Performance implications of assembly work teams

Journal of Operations Management 22 (2004) 387–412

Performance implications of assembly work teams John K. McCreery a,∗ , Lee J. Krajewski b,1 , G. Keong Leong c,2 , Peter T. Ward d,3 a

b

College of Management, North Carolina State University, Raleigh, NC 27695-7229, USA College of Business Administration, University of Notre Dame, Notre Dame, IN 46530-0399, USA c College of Business, University of Nevada, Las Vegas, NV 89154, USA d Fisher College of Business, The Ohio State University, Columbus, OH 43210, USA Available online 17 June 2004

Abstract This paper explores the role of selected workforce management practices in developing mix, volume, and product flexibility. Using a model of a manually-paced assembly area, we examine three workforce management practices—the configuration of work teams, the extent of cross training, and the deployment of workers—that have the potential to enhance the level of manufacturing flexibility. We examine the effects of these practices at the level of the individual operation and individual worker, with the goal of maximizing overall system performance in a variety of manufacturing environments. Our results indicate that the value of workforce flexibility is contingent upon characteristics of the operating environment. Environments having high levels of product variety call for the use of a larger number of parallel work teams, while environments with highly complex tasks tend to require a smaller number of parallel teams. Further, the value of cross training and worker task sharing is diminished as work tasks become more complex, due to learning and forgetting effects on the workforce. The overall implication is that more worker flexibility does not always yield improved system performance. © 2004 Elsevier B.V. All rights reserved. Keywords: Workforce flexibility; Workforce management; Work teams; Learning

Manufacturing flexibility is increasingly viewed as a source of sustainable competitive advantage for the firm. Support for this view is found in the practitioner literature (Hayes and Pisano, 1994), as well as in case study research (Upton, 1994) and broad-based survey work (De Meyer et al., 1989; Ferdows and DeMeyer, ∗ Corresponding author. Tel.: +1 919 515 4093; fax: +1 919 515 6943. E-mail addresses: [email protected] (J.K. McCreery), [email protected] (L.J. Krajewski), [email protected] (G.K. Leong), [email protected] (P.T. Ward). 1 Tel.: +1 219 631 9063. 2 Tel.: +1 702 895 1762. 3 Tel.: +1 614 292 5294.

1990). While there are numerous dimensions of manufacturing flexibility (Gerwin, 1987, Sethi and Sethi, 1990), of interest in this research are the plant-based flexibilities of mix—the ability to produce a number of different products at the same point in time, volume—the ability to change the aggregate amount of production volume, and product—the ability to handle additions to and deletions from the product mix over time as older products are retired and new products are introduced. The role of workforce management practices in developing mix, volume, and product flexibility is central to developing competitive capabilities. In this research we focus on two important decision areas: team creation, which determines the number of work

0272-6963/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jom.2004.05.004

388

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

teams, and task sharing, which determines the degree of cross training of the workforce and how workers should be deployed. We examine the effects of workforce management practices at the level of the individual operation and individual worker with the goal of maximizing overall system performance in a variety of manufacturing environments.

tasks, have an impact on the ability of the workforce to learn and retain assembly-related skills. While high levels of variety may require workers to be flexibly configured, trained, and deployed to eliminate load imbalances, high levels of complexity may degrade worker proficiency as workers roam from task to task. 1.2. Workforce flexibility decisions

1. Background We focus our study of workforce flexibility on assembly line processes in environments where learning and forgetting takes place. The study of workforce flexibility in assembly areas is a managerially significant issue. Our study is motivated by visits to several companies that utilized six assembly areas with batch processing, where we conducted detailed interviews with their managers. The types of products assembled in these firms include industrial motors and process control sensors, standard and customized air conditioning units, multi-parameter patient monitors, paper processing measuring equipment, and electromechanical blood pressure machines. The plant visits and interviews with managers helped to delineate two types of concerns, categorized as either environmental conditions or workforce flexibility decisions. 1.1. Environmental conditions The first set of managerial concerns were centered on characteristics of the assembly area that are somewhat exogenous to manufacturing, since they are beyond the direct control and manipulation of manufacturing management, at least in the short run. One concern is the amount of product variety that the assembly area must deal with, due to end-user demands for product customization. If there is a large amount of product variety the diversity of tasks the workforce performs will increase, with a corresponding increase in the potential for workload fluctuations and imbalances. Another key concern is the inherent difficulty and complexity of the manufacturing tasks that workers must perform. Based on the design and nature of the products, worker tasks may vary from being very simple to extremely difficult to perform in an efficient manner. The complexity of the tasks workers perform, as well as the variety of those different

The second set of concerns important to management is related to creating a flexible workforce, and subsequently using that flexibility to advantage when necessary. This set of concerns focuses on decision-making, since management has control over how these issues are operationalized in the assembly area. A key issue related to workforce flexibility is that of cross training. The managers revealed that they were uncertain as to how many skills they should train their workforces for. The approaches to cross training that we observed were ad hoc in nature, without any guidelines for what is best or most appropriate in a given assembly environment. All managers wanted to maximize the product throughput of their respective assembly operations but did not have a good basis for making the necessary workforce cross training decisions. Another issue tied to the creation of a flexible workforce is the design of work teams. While the managers were certainly aware of the potential benefits of teaming, they were unsure of the operational implications of work teams. They questioned whether work teams should be used in their assembly areas. And if teams are to be utilized, how should they be configured? A third issue expressed by the managers was how to deploy workers to take advantage of the flexibility inherent in a multi-skilled, team-based workforce. In particular, what methods are appropriate for allowing workers to share workloads and to adapt to changing load conditions in the assembly area? And, how should workers be dynamically assigned to tasks, given that shop conditions are in a constant state of flux? This study incorporates the key concerns of our industrial contacts while maintaining ties to the relevant bodies of academic literature. Based on iterative input from the industry contacts, as well as reliance upon prior relevant literature, we developed a model to study the important issues posed above. In particular, we examine the impact of teaming, cross training, and worker deployment policies on assembly area

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

389

performance, while recognizing that performance will also be a function of individual worker learning and forgetting.

2. Problem setting Workforce flexibility is made operational through the dynamic assignment of workers to a variety of tasks within a prescribed work area. The DRC literature (Treleven, 1989) has examined a number of issues related to worker deployment policies and job prioritization. These issues have been explored in a variety of plant settings and have included a wide range of experimental factors. Germane to our study, Bobrowski and Park (1993) investigated the effects of worker cross training and deployment policies where workers were not equally proficient on all tasks, and Malhotra et al. (1993) studied the effects of cross training, worker deployment, and worker learning on plant performance. These studies suggest that there are diminishing returns as the extent of cross training is increased. More recently, McCreery and Krajewski (1999) examined the benefits of cross training and worker deployment policy choice in a set of flow shop environments where worker learning and forgetting exist. They found that cross training and deployment decisions depended on the nature of the work environment, as characterized by product variety and task complexity. No DRC study has explicitly inquired into the operational benefits of workforce teaming in environments with individual worker learning and forgetting, particularly when the production task varies in terms of variety and complexity. 2.1. Assembly process Workforce management and flexibility issues are studied in the context of an assembly area for a moderate volume discrete-parts manufacturer. This environment is typified by moderate amounts of product mix, with assembly task times not tightly balanced on a product-by-product basis as is often found in high volume, repetitive line flow manufacturing. The assembly department utilized in this research has one or more lines laid out in a U-shaped configuration, as seen in Figs. 1–4. There are twelve distinct

Fig. 1. Team and worker configurations—single team design. Worker: W; task: circled number.

tasks, or processes, to be performed for any product. Each task is said to take place at a station. Individual manufacturing orders, which are batch quantities of a given product, are released to the assembly area based on a finished goods schedule. Setup times for these orders are assumed to be negligible. As is typical in assembly processes, the individual units in a batch order, or lot, are processed and then move down the assembly line separately. Because the focus of this research is to examine issues of worker flexibility, material shortages are assumed to never occur. Also, the assembly stations are designed so that, at most, two workers can perform the same task at the same instant in time. Depending on how workers are deployed in the assembly area, this flexible station design will allow two workers to work side-by-side at a station, with each person working on a distinct unit of product. Given the fact that assembly areas for discrete product batch manufacturers are often low in capital intensity, this design characteristic of the assembly area is not unrealistic. A benefit of having this type of station-related flexibility is that workers will be able to assist each other to alleviate temporary load imbalances. This design feature also focuses attention

390

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

Fig. 2. Team and worker configurations—two team design. Worker: W; task: circled number; shaded area: “coverage training” tasks for associated worker W.

on workforce management issues, since workers will tend to be the primary scarce resource. So, within the bounds that each work station can accommodate up to two workers, equipment and work station utilization

will not be a significant factor with regard to system performance. For purposes of this research, the assembly area operates on an 8 h shift, single shift per day basis. No

Fig. 3. Team and worker configurations—three team design. Worker: W; task: circled number; shaded area: “coverage training” tasks for associated worker W.

