the impact of unions on operations strategy

THE IMPACT OF UNIONS ON OPERATIONSSTRATEGY*

MARK PAGELL AND ROBERT HANDFIELDDepartment of Management, College of Business Administration, Kansas State

University, Manhattan, Kansas66506-0507College of Management, North Carolina State University, Raleigh,

North Carolina27695-7229

This paper explores the effect that unions have on a firm’s ability to reengineer manufacturingprocesses. We begin by exploring the various effects that a union may have in a manufacturingenvironment. Next, we briefly review how unions may affect managerial initiatives to reengineerprocesses and improve manufacturing performance. The third section analyzes an existing databaseto test for differences in cycle time and manufacturing performance between union and nonunionfirms. Finally, we discuss the implications of the study for future operations strategy research andnote how a different form of union-management relationships is beginning to evolve.(OPERATIONS STRATEGY; LABOR RELATIONS; UNIONS; REENGINEERING)

1. Introduction

Over the last decade, the operations management literature has advocated the reengineer-ing of business processes to increase competitiveness (Womack, Jones, and Roos 1990;Hayes and Pisano 1994). Total Quality Management and Just-in-Time make the shop floora place of continuous improvement where products with zero defects are produced as needed,while employing relatively little inventory (Mehra and Inman 1992; Anderson, Rungtusan-atham, Schroeder, and Devaraj 1995; Flynn, Schroeder, and Sakakibara 1995). Flexibility,achieved through cross trained workers and or advanced manufacturing technologies (AMT),is lauded as another competitive priority (DeMeyer, Jinichiro, Jeffrey, and Ferdows 1989;Gerwin 1993). Finally, manufacturing is being viewed by some as a service industry(Goldhar and Lei 1994) that will have to provide mass customization to serve demandingcustomers in fragmented markets, and do so through ever-shrinking cycle time management(Stalk and Hout 1990; Istvan 1992; Pine 1993).

In many settings, especially manufacturing, it is the operational employees who determinethe relative success of business process reengineering. For instance, it has been noted by anumber of authors that the enabler of advanced manufacturing technologies is not thehardware, but rather the skills of the people running the equipment and working on the shopfloor (i.e., Adler 1988; Snell and Dean 1992). By the same token, continuous improvementon the shop floor does not work unless the operators of equipment (or the service employees)

*Received August 1996; revisions received May 1997, January 1998, and September 1998; accepted March 1999.

PRODUCTION AND OPERATIONS MANAGEMENTVol. 9, No. 2, Summer 2000

Printed in U.S.A.

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Copyright © 2000, Production and Operations Management Society

are trained, willing to participate, and willing to make changes to existing processes (Lawler,Mohram, and Ledford 1992; Anderson, Rungtusanatham, and Schroeder 1994; Handfield andGhosh 1994). An empty suggestion box will not lead to continuous improvement. Cycle timereduction requires shop floor employees who are willing and able to work in a more flexibleenvironment (Charney 1991), where inventory cannot be used to mask differences in output.

Ignored in many of these discussions is the effect a union may have on a firm’s ability toadopt reengineering initiatives. Unions can severely limit managerial discretion, or in certainsituations may actually increase the rate of adoption of change. The decline in overall unionmembership in the United States obscures the large number of manufacturing plants that arestill unionized, as well as the number of services (such as state and local governments, hotels,and laborers) that have recently become unionized (Bernstein 1997).

This paper explores the effect that unions have on a manufacturing organization’s perfor-mance outcomes. The first section explores several attributes of the shop floor environmentin the presence of a labor union. The second section briefly reviews how unions are expectedto affect the adoption of business process reengineering initiatives. The third section analysesdata from an existing study of manufacturing shop floor performance, to determine if thereare indeed differences in union and nonunion performance outcomes. The final sectiondiscusses the implications of the study for operations managers and identifies how thepresence of a union may affect future research in operations strategy.

2. THEORY AND HYPOTHESES

2.1. The Union Environment

2.1.1. PRIOR STUDIES ON UNION–MANAGEMENT RELATIONSHIPS. The study of relationshipsbetween management and organized shop floor employees resulted in an entirely new branchof sociology (Industrial Relations) that evolved over much of the century (see Kaufman 1993for a complete history of this field.) The majority of studies in this field focus on the outcomesof the labor/management relationship, including wages, skills, job security, and conflicts. Forinstance, Braverman’s (1974) seminal work on the “negative” effects of new technology onthe workforce is concerned with the effect of numerically controlled (NC) equipment on labor.

Even the industrial relations research that is philosophically neutral tends to be moreinterested in the effects on employees rather than on the business enterprise. For instanceForm, Kaufman, Parcel, and Wallace (1994) suggest that 30 years of research on the effectof NC and computer numerically controlled (CNC) equipment on workforce skill levels shouldnow begin to focus on why the effects occur. Moreover, the industrial labor relationsliterature provides managers with few clues on how to better manage workers in order toimprove manufacturing performance.

On the other hand, the philosophical underpinnings of operations management (OM)research tends to focus primarily on the effects of various strategic initiatives on manufac-turing and financial performance. A relative paucity of literature inOM examines how therelative success of such programs are affected by labor relations. While the domain of thefield should probably not attempt to derive possible explanations for the existence of unions,their existence should nevertheless be acknowledged. This paper adopts this perspective; wedraw on prior theory that predicts how unions might influence manufacturing outcomes, butdo not seek to uncover why a union forms in the first place.

