. Swanson / Int. J. Production Economics 83 (2003) 45–6446

Manufacturing diversity includes characteristics such as variability of demand patterns and the complexityof the products being produced. Process diversity is determined by the characteristics of process technology(i.e., job shop, batch, continuous) in use as well as the product volume/variety trade-offs found in theproduct–process matrix.

As a function located within the overall organization, the maintenance department is somewhat shieldedfrom external, market-related factors that contribute to uncertainty. However, several internal factors thatadd complexity for the overall manufacturing organization have a similar effect on the maintenancefunction. Labor diversity may be a significant source of complexity for maintenance. The maintenancefunction may have to manage many different craft classifications such as electricians, mechanics and pipefitters. The maintenance department may also have a tall organizational structure with several reportinglevels.

Process diversity is also important to the maintenance function because it describes the actual equipmentthat the maintenance function is responsible for maintaining. Studies have found that mass outputorientation impacts the overall supporting infrastructure for manufacturing organizations (Woodward,1965; Blau et al., 1976; Ward et al., 1992). More recently, studies have found that organizationaladjustments are required in order to successfully implement advanced manufacturing technologies (Deanand Snell, 1991; Nemetz and Fry, 1988). Logically, it may be assumed that this effect may be extendedto the organizational structure and practices of specific functions within manufacturing. Further, theuse of advanced manufacturing technology (AMT) has been found to be associated with maintenancepractices that support communication and coordination and technical expertise within the organization(Swanson, 1999).

3. Information-processing alternatives for maintenance

Flynn and Flynn (1999) recognized that Galbraith’s model provides some helpful direction inunderstanding the effect of complexity on the manufacturing organization and used the model toinvestigate the role of different manufacturing practices in mitigating the effect of complexity onmanufacturing performance. In Galbraith’s model (1977), complexity has a direct effect on anorganization’s information-processing needs. Organizations have two alternatives for coping withcomplexity. The first alternative is to reduce the need for information processing. The second alternativeis to increase the organization’s information-processing capacity. Specific maintenance practices areconsistent with the information-processing alternatives discussed by Galbraith. These practices arepresented in the following sections.

3.1. Reducing information-processing needs

Galbraith (1977) proposed three methods for reducing an organization’s information-processingrequirements. The first method involves changing the organization’s environment to reduce uncertainty.Environmental management involves reducing complexity by reducing the number of product offerings,reducing time pressure or reducing the need to forecast. For maintenance, environmental managementmeans managing the production environment instead of the external environment to make the process ofplanning less complex. One way that the maintenance function can gain greater control of its work andreduce uncertainty is to use preventive and predictive maintenance.

Preventive maintenance is work performed after a specified period of time or machine use (Gits, 1992).Preventive maintenance restores equipment condition in order to avoid more catastrophic failures thatwould cause more extended downtime. Predictive maintenance is based on the same principle as preventivemaintenance. Under predictive maintenance, diagnostic equipment is used to measure the physical

L. Swanson / Int. J. Production Economics 83 (2003) 45–64 47

condition of equipment such as temperature, vibration, lubrication and corrosion. When one of theseindicators reaches a specified level, work is undertaken to restore the equipment to proper condition(Vanzile and Otis, 1992; Herbaty, 1990).

Preventive and predictive maintenance provide the maintenance organization with a more predictableand manageable workload. These practices also allow the production function to more easily determine itsability to fill orders on time. This ability is especially important as the diversity of equipment to bemaintained and the number of different types of workers to be managed increases.

Hypothesis 1. The use of preventive and predictive maintenance to manage the maintenance environmentwill be greater in plants with greater process and labor diversity.

The second method that Galbraith (1977) proposed for reducing information-processing requirementswas to create slack resources. Galbraith characterized the creation of slack resources as the reduction ofperformance standards to create additional resources. Manufacturing organizations can create slackresources by carrying inventory or adding excess capacity to meet variations in demand. Flynn and Flynn(1999) observed that the creation of slack resources adds excess costs and would not be a viable approach tocombating complexity in most organizations. On the other hand, as a functional area within themanufacturing organization, maintenance plays a critical role in ensuring that schedules are met andquality is maintained. It is possible that an organization would consider carrying slack resources inmaintenance to be a viable option for preserving overall plant performance.

Since maintenance is a service, it would be impossible to carry an inventory in response to complexity.However, it is possible that maintenance would carry excess capacity in the form of extra workers to easedecision making about allocating resources in a complex environment.

Hypothesis 2. The maintenance workforce will be larger in plants with greater process and labor diversity.

Galbraith’s (1977) third approach to reducing information-processing requirements is to use self-contained tasks. With self-contained tasks, groups are created with each group being provided withsufficient resources to perform its own task. This means that instead of being organized by function, groupsare organized according to outputs and given the resources required to produce that output. The use of self-contained tasks reduces complexity by reducing goal diversity, or the number of different constituentsplacing demands on the group. This reduces environmental complexity and, in turn, information-processing requirements. Flynn and Flynn (1999) used group technology as an example of self-containedtasks in a manufacturing environment. Group technology assigns a group of machines to produce a specificset of products rather than the universe of product offerings.