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

391

Fig. 4. Team and worker configurations—four team design. Worker: W; task: circled number; shaded area: “coverage training” tasks for associated worker W.

preemption of units is allowed, and each shift picks up exactly where the last one ended. 2.2. Research model overview An overview of the research domain is presented in Fig. 5. As indicated in the figure, two dimensions of environmental conditions, namely product variety (PV) and task complexity (TC), are relevant to this

study. Jointly, these dimensions create distinct product line environments, or profiles. PV and TC work together to place differing demands on the workforce, both in terms of creating shop load imbalances and in regulating the ability of the workforce to learn and retain that learning. Also shown in Fig. 5 are the decisions that management makes in the area of workforce flexibility, in terms of configuring assembly workers into subteams, choosing how extensively to cross train

392

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

Fig. 5. Overview of research domain.

the workers, and deciding how to allow workers to assist each other on a real-time basis by sharing tasks. In this research we use the environmental conditions to define realistic operating conditions, or scenarios, for the assembly area. For each scenario, management then makes its workforce decisions, and the impact on worker learning, worker forgetting, and system performance is assessed. The focus of the research is on the management decisions, while the scenarios provide the settings within which the decisions take place.

2.3. Product variety The four parameters comprising the product variety factor, as well as the high and low settings for each parameter, are shown in Table 1. The choice of these particular parameters for the product variety factor is guided by definitions suggested by Gerwin (1987, 1993), Slack (1987), Sethi and Sethi (1990), Cox (1989), and Upton (1994), along with consultation and iteration with our industrial contacts. All of

Table 1 Parameters for product variety and task complexity exogenous factors Characteristic

Parameters

Low setting

High setting

Product variety

Number of products in product line Task time variability within product Variability in product routings Rate of product turnover

Few (5) Small (±10%) Low (±10%) Slow (25%/year)

Many (10) Large (±20%) High (±20%) Rapid (50%/year)

Task complexity

Learning rate (r) Amount of learning possible (in multiples of task standard time) Predominant type of learning Speed of forgetting (Ftotal )

Rapid (70%) Small (home = 1; other = 2)

Slow (80%) Large (home = 2; other = 3)

Process Slow (total = 60 days)

Product Fast (total = 30 days)

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

these parameter values are set in unison for either the low or high settings of product variety. The first parameter in Table 1 is number of different products within a product line. When product variety is low, there are five distinct products in the product line. When product variety is high, the product line has ten products in it. These values are similar to those used in Smunt (1986). Each product within a product line has a standard unit processing time ranging from 50 to 70 min, which is the total amount of time that a unit of product is worked on in the assembly area, assuming all workers who work on the unit are fully proficient in performing the tasks. This total unit processing time is spread across all the tasks that must be performed on a product as it moves through the assembly area. For twelve tasks, this translates to an average task standard processing time of 5.0 ± 0.8 min. Boothroyd (1992) offers empirical evidence of times for discrete assembly processes that range from 30 s to 2 h, with 5 min per task being a reasonable value. The second parameter in Table 1 is the variation around each product’s average task time. In a perfectly balanced line, each task’s standard time is equal. However, in manually paced, moderate volume assembly lines such as those of concern in our study, there is typically some variability seen in a product’s standard task times. For the low setting of product variety there can be deviations of up to 10% for the standard processing times of any task. For the high setting of product variety the deviations can be up to 20%. In discussions with manufacturers, we have found task time variations of ±10 to ±20% to be representative of their product lines. The third parameter is the probability that a task for a product may be skipped over, which means that the standard processing time for that task is 0 min. This parameter, called variability in product routing, captures the degree of different product features and options in the product mix. For different products within a product line, it is possible that some/all of these products will not have a complete set of features built into them. A task processing time of 0 min is equivalent to a product line feature that is not included in a particular product. Actual standard processing times for tasks are determined by using the values for both the second and third parameters in Table 1, using a randomized allocation scheme for parceling out a unit’s total pro-

393

cessing time (xtotal ) to all of its associated tasks, starting with upstream tasks and proceeding downstream. For each task, it is first determined whether that task will receive a processing time of zero, by sampling from a uniform [0, 1] distribution and comparing the sample to the probability that a task will receive a zero processing time. For low product variety, if the random number is less than or equal to 0.10 (i.e., 10%), this task receives a zero processing time. For high product variety, if the random number is less than or equal to 0.20 (i.e., 20%) the task’s processing time is set to zero. If the task is not skipped over by receiving a zero processing time, its standard time is randomly sampled from a uniform distribution centered around the value (xtotal /12). For a product line with low product variety, the sample is taken from the uniform distribution U[(0.90 × (xtotal /12)), (1.10 × (xtotal /12))]. If the setting of product variety is high, the sample comes from U[(0.80 × (xtotal /12)), (1.20 × (xtotal /12))]. All tasks go through this same procedure to determine their times. Because the procedure for determining task times uses random sampling, the sum of the task times may not be equal to the total processing time for a product. If the times are not equal, all task times are adjusted upward or downward proportionally so their sum will now equal the total processing time. The last parameter within the product variety factor is the rate of product turnover. Over time, the portfolio of products will change due to old product retirements and new product introductions. In our study, when product variety is low the rate of product turnover is 25% per year, while at the high product variety setting the product line turns over at a rate of 50% per year. These rates of product turnover are based on recent practitioner accounts of the accelerating pace of new product introduction, and are also used in Smunt (1986). 2.4. Task complexity Our interest in the task complexity factor is primarily in its effects on worker learning and forgetting. Task complexity as modeled here reflects findings in the learning/forgetting literature that relate to the issue of workforce flexibility, and is consistent with the insights and views of our industrial contacts.

394

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

2.4.1. Basic learning model The most common model of production learning in the literature is the log-linear model (Yelle, 1979). The model states that production time (or cost) for a unit of product decreases by a fixed percentage each time cumulative volume doubles. However, as noted in Dutton and Thomas (1984) and Dar-El (2000), there is a large amount of variability in learning rates across different industries, products, and processes, and even for similar products and processes within the same industry. Studies by Levy (1965), Muth (1986), Adler (1990), and Adler and Clark (1991) indicate that learning is an inherently complicated and iterative process, with activities other than cumulative volume influencing the rate and progression of learning. Our model for worker learning and forgetting has as its foundation the log-linear model described by Yelle (1979). With the basic log-linear model, learning is assumed to continue indefinitely as a function of cumulative production volume, resulting in a marginal unit processing time that ultimately approaches zero. To overcome the unrealistic nature of this assumption we base our learning and forgetting model on the DeJong (1957) learning model, shown in Eq. (1). The DeJong model allows for a plateau at some non-zero time as cumulative volume increases. For an individual worker performing a task, the minimum task time possible is defined as the task’s standard time. We define this standard time as the amount of time an initially trained and fully proficient worker (made fully proficient through experience at the task in question) needs to perform the task. y(z) = a × [c + (1 − c)zb ]

(1)

where y(z) is the time of the zth unit, a the time to produce the first unit, z the cumulative number of units, b the learning index = log (r)/log (2), r is the learning rate, c the incompressible factor, which is the proportion of the task that cannot be eliminated regardless of the cumulative number of units produced, (0 ≤ c ≤ 1) and a × c the standard time. This definition of standard time incorporates two simplifying assumptions. First, the workforce is assumed to be homogeneous with respect to their ability to attain standard times for tasks. While standard time variance for workers performing similar tasks may exist in actual production settings (Hancock and Bayha, 1992), this assumption allows us to more readily iso-

late the effects of workforce management practices on assembly area performance in our model. Second, non-productive time due to worker allowances (e.g., worker fatigue, personal needs) is not included in this definition of standard time. Including an allowance for non-productive time would have the effect of inflating a task’s standard time. However, allowance time is often observed to be a relatively small percentage of total time in production settings (Niebel, 1992). Also, since workers are assumed to be homogeneous with respect to their abilities to attain full proficiency and to meet standard times, it is reasonable to further assume that, if they were to require allowance time, the proportion of allowance time to total time would be constant across workers. Therefore, the net effect of incorporating allowance time in our model would be negligible. 2.4.2. Learning and forgetting model A number of empirical studies have examined the determinants of forgetting in a manufacturing environment. Based on personal observation at a number of manufacturing plants, Carlson and Rowe (1976) noted that forgetting appears to be a function of the elapsed time of worker interruption and the amount learned prior to interruption. Globerson et al. (1989) performed a laboratory study in which they found that both initial and total forgetting increase exponentially over time of interruption, with the rate of forgetting being more rapid at smaller interruption intervals and tapering off as the interruption time continues to increase. Bailey (1989) defined two different tasks: continuous control tasks that emphasize repetitive movement, and procedural tasks that call for discrete, non routine actions. For the continuous control task the amount of forgetting was negligible, regardless of the interruption interval or the amount learned prior to the interruption. In contrast, forgetting for the procedural task is a function of the learning prior to interruption and the elapsed time of the interruption. Hewitt et al. (1992) essentially supported the findings of Bailey. More recently, Shafer et al. (2001) captured worker forgetting as a function of task tenure, which, in the shop environment studied, affects the worker’s amount learned prior to interruption and the interruption interval. We have modified the basic DeJong learning model to accommodate instances of forgetting in accordance with our research design, and to agree with the em-

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

pirical evidence noted in the literature. The modified model is shown in Eqs. (2)–(5). kn = tn − (tn−1 + y(zn−1 ))

(2)

 if kn = 0  0 fn = h{(kn /Ftot )×[y(1) − y(zn−1 + 1)]} if 0 < kn < Ftot   zn−1 if kn ≥ Ftot

(3)

zn = zn−1 + 1 − fn

(4)

y(zn ) = a × [c + (1 − c)zbn ]