2.1.2. EFFECT OF JOB DEMARCATION. To illustrate the influence of unions on manufacturingperformance, consider the 600 plus page contract between the United Auto Workers (UAW)and General Motors (GM). The document provides information about how much employeesare to be paid, who gets promoted, how to hire and fire, procedures for closing a plant,procedures for outsourcing work, as well as rules on a number of other issues. Quite simply,the contract is a rule book that management must follow if they wish their employees to work.

142 MARK PAGELL AND ROBERT HANDFIELD

In addition, most plants also have a local agreement with rules governing specific localconditions. This complicated situation is compounded by the fact that a strike at any singlefacility can disableGM’s entire network of plants, as a recent strike at a GM brake componentplant in Dayton, Ohio illustrated. In this manner, unions can severely limit managerialdecision-making through work rules, seniority systems, and provisions on outsourcing.

To further aggravate this situation, union environments are often characterized by lowerskilled, narrow jobs. Tightly defined jobs have been a component of unionized workforces formany decades. Slichter, Healy, and Livernash (1960) noted that unions seek narrow jobdemarcations in order to protect members’ jobs. Other work rules include limits on promo-tion, control over hiring, policies that forbid laying-off idle employees, and a host of otherpotentially restrictive policies.

Management’s response is often to limit a union’s influence over what happens on the shopfloor by keeping jobs simple and easy to monitor and control. Simple jobs limit employeeflexibility. Employee flexibility may also be limited by job descriptions. Union employees areoften prohibited, either through formal work rules or informal peer group pressure, to dowork that is not specifically assigned to their job. This problem is compounded by thepresence of multiple unions on a site (Guest 1987). In many locations, skilled tradesemployees are responsible for maintenance. An operator may know what is wrong and havethe ability to fix the machine. However, that is not their job, and to do so is to take work fromother union employees.

2.1.3. EFFECT OF CONTROL ON PROMOTIONS. Seniority systems allow unions to controlpromotions. In a static environment a seniority systems ensures that the employees with themost experience (theoretically, the ones with the largest bank of stored knowledge) are theones who get promoted. However, many of the strategic initiatives that firms are presentlyattempting to adopt require a new skill set, which many senior employees may not possess.

Seniority systems in a union environment may eliminate managerial control over theemployees allowed to perform certain jobs. This problem may be compounded in unionenvironments because of the composition of union firms. In general, union firms have olderemployees working in older plants than nonunion employers (Kochan, McKersie, andCappelli 1984). Older employees are less likely to want to change (Gupta 1989), even if theyhave the skills required to do so. Additionally, the time between major changes in a factoryis negatively related to the ease of change (Schroeder, Congden, and Gopinath 1995). Olderemployees, in older plants, who are promoted on the basis of seniority can pose a significantproblem for management. The employees with the “best” or most important jobs are alsothose least likely or able to learn the new skills they will need. When combined with an oldplant, many firms find themselves doing the same things they have always done regardlessof the appropriateness of the strategy, because change is simply too difficult.

2.2.1. UNION EFFECTS ON BUSINESS PROCESS REENGINEERING. Almost every North Americanmanufacturing organization has undergone some form of reengineering within the lastdecade. Driven by the need to reduce inventory and cycle time, improve quality, increaseflexibility, and lower costs, organizations have sought to dramatically change or “reengineer”their business processes.

Business Process Reengineering (BPR) spans the functions of sales, order entry, engineer-ing, purchasing, manufacturing, and logistics and involves the activities required to dramat-ically improve processes (Stalk and Hout 1990; Hammer and Champy 1993; Handfield1995). To effectively introduce change, an organization must eliminate non–value-addedactivities within every process in the product/value supply chain. Concurrently, organizationsmust identify processes that are not a core competence and outsource them to supply chainmembers better positioned to effectively manage the process. Because of the need forintegration between activities demanded by BPR, every value-chain participant must becomeinvolved in this cross-functional undertaking. Hammer emphasized the importance of man-

143IMPACT OF UNIONS ON OPERATIONS STRATEGY

aging “people issues” in reengineering: “The real point of this is longer-term growth on therevenue side. It’s not so much getting rid of people. It’s getting more out of people (Hammerand Stanton 1995).

2.2.2. FLEXIBILITY . Union contracts are often very inflexible regarding change of any sort.Nowhere is this more apparent inBPR than in an organization’s ability to respond to changingcustomer requirements (a concept known as “flexibility”). Although there are different waysto operationalize manufacturing flexibility (Gerwin 1993), one way of doing so is byidentifying the relative mix ofmake-to-order(MTO), assemble-to-order(ATO), andmake-to-stock(MTS) products produced within the facility. AMTO product is finished after receipt ofa customer’s order, and is usually a combination of standard items and items designed tomeet the special needs of the customer. Such products typically involve more internalmanufacturing of components thanATO products. AnATO product often has key components(subassemblies, semifinished, intermediate items, etc.) stocked prior to customer ordersarriving, and therefore involves less manufacturing time (McClelland and Marucheck 1986).MTS products are easiest to manage; since there are no differences between orders, they canbe produced for finished goods inventory. As shown in Figure 1,MTS environments arebuffered from external demand fluctuations through finished goods inventory, whileATO/MTO

environments are not buffered from the demands of individual customers. Because of theneed to respond to a range of customer requirements,MTO/ATO products typically have fewercommon components and are thus not as standardized.MTO/ATO organizations are frequentlyunder pressure to stock a greater variety of component parts, reduce delivery lead-time,improve on-time delivery, attain an advanced level of technical proficiency, and changeproduct designs on an ongoing basis to meet customers’ requirements (Raturi, Meredith,McCutcheon, and Camm 1990).