For maintenance, one way to create self-contained tasks is through the use of decentralized, areamaintenance crews. In many plants, maintenance workers are dispatched from a central shop. In thissetting, the central shop experiences goal diversity as it attempts to meet the varying maintenance needs ofdifferent departments. The differing needs may be as a result of the repair needs of different types ofproduction equipment. Further complexity may be added by the need to allocate workers in a variety ofmaintenance job classifications to meet the needs of different production areas. By creating areamaintenance crews assigned to specific plant areas, the maintenance function reduces complexity bydedicating crews to specific areas of the plant rather than trying to juggle and meet the needs of multipleproduction areas with a single, central shop (Heintzelman, 1976).

Hypothesis 3. The use of area maintenance crews will be higher in plants with greater process and labordiversity.


3.2. Increasing information-processing capacity

Galbraith (1977) proposed two methods for increasing an organization’s information-processingcapacity. The first method involves investments in vertical information systems. According to Galbraith,vertical information systems allow an organization to process information without overloading theorganization’s normal communication channels. A computer information system is one example of avertical information system. The value of vertical information systems is that their capabilities forsupporting communication and decision making mean that fewer exceptions are referred upward in theorganizational hierarchy.

In maintenance, there has been an increasing movement toward computerized maintenance managementsystems (CMMS). CMMS assists in managing a wide range of information on the maintenance workforce,spare-parts inventories, repair schedules and equipment histories. It can also be used to automate thepreventive maintenance function, and to assist in the control of maintenance inventories and the purchaseof materials. CMMS may also be used to plan and schedule work orders and to manage the overallmaintenance workload (Hora, 1987; Wireman, 1991). Another capability offered by CMMS is the potentialto strengthen reporting and analysis capabilities (Wireman, 1991; Callahan, 1997; Hannan and Keyport,1991). Finally, CMMS has been described as a tool for coordination and communication with production(Dunn and Johnson, 1991).

While the capabilities offered by CMMS do not in any way reduce the amount of information to beprocessed by the maintenance organization, they do assist the maintenance function in managing the everincreasing complexity brought about by more complex and varied technologies and a workforce with highlyspecialized skills.

Hypothesis 4. The use of computerized information systems by the maintenance function will be higher inplants with greater environmental complexity.

Galbraith (1977) also suggested that lateral relations assist in increasing information-processing capacity.Lateral relations allow problems to be solved at the level that they occur rather than being passed up theorganizational hierarchy. Some examples of lateral relations include direct contact between individuals whoshare a problem, liaison roles that provide a link between departments, task forces for solving problems onan as needed basis and teams that work on interdepartmental problems on a continuing basis.

As a support function, maintenance must communicate and coordinate effectively with production. Allof the proposed types of lateral relations may be used to create links between maintenance and production.As the production environment becomes more complex, coordination between maintenance andproduction becomes more critical and may require the use of more than one type of lateral relation inorder to effectively support the ability to maintain quality and meet production schedules.

Hypothesis 5. The use of lateral relations by the maintenance function will be higher in plants with greaterprocess and labor diversity.

3.3. Performance

By reducing the need for information processing or increasing information-processing capacity, each ofthe maintenance practices discussed above will help the maintenance function to operate more effectively.Problems can be addressed and solved more quickly, and communication and coordination withproduction is carried out more effectively. Thus,

Hypothesis 6. The use of maintenance practices that reduce the need for information processing or increaseinformation-processing capacity will be associated with higher maintenance performance.


4. Methodology

4.1. Sample

A sample of 354 plants was identified using the Harris Indiana Industrial Index (1992). To be included inthe sample, each plant had to meet two requirements. The first requirement was that the plant had to haveat least 200 employees. This size limitation is consistent with previous research. Empirical studies havegenerally limited their samples to larger plants with at least 200–250 employees (Blau et al., 1976; Hull et al.,1988; Collins et al., 1988; Dean et al., 1992; Snell and Dean, 1992). Plants with at least 200 employees willhave a significant investment in technology and are likely to require an internal maintenance group to carefor equipment.

The second requirement was that the plant had to be primarily involved in a metalworking industry. Theindustries included: primary metals (Standard Industrial Classification [SIC] 33), fabricated metal products(SIC 34), industrial and metalworking machinery (SIC 35), precision instruments (SIC 36), andtransportation equipment (SIC 37). This requirement is comparable to several surveys of manufacturingthat have concentrated on plants primarily involved in metalworking industries (Dean and Snell, 1991;Deane et al., 1990; Ettlie and Reza, 1992; Majchrzak et al., 1986). Within these industries, a variety ofdifferent technologies are in use including a significant level of automated and advanced manufacturingtechnologies (Majchrzak et al., 1986). Conversely, these industries should be fairly uniform in theexogenous factors that might affect maintenance management. Exogenous factors might include thecharacteristics of the material being worked on and governmental regulations regarding the handling ofmaterials, products and equipment. For example, chemical and food-processing industries may havedifferent requirements for cleaning and maintaining equipment and for handling products. Thesedifferences could influence maintenance management.