(5)

y(zn ) the time to produce the nth unit, a the time to produce the first unit = y(1), b the learning index = log (r)/log (2), where r is the learning rate, c the incompressible factor, which is the proportion of the task that cannot be eliminated regardless of the cumulative number of units produced (0 < c ≤ 1), a × c the standard time for the product/task, zn the effective unit count for the nth unit, net of forgetting, n the actual unit count (n = 1, 2, 3, . . . ), kn the interruption time between units (n) and (n − 1), fn the number of units backtracked on learning curve due to break in service time kn , h(·) a function that transforms a processing time y into an effective unit count z, according to the DeJong relationship: y = a × [c + (1 − c) zb ], Ftotal the interruption time at which complete forgetting occurs, tn the time at which processing of the nth unit begins, tn − (tn−1 + y(zn−1 )) the interruption time between units, y(1) − y(zn−1 + 1) the amount learned to date on the product/task. This model is operationalized through a set of four parameters for learning and forgetting, shown in Table 1. They are: the learning rate, the amount of learning possible, the predominant type of learning, and the speed of forgetting. Values for the learning rate parameter, r, are based on a large number of studies carried out in a variety of industries. This empirical evidence is summarized in Dutton and Thomas (1984). The second task complexity parameter in Table 1 is the amount of learning possible. This is calculated as ((1/c) − 1), where c is a task’s incompressible factor as defined in Eq. (1). The calculation yields a parameter that is a multiple of a task’s standard time. Therefore, the amount of learning possible parameter is the difference between the time it takes a worker to perform the first repetition of a task and the task’s standard time, stated in multiples

395

of that standard time. This parameter can vary, per Table 1, depending on the particular task a worker is performing. When a worker is performing a task he or she is initially hired for and assigned to, this task is designated as a “home” task. The home tasks for all workers can be seen in the worker-task assignments of Figs. 1–4. When a worker performs tasks other than those assigned per Figs. 1–4, the task is designated as an “other” task for that worker. The amount of learning possible parameter is relatively lower for home tasks than for other tasks since workers are presumed to be initially more proficient on their assigned home tasks. The increased proficiency is due to each worker’s specific skills acquired by experience, education, and training prior to working in this assembly area. The values in Table 1 are supported by Carlson and Rowe (1976) and Dar-El (2000) as acceptable for manual manufacturing activities. A further explanation of home and other tasks is done in Section 3 of this paper, in the context of the workforce teaming, cross training, and deployment decisions that management faces. The third task complexity parameter of Table 1 addresses the type of learning taking place in the assembly area. Our model assumes that the driver of learning is dependent upon the nature of the worker’s task. In general, we assume that the worker learns processes (i.e., tasks). If the process in question is relatively simple and there is little difference in the performing of that task across different products, then “process” learning is said to occur. On the other hand, if the task in question is relatively difficult and there are significant differences in the detailed performing of that task across different products, then “product” learning is said to occur. Process learning makes the assumption that the knowledge a worker gains by performing a given task is generic, and therefore transferable to any product that needs that task. As the worker performs a task on a sequence of products in a product line, the experience gained with each product reduces the processing time needed for any product. Product learning operates under the assumption that both the process and the specific products routed through a process affect worker learning. Because of this, there is a separate learning mechanism for each process–product combination the worker works on, with no learning transference across different products or processes. Task complexity determines the dominant type of learning mechanism used in

396

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

the model. Less complex tasks will induce process learning, while more complex tasks result in product learning. Support for these types of learning is provided by Globerson and Millen (1989), Engwall (1992), and Nembhard and Osothsilp (2001). In addition, our industrial contacts supported this approach to capturing the types of learning that occur in their assembly areas. Finally, the fourth parameter, speed of forgetting, has values based on the controlled experiments of Bailey (1989) and Hewitt et al. (1992) that measured the intervals at which complete forgetting occurs, for both simple and complex tasks that use motor and cognitive skills. In order to keep the notation in Eqs. (2)–(5) simple, these equations are shown without subscripting for workers, tasks, or products. For process learning, each worker will learn and forget per Eqs. (2)–(5) for every eligible task that worker is able to perform. Alternately, for product learning each worker will learn and forget per Eqs. (2)–(5) for every eligible task-product combination that worker is able to perform. The Appendix A provides a graphical representation and associated step-by-step account of how Eqs. (2)–(5) operate to dynamically track learning and forgetting each time a worker begins a task on a unit of product.

3. Management decisions The workforce flexibility decisions examined in this research are (1) the configuration of work teams; (2) the number of skills for which workers are cross trained; and (3) the dynamic deployment of the workers as reflected in the policies defining when workers can move from their work stations to assist other workers in reducing their workload. These workforce flexibility decisions are studied under conditions of varying product variety and task complexity. This research builds upon the work of McCreery and Krajewski (1999), which examined the impact of decisions (2) and (3) above, but only in a single team setting. The first workforce flexibility decision is termed the Team Creation decision, which defines the appropriate team configuration. Then, once a configuration is chosen and coverage training is achieved for the work-

force, decisions on the extent of cross-training and the method of worker deployment must be made. These latter decisions are identified as task sharing decisions. 3.1. Team creation decision There is a large body of research related to the use and potential benefits of work teams in manufacturing (see Cohen and Bailey, 1997). Many different types of teams are used in manufacturing, ranging from ad hoc problem-solving teams to permanent self-directed teams with a broad range of responsibilities (McCreery and Bloom, 2000). The team design relevant to our present research is that of multiskilling, whereby team members may assist each other with the performance of manufacturing tasks on units of product (Dunphy and Bryant, 1996). These teams are not explicitly empowered to perform human resource functions or work with suppliers or customers, although there is nothing in the design of our study that would preclude them from doing so. We use multiskilling in order to allow for the creation of parallel subsets of workers. Each parallel team of workers is configured similarly, and each team’s workers will have the capability, to varying degrees, of assisting other workers within their same team in the performance of assembly tasks. Parallel teams allow for the possibility that product may move through the assembly area more rapidly or efficiently because of the multiple parallel paths by which it may flow. While the literature on manufacturing work teams is large, there is little that specifically addresses the question of how many sets of parallel teams should be used in a manufacturing assembly area in order to improve operational performance (Dunphy and Bryant, 1996). Accordingly, our research examines this open question. 3.2. Work team configuration Figs. 1–4 show the team configuration options, prior to taking cross training into account. For the single team configuration of Fig. 1, with twelve workers and twelve tasks, each worker can be matched to a single task. The two team configuration of Fig. 2 shows that each worker (W) is matched to two tasks, because there are now two parallel assembly areas to be staffed. Increasing the number of teams to three, as in Fig. 3,

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

yields a three-to-one ratio between tasks and workers. Finally, the four team configuration of Fig. 4 results in four tasks per worker. The key issue to note here is that, for each of these configurations, workers must be able to perform a minimum number of tasks if units are to pass through the assembly areas. To illustrate, for the three team design, workers must be able to perform an average of at least three tasks if any work is to be accomplished. This type of training is fundamentally different than what is termed cross training in our study. For the three team configuration, if each worker is trained to perform three tasks, there is no sharing of tasks or workload among workers within a team. Hence, we use the term “coverage training” to describe this minimum required level of training. Coverage training, then, is the minimum amount of training required to cover all the tasks in an assembly process. These tasks that a worker is matched to because of coverage training are known as that worker’s “home tasks”. A potentially important benefit of having a large number of parallel teams is that the occurrence of a bottleneck situation for one team will have no effect on any other team’s performance. Therefore, blocking and starving of workers and stations is less likely to be a problem for the multiple team designs. However, the potential downside of having multiple teams is that, even without additional cross training, workers are asked to perform multiple tasks. Particularly when tasks are difficult, the fact that workers perform multiple tasks may result in a lower level of worker efficiency. This in turn may result in degraded system-wide performance. 3.3. Task sharing decisions Once a decision has been made on the configuration of the workforce into teams, the question of worker task sharing must be addressed. Task sharing encompasses two issues: the extent of cross training whereby workers are capable of performing tasks that may be shared, and the deployment of workers to actually share tasks with each other. 3.3.1. Worker cross training We define “cross training” as the training that allows workers to share workloads among themselves within any given team configuration. Cross train-

397

ing goes beyond the minimum amount of “coverage training” required for a particular team configuration. With cross training, workers have the capability to work side-by-side at the same assembly station. Using the three team example of Fig. 3, should worker W1 now be trained to perform task 4, worker W1 could help worker W2 to alleviate load imbalances as they occur. It should be noted that there is a certain amount of flexibility built into the assembly system if the team creation decision results in the formation of multiple work teams. So the question is when/whether the additional flexibility possible through cross training will be of value. There are four levels of cross training possible. At the lowest level, that of no cross training, each worker is able to perform only his or her home tasks. This level of cross training, along with examples of the other levels of cross training, is displayed in Table 2 for each of the work team configurations. The next level of cross training is defined as low cross training. Here, in addition to being able to perform home tasks, each worker is trained to perform the immediately adjacent downstream task. Because of the U-shaped design of the assembly areas, workers can easily and rapidly move between the most upstream stations and the most downstream stations when needed. Low cross training obviously offers more flexibility and more task coverage than no cross training because workers within each team are able to share tasks as bottlenecks occur. The third level is medium cross training. Here, in addition to being able to perform home tasks, each worker is trained to perform the immediately adjacent upstream task and downstream task. The final level of cross training is defined as high. Here, in addition to being able to perform home tasks, each worker is trained to perform the immediately adjacent upstream task and two adjacent downstream tasks. Of the cross training options, the high level obviously offers the highest degree of worker flexibility and task coverage. 3.3.2. Worker deployment In our discussions with a number of plant managers, there was a great deal of concern on how to best address and manage the problem of worker specialization versus worker flexibility. In these discussions, a common question related to when workers should