Given the increased flexibility required to produceMTO andATO products, it follows thatunion firms are less likely to produce such products. Narrow low skilled jobs frequentlyfound in union environments (Kelly 1990) make it difficult to deal with the variety of tasksthe environment requires of the employees. Instead it is suggested that unions may focus onstandardizedMTS products. Both strict job classifications and seniority would help ensurerigid processes and personnel assignments—both oppose the needs ofATO and MTO plants.

FIGURE 1. Make-to-Order, Assemble-to-Order, and Make-to-Stock processes


Mefford (1986) and Kelly (1990) also found that union employees are generally moreefficient (but less flexible) than their nonunion counterparts. Greater flexibility is required tomanage the large number of nonstandard components produced, design changes, and volumechanges inherent in theMTO environment. We therefore posit that firms with unions will beless able to adopt to such changes because of the nature of unionization for most of thiscentury.

HYPOTHESIS 1. Union firms produce a smaller percentage ofMTO and ATO products thannonunion firms.

2.2.3. OUTSOURCING. Another major initiative ofBPR is the outsourcing of work notconsidered to be part of a firm’s core competence (Hammer and Champy 1993; Hammer andStanton 1995; Handfield 1995). Most employees view outsourcing as a threat to their jobs.Unions are against most outsourcing, especially in slow economic times. Recent labordisputes at Boeing, Caterpillar, andGM identified outsourcing as a central negotiation issue.Union agreements often provide a specific justification procedure for outsourcing, which mayrequire the firm to prove that outsourcing is cheaper by a given percentage or that theemployees necessary to perform the work are not presently available in the local market. Onemanifestation of this environment is in the percentage ofMTO/ATO product assembliesproduced in-house compared to the percentage outsourced. Unions are more likely to protestthe increased use of outsourcing in “make or buy” decisions, and may even go so far as to“hide” internal manufacturing costs in order to favor the insourcing decision. Using thisargument, the following hypothesis can be formed.

HYPOTHESIS 2. MTO/ATO union firms manufacture a larger percentage of assemblies in-house thanMTO/ATO nonunion firms.

2.2.4. SHOP FLOOR PERFORMANCE. One way of distinguishing the effects of unions onreengineering initiatives is through a comparison of shop floor variables related to laborutilization, process flow, quality, and lead time. For instance, as process redesign andimprovement takes place, one would expect to find less unscheduled downtime, less laborslack, and increased capacity utilization in manufacturing processes. This occurs because ofthe improved focus on preventive equipment maintenance and less non–value-added time(Monden 1981; Womack, Jones, and Roos 1990; Handfield 1995). Reengineering efforts alsoseek to limit the number of product routings, reduce the distance traveled by productsthroughout the plant, and limit the “queues” of products waiting at work centers and fewerdefective products requiring less rework (Mehra and Inman 1992; Hammer and Stanton1995; Handfield 1995). However, we believe that these effects may be mitigated by thepresence of a union.

There is mixed evidence as to how the presence of a union effects the decision to changeprocesses and improve quality. Lawler, Mohrman, and Ledford (1992), in a study of Fortune1000 firms, found that the rate of quality improvement was negatively correlated to firms’union status. Osterman (1994) found no relationship between unionization and the adoptionof quality programs. Osterman’s findings also indicate that unions are not good predictors ofwhether or not a firm will adopt what he deems “flexible work practices.” Osterman’sfindings contradict his own earlier findings (Osterman 1987) but are in line with similarfindings regarding work place practices by Jackson, Schuler, and Rivero (1989) and Woodand Albanese (1995).

We formally state these relationships in the following hypothesis.

HYPOTHESIS3. MTO/ATO union plants have(1) lower utilization(lower capacity utilization,more labor slack, more downtime); (2) longer process flows(a larger number of routings,longer distances traveled by products in the plant, more queue hours); and(3) higher qualityrejects compared toMTO/ATO nonunion plants.


The final hypothesis related to shop floor performance is based on the proposition that oneoutcome ofBPR is reduced cycle time. As processes are redesigned so as to eliminatenon–value-added activities and jobs, overall lead times are reduced (Hammer and Champy1993; Handfield 1995). The reengineering process may draw on a variety of tactics, includingintroduction of new manufacturing technology, variance reduction, parallel processes, excesscapacity, system simplification, or integration (Handfield 1995).

Authors such as Walton and Sussman (1987), Adler (1988), Saraph and Sebastian (1992),Huq (1992), and Prickett (1994) have all suggested that operators in complex manufacturingenvironments should be cross trained, highly skilled, able to work in self managing groups,paid for knowledge rather than for the job they possess, and selected and promoted based ontheir ability to learn. Many of these programs are the antithesis of the traditional unionenvironment. There is also evidence that union firms do not follow this model. For instance,Kelly (1990) found that union firms, and especially union firms with a seniority system werelikely to use lower skill employees than nonunion firms.

These findings suggest that when management is faced with a union, they are restricted indefining the scope of operator jobs. This restriction leads to simple narrow jobs, rather thanthe knowledge work identified by authors such as Walton and Sussman (1987). Such jobdemarcations can impede the firm’s efforts to reengineer processes, and thereby inhibit theorganization’s responsiveness. Following this line of reasoning, it follows that unions are alsomore likely to have longer cycle times associated with their product delivery systems. Thisis operationalized in the following hypothesis.

HYPOTHESIS 4. MTO/ATO union firms have(1) longer customer delivery lead-times, (2)longer manufacturing lead-times, and (3) longer lead times compared toMTO/ATO nonunionfirms in the same industry.

Note that some of the variables tested in Hypothesis 3 have the potential to moderate thevariables tested in Hypothesis 4. Consequently, we also note that if Hypothesis 3 is supportedthen Hypothesis 4 would also probably be supported. Despite this fact, Hypotheses 3 and 4are treated independently, bearing in mind that different effects may be found for union firmsin each case.