Questionnaires were sent to the maintenance manager and production manager in each plant. Thus, atotal of 708 questionnaires were sent. The name of the plant manager was obtained from the Harris Index(1992). The name of the maintenance manager was obtained directly, through telephone calls to the plants.The survey respondents included 125 plant managers and 162 maintenance managers from 232 plants.1

Twelve of the questionnaires were incomplete and were eliminated from the sample. An additional 53questionnaires were eliminated because they reported having fewer than 200 employees in their plant. In all,there were 222 usable questionnaires from 180 different plants, representing a response rate of 31.4%.Using data available from the Harris Index (1992), comparison of responding plants to non-respondingplants showed no significant difference for size, age or industry.2

4.2. Variables

For the first five hypotheses in this study, the independent variables describe the maintenanceorganization and the production technology in the plant. The dependent variables are measures of the useof information-processing alternatives by the maintenance department. In the sixth hypothesis, theinformation-processing alternatives become the independent variables and a measure of maintenanceperformance is the dependent variable. The descriptions and names of the variables are shown in Table 1.Appendix A lists specific items.

1Dual responses were received from 55 plants and were used to assess interrater reliability.2Chi-square tests were used to assess the representativeness of the sample using plant size, age and 2-digit SIC industry. For each

characteristic, the null hypothesis of homogenous distribution for respondents and non-respondents was accepted at a 0.05 level.


4.2.1. Complexity

Complexity was operationalized by taking into account characteristics of the production equipmentmaintained by the maintenance organization and characteristics of the organizational structure of themaintenance department.

4.2.2. Production technology characteristics

Three measures were used to describe the production technology in use. The first variable measures thediversity of equipment that the maintenance function is expected to maintain. Previous literature provideslittle guidance into the measurement of the diversity of plant equipment. Variety was operationalized usingtwo items. In both items, the respondent was asked to indicate where his/her organization fell on a bipolarscale describing a range from the use of relatively few equipment types and original equipmentmanufacturers (OEMs) to a relatively broad set of equipment types and OEMs.

The second variable measured the extent of use of AMT in the plant. The measure of AMT was based onan instrument used by Snell and Dean (1990). AMT was based on nine items that measured the extent towhich the plant had implemented different computer technologies (e.g., FMS, NC, DNC). Greater use ofAMT means that equipment is more complex to maintain (Robinson, 1987).

The third variable describing process technology is mass output orientation. The measure is similar toone developed by Khandwalla (1974). Respondents were asked to rate on five-point scales the extent towhich each of the five major technologies were used in their plants.3 The ratings on the use of each of the

Table 1

Variable definitions

Variable Definition

Plant size (SIZ) Log of number of employees in plant

Unionization (UNION) Unionized=1, Not unionized=0

Technology variety (VARI) Average of the reported variety of equipment types and the

number of OEMs (see appendix)

Advanced manufacturing technology (AMT) Average of the reported extent of use of nine different computer

technologies (see appendix)

Mass output orientation (MASS) Weighted average of the extent of use of five different types of

process technologies (see appendix)

Classifications (CLASS) Total number of maintenance craft classifications in the plant

Organization levels (LEVEL) Number of levels in the longest line between the craft worker and

the highest level maintenance employee in the plant

Preventive/predictive maintenance (PMV) Extent of use of preventive and predictive maintenance (see

appendix)

Maintenance department size (LMTCSIZ) Log of the number of hourly maintenance employees in the plant

Area maintenance (AREA) Log of the percent of the maintenance workforce assigned to area

maintenance

Computerized maintenance management systems (CMMS) Average of the reported extent of use of CMMS for 11 different

maintenance activities (see appendix)

Lateral relations (LATREL) Average of the reported extent of use of interdepartmental

committees, task forces and liaison personnel (see appendix)

Performance (PERF) Average of the extent to which maintenance had contributed to

improvements in plant performance in product quality, equipment

availability and production costs (see appendix)

3The technologies include custom technology, small-batch or job shop technology, large-batch technology, mass-production

technology and continuous-process technology. These categories represent increasing technical complexity.


five technologies are weighted by the type of technology. Custom technologies received a weight of one andthe use of continuous technologies received a weight of five. The weighted ratings were then divided by thesum of the weights (one through five) to produce an index of the extent mass output orientation inthe plant.

4.2.3. Organizational characteristics

Two measures were used to describe the maintenance organization. The first variable measuresthe number of levels in the organization. Respondents were asked to count the number of levels inthe longest line between the craft worker and the highest maintenance level employee in the plant. Thesecond item asked the respondent to report the total number of maintenance craft classifications in theplant.

4.2.4. Maintenance approaches

One way for the maintenance function to control its environment is through the use of preventive andpredictive maintenance. Respondents were asked to report, on five-item Likert scales (1=0–20% to 5=81–100%), the extent to which equipment in their plant is covered by preventive and predictive maintenance.The responses on the two items were averaged.