398

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

Table 2 Task coverage and task sharing for different levels of cross training Team A

T1

Single team design W1 C W2 M W3 W4 W5 W6 W7 W8 W9 W10 W11 H W12 L

T2

T3

L C M

H L C M

T4

T5

T6

T7

T8

T9

T10

T11

T12 M

H L C M

H L C M

H L C M

H L C M

H L C M

H L C M

H L C M

H

Two team design (example: only team A shown) W1 C C L H W2 M C C W3 M W4 W5 W6 L H Three team design (example: only team A shown) W1 C C C L W2 M C W3 W4 L H Four team design (example: only team A shown) W1 C C C C W2 M W3 L H

H L C M

H L C M

L C

H C

L C

H C M

L C

H C M

L C

H C M

L C

H C M

C M

H C

L C

H C

C M

L C

L C

H C

H C

C M

C

C M

C

C

W, worker; T, task; C, home task for worker because of task coverage; L, additional task for worker due to low cross training; M, additional task for worker due to medium cross training; H, additional task for worker due to high cross training.

specialize on specific tasks, as opposed to allowing workers to move across a wider range of tasks to respond to bottlenecks. This issue of specialization versus flexibility is operationalized by classifying workers as either fixed or floating. The overall objective of fixing workers to tasks is to increase worker specialization, with the goal of developing a high level of narrowly-focused proficiency. Workers classified as “fixed” stay focused on their preassigned home tasks whenever they will be kept busy, moving away from these tasks only when they would otherwise be idle. If a fixed worker leaves a home task to temporarily assist another worker, the worker will look back to his home tasks upon each completion of a non-home task to see if there are units awaiting

work. When a unit of product arrives and is awaiting processing at a home task, the worker will return to the home task to begin work upon completion of the non-home task. Conversely, when a worker is classified as “floating”, the worker will look at all tasks he is trained for, and move to the task that has the largest queue of work. This may take the worker to a home task, or it may take the worker to a non-home task to assist another worker in responding to a potential bottleneck situation. The classification of workers as fixed or floating depends upon the level of worker cross training. If workers are not cross trained at all, classifying workers as either fixed or floating is not possible. Without cross training, they simply remain at their home tasks

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

399

Table 3 Experimental and fixed factors Factors

Factor settings

Experimental factors Product line environments Product variety Task complexity

Low; high Low; high

Management decisions Work team configuration: number of teams Degree of cross training Proportion of fixed workers (%) Fixed factors Number of workers Number of distinct tasks per product Variability of product turnover interval Shape of forgetting function Manufacturing order lot size Queue priority rule Queue selection rule Maximum queue size Worker movement time between work stations Maximum number of workers at a station at any time

regardless of the workload in the shop. However, if workers are cross trained to at least a low level, then allowing workers to perform non-home tasks is possible. At one extreme, all 12 workers are fixed (i.e., proportion of workers fixed = 100%). At the other extreme none of the workers are fixed (i.e., proportion of workers fixed = 0%). Between these two extremes the possibility of either four fixed workers (proportion of workers fixed = 33%) and eight fixed workers (proportion of workers fixed = 67%) was considered. However, a series of screening experiments determined that the performance of these intermediate levels of fixed workers is always dominated by either the 0% fixed workers choice or the 100% fixed workers choice. Therefore, only the two extreme points are included in this study. Table 3 provides a summary of fixed and experimental factors. Consistent with the overview presented in Fig. 5, the experimental factors in this table are divided into two distinct subgroups: product line environment factors and management decision factors. In this research, the product line environment factors are used to create operating scenarios within which man-

1; 2; 3; 4 None; low; medium; high 0; 100 12 Maximum of 12 (equal to 12, less any tasks skipped because of the probability of product routing gaps) None (interval is constant for a given yearly turnover rate) Linear with respect to interruption interval 50 units First in system first served Largest queue 4 0 2

agement must make workforce decisions. The parameters and associated values for these factors are set in order to characterize extreme, yet realistic, operating environments that are representative of the assembly areas of our industrial contacts. Given an operating environment, the focus of the experiments is to gauge the impact of management’s decisions in the areas of teaming, cross training, and worker deployment.

4. Research hypotheses There is a set of six hypotheses for each product line profile, listed below in null hypothesis format. 4.1. Team creation This hypothesis examines whether the investment in creating parallel teams of workers improves performance. H1. When workers have no cross training that would allow the sharing of tasks, there is no performance difference between:

400

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

(a) the single team process design and the two team process design; (b) the two team process design and the three team process design; (c) the three team process design and the four team process design. While workers in multiple team designs must be trained to perform their multiple home tasks, for team creation hypotheses H1(a)–(c) they do not have additional training that allows them to share tasks with each other.

Cross training Process design

No vs. low

Low vs. medium

Medium vs. high

Single team Two team Three team Four team

H3(a) H4(a) H5(a) H6(a)

H3(b) H4(b) H5(b) H6(b)

H3(c) H4(c) H5(c) H6(c)

The alternate hypotheses for all six null hypotheses listed above are tailored for each product line profile. 4.3. Performance measures

4.2. Task sharing There are five hypotheses that examine the performance ramifications of worker cross training and deployment policies when workers are configured in teams and are able to share tasks. The first of these five hypotheses examines the worker deployment question, while the following four focus on cross training. H2. There is no performance difference between the deployment choice of 0% of workers fixed and the deployment choice of 100% of workers fixed, for: (a) (b) (c) (d)

the single team process design; the two team process design; the three team process design; the four team process design.

H2(a)–(d) examines the relative performance of the two deployment policies, where deployment policy performance for each team design is averaged across all levels of worker cross training. Following the approach and findings of McCreery and Krajewski (1999), this hypothesis is to be tested first in order to determine which deployment policy is best for each product line profile. Once this is determined, the appropriate deployment policy will be used to test the following multi-part hypotheses concerning the value of cross training when workers are configured in teams. H3–H6. There is no performance difference between the following levels of cross training for each of the following team process designs:

Performance is measured at the completion of each batch means run in the following ways: product throughput (WORK), as measured by the standard work content of products built in the assembly operation per day; labor utilization (UTIL); and labor efficiency (EFFIC). WORK is defined by the average amount of work performed by the assembly area per day per worker in terms of standard time, not actual time. For example, a completed unit of a product with standard times of 5 min for each of twelve tasks will contribute 60 min to the throughput calculation. It is likely that the actual amount of time spent on this unit was greater than 60 min, due to workers being less than fully proficient at various tasks. However, this lack of proficiency should not be included in throughput calculations, since the standard work content of units produced is of interest here. Accordingly, throughput is standardized by determining how many minutes per day each worker is “productive”, on average. When a worker performs a task on a unit of product, the product’s standard time for that task is recorded. At the completion of each batch means run for an experimental design point (i.e., at the end of each year of simulated time), all of the standard times for completed tasks on units are added together, divided by the number of workers, and divided by the number of days on which the throughput calculation is based. Consequently, WORK has a lower bound of 0 min and an upper bound of 480 min, with higher values of WORK associated with high performance. UTIL is also a commonly used measure of performance within the DRC literature, and is defined here

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

as the proportion of total available time that workers are actually busy performing tasks. In this study, however, we relate low levels of UTIL to high performance. The reason is that the assembly operation in this study is labor paced and, consequently, for a given level of WORK, lower levels of UTIL allow for more flexibility to accommodate volume and product mix variations. The third performance measure is worker efficiency. EFFIC is calculated as: EFFIC =

WORK m × UTIL

(6)

where m is the total time available per day per worker. The numerator is the average output per worker using standard times for each task while the denominator is the actual time the average worker was busy. The numerator and the denominator can be different because of the cross training and deployment policies in effect and the amount of learning and forgetting that has taken place. So, even though EFFIC is a function of the other two measures used in this study, it nonetheless provides additional insights into performance. High performance is associated with high levels of WORK and EFFIC and a low level of UTIL. Per our industrial contacts, worker utilization and worker efficiency were thought of as ancillary measures associated with the desired primary objective of high product throughput. That is, counterbalancing the goal of high product throughput is the consideration of how busy workers have to be kept and how efficiently they work while busy, in order to attain a certain level of product throughput. Stated another way, for a given level of throughput, high worker efficiency and low worker utilization are desirable.

401

5.1. Model The assembly area is modeled using SLAM II (Pritsker, 1986), a discrete event simulation software tool. The model is designed with extensive calls to user-specific subroutines written in Fortran, version VS2. SLAM II is used to keep track of simulation time, handle events and entity attributes on the event calendar, and collect performance statistics. All of the other detailed processing is done through the use of Fortran subroutines. The batch means approach is used to obtain independent observations for each experimental run. A total of twenty batches is used for each experimental run, with each batch being 1 year in length. Prior to the beginning of the first batch in any run, the model goes through a transient startup period. Welch’s procedure (1983) is used to determine the end of the startup period, at which time the model is assumed to have reached steady-state (Law and Kelton, 1991). All performance data generated by the model during the startup period is discarded prior to the beginning of the first batch. At the end of each batch, performance statistics are collected on product throughput, worker utilization, and worker efficiency. At the end of each batch the manufacturing order file is recalibrated so that, for a given setting for product variety, the same batch number across runs always starts with the same manufacturing order. This provides equivalent starting points for each batch and acts to reduce unwanted variance in the results. To insure independence across batches for a given experimental run, Fishman’s (1978) test for autocorrelation is used. For all of the runs in this study, no significant autocorrelation was found. 5.2. Experimental design

5. Research methodology DRC studies predominantly use simulation as their primary research tool, since the inherent complexity of the DRC environment precludes analytical methods for examining labor assignment issues. Similarly, our study employs discrete-event simulation as the modeling tool. The key characteristics of the model will be presented later in this section, after the experimental factors and modeling environment are described.