3. METHODS

3.1. Sample

In order to demonstrate some of the potential differences in union and nonunion firms, datafrom a study of time-based competition inMTO/ATO environments were analyzed (seeHandfield 1995). Rather than attempting to generate a completely random sample, theresearchers employed deliberate purposive sampling for heterogeneity (Cook and Campbell1979). A detailed description of this sampling approach is provided in the Appendix.

The sample was limited to North AmericanMTO firms located midway in the value-chain(i.e., products used in the provision of other products or services to individuals). Althoughsome of the firms produced some proportion of make-to-stock (MTS) products, the datacollected focused exclusively onMTO/ATO products. The attributes used to generate thesample included size, industry, unionization, and location within North America. The lattercriterion was considered in order to include firms in different manufacturing environmentsand union settings. The plants were located in eight states in the U.S. and three provinces inCanada. The plant managers at these firms were approached, and it was determined firstwhether they were in a make-to-order industry. The sample of 40 included firms producingcomputers, electronics, farm equipment, fiber optic cable, industrial equipment, telecommu-nications products, textiles, furniture, aeronautical equipment, and plastics. The unit ofanalysis in each case was the product line. Annual sales for the plants visited varied from$300,000 to over $1.6 billion, with a mean of $233 million in sales per facility (although this


figure was not released by all of the respondents). In some cases, data for two product linesin a single firm were obtained when independent production activities for each product wereevident. Tables 1 and 2 show the distribution of the sample across industries, geographiclocation, and by union status. Table 4 shows the mean, standard deviation, and standard errorfor each of the variables measured for each product line at each location. In comparing thedata to other recent survey data collected at random, nonresponse and sampling bias did notappear to be a factor across the variables of size, location, and unionization.

3.2. Measures

Data for 50 independent product lines were obtained using structured interviews. Becausethe source of the data collected required multiple inputs from different individuals in eachorganization, interviews were selected over mail-out surveys, which typically result in agreater amount of missing data when filled out by a single respondent. Also, the use ofinterviews reduced the potential for bias, as responses could be checked against secondarysources and other personnel within the organization. Finally, the use of standardized mea-sures with a commonsense referent (e.g., similar units) was used to reduce the risk of errormeasurement (Cook and Campbell 1979).

All data were collected by the researcher through on-site structured interviews using pairedresponses from production control, quality, sales, purchasing, and engineering personnel.Using a structured interview format, questions were asked of the two functional employeesmost familiar with each area, with the responses recorded by the researcher. In almost allcases, this included the plant manager and the materials manager. Because all of the variablesof interest are quantitative performance measures, single measures of the performancecriteria were used. Responses were obtained from two different employees in order to controlfor respondent bias. In the case of the variables included in this study, discussions typically

TABLE 1

Firms Geographic Location

Geographic Location Number of Union Firms Number of Nonunion Firms

Southeast (North Carolina, Virginia) 0 25Northeast (Massachusetts, Vermont, New York) 5 0Midwest (Minnesota, Michigan, Iowa, Wisconsin) 10 2West Coast (California) 0 1Canada (Alberta, Saskatchewan, Quebec) 7 0

TABLE 2

Industry Unionization

IndustryNumber of Union Firms

n 5 22Number of Nonunion Firms

n 5 28

Textiles 0 4Telecommunications 5 6Industrial Products 3 13Computer 4 2Furniture 3 1Aerospace 5 0Consumer products 0 2Automotive 2 0

Note: The seven Canadian plants were in the telecommunications industry (6) and the computerindustry (1).


took place with at least two or more of the following individuals: the plant manager, the headproduction planner, a quality manager, a materials manager, a sales representative, and a shopfloor supervisor. It was found that sales representatives were often a good source ofknowledge on external competitor lead times, but were not familiar with shop floor data. Incases when the estimate of two measures differed by approximately 10%, the differenceswere resolved by directly consulting company records, when available. Descriptions of themeasures are provided in Table 3, and all of the means and standard errors for the variablescollected are shown in Table 4.

TABLE 3

Description of Measures

Variable Description

Percent of MTO/ATO/MTSProducts

Estimated by plant manager and sales manager as percentages oftotal product line

Number of Assemblies forProduct Family

Estimated by plant manager and purchasing manger for highestselling MTO/ATO product family

Percent of Assemblies In-House for Product Family

Estimated by plant manager and purchasing manger for highestselling MTO/ATO product family

Percent Capacity Utilization Determined by shop floor supervisor as actual output of equipment/labor divided by theoretical output of equipment for the lastquarter

Percent Labor Slack Determined by shop floor supervisor as the total number of hours notavailable for processing, set-ups or equipment downtime dividedby total number of hours of production in the last quarter

Percent Downtime Determined by shop floor supervisor as the number of hours lost tomachinery failure divided by the total hours available forproduction in the last quarter

Number of Routings Determined by shop floor supervisor as the number of differentalternative routings that exist for the product family on the shopfloor

Distance Traveled (feet) Determined by shop floor supervisor as the total number of feet theproduct family traveled as it moved from work station to workstation

Queue Hours Determined by shop floor supervisor as the average amount of timethe product waits at work stations to be processed while beingprocessed in the shop

Quality, Percent Rejects Determined by shop floor supervisor and or the resident qualitymanager as the percentage of product family that does not meetquality standards

Customer Lead Time (days) Plant manager determined average time from order placement toorder delivery. Note that these and all other lead time measureswere verified by comparing actual orders to estimates

Expediting Lead-time (days) Plant manager determined average time from order placement toorder delivery for expedited orders.