Maintenance department size was based on the log of the number of hourly maintenance workers inthe plant.

Use of area maintenance was employed to indicate the extent to which self-contained tasks were used toreduce information-processing capacity requirements for the maintenance function. Use of areamaintenance was measured as the percent of the maintenance workforce assigned to production areasthroughout the plant (versus assigned to a centralized maintenance area).4

Investment in vertical information systems was operationalized as the extent of use of CMMS. Themeasure of CMMS use included eleven items asking the respondent to report the extent to which computersystems were used to support different maintenance activities (e.g., work-order planning and scheduling,equipment failure diagnosis, inventory control).

The use of lateral relations was measured using three items. Respondents were asked to report on a five-point Likert scale the extent to which interdepartmental committees, task forces, and liaison personnel wereused to coordinate interdepartmental communication and activities.

4.2.5. Performance

For this study, a plant level measure of maintenance performance was needed. At the plant level,maintenance performance is evident in equipment availability, the ability to meet production schedules andproduct quality (Pintelon and Gelders, 1992; Teresko, 1992; Macaulay, 1988). However, in the case of plantequipment condition and availability, uniform plant-level measures of maintenance performance aredifficult to identify. It is only in the past few years that researchers have started to discuss uniform methodsof measuring maintenance performance (Arts et al., 1998; Tsang, 1998). Many plants track equipmentdowntime on individual pieces of equipment, but overall plant indicators of downtime are often notavailable.

Using plant level measures of production schedule compliance or product quality levels might be ways totry to measure maintenance performance, but so many other factors may affect performance on thesevariables it would be difficult to isolate the impact of maintenance practices.

Because of the problems associated with identifying and obtaining data for objective, uniform measuresof performance, a qualitative measure of maintenance performance was used. Respondents were asked to

4The measure for area maintenance was skewed toward zero. To reduce the effect of heteroscedasticity, the log of the measure was

used.


evaluate the extent to which the maintenance department had contributed to plant improvements inproduct quality, equipment availability and production costs. Responses were recorded using a five-pointLikert scale (1 = less than 20% of performance improvement was the result of maintenance efforts, 5 =more than 80% of performance improvements was the results of maintenance effort). The average of theresponses were used to obtain the measure of performance.

4.3. Analysis

The hypotheses concerning the relationship between environmental complexity and maintenanceorganization and maintenance practices were tested using hierarchical regression analysis (Cohen andCohen, 1975). Hierarchical regression allows groups of variables to be entered into the regression equationin steps. The first group of variables is allowed to explain as much of the variability of the dependentvariable as possible. As subsequent variables are entered, the amount of variance of the dependent variablethat is explained by the newly entered independent variables is calculated. In this study, the variablesdescribing the plant environment (plant size and union status) were entered in the first step. In the secondstep, the production technology variables measuring production technology characteristics were entered. Inthe third step, variables measuring the number of maintenance classifications and number of levels in themaintenance organization were entered. A significant incremental R2 in the second or third step could beinterpreted as support for the hypotheses that there are relationships between production technology ormaintenance organization and maintenance practices. The F-statistics reported in the tables areincremental. That is, they are associated with the change in R2 occurring when the variables were entered.The variables were measured so that positive b’s are consistent with the hypotheses. Positive b’s wouldindicate that plants with greater complexity would make more extensive use of the particular maintenancepractice than plants with lower levels of complexity. The form of the regression equation is shown below:

MtcPraci ¼ b0 þ ðb1 Sizei þ b2 UnionizationiÞ þ ðb3 VARIi þ b4 AMTi þ b5 MASSiÞ

þ ðb6 CLASSi þ b7LEVELiÞ þ eI : ð1Þ

The hypothesis concerning the relationship between maintenance practices and maintenanceperformance were tested using linear regression analysis.

5. Results

The means, standard deviations and correlations for the variables are shown in Table 2. The alphas forthe variables measuring Variety, AMT, CMMS, Lateral Relations and Performance are also shown inTable 2. The results for Hypotheses 1–5 are presented in Tables 3 and 4. The results for Hypothesis 6 areshown in Table 5.

5.1. Environmental management

Hypothesis 1 predicted greater use of preventive and predictive maintenance to control more complexmaintenance environments. For preventive and predictive maintenance, the incremental F-test for the entryof process technology characteristics was significant (F ¼ 2:34; po0:10), which indicates evidence of theeffect of the process technology on the use of preventive and predictive maintenance. Specifically, AMT hasa positive and significant relationship (b ¼ 0:463; po0:05) with the use of preventive and predictivemaintenance. However, organizational characteristics such as the number of maintenance classificationsand the number of levels in the maintenance organization were not significantly related to the use of


Table

2

Correlations

Variable

Means

s.d.