Prior to testing the research hypotheses, a series of preliminary experiments was run to observe the sensitivity of the model to selected parameter settings. These experimental runs varied the values of a select number of parameters believed most likely to significantly affect the model’s performance. The parameters tested were manufacturing order lot size, the speed of forgetting, and the order release mechanism of units to the assembly area. In all of these cases, varying

402

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

the parameters did not substantively alter the results or conclusions of the experiments of interest in this research. As indicated in Table 3, there are five experimental factors to be examined in this study. The first two, product variety and task complexity, have been described as exogenous to manufacturing management, since the factors are beyond their short term control. The other three factors, work team design, worker deployment policy, and degree of cross training, are workforce management practices within the control of manufacturing management. As a first test, a full factorial analysis of variance was run to determine whether all of the factors have significant effects on performance. The ANOVA results showed all factors significant at the P < 0.001 level. Given this result, a set of experimental contrasts was designed to more specifically test the hypotheses presented in the prior section. These contrast tests are run for each of the four combinations of product variety and task complexity settings (high and low for each factor), effectively capturing

the nature of the interaction between these two exogenous experimental factors. Testing of the hypotheses will be done through analysis of variance techniques, in particular through the use of customized contrasts that are derived from the hypotheses.

6. Results The results of the contrast tests are presented in Tables 4–6. The tables show the percentage differences for each of the three performance measures for each hypothesis, along with an indication of each contrast estimate’s significance level. The calculations used to determine percentage changes for each hypothesis are displayed as footnotes to the tables. In addition, the raw values for product throughput (WORK), worker utilization (UTIL), and worker efficiency (EFFIC) for each environment’s “base case” of no worker cross training and a single team process design configuration are also shown in Table 4.

Table 4 Results—team creation hypotheses

Base case values H1(a) H1(b) H1(c)

PV = low, TC = low

PV = high, TC = low

PV = low, TC = high

PV = high, TC = high

WORK

UTIL

EFFIC

WORK

UTIL

EFFIC

WORK

UTIL

EFFIC

WORK

UTIL

EFFIC

409.3 4.1∗∗ 1.6∗∗ 2.3∗∗

0.864 4.7∗∗ 1.9∗∗ 2.5∗∗

0.987 −0.6 −0.3 −0.2

333.5 9.4∗∗ 5.8∗∗ 6.1∗∗

0.712 10.7∗∗ 6.2∗∗ 6.3∗∗

0.976 −1.2∗ −0.4 −0.2

271.1 −0.5 −0.4 0.6

0.862 4.3∗∗ 2.0∗∗ 2.0∗∗

0.655 −4.6∗∗ −2.2∗ −1.5

206.6 7.1∗∗ 4.9∗∗ 5.2∗∗

0.707 10.5∗∗ 6.4∗∗ 5.8∗∗

0.609 −3.1∗ −1.4 −0.5

Base case values: single team design, with no worker cross training. For H1(a): {[(# teams = 2) − (# teams = 1)]/(# teams = 1)} × 100%. For H1(b): {[(# teams = 3) − (# teams = 2)]/(# teams = 2)} × 100%. For H1(c): {[(# teams = 4) − (# teams = 3)]/(# teams = 3)} × 100%; PV, product variety; TC, task complexity. ∗ P < 0.01. ∗∗ P < 0.001. Table 5 Results—task sharing, worker deployment hypotheses

H2(a) H2(b) H2(c) H2(d)

PV = low, TC = low

PV = high, TC = low

PV = low, TC = high

PV = high, TC = high

WORK

EFFIC

WORK

UTIL

EFFIC

WORK

0.8 −0.2 −0.5 −1.1∗

3.7∗∗

3.3∗∗

0.4 −1.1∗ −1.5∗ −1.8∗

−17.6∗∗

1.6∗∗ 2.8∗∗ 3.1∗∗ 1.9∗∗

UTIL 0.9 2.9∗∗ 3.6∗∗ 2.9∗∗

5.2∗∗ 6.1∗∗ 4.4∗∗

6.2∗∗ 7.5∗∗ 6.1∗∗

−12 3∗∗ −7.6∗∗ −5.3∗∗

UTIL

EFFIC

WORK

UTIL

EFFIC

0.2 1.8∗∗ 3.2∗∗ 3.0∗∗

−17.8∗∗

–10.8∗∗

2.1∗∗

−13.0∗∗ −11.8∗∗ −8.5∗∗ −8.4∗∗

−14 3∗∗ −11 2∗∗ −8.5∗∗

−6.8∗∗ ∗∗ − 2.4 −1.8∗

4.5∗∗ 5.7∗∗ 6.2∗∗

For H2(a): {[(0% fixed) − (100% fixed)]/(0% fixed)} × 100%, for # teams = 1. For H2(b): {[(0% fixed) − (100% fixed)]/(0% fixed)} × 100%, for # teams = 2. For H2(c): {[(0% fixed) − (100% fixed)]/(0% fixed)} × 100%, for # teams = 3. For H2(d): {[(0% fixed) − (100% fixed)]/(0% fixed)} × 100%, for # teams = 4; PV, product variety; TC, task complexity. ∗ P < 0.01. ∗∗ P < 0.001.

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

403

Table 6 Results—task sharing, cross training hypotheses

H3(a) H3(b) H3(c) H4(a) H4(b) H4(c) H5(a) H5(b) H5(c) H6(a) H6(b) H6(c)

PV = low, TC = low

PV = high, TC = low

PV = low, TC = high

PV = high, TC = high

WORK

UTIL

EFFIC

WORK

UTIL

EFFIC

WORK

UTIL

EFFIC

WORK

UTIL

EFFIC

9.4∗∗

10.6∗∗

−1.1∗

22.0∗∗

24.3∗∗

−1.8∗

3.7∗∗

9.6∗∗

−5.4∗∗

9.6∗∗

20.2∗∗

0.5 6.7∗∗ 0.7∗ 0.1 5.8∗∗ 1.0∗ −0.6 3.3∗∗ 0.8∗ −0.4

1.0∗ 7.8∗∗ 1.4∗ 0.8 7.3∗∗ 0.8 0.2 5.1∗∗ 0.6 0.1

2.2∗∗ 15.8∗∗ 2.1∗∗ 1.6∗∗ 12.9∗∗ 1.6∗∗ −0.5 7.4∗∗ 1.3∗∗ −0.4

3.2∗∗ 18.9∗∗ 3.0∗∗ 2.6∗∗ 15.5∗∗ 2.2∗∗ 0.7 9.9∗∗ 1.7∗∗ 0.3

−1.0 −2.6∗ −0.9 −0.9 −2.3∗ −0.6 −1.2∗ −2.3∗ −0.4 −0.7

0.9 −0.4 1.4∗ 2.3∗∗ 1.6∗ 0.3 2.5∗∗ 1.6∗ 0.4 1.7∗ 0.8

1.1∗ 3.2∗∗ 3.9∗∗ 2.3∗∗ 1.0 3.8∗∗ 2.1∗∗ 0.7 2.4∗∗ 1.1∗

−1.5 −1.8∗ −1.5 −0.7 −0.6 −1.2 −0.5 −0.3 −0.8 −0.4

0.3 6.7∗∗ 5.3∗∗ 3.2∗∗ 2.9∗∗ 3.6∗∗ 2.8∗∗ 0.7 2.0∗∗ 1.7∗

3.4∗∗ 10.3∗∗ 8.2∗∗ 4.9∗∗ 4.4∗∗ 5.5∗∗ 4.2∗∗ 1.5∗ 3.3∗∗ 2.4∗∗

−8.9∗∗ −3.2∗ −2.9∗ −3.3∗ −2.6∗ −1.7∗ −1.4 −1.8∗ −1.2 −0.8 −1.3 −0.7

2.6∗∗

3.2∗∗

−0.6 −0.6 −1.1∗ −0.7 −0.7 −1.4∗ 0.2 −0.8 −1.7∗ 0.2 −0.5

6.2∗∗

7.5∗∗

−1.1∗

3.0∗∗

−2.0∗

4.4∗∗

7.9∗∗

# Teams = 1: for H3(a): {[(low XT) − (no XT)]/(no XT)} × 100%; for H3(b): {[(medium XT) − (low XT)]/(low XT)} × 100%; for H3(c): {[(high XT) − (medium XT)]/(medium XT)} × 100%. # Teams = 2: for H4(a): {[(low XT) − (no XT)]/(no XT)} × 100%; for H4(b): {[(medium XT) − (low XT)]/(low XT)} × 100%; for H4(c): {[(high XT) − (medium XT)]/(medium XT)} × 100%. # Teams = 3: for H5(a): {[(low XT) − (no XT)]/(no XT)} × 100%; for H5(b): {[(medium XT) − (low XT)]/(low XT)} × 100%; for H5(c): {[(high XT) − (medium XT)]/(medium XT)} × 100%. # Teams = 4: for H6(a): {[(low XT) − (no XT)]/(no XT)} × 100%; for H6(b): {[(medium XT) − (low XT)]/(low XT)} × 100%; for H6(c): {[(high XT) − (medium XT)]/(medium XT)} × 100%. PV, product variety; TC, task complexity; XT, cross training. ∗ P < 0.01. ∗∗ P < 0.001.