Manufacturing Lead Time(days)

Based on estimates by shop floor supervisor (as well as comparisonswith previous months records) this was measured as total numberof working days required to manufacture a typical productconfiguration once it entered the shop.

Customer Lead-time Comparedto Industry Average(percent)

Ratio of the firm’s customer lead time to the industry average asdetermined by a marketing representative (since they were closestto this data)

Note:Size was measured as the number of employees within the facility. Industry classification was done using effectscoding to categorize a plant as either in an industry or not in an industry. Therefore 7 variables are used to represent the8 industries. [Plants in the 8th industry (automotive) are coded as a21 in all categories]. The results obtained using effectscoding and dummy variable coding are the same, in terms of variance explained. However, Cohen and Cohen (1983) notethat effects coding is most appropriate when you wish to compare one group (industry) with the entire set of groups, ratherthan a single reference group, as is facilitated with dummy variable coding.


The one variable with the greatest amount of variation was customer lead time. This wasbecause the “average lead time” was often a function of multiple variables includingcapacity, specific customers, etc. For 11 of the sites visited, customer orders on the shippingdock were scanned and the time from when the order was placed to the date of deliveryrecorded. Using a t-test, it was found that no significant difference existed (p5 0.40) betweenthe average of the five customer order lead times and the average lead time described by therespondent(s). This provided some assurance that the individuals interviewed were providingreasonably good estimates of their customer lead times.

Despite the potential presence of measurement error in our data, we believe that our resultsare nevertheless valid. The presence of measurement error is a common assumption in allpublished research in Operations Management. For instance, a review of top level journalsin the field ofOM from 1990 to 1995 reveals that every important study contains some degreeof measurement error and some degree of sampling error (Malhotra and Grover 1998).However, appropriate steps through the use of multiple respondents, secondary data sources,and comparison of our data to similar data collected in another study reduces the possibilityof measurement error.

3.3. Methods

The statistical analysis was carried out in two stages. In the first stages the data set wasstratified into union and nonunion firms. Independent t-tests were performed on the variablesproposed in the hypotheses. T-tests were performed first because this simple test produceseasily interpreted results regarding the differences between union and nonunion firms on aspecific variable, assuming no other moderating variables are present.

However, it is naive to think that all differences between union and nonunion firms(especially on items that relate to quality, lead time and product mix) are due solely to thepresence of an organized workforce. Two other important variables were therefore intro-duced in the subsequent analysis: plant size and industry.

In the second stage, variables for which the t-test indicated that union and nonunion firmswere indeed different were examined using step-wise regression. This analysis sought toassess whether the effect was due to the presence of a union, or if other variables explained

TABLE 4

Performance Measures—Sample Data

Variable MeanStandardDeviation

StandardError

Percent of MTO Products 22.8 36.87 5.21Percent of ATO Products 49.54 43.2 6.11Percent of MTS Products 27.66 33.78 4.78Number of Assemblies 30.82 63.3 8.95Percent of Assemblies 35.56 75.53 10.68In-house Percent Capacity Utilization 73.64 23.68 3.35Percent Labor Slack 9.2 8.82 1.25Percent Downtime 4.48 .737 1.32Number of Routings 9.36 26.52 3.75Distance Traveled (feet) 1376.84 1835.52 259.55Queue Hours 91.17 105.08 15.84Quality, Percent Rejects 5.21 6.97 0.99Customer Lead Time (days) 74.22 85.82 12.14Expediting Lead Time (days) 22.42 38.51 5.45Manufacturing Lead Time (days) 17.43 17.35 2.45Customer Lead Time Compared to

Industry Average (percent)64.45 81.95 11.59


some or all of the differences. To ensure that the union variable was capturing as much of thevariance in performance outcomes as possible, the first step was to regress the union variableon the variable of interest. The second step involved entering plant size into the model. Thethird step involved adding industry into the model. Industry was entered into the model lastbecause the effects coding requires seven variables, which reduces statistical power. In otherwords, if industry is introduced earlier into the regression model, it becomes difficult todetermine the effect of other variables on performance outcomes.

4. RESULTS

4.1. Sample Characteristics

Initially, the sample was stratified to identify the number and location of the union andnonunion firms. As shown in Table 1, union firms were predominant in the Northeastern andMidwest states, as well as in Canada.

In assessing the extent of unionization across industries (Table 2), it appears that industryand geographic origin are important. The observations apparent in Table 2 are supported bya regression of industry on unionization (Table 5). Industry accounts for a significant portionof the variance in the union variable (R squared5 0.415 and p5 0.001), and specificindustries such as aerospace, textiles, and consumer products differ significantly from thegroup as a whole (see Table 5). Similar results are observed in Table 6, when regressingindustry on plant size (R squared5 0.382 and p5 0.003). Finally, there is no relationshipbetween union and plant size (correlation5 0.011 and p5 0.938).