Correlationsa

12

34

56

78

910

11

12

1.Size

6.24

0.77

2.Unionization

0.59

0.49

0.22

3.Variety

3.49

1.02

0.21

�0.04

(0.67)

4.AMT

2.60

0.75

0.34

0.10

0.18

(0.77)

5.Mass

outputorientation

2.96

0.72

0.21

0.14

0.14

0.04

6.Classifications

4.53

4.01

0.45

0.32

0.05

0.17

0.12

7.Organizationlevels

2.96

1.33

0.12

�0.00

0.07

0.06

0.03

0.09

8.Prev/PredMaintenance

2.43

0.91

0.05

�0.11

�0.01

0.16

0.08

�0.00

0.06

9.Maintenance

dept.size

2.90

1.51

0.73

0.43

0.14

0.32

0.22

0.59

0.02

0.03

10.Areamaintenance

�3.13

2.90

0.37

0.17

�0.03

0.21

0.13

0.31

0.21

0.05

0.33

11.CMMS

2.62

1.10

�0.02

�0.14

0.03

0.25

�0.18

0.08

0.26

0.34

0.02

0.05

(0.88)

12.Lateralrelations

2.60

1.01

0.26

�0.18

0.13

0.29

0.03

0.11

0.00

0.16

0.22

0.07

0.33

(0.74)

13.Perform

ance

2.87

0.80

0.02

�0.14

�0.03

0.10

0.04

�0.04

�0.04

0.30

0.07

�0.03

0.20

0.21

(0.73)

aCorrelationsabove0.15are

significantat

po0:05:Coefficientaare

inparentheses.


Table 3

Results for Hypotheses 1–4

H1: Extent of preventive and

predictive maintenance (PMV)

H2: Maintenance department size

(LMTCSIZ)

H3: Use of area maintenance

(AREA)

Variables b DR2 R2 F b DR2 R2 F b DR2 R2 F

0.0143 0.0143 1.14 0.6052 0.6052 121.85**** 0.0041 0.0041 0.22

Constant 4.211 �5.845 �1.426

SIZ 0.140 1.318 0.049

UNION �0.435 0.844 0.066

0.0433 0.0576 2.34* 0.0072 0.6124 0.97 0.0202 0.0243 0.70

Constant 3.924 �6.034 �1.546

VARI �0.171 �0.179 �0.098

AMT 0.463** 0.166 0.086

MASS 0.153 0.077 0.087

0.0021 0.0598 0.17 0.0612 0.6553 13.15**** 0.0069 0.0275 0.34

Constant 3.736 �4.988 �1.319

CLASS �0.008 0.104**** 0.016

LEVEL 0.061 �0.076 0.018

Overall F 1.37 3.60*** 0.39

N 158 157 104

H4: Use of comp. maintenance

management systems (CMMS)

H5: Use of lateral relations (LATREL)

Variables b DR2 R2 F b DR2 R2 F

0.0245 0.0245 1.23 0.1356 0.1356 12.16****

Constant 2.991 �0.013

SIZ �0.021 0.459****

UNION �0.337** �0.459****

0.1436 0.1681 5.47*** 0.0656 0.2012 4.16**

Constant 3.406 �0.195

VARI �0.075 0.003

AMT 0.429*** 0.355****

MASS �0.405** �0.023

0.0994 0.2675 6.31*** 0.0067 0.2079 0.64

Constant 3.346 0.043

CLASS 0.303 0.015

LEVEL 0.258** �0.054

Overall F 4.85**** 5.62****

N 100 157

*po0:10; **po0:05; ***po0:01; ****po0:001:


preventive and predictive maintenance. This provides partial support for Hypothesis 1. However, therelationship is restricted to the use of AMT.

5.2. Slack resources

The second hypothesis predicted that the maintenance department workforce would be larger in plantswith more complex maintenance environments. For this hypothesis, the incremental F-test for the entry ofprocess technology characteristics was not significant. However, for organizational characteristics, theincremental F-test was significant (F ¼ 13:15; po0:001). In this case, the number of maintenance craftclassifications was positively and significantly related to the size of the maintenance workforce (b ¼ 0:104;po0:001), giving this hypothesis partial support.

5.3. Self-contained tasks

Hypothesis 3 predicted more extensive use of area maintenance would be associated with increasinglycomplex maintenance environments. The measure of area maintenance was skewed toward zero, with aboutone-third of the sample relying totally on centralized maintenance shops. This meant that the dependentvariable violated the assumption of normality required for linear regression. To address this problem, theanalysis was carried out in two steps. First, to perform the hierarchical regression, all samples with zeroarea maintenance were eliminated from the sample, and the log of the measure of area maintenance was

Table 4

Logistic regression results for Hypothesis 3

Variable b Std Error Wald Chi-square

Constant 8.518 2.544 11.210***

SIZE �1.147 0.418 7.535**

UNION �0.213 0.386 0.305

VARI 0.101 0.206 0.243

AMT �0.265 0.257 1.066

MASS �0.137 0.268 0.261

CLASS �0.175 0.043 4.288*

LEVEL �0.244 0.160 2.315

Chi-square 36.17***

*po0:05; **po0:01; ***po0:001:

Table 5

Results for Hypothesis 6

Variables b F N

PMV 0.13**** (0.032) 17.15**** 177

AREA 0.12 (0.178) 0.57 176

CMMS 0.15**** (0.069) 4.96** 112

LATREL 0.17*** (0.059) 7.92*** 174

MTCSIZ �0.000 (0.00) 0.021 174

Dep Var: PERF.