The results for the team creation hypotheses are presented first, followed by the results of the Task Sharing hypotheses tests. 6.1. Team creation Hypotheses H1(a)–(c) examine the performance implications of creating a varied number of work teams without any task sharing by workers. 6.2. Low variety, low complexity As indicated in Table 4, product throughput for the low variety, low complexity environment is at its lowest level for the single team design, with a base case value for WORK of 409.3 out of a maximum of 480. Each increment in the number of teams yields a small, but statistically significant, increase in product throughput, with roughly half of this throughput improvement obtained by the change from a single team

to dual teams. However, the worker utilization performance measure yields somewhat opposing results, as worker utilization increases by approximately 9% over the base case as the number of teams reaches four. Worker efficiency for the single team, base case design is 0.987. Thus, for the single team base case, when workers are busy they are operating at 98.7% efficiency. As indicated in Table 4, the losses in efficiency when increasing the number of teams over the base case are nominally small and not statistically significant. Overall, these results show slight increases in throughput coming at the expense of corresponding increases in worker utilization. Given these results, the selection of how many teams to create in this environment must be guided by managerial preference. In accordance with our industrial contacts, if product throughput is used as a primary performance measure, and if management does not plan to make use

404

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

of additional worker cross training, then a four team design may be a plausible choice in a low variety, low complexity environment. However, it should be noted that the magnitude of the performance differences across all of the team configurations is relatively minor. 6.3. High variety, low complexity The results of Table 4 clearly show large, significant improvements in product throughput as the number of teams is increased. As compared to the single team, base case value of 333.5, the incremental improvements in throughput are 9.4, 5.8, and 6.1% as the number of teams is increased from two to four. However, similar to the low variety, low complexity environment, product throughput in this environment requires corresponding increases in worker utilization as the number of teams gets larger. An examination of worker efficiency in this environment yields results similar to those previously discussed in the low variety, low complexity environment. The worker efficiency for the base case is 0.976. Compared to the single team base case, the efficiency loss by moving to the two team design is only 1.2%. Further changes to three team or four team designs do not yield statistically significant losses in worker efficiency. So, in this high variety, low complexity environment, worker efficiency is degraded only slightly as throughput increases. The resultant choice in this environment is to use a high number of parallel teams. Common to both environments where task complexity is low, the amount of product throughput is correlated to worker utilization. Learning is rapid and forgetting is slow in low complexity environments. Hence, worker proficiency is not severely degraded by the lack of job specialization resulting from workers being assigned multiple home tasks in the multi-team designs. The key difference in the two low complexity environments is the amount of product variety. When variety and complexity are both low, there is relatively less to be gained in throughput performance over the base case. In contrast, for the low complexity environment with high variety, each step in the number of teams adds substantially to product throughput. Overall, the preferred choice in a low complexity environment is to use multiple teams.

6.3.1. Low variety, high complexity Product throughput for this environment does not substantively change as the number of teams is varied. In Table 4, there are no significant changes from the single team, base case throughput of 271.1 as the number of teams increases. And even though throughput remains steady, levels of worker utilization increase as the number of teams rises. Further evidence for the lack of desirability of multi-team designs in this environment is indicated by changes in worker efficiency as the number of teams is increased. Compared to the single team design, there are statistically significant efficiency losses for the two and three team configurations. All of these results indicate that keeping workers busier (i.e., more highly utilized) does not necessarily translate into improvements in throughput or more efficient workers. In this environment, the single team design yields the best overall performance. Learning is relatively slow and forgetting is rapid in a high complexity environment. Hence, the job specialization resulting from a single team design tends to be of value, particularly when there is a low level of product variety for workers to manage. 6.3.2. High variety, high complexity In this environment, creation of multiple teams yields moderately large, statistically significant improvements in product throughput. Starting from the single team, base case throughput of 206.6, there are significant gains in throughput each time the number of teams is increased. Worker utilization levels in this environment also rise substantially as the number of teams is incremented. Examination of worker efficiencies in this environment indicates only a slight degradation as the number of teams is increased. The change from one team to two results in an efficiency loss of 3%, with smaller, statistically insignificant losses for the changes to three and four teams. The high variety, high complexity environment exhibits a pattern of performance similar to that of the low variety, low complexity environment. Increases in product throughput come at the expense of proportionate worker utilization increases, with negligible changes in worker efficiency. Given this pattern of performance, multiple team designs appear to be reasonable selections. Again, if the managerial preference is to use product throughput as a primary performance

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

measure, and if management does not plan to make use of additional worker cross training, then a four team design is a plausible choice in this environment. 6.4. Task sharing 6.4.1. Low variety, low complexity This environment is relatively simple and homogeneous, and therefore the most forgiving with regard to workforce flexibility decisions. For a product line with low variety, the transient load imbalances are at a relatively low level, thereby reducing the need for workers to react frequently or quickly to line imbalances. In addition, when workers do move to alternate stations, low complexity makes it relatively easy to gain proficiency at the alternate tasks they perform, with little penalty due to forgetting. Hypotheses H2(a)–(d) examine the value of the two deployment policies for each product line profile and all four distinct work team configurations. The results of these hypotheses tests are found in Table 5, with the table entries being percent differences in each of the performance measures. For product throughput performance in the low variety, low complexity environment, the flexible deployment policy of “0% workers fixed” outperforms the restrictive “100% workers fixed” policy by up to 3% as the number of teams rises incrementally from one to four. The only negative aspect of the flexible deployment policy is that worker utilization levels tend to be slightly higher than for the restrictive deployment policy, as indicated in Table 5. However, calculating worker efficiencies for the deployment policies shows that, on average, the efficiency differences between the deployment policies are negligible. Consequently, the flexible deployment policy is employed in all the low variety, low complexity experiments. Hypotheses H3(a)–(c) addresses the question of how extensively to cross train the workforce for the single team design. As indicated in Table 6, there is a sizable jump in product throughput when moving from no cross training to low cross training, with little to no further improvement when moving to higher cross training levels. Utilization, however, is also affected by the extent of cross training, with most of the gain in utilization seen when moving from no to low cross training. There are very slight differences in worker efficiencies for the single team configu-

405

ration as the extent of cross training is varied. This same pattern for worker efficiencies holds for the two, three, and four team designs as well. For the single team design, then, a low to medium level of cross training seems to be the best managerial choice in the low variety, low complexity environment. These increases in cross training yield fairly sizable increases in throughput without too severe a degradation in efficiency, and there is no further gain in throughput beyond a medium level of cross training. Analysis of cross training performance for the two, three, and four team designs yield somewhat similar results. The key difference is that, as opposed to the single team’s product throughput performance increasing at both the low cross training and medium cross training levels, all of these multi-team designs have much less improvement in throughput when moving from the low cross training to medium cross training level. These multi-team designs hit the point of diminishing returns of additional cross training faster than does the single team design. Since the multi-team configurations all have some degree of worker flexibility implicitly built in because of their parallelism, they can only gain performance benefits from a smaller incremental amount of worker flexibility. Any more cross training in these team designs is wasted. Therefore, this environment calls for the use of a flexibly deployed workforce, with either low-to-medium cross training in a single team design, or low cross training in a multi-team design. 6.4.2. High variety, low complexity In this product line environment, high variety will require a flexible workforce in order to alleviate load imbalances throughout the assembly line. Fortunately, the low complexity of the tasks minimizes performance penalties related to worker learning and forgetting. As was true in the low variety, low complexity environment, product throughput is maximized in this environment by using the flexible deployment policy of 0% workers fixed. The throughput differences between these deployment policies are substantial, up to 6% greater for the 0% workers fixed versus the 100% workers fixed policy. However, worker utilizations are also at higher levels when all workers are flexibly deployed.

406

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

Again, worker efficiencies for each team design can be used to compare and evaluate the joint increases in product throughput and worker utilization. While there are slight efficiency advantages associated with the restrictive deployment policy, they are only on the order of 1–1.5%. It seems clear that the very large product throughput gains from a flexible deployment policy outweigh its relatively small efficiency losses. With respect to cross training, the largest improvements in product throughput are gained by increasing cross training from none to a low level, regardless of team design. In Table 6, the largest throughput gain is found for the single team design (22.0%), while the four team design yields a still-substantial marginal gain of 7.4%. As was found in the low variety, low complexity environment, the best levels of cross training for the single team design here are either low or medium. For all of the multi-team configurations, there are diminishing returns to further cross training. As indicated in Table 6, the low level of cross training produces virtually all of the incremental improvement in product throughput. For all work team designs, worker efficiency losses associated with increases in cross training are negligible. Taken in conjunction with the team creation results of Table 4, this environment calls for a high number of work teams, with workers receiving a low amount of additional cross training and being flexibly deployed. The multi-team configurations allow workers to cover a large number of work stations without severe degradation in performance, because tasks are of low complexity. These multiple team designs provide an inherent degree of flexibility to the assembly area since each team operates independently of the others, so blocking or starving in one team has no effect on the other teams. Beyond this degree of flexibility offered by multiple teams, low worker cross training appears to provide sufficient capability for workers to help each other by sharing tasks whenever high product variety causes transient workload imbalances to occur. 6.4.3. Low variety, high complexity A high complexity product line causes task learning to proceed slowly, with subsequent loss of task proficiency due to forgetting occurring rapidly. The product line’s low variety, however, allows workers to stay focused on their home tasks.