The preliminary results support testing for union, plant, and industry effects. Industry andgeographic origin appear to be important variables that affect unionization. The results

TABLE 5

Regression of Industry on Union

Variable CoefficientStandard

ErrorStandard

Coefficient T P

Constant 0.470 0.071 0.000 6.66 ,0.000Textile 20.470 0.193 20.354 22.44 0.019Tel-communication 0.075 0.129 0.076 0.582 0.564Industrial Products 20.337 0.116 20.373 22.89 0.006Furniture 0.280 0.193 0.211 1.451 0.154Consumer Products 20.470 0.263 20.299 21.79 0.081Computers 0.196 0.163 0.166 1.21 0.234Aerospace 0.530 0.175 0.425 3.02 0.004

TABLE 6

Regression of Industry on Plant Size

Variable CoefficientStandard

ErrorStandard

Coefficient T P

Constant 258.115 45.128 0.000 5.72 ,0.000Textile 379.385 123.259 0.459 3.078 0.004Tel-communication 213.206 82.587 20.022 20.160 0.874Industrial Products 2209.935 74.464 20.374 22.82 0.007Furniture 229.885 123.259 0.278 1.87 0.069Consumer Products 2214.115 168.372 20.219 21.27 0.210Computers 150.218 103.959 0.204 1.445 0.156Aerospace 2124.115 112.079 20.160 21.11 0.274


suggest that unionized firms are more likely to be located in specific industries and geo-graphic locations. However, it must be emphasized that the relationships between industryand unionization do not account for all of the variance, and many of the represented industrieshave both union and nonunion members.

4.2. Hypothesis Testing

The discussion will focus on four sets of variables corresponding to the four hypotheses.The types of products made by the firms (MTO/ATO//MTS), the proportion of assembliesproduced in-house, shop floor variables, and cycle time performance. As noted earlier, eachhypothesis is tested using both a simple test of differences between union and nonunionvariables (Table 7), and a moderated step-wise regression controlling for plant size andindustry (Table 8).

4.2.1. HYPOTHESIS1. The analysis first examines the relative percentage ofMTO, MTS andATO products produced in each location. It was hypothesized that nonunion firms wouldproduce a higher proportion ofMTO andATO products in any given facility.

The results (see Tables 7 and 8) show significant differences forMTS products but not forthe ATO andMTO products. Union and nonunion firms produce approximately the same ratioof MTO and ATO products. However union firms, even when industry and plant size arecontrolled for, produce fewerMTS products. While this result fails to support Hypothesis 1,it is nevertheless of interest. Mefford (1986) noted that unionized plants have higherproductivity than similar nonunion plants while Kelly (1994) noted that the employees ofunion firms, regardless of product, are more efficient than employees in nonunion settings.Union firms may capitalize on the efficiency required forMTS products by making a smallerrange of products at higher volumes.

4.2.2. HYPOTHESIS2. The percentage and number of major assemblies produced in-housewas different for union and nonunion firms. As shown in Table 7, union firms use a

TABLE 7

Union/Nonunion Results

VariableUnionMean

NonunionMean t-Statistic Probability

Percent of MTO Products 53.4 46.5 20.56 0.58Percent of ATO Products 30.9 16.43 21.3 0.195Percent of MTS Products 15.68 37.07 2.44 0.018*Number of Assemblies 58.09 9.4 22.57 0.018*Percent of Assemblies In-house 70.36 8.21 22.78 0.011*Percent Capacity Utilization 69.96 76.54 0.96 0.342Percent Labor Slack 10.14 8.46 26.23 5.38Percent Downtime 4.91 4.13 20.495 0.623Number of Routings 15.96 4.19 21.42 0.17Distance Traveled (feet) 2026.86 866.1 22.128 0.043*Queue Hours 45.64 95.22 2.01 0.050*Quality, Percent Rejects 6.18 4.44 20.876 0.385Customer Lead Time (days) 118.41 39.5 23.22 0.004*Expediting Lead Time (days) 23.24 12.44 21.878 0.070Manufacturing Lead Time

(days) 23.57 12.61 22.2 0.035*Customer Lead Time

Compared to IndustryAverage (percent) 92.027 42.77 22.00 0.056

* 5 significant at p, .05.Bold 5 significant at p, .10.


significantly larger number of assemblies (p5 0.018) and also produce a significantly greatershare of them in-house (p5 0.011). These results are supported by the subsequent regressionanalysis as well (Table 8).

The production of a larger number of assemblies may mean that union firms maintain theirflexibility by using simple, narrow jobs to assemble modules, rather than fully customized(hence more complex) products. The significantly greater percentage of assemblies madein-house is consistent with notion of union resistance to outsourcing, thus supportingHypothesis 2.

4.2.3. HYPOTHESIS 3. There is no statistical difference between the level of union andnonunion firms’ capacity utilization, routings, percent downtime, percent labor slack, anddefects. Additionally the regression analysis suggests that the longer distances traveled inunion plants are a result of the industries that tend to be unionized, not the existence of theunion per se. However, parts spend more time (p5 0.05) in queues at union firms, a resultthat is confirmed by the lack of either plant size or industry effects in the regression.Hypothesis 3 is not supported, suggesting that union and nonunion firms in the sample mayhave the same level of shop floor performance.

4.2.4. HYPOTHESIS 4. The analysis confirmed that union companies had significantlylonger lead times. Customer lead times (including backlogs) were almost three times as longfor union firms as nonunion firms. However, this result is due to industry and plant size, notthe existence of a union.

On the other hand, both manufacturing lead times and expediting lead times were bothsignificantly longer for union firms. These differences were explained solely by the existenceof a union, not by plant size or industry. Lead time compared to industry average alreadycontrols for industry, so the t-test was the only statistical analysis reported. Compared to theindustry lead-time average, union firm lead times were close to the average, while nonunion

TABLE 8

Stepwise Regression of Union, Plant Size, and Industry Variables

DependentVariable

Step 1 Union Step 2 Plant Size Step 3 Industry

R squared PChange inR squared

P formodel P for Beta

Change inR squared

P formodel

Percent MTS 0.101 0.025 10.071 0.009 0.036 10.22 0.008**Number of

Assemblies 0.149 0.006 10.002 0.022 0.761 10.288 0.003**Percent of

Assembliesin House 0.170 0.003 10.003 0.012 0.715 10.165 0.037**

Queue Hours 0.069 0.066 0.0 0.188 0.954 10.198 0.144Distance

Traveled 0.101 0.025 10.054 0.019 0.088 10.167 0.051*Customer

Lead Time 0.213 0.001 10.001 0.003 0.767 10.423 <0.000*Manufacturing

Lead Time 0.100 0.025 10.002 0.784 0.080 10.177 0.117Expediting

Lead Time 0.070 0.063 0.070 0.180 0.965 10.206 0.122

Note: Step 2 shows the increase in R2 that occurs when the plant size variable is included, as well as the p valuefor the standardized Beta associated with plant size. Step 3 shows the increase in R2 that occurs when the sevenindustry variables are included, as well as the p value for the regression model. Significant p values are in bold type.