*po0:10; **po0:05; ***po0:01; ****po0:001:


used as the dependent variable. With this adjustment, the heteroscedasticity was eliminated. In this step,none of the process technology or organizational factors was found to be related to the level of areamaintenance.

To test for differences between firms that did and did not use area maintenance, logistic regression wasused. Logistic regression is appropriate when analyzing models with categorical dependent variables andcategorical and continuous independent variables (Liao, 1994). Logistic regression provides an estimate ofthe probability of each categorical response using the maximum likelihood method.

For this analysis, plants with no area maintenance crews, the dependent variable was given a value ofzero. For plants that used area maintenance crews, the dependent variable was given a value of one. Theresults of the logistic regression are shown in Table 4. Each of the parameter estimates indicate the partialeffect of each independent variable on whether or not area maintenance crews are used. The number ofclassifications (b ¼ �0:175; po0:05) is significant in a negative direction, counter to the hypothesis. Thisindicates that plants with more maintenance craft classifications are less likely to have decentralized areamaintenance crews.

5.4. Vertical information systems

The fourth hypothesis predicted more extensive use of CMMS in more complex maintenanceenvironments. The incremental F-test for the entry of process technology was significant (F ¼ 5:47;po0:01) which indicates evidence of a relationship between process technology characteristics and the useof CMMS. Both AMT and complexity were significantly related to the use of CMMS. For AMT, therelationship was positive and significant (b ¼ 0:429; po0:01). For complexity, the relationship was negativeand significant (b ¼ �0:405; po0:05). The F-test for the entry of maintenance organization characteristicswas also significant (F ¼ 6:31; po0:01). Specifically, the relationship between the number of levels inthe maintenance organization and the extent of use of CMMS was significantly and positively related(b ¼ 0:258; po0:05). While the significance of the relationship between maintenance environment and theuse of CMMS was confirmed for this hypothesis, the direction of the relationship received mixed support.

5.5. Lateral relations

In the fifth hypothesis, more extensive use of lateral relations was predicted to be related to greaterprocess and labor diversity. The incremental F -test for the entry of process technology characteristics wassignificant (F ¼ 4:16; po0:05). The relationship was positive and significant for AMT (b ¼ 0:355;po0:001). The F-test for the entry of maintenance organization characteristics was not significant. So thishypothesis received partial support.

5.6. Performance

The sixth hypothesis predicted a positive relationship between maintenance practices that enhanceinformation-processing capacity or reduce the need for information processing and maintenanceperformance. Three out of the four practices were significantly related to performance. For preven-tive and predictive maintenance, the relationship was positive and significant (b ¼ 0:130; po0:001).The relationship with performance was also positive and significant for CMMS (b ¼ 0:150; po0:001) andlateral relations (b ¼ 0:166; po0:01). These results provide support to the fifth hypothesis.


6. Discussion

This study examines the relationship between the maintenance environment and the information-processing capabilities of the maintenance department. Each of the five hypotheses tested in this studyreceived some support. Taken together, the findings seem to indicate that the maintenance functionresponds to complexity in the production environment by undertaking different maintenance practices andapproaches. Preventive maintenance, increased maintenance department size and use area maintenance aremaintenance practices that reduce the need for information processing by managing the environment,creating slack resources and creating self-contained tasks. These practices were used in response to moreautomated technologies and a more varied maintenance workforce. CMMS and lateral relations to increaseinformation-processing capacity were used in response to the use of AMT. It also appears that some of theinformation-processing alternatives used by maintenance in response to complexity contribute to improvedmaintenance performance.

The findings also seem to indicate that different factors contributing to complexity elicit different types ofresponses. Production technology, in the form of AMT was found to be strongly associated with moreextensive use of preventive and predictive maintenance and CMMS and lateral relations. Organizationalcharacteristics were found to be only marginally associated with maintenance practices. The number oflevels in the organization was related to the extent of use of CMMS and maintenance crew centralization.The number of craft classifications was related to maintenance crew size.

AMT was strongly associated with several maintenance practices. AMT such as flexible manufacturingsystems replace both ‘physical’ human effort and some ‘mental’ human effort. Introduction of AMT meansthat equipment is more complicated to maintain (Robinson, 1987). AMT implementation also means thatproduction steps that were previously distinct may be combined into a single operation. Increasedintegration means that equipment failures lead to more immediate and costly consequences (Finch andGilbert, 1986; Walton and Susman, 1987). Therefore, maintenance resources must be quickly and properlydirected to solve problems.