Accordingly, the best worker deployment policy is a restrictive one, where all workers are fixed to their home tasks. Large product throughput losses occur if a flexible deployment policy is used. As indicated in Table 5, these losses range from 5.3% in the four team design, up to 17.6% for the single team design. In addition, these product throughput improvements occur with lower worker utilization levels and better worker efficiencies for the restrictive deployment policy than for the flexible policy. Per the results in Table 6 for all team designs, the case of no cross training, and therefore no worker sharing of tasks, performs reasonably well in terms of product throughput and worker utilization. The gain in product throughput over the base case is slight, less than 4% in all cases, and it comes at the expense of corresponding increases in worker utilization. The incremental gain in product throughput for medium cross training is even smaller on average, and going to a high level of cross training is of very little further value. The choice of a single team design with little to no cross training appears to be justified for the low variety, high complexity environment. As seen both in the team creation and task sharing results in Tables 4 and 6, improvements in product throughput in this environment are difficult to achieve. Compared to the single team base case, WORK results of Table 6 show that there is little gain possible in product throughput for any other workforce flexibility policy. This is in stark contrast to the high variety, low complexity environment, where maximum product throughput gains over the base case can exceed 20%. Also, the penalties for making a poor deployment policy choice are large, with greatly reduced throughput and much higher worker utilization. The clear implication is that workers can be kept busy performing the wrong tasks in this environment. Overall, this environment calls for a single work team, with workers receiving little or no cross training and being restrictively deployed. 6.4.4. High variety, high complexity This is the most demanding operating environment in our study: tasks are difficult and variety is high. For this type of product line, variety and complexity act as competing forces. The high variety calls for workers to be flexibly configured, trained, and deployed to eliminate load imbalances. Conversely, the high complex-

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

ity limits and penalizes worker proficiency as workers roam from task to task. Thus, the team creation and task sharing decisions are not intuitively clear. The results for this environment show that product throughput is best when all workers are fixed. Table 5 shows that product throughput losses of 11% are possible if a flexible deployment policy is used in the single team design. Even for the four team design, a flexible deployment policy will generate product throughput losses of 2%. Also, worker utilization is reduced under a restrictive deployment policy, by as much as 6%, with subsequent large gains in worker efficiency. Thus, the restrictive deployment policy is the better choice in this environment. Per the results in Table 6, the choice of the appropriate level of cross training appears to depend on the number of teams. For lower numbers of teams, there are moderately large gains in product throughput at both the low and medium levels of cross training. At the higher numbers of teams, however, the value of additional cross training is relatively smaller. Overall, this environment requires a restrictive deployment policy and a moderate amount of worker flexibility, in the form of either a high number of teams with no cross training, or a smaller number of teams that do use cross training.

7. Conclusions Table 7 presents in summary form conclusions obtained from testing the team creation and task shar-

407

ing hypotheses. The team creation results, shown in the top portion of Table 7 and in Table 4, are applicable when no cross training and no worker movement are allowed. Overall for team creation, it appears that product variety and task complexity, as characterized in this study, act as competing forces with respect to determining how many parallel teams to employ. High product variety tends to call for a higher number of teams, while high task complexity calls for a lower number of teams. When variety is high and complexity is low, variety’s impact appears to take precedence, resulting in a higher number of teams being preferred. Conversely, when complexity is high and variety is low, complexity’s impact takes precedence and the preferred number of teams is low. When variety and complexity are either both low or both high, they appear to have opposing effects on team design, resulting in a less definitive preference for the number of teams to be employed. The relationship of product variety to team design may be explained by considering the effects of variety in serial assembly areas. High variety tends to produce load imbalances across work stations, which in turn causes transient blocking and starving at work stations. When the workforce is configured as a single team, starving or blocking at any point will degrade overall assembly area performance. However, when the workforce is configured into multiple teams the harmful effects of load imbalances are more likely to be reduced, for two reasons. First, each team operates independently of the others, so blocking and starving of one team will not necessarily translate into

Table 7 Summary results Product variety

Task complexity

Preferred number of work teams

Extent of cross training

Worker deployment

Low–medium Low Low None–low Low–medium None

Flexible Flexible Flexible Restrictive Restrictive Restrictive

Team creation results (no cross training, no worker movement) Low Low 4 High Low 3–4 Low High 1 High High 4 Task sharing results (cross training and worker movement allowed) Low Low 1 Low Low 2–4 High Low 3–4 Low High 1 High High 1–2 High High 2–4

408

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

blocking or starving for any other team. Thus, one team’s slowdown in a multiple team design will have a lower impact on overall performance than it would in a single team design. Second, in multiple team designs each worker in a team performs multiple tasks. The load variability across workers would tend to be reduced, since a worker’s total time spent on a unit of product is the sum of the times spent on each task performed by that worker. This summing of individual task times will cause the variability of the total times across workers to be reduced, thereby effectively reducing the harmful effects of load imbalance for multiple team designs. The relationship of task complexity to team design can be explained by considering the role of job specialization in learning and forgetting environments. When tasks become more complex, the ability to quickly learn to perform them efficiently is diminished. Furthermore, once complex tasks are learned they are more rapidly forgotten if workers do not stay experienced at the tasks. In a single team design, each worker has a high degree of job specialization, with primary responsibility for one task. This allows workers more repetition, promoting a more rapid descent down the corresponding learning curves. In multi-team designs, workers have enlarged jobs, resulting in greater breadth of experience but less depth. In these multi-team designs, workers will tend to take longer to move down their learning curves. Further, once they do move down their learning curves, it becomes more difficult to keep their experience current, since they will tend to perform a higher variety of tasks. They are also more likely to have longer breaks between subsequent manufacturing orders of the same product, since orders are now being allocated to a number of different teams. The task sharing summary results, shown in the lower portion of Table 7, are applicable when teaming, cross training, and worker deployment are all allowed. Focusing first on the worker deployment policy, it can be seen that the low complexity environments call for a flexible deployment approach, while the high complexity environments call for restrictive deployment. These results are consistent with those of McCreery and Krajewski (1999), where deployment policies were examined in single team designs. There does not appear to be a significant penalty in allowing workers to flexibly assist other workers when tasks

are relatively simple to perform. However, when tasks are difficult to learn and easy to forget, it is better to keep workers focused on their home tasks whenever possible. Examining the team design and cross training choices on the bottom portion of Table 7 yields some interesting insights. When variety and complexity are either both low or both high, results of Table 7 indicate that relatively larger amounts of cross training are preferred when the number of work teams is low. But when the number of teams is higher, less cross training is desirable. Thus, it appears that teaming and cross training work together in an additive fashion to create an overall moderate amount of worker flexibility. Results of Table 7 for task sharing in a high variety, low complexity environment are also noteworthy. The preferred policies in this environment are to create a high number of teams and a low level of cross training. However, for this environment McCreery and Krajewski (1999) found that in a single team design workers should receive a high level of cross training. Taking these results together, it appears again that teaming and cross training act in somewhat of a complementary fashion, and that they both provide a degree of workforce flexibility that may be of value in a given shop environment. The variability of results across different product line environments is potentially useful information for firms operating discrete-part, worker-paced assembly areas. The results suggest that more cross training and more work teams are not universally appropriate actions to take. Rather, the appropriate creation and use of cross training and teaming is at least somewhat contingent on other operating conditions. Taking our industrial contacts as examples, their product lines run the gamut from low product variety and low task complexity up to high levels of each. The results of this research suggest quite different workforce practices for these firms’ assembly areas. We are currently working with one of the contact firms to assist them in applying the research findings in their plant. 7.1. Limitations and extensions There are a few points to consider when interpreting the results of this study. First is the fact that there are implicit costs related to teaming not captured by our

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

model. On the cost side, there are outlays related to setting up the work space into multi-team configurations. More floor space and equipment may be needed, and higher wages may be necessary to secure a workforce capable of performing the enlarged jobs that exist in the multi-team designs. As the number of teams increases, these costs may also increase. There are also work in process inventory changes that may be considered by management when making their workforce flexibility decisions. In addition to extra floor space, hand tools, and other equipment, multi-team configurations will also result in higher levels of work in process inventory present in the assembly area. On average, work in process will tend to increase in the range of 80–90% per additional team beyond the single team design. Also, work in process levels will tend to be smaller as workers are allowed to move freely to alleviate load imbalances. For the assembly area, flow line experiments conducted in this research, the average amount of work in process inventory is much less than one day’s worth of work. While these work in process levels are relatively small when compared to levels often seen in job shop manufacturing studies, nonetheless management may want to consider the impact of its workforce decisions on work in process inventory. Further, it is important to note that any potential “behavioral” benefits to teaming such as improved morale, higher dedication to the job, and greater worker retention are also outside the scope of this model. In this research we focus on the operational performance implications of teaming. Finally, the research findings presented here are for a given technological profile within the assembly area. As noted by Dutton and Thomas (1984), learning depends on more than cumulative volume alone. Over time, significant technological investments to increase the level of automation in the assembly area are likely to affect worker learning and forgetting. This study’s findings are restricted to a manually paced, low automation assembly area, where the level of technology stays relatively constant over the time span of the experiments. There are a number of open issues stemming from this research, which is fundamentally exploratory in nature. First, the contingency nature of workforce flexibility practices needs to be studied further. We have examined the performance effects of teaming, cross

409

training, and deployment when task complexity and product variety are varied, under conditions of individual learning and forgetting. The results have shown that heightened workforce flexibility does not necessarily translate into improved shop-wide performance. Empirical work is needed to further explore these complex interrelationships, and to provide guidance to managers when configuring, training, and deploying their workforces. Additional work is needed in sorting out the tradeoffs and interrelationships of parallel teaming and cross training. Both practices can be used to create a more flexible workforce, but more work needs to be done to understand which of these practices are more suitable and beneficial in differing shop environments. Also, more research that explores the drivers of product variety and task complexity is necessary. This research created factors for product variety and task complexity that are amalgamations of multiple parameters. While the impact of workforce practices on performance does appear to be affected by the levels of these factors, it is not clear which parameter(s) within each factor are most significant. Future modeling and empirical research can dissect these factors and examine their constituent parameters in isolation. Our research focused on the operational performance impact of selected workforce practices. The operational impact of other workforce practices needs to be further studied. These include commonly-used practices such as the systematic rotation of workers to different jobs or tasks, and the expansion of worker responsibilities through such “off-line” activities as product quality inspection, equipment adjustment and maintenance, and process improvement efforts. Finally, a deeper understanding of the joint operational and behavioral implications of the use of workforce practices to increase flexibility is necessary. Analytical modeling and empirical work are both required to develop further insights into these complex issues.