* Overall model significant but Beta coefficient for union is not significant. ** Overall model is significant andBeta coefficient for union is significant.


firms had significantly lower lead times compared to their industry (i.e., lead times were 43%of average). There was a significant difference between these two industry-adjusted lead-timescores (p5 0.099) indicating that the nonunion firms in the sample had a competitiveadvantage in terms of lead time while the union firms in the sample did not.

These results suggest that union firms do need more time to get aMTO product through thefactory, and support Hypothesis 4. These results suggest that reengineering initiatives may bemore easily deployed within nonunion environments and may result in a significant advan-tage in terms of competitive customer lead times. Since reduced lead times are associatedwith lower inventories, lower capital expenses, and reduced costs (Handfield 1995), it is verypossible that for firms in the same industry, nonunion firms are enjoying a significantcompetitive advantage over their unionized competitors.

To summarize, the data provide evidence to support the overall proposition that union andnonunion firms differ, and that union firms may have less organizational flexibility and beless open to the adoption of organizational innovations. The results also support some of theliterature on union environmental attributes. The union firms produced more assembliesin-house and employed outsourcing to a lower extent. Union firms have longer lead times andtake longer to manufacture a product (a sign of less shop floor flexibility).

5. DISCUSSION

The data analysis reinforces the importance of union workforce issues in developing anoperations strategy. Union firms had longer lead times and a smaller number ofMTS productfamilies, made more assemblies in house, and were in industries where lead times werelonger than the industries where the nonunion firms competed.

Such differences in union and nonunion environments have a number of potential impli-cations for researchers and practitioners. Operations Management research becomes morevaluable when it is useful to managers. Studies on the effect of operations strategies onperformance that ignore the existence of a union can only be descriptive. In order for researchto be prescriptive, it must explicitly recognize that a significant number of companies mustwork within the set of constraints imposed by a unionized workforce. Managers at thesecompanies will be unwilling to explore the adoption of innovations if they believe thatadoption success is contingent on a human resource policy that is at odds with the nature oftheir existing workforce. Worse yet, they may simply choose to ignore the outcomes of suchresearch.

The findings also have serious implications for operation managers. Even when controllingfor industry, union firms had longer manufacturing and expediting lead times. Thereforethese firms are at a competitive disadvantage when time is a competitive priority (as it shouldbe for all theMTO firms, the focus of the research). This disadvantage is most apparent whenexamining the data on lead time as compared to the industry average. The union firms havelead times that are generally about average while the nonunion firms have lead times that arehalf those of the average competitor. This leads us to believe that nonunion firms in thesample, regardless of industry, have competitive customer cycle times, while union firms, onaverage, do not. This result suggests that in a predominately union industry, a nonunion firmmay enjoy a significant competitive advantage; conversely union firms in a predominatelynonunion industry may be at a serious disadvantage.

However, other results from our study and the recent literature on union-managementrelations suggests that unionized firms may not always experience a competitive disadvan-tage. Union firms can differentiate themselves on dimensions other than time. The datasuggest that union and nonunion firms generally do not differ on shop floor performance(Hypothesis 3). Therefore union firms can compete and can successfully differentiatethemselves on quality and effectiveness, even if cycle times may be somewhat longer. Ourresults also suggest that union firms may be able to compete by being more efficient for


standard products, based on the fact that union firms produced fewerMTS products at highervolumes than nonunion firms. This result corroborates studies by Mefford (1986) and Kelly(1990), who found that union firms are more efficient than nonunion firms. One can surmisethat union firms might also be able to compete on cost, at least in industries where economiesof scale are prevalent and labor costs are not a significant component of total costs. On thewhole, the findings on quality and efficiency suggest that union firms may be able to gain acompetitive advantage by focusing on a smaller variety of high volume goods that are soldbased on their quality and cost.

The aforementioned strategy has merit in an environment where there is a potential nichefor a firm that competes on cost and quality. However, recent literature suggests thatflexibility (i.e., DeMeyer, Jinichiro, Jeffrey, and Ferdows 1989) and time (i.e., Handfield1995) are important competitive dimensions. Although the data analysis suggests that unionfirms are at a disadvantage on these competitive dimensions, there may be instances wheremanagement and the union can form a partnership to jointly improve performance.

There is evidence that some unionized firms have begun to move away from traditionalrelationships that emphasized narrow jobs, promotion by seniority, and adversarial relations.Recent discussions between management and union representatives from theAFL-CIO, labor-ers, machinists, need trades, and steelworkers unions are resulting in partnerships that resultin improved productivity (Bernstein 1997). Prior research by Jackson et al. (1989) and Woodand Albanese (1995) show that many union firms are moving away from human resourcepolicies such as promotion by seniority, narrow job demarcations, etc.