In Hypothesis 1, AMT was found to be significantly and positively associated with the use of preventiveand predictive maintenance. By helping to manage the maintenance environment and create a predictableworkload, the use of preventive and predictive maintenance allows the maintenance function to maintainthe equipment in better condition. As a result, equipment failures are prevented and high equipmentutilization rates are supported. This is critical considering the large capital investment associated withAMT. None of the other technology or organizational characteristics were found to influence the use ofpreventive and predictive maintenance.

AMT was strongly associated with the use of CMMS, as predicted in the fourth hypothesis. Theinformation-processing capabilities of CMMS provide the ability to quickly communicate and coordinatethe need for repairs. This result also makes sense in that organizations with computer-assistedmanufacturing technologies would be very comfortable with using a computer-based system forcommunicating and coordinating maintenance activities.

In Hypothesis 5, AMT was strongly associated with the use of lateral relations. The complexity ofadvanced manufacturing technologies makes production more dependent on the ability of the maintenancedepartment to maintain and repair these very capital-intensive technologies. Problems need to be fixedquickly and activities need to be coordinated carefully to maintain high utilization levels. Lateral relationswould complement the use of CMMS by creating close links between production and maintenance tofacilitate decision making and coordinate maintenance activities with production schedules.

Aside from AMT, the only technology characteristic significantly associated with a maintenancepractices was mass output orientation. Mass output orientation was significantly and negatively associatedwith the use of CMMS. This is counter to the direction predicted in Hypothesis 4. It was expected thatorganizations with higher mass output orientation would be more likely to use CMMS. As with AMT,


higher mass output orientation is generally associated with more automated, capital intensive equipment.Therefore, high utilization and rapid response would be more critical. An explanation for the negativerelationship may be that organizations with lower mass output orientation such as job shops may have avery large number of different pieces of equipment operating. By contrast, organizations with high massoutput orientation such as continuous processors may tend more toward a large monolithic piece ofequipment with many component parts. The complexity of tracking and scheduling work on the largenumber of different pieces of operating equipment in a job shop type environment may be a greaterincentive toward the use of CMMS to increase information-processing capacity.

Maintenance organization structure was also related to maintenance practices. The results of the secondhypothesis showed that maintenance department size was positively and significantly associated with thenumber of maintenance craft classifications. One way to reduce the complexity of allocating workers inseveral crafts to different areas in a plant is to create slack resources by having more workers in eachclassification. This leads to a larger overall maintenance organization.

The number of maintenance classifications was found to be related to whether or not maintenance useddecentralized, area maintenance crews. However, counter to hypothesis three, organizations with moreclassifications were found to be less likely to use area maintenance. This may be a case of utilizationoverriding information processing. In a maintenance department that has many craft classifications,assigning area maintenance crews may result in the need for an excess number of workers in some lessutilized classifications. For example, a plant may only need one pipe fitter, but using area maintenancecrews could mean that the plant would have to employ two pipe fitters so that each area crew would be fullystaffed. By having a central maintenance shop, fewer maintenance workers would be needed and higherutilization would be achieved.

Another reason for the results for Hypothesis 3 may be that the use of area maintenance simply does notreduce the need for information processing. In fact, area maintenance workers required to work on a widevariety of different types of equipment may result in greater information-processing requirements thancentrally located maintenance workers who specialize in maintaining different types of machines. This maybe especially significant with the increasing use of modular technology that allows for the rapid changeoutof failed components. These components may then be brought to a centralized shop for repairs, reducingthe need for area maintenance crews.

The number of maintenance organization levels was significantly and positively associated with theuse of CMMS. This makes sense in that the CMMS can be a channel for swiftly and accuratelycommunicating information on equipment status and schedules throughout an organization. CMMS helpsto speed the otherwise slow process of pushing information up and down through several organizationallevels.

Organizational levels and the number of maintenance classifications were not significantly associatedwith any of the other maintenance practices studied. While organizational characteristics can play animportant role in influencing the use of practices related to information-processing requirements, formaintenance, it may be that equipment characteristics simply outweigh any organizational characteristics ininfluencing these practices.

Consistent with Hypothesis 6, three out of the five maintenance practices were found to be positivelyassociated with maintenance performance. This finding would appear to confirm the widely discussedimportance of these practices in improving maintenance performance. By creating a more predictable andmanageable operating environment, preventive and predictive maintenance allow the maintenance functionto better support equipment availability and performance. By accelerating the otherwise slow process ofpushing information up and down through maintenance and production organizational levels, CMMSallows maintenance to respond more quickly and better manage equipment performance. Lateral relationsalso play a role in improving maintenance performance by allowing production and maintenancedepartments to more effectively communicate and coordinate their activities.


The findings in this study provide support for Galbraith’s (1977) argument that use of information-processing alternatives is related to complexity. In responding to complexity, maintenance organizationsused environmental management, slack resources, self-contained tasks, vertical information systems andlateral relations. This study also expands the Flynn (1999) study by applying it to a sub-unit of amanufacturing organization and confirming the use of information-processing alternatives within themanufacturing organization.