Appendix A Fig. 6 graphically demonstrates how Eqs. (2)–(5) operate to dynamically track learning and forgetting each time a worker is to begin a task on a unit of product. Step 1 of this figure shows a worker progressing

410

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

Fig. 6. Learning and forgetting process.

down a learning curve through performing a task on a series of products without an interruption. In this case, kn will be zero in Eq. (2), fn of Eq. (3) will be zero since there is no forgetting between subsequent units, the effective unit count zn of Eq. (4) is incremented by one with each subsequent unit, and the DeJong learning relationship of Eq. (5) determines the amount of time the worker needs to perform each subsequent task. However, at step 2 the worker has an interruption between unit (n∗ − 1) and unit (n∗ ), which will cause some worker forgetting to occur. Once the length of the interruption can be determined, the worker will regress back on the learning curve to some less efficient point, as seen in step 3. This new point on the learning curve is determined by calculating a new effective unit count zn∗ . Eqs. (2)–(5) determine this new effective unit count as follows. First, kn∗ of Eq. (2) is calculated, to determine the length of the interruption. If the interruption is equal to or greater than the interruption time at which complete

forgetting occurs, then the “number of units forgot”, fn∗ , will be equal to the entire number of units worth of learning to date. In other words, the worker will forget all he or she has learned up to this point for this task and product. Eq. (4) will then yield an effective unit count of 1, and the worker performs the task as if for the first time. If, however, the interruption is greater than zero but less than the interruption time at which complete forgetting occurs, then the “number of units forgot”, fn∗ , will be greater than zero but less than the entire number of units worth of learning to date. In this case, the middle expression of Eq. (3) is used to determine the value of fn∗ . The improvement in worker efficiency to date, due to worker learning, is given by [y(1) − y(zn−1 + 1)]. This amount (in min) is then multiplied by a fraction (kn /Ftotal ), since forgetting is calculated as a linear function of the length of the interruption. The resultant value is a measure of the processing time “loss of efficiency” due to forgetting.

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

The DeJong relationship of Eq. (1) uses this “loss of efficiency” time, in conjunction with the processing time that would have occurred if there were no break in service, to determine an equivalent number of units forgot, fn∗ . Next, Eq. (4) determines the new effective unit count zn and Eq. (5) transforms that effective unit count into a processing time. After the interruption is over, step 4 of Fig. 6 shows the worker proceeding down the learning curve in the same manner as described in step 1. Therefore, the end result of the interruption is to move the worker up the learning curve to a less efficient point, from which the worker will continue to learn with experience.

References Adler, P.S., 1990. Shared learning. Management Science 36 (8), 938–957. Adler, P.S., Clark, K.B., 1991. Behind the learning curve: a sketch of the learning process. Management Science 37 (3), 267–281. Bailey, C.D., 1989. Forgetting and the learning curve: a laboratory study. Management Science 35 (3), 340–352. Bobrowski, P.M., Park, P.S., 1993. An evaluation of labor assignment rules when workers are not perfectly interchangeable. Journal of Operations Management 11, 257–268. Boothroyd, G., 1992. Assembly Automation and Product Design. Marcel Dekker, New York. Carlson, J.G., Rowe, A.J., 1976. How much does forgetting cost? Industrial Engineering 8, 40–47. Cohen, S.G., Bailey, D.E., 1997. What makes teams work: group effectiveness research from the shop floor to the executive suite. Journal of Management 23 (3), 239–290. Cox, T., 1989. Toward the measurement of manufacturing flexibility. Production and Inventory Management Journal 30 (1), 68–72. Dar-El, E.M., 2000. Human Learning: From Learning Curves to Learning Organizations. Kluwer Academic Publishers, Dordrecht. DeJong, J.R., 1957. The effects of increasing skill on cycle time and its consequences for time standards. Ergonomics. De Meyer, A., Nakane, J., Miller, J.G., Ferdows, K., 1989. Flexibility: the next competitive battle—the manufacturing futures survey. Strategic Management Journal 10, 135–144. Dunphy, D., Bryant, B., 1996. Teams: panaceas or prescriptions for improved performance? Human Relations 49 (5), 677–699. Dutton, J.M., Thomas, A., 1984. Treating progress functions as a managerial opportunity. Academy of Management Review 9 (2), 235–247. Engwall, R.L., 1992. Learning Curves. In: Hodson, W.K. (Ed.), Maynard’s Industrial Engineering Handbook, fourth ed. McGraw-Hill, New York.

411

Ferdows, K., DeMeyer, A., 1990. Lasting improvements in manufacturing performance: in search of a new theory. Journal of Operations Management 9 (2), 168–184. Fishman, G.S., 1978. Principles of Discrete Event Simulation. Wiley, New York. Gerwin, D., 1987. An agenda for research on the flexibility of manufacturing processes. International Journal of Operations and Production Management 7 (1), 38–49. Gerwin, D., 1993. Manufacturing flexibility: a strategic perspective. Management Science 39 (4), 395–410. Globerson, S., Levin, N., Shtub, A., 1989. The impact of breaks on forgetting when performing a repetitive task. IIE Transactions 21 (4), 376–381. Globerson, S., Millen, R., 1989. Determining learning curves in group technology settings. International Journal of Production Research 27 (10), 1653–1664. Hancock, W.M., Bayha, F.H., 1992. The learning curve. In: Salvendy, G. (Ed.), Handbook of Industrial Engineering, second ed. Wiley, New York. Hayes, R.H., Pisano, G.P., 1994. Beyond world-class: “the new manufacturing strategy”. Harvard Business Review January/February, 77–86. Hewitt, D., Sprague, K., Yearout, R., Lisnerski, D., Sparks, C., 1992. The effects of unequal relearning rates on estimating forgetting parameters associated with performance curves. International Journal of Industrial Ergonomics 10, 217–224. Law, A.M., Kelton, W.D., 1991. Simulation Modeling and Analysis, second ed. McGraw-Hill, New York. Levy, F.K., 1965. Adaptation in the production process. Management Science 11, B136–154. Malhotra, M.K., Fry, T.D., Kher, H.V., Donahue, J.M., 1993. The impact of learning and labor attrition on worker flexibility in dual resource constrained job shops. Decision Sciences 24 (3), 641–663. McCreery, J.K., Bloom, M.C., 2000. Design and implementation of manufacturing work teams. In: Swamidass, P.W. (Ed.), Innovations in Competitive Manufacturing. Kluwer Academic Publishers, Dordrecht. McCreery, J.K., Krajewski, L.J., 1999. Improving performance using workforce flexibility in an assembly environment with learning and forgetting effects. International Journal of Production Research 37 (9), 2031–2058. Muth, J.F., 1986. Search theory and the manufacturing progress function. Management Science 32 (8), 948–962. Nembhard, D.A., Osothsilp, N., 2001. An empirical investigation of forgetting models. IEEE Transactions on Engineering Management 48 (3), 283–291. Niebel, B.W., 1992. Time study. In: Salvendy, G. (Ed.), Handbook of Industrial Engineering, second ed. Wiley, New York. Pritsker, A.A.B., 1986. Introduction to Simulation and SLAM II. Systems Publishing Corporation. Sethi, A.K., Sethi, S.P., 1990. Flexibility in manufacturing: a survey. International Journal of Flexible Manufacturing Systems 2 (4), 289–328. Shafer, S.M., Nembhard, D.A., Uzumeri, M.V., 2001. The effects of worker learning, forgetting, and heterogeneity on assembly line productivity. Management Science 47 (12), 1639–1653.

412

J.K. McCreery et al. / Journal of Operations Management 22 (2004) 387–412

Slack, N., 1987. The flexibility of manufacturing systems. International Journal of Operations and Production Management 7 (4), 35–45. Smunt, T.L., 1986. Incorporating learning curve analysis into medium-term capacity planning procedures: a simulation experiment. Management Science 32 (9), 1164–1176. Treleven, M.D., 1989. A review of the dual resource constrained system research. IIE Transactions 21 (3), 279–287.

Upton, D.M., 1994. The Management of Manufacturing Flexibility. California Management Review 72–89. Welch, P.D., 1983. The statistical analysis of simulation results. In: Lavenberg, S.S. (Ed.), The Computer Performance Modeling Handbook. Academic Press, New York. Yelle, L.E., 1979. The learning curve: historical review and comprehensive survey. Decision Sciences 10, 302–328.