Traditional, adversarial, union management relationships dictate that each side bargains towin as much of the proverbial pie as possible. However this “traditional” model has been ina state of flux for a number of years. Kochan, McKersie, and Cappelli (1984) note that basicemployment relations have changed over time. This is a finding echoed by a number ofauthors such as Guest (1987), who notes that trade unions have reduced pressure onmanagement, which should lower managerial need to control the workforce.

Recent economic history provides clues as to why this may be the case. In many industrialsettings, American companies, who for years enjoyed large profits, suddenly found them-selves under attack. Negotiations with the union used to be over who got how much of the“pie.” Suddenly there was a chance that there would be no pie! Some of the most contentiousrelationships became much closer under the threat of job losses on both sides of thebargaining table. (For instance, consider the fact that the UAW president once served on theboard of Chrysler!)

The net result of economic pressure created by true global competition is that unions havebeen forced to collaborate with management. Both Thomas (1991) and Smith (1992) notethat the political process (negotiation) often affects workplace outcomes. Smith found thatsome unions were able to negotiate control (or at least a voice) in technological changes andreengineering initiatives. These changes indicate that it is possible to negotiate more flexiblework practices, which could enable union firms to compete on time and or flexibility.

Management in a unionized organization that develops a partnership with their union may beable negotiate work practices that enable increased flexibility and decreased lead times. This mayeven create a competitive advantage, especially in a highly unionized industry. This is not toimply that such a change of attitude is easily achieved; it is not! However, it is possible for aunionized organization to reengineer their workplace and improve performance.

6. CONCLUSIONS

The type of relationship that management has with its union may have a major effect onthe success or failure of strategic action programs, especially the adoption of innovations thatmay make manufacturing more competitive. Because of the significant effect of a unionizedworkforce on operations strategy deployment, we believe that future studies of operations


strategy should be moderated with at least a union/nonunion dichotomous variable. In a testof MTO/ATO/MTS firms, the union/nonunion dichotomy showed significant differences acrossimportant variables, even when controlling for industry and plant size. The findings suggestthat union firms may compete in a different manner than nonunion firms, hence they may bemore successful when deploying reengineering-driven operations strategies.

Our results confirm the need for a broader set of variables addressing the effect ofmanagement–labor relationships on performance. At the very least, future studies of thedegree of managerial discretion over workforce decisions in operations should address thetype of union management relationship, the number of unions present at the sight, and thepresence or absence of a seniority system. Firms with poor labor relationships, restrictiveunion rules, and restrictions on outsourcing may be limited in terms of the types of operationsstrategies that can be successfully deployed.

This research suggests that unionized firms can gain a competitive advantage regardless ofindustry by either capitalizing on their efficiency or by working to change relationships with theunion to allow more flexible work practices. Although our results suggest that unionized firmsmay be at a competitive disadvantage in terms of cycle time, this problem can be mitigated incases when management makes an effort to improve their relationship with the union.

Unions still represent a significant portion of the labor force. A union can, in some situations,severely limit managerial discretion about who does what, as well as where parts are produced.However, researchers such as Mefford (1986) and Kelly (1994) have shown that unions can havea positive effect. Research that explicitly considers the existence of unions may identify mana-gerial innovations that are more beneficial to union firms, as well as innovations that are untenablein union environments. In failing to include this variable, Operations Management research willbe of far less value to practitioners because the descriptions we provide cannot be translated intoprescriptive plans of action for unionized firms.

Appendix: Sampling Approach

Deliberate Sampling for Heterogeneity

This approach, first described by Cook and Campbell (1979), involves defining target classes of persons, settings,and times and ensuring that a wide range of instances from within each class are represented. Deliberate samplingis generally considered preferable to random sampling, since researchers are generally unlikely to be able to begranted access to a reasonable number of randomly selected organizations (Cook and Campbell 1979). Whiledeliberate sampling does not require random selection of sites, one can only deduce whether the effect has or hasnot been obtained across the particular variety of settings with the specified attributes. As such, it is advantageousto obtain opportunistic samples that differ as widely as possible from one another on the variables of interest. Thedescription of the sampling procedures in Section 3.2 is therefore intended to help future researchers in replicatingthe study, in order to further advance the generalizability of our results.

An important goal of this study is to encourage further replication of the study, which is critical to enhancing theexternal validity of any field study. When the results from deliberate samples are replicated in different times andsettings using the same range of specified sampling variables, the external validity of the results are greatlyenhanced. In seeking to replicate the sample, researchers should set out to sample a set of organizations with asimilar distribution and range of industries, geographic locations, and union status, and compare the same set ofvariables. While it would clearly be impossible to find a set of organizations with exactly the same variation acrossthese variables, a sample with a reasonably similar distribution would be comparable. It is important to note that nosample is ever completely generalizable, because of the time and context that is specific to every data collectioneffort. It is only by replicating studies in different contexts and in different samples, with the same results, that apattern of outcomes becomes generalizable.

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Mark Pagell is Assistant Professor of Management at Kansas State University. He received hisdoctorate in operations management from Michigan State University in 1996. He has published anumber of articles in theJournal of Operations Managementand the International Journal ofPurchasing and Materials Management. His research involves studies of skilling/deskilling humanresource strategies on manufacturing automation performance, and the alignment of purchasing andoperations strategy.

Rob Handfield is the Bank of America University Distinguished Professor of Supply ChainManagement at North Carolina State University. He received his doctorate in operations managementfrom the University of North Carolina at Chapel Hill in 1990. He has published articles in theJournalof Operations Management, IEEE Transactions on Engineering Management, Journal of ProductInnovation Management, and is co-author of the bookIntroduction to Supply Chain Management. Hisresearch spans the areas of purchasing and supply chain management, environmental management, andglobal supplier development.


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