6.1. Managerial implications

One important implication of this study is in supporting the idea that the maintenance function mustadapt to the environment in which it operates. More complex environments require different maintenancepractices and approaches than less complex environments. The study also supports many maintenancepractices that have been promoted as being important to maintenance performance. The importance ofthese practices has been supported in two ways. First, the strong link between the use of AMT and the useof preventive and predictive maintenance, CMMS and lateral relations suggests the critical role thesepractices may play in the successful implementation of new manufacturing technologies. Further, the linkfound between preventive and predictive maintenance, CMMS, lateral relations and maintenanceperformance further supports the importance of these ‘‘world class’’ maintenance practices.

Appendix A. Survey measures

Technology variety

How would you characterize equipment in your plant along the following dimensions? (circle number)

A single type of productionequipment represents more than80% of total plant productionequipment

1 2 3 4 5 No single type of productionequipment represents more than20% of total plant productionequipment

Supplied by a few originalequipment manufacturers (OEM’S)

1 2 3 4 5 Supplied by many different OEM’S

Advanced manufacturing technologies

To what extent are each of the following advanced manufacturing technologies used in your plant?(circle number)

Not at all Usedmoderately

Usedextensively

Computer-aided design (CAD) 1 2 3 4 5Numerical control (NC) 1 2 3 4 5Computer numerical control (CNC) 1 2 3 4 5Direct numerical control (DNC) 1 2 3 4 5Automated material load/unload atworkstations

1 2 3 4 5

Automated material handling betweenworkstations

1 2 3 4 5

Flexible manufacturing systems (FMS) 1 2 3 4 5Computer-aided testing and inspection 1 2 3 4 5Computer-aided process planning 1 2 3 4 5


Mass output orientation

To what extent are each of the following types of production technologies used in your plant?

Donothave

Usedminimally

Usedmoderately

Usedextensively

Custom technology 0 1 2 3 4 5Used in the production or fabrication ofa single unit or a few units of products tocustomers specifications or needs, e.g.,missile prototypes

Small batch, job shop technology 0 1 2 3 4 5Used in the creation of small batches ofsimilar units, such as tools and dies

Large batch technology 0 1 2 3 4 5Used in the manufacture of large batchesof drugs and chemicals, parts, cans andbottles, counts of yarn, etc.

Mass production technology 0 1 2 3 4 5Used in mass production of autos,standard textiles, etc.

Continuous process technology 0 1 2 3 4 5Used in oil refining and other automatedindustries, in which the output is highlystandardized and mechanized and isproduced continuously rather than inbatches or shifts

Use of preventive and predictive maintenance

What percentage of the equipment in your plant is covered by preventive maintenance work orders?1 0 TO 20%2 21 TO 40%3 41 TO 60%4 61 TO 80%5 81 TO 100%

What percentage of the equipment in your plant is covered by predictive maintenance work orders?1 0 TO 20%2 21 TO 40%3 41 TO 60%4 61 TO 80%5 81 TO 100%


Use of area maintenance

What is the allocation of maintenance personnel between central shops and area maintenance?% in Central Shops (Centralized maintenance areas which respond to calls from

all areas of the plant)% in Area Maintenance (Maintenance crews dedicated to a single production

department or plant area)100% Total

Use of computerized maintenance management systems

To what extent are each of the following computerized maintenance management system modules used?

Do nothave

Usedrarely

Usedmoderately

Usedfrequently

Work-order planning and scheduling 0 1 2 3 4 5Preventive-maintenance planning andscheduling

0 1 2 3 4 5

Predictive maintenance data analysis 0 1 2 3 4 5Equipment failure diagnosis 0 1 2 3 4 5Equipment repair history 0 1 2 3 4 5Equipment parts list 0 1 2 3 4 5Manpower planning and scheduling 0 1 2 3 4 5Inventory control 0 1 2 3 4 5Spare parts requirements planning 0 1 2 3 4 5Material and spare parts purchasing 0 1 2 3 4 5Maintenance budgeting 0 1 2 3 4 5

Use of lateral relations

In assuring compatibility among decisions in maintenance and other functions such as production andengineering, to what extent are the following coordination methods used? (circle number)

Usedrarely

Usedmoderately

Usedfrequently

Interdepartmental committees which are set up toallow departments to engage in joint decision makingon an on-going basis

1 2 3 4 5

Task forces which are temporary bodies set up tofacilitate interdepartmental collaboration on a spe-cific project

1 2 3 4 5

Liaison personnel whose specific job it is to coordinatethe efforts of several departments for purposes of aspecific project

1 2 3 4 5


Performance

Less than 20%of performanceimprovementwas the resultof maintenanceefforts

About 50% ofperformanceimprovementwas the resultof maintenanceefforts

More than 80%of performanceimprovementwas the resultof maintenanceefforts

Over the past 2 years,how much has maintenancecontributed to theimprovement of product

quality?

1 2 3 4 5

Over the past 2 years,how much has maintenancecontributed to theimprovement of

equipment availability?

1 2 3 4 5

Over the past 2 years,how much hasmaintenance contributedto the reduction of

production costs?

1 2 3 4 5

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