Work flow policies are shown to induce a change in average between-workers variability (worker heterogeneity) and within-worker variability in performance times. In a laboratory experiment, the authors measured the levels of worker heterogeneity and within-worker variability under an individual performance condition, a work sharing condition, and a fixed assignment condition. The work sharing policy increased the levels of worker heterogeneity and worker variability, whereas the fixed assignment policy decreased them. These effects, along with work flow policy main effects on mean performance times and variability are examined. This article represents an initial step in understanding effects that may be important in the selection of an operating policy, the ignorance of which may lead to costly misestimates of performance.

We propose a model in which between-individual differences in performance (heterogeneity) and within-individual differences in periormance over time (variability) affect flow line performance. The impact of heterogeneity and variability is contingent upon the flow line context, particularly the rules governing the way work moves between employees (work flow policy). We show how subtle changes in this policy can have a motivational effect on heterogeneity and variability and how these, in turn, can impact the relationship between work flow policy and flow line periormance.In this paper we develop a model of production line performance in a particular operational context, and we integrate elements of the operations management literature on flow lines and the organizational behavior literature on workgroups and motivation to develop a behavioral model of flow line performance. We show that these two bodies of literature interrelate in important ways that have implications for both organizational behavior and operations management theory and research. Given the potential difficulties of integrating these areas, we build our model by focusing narrowly on specific operating policies of production flow lines.A production flow line involves multiple employees completing tasks that are sequenced in a particular way. Flow lines are used to produce such goods as automobiles, jet aircraft, and perWe thank Chris Earley, Kevin Gue, Steven Powell, Ken Schultz, Ruth Wageman, and the anonymous reviewers for their helpful comments on an earlier draft of the manuscript. sonal computers; this is the recommended form of production for discrete-item mass production (Hayes & Wheelwright, 1984). The detailed study of these lines is important, because flow lines are used in situations of high-demand volume, and even small improvements in their per-unit operation can yield large gains in profitability (Wild, 1972). Because of the popularity and efficiency of this type of line, it has been the subject of considerable research in the field of operations management (Gagnon & Ghosh, 1991;Ghosh & Gagnon, 1989). Another reason for the attention given to the context of flow lines is that, to our knowledge, no production method has yet been developed that can rival its efficiency.However, historically, individual attributes have been virtually ignored in operations management flow line models, in spite of a wealth of evidence suggesting that significant individual differences exist, even for simple manual tasks (Hunter, Schmidt, & Judiesch, 1990;Schmidt & Hunter, 1983;Schmidt, Hunter, Outerbridge, & Goff, 1988), and that those differences are re- Mitchell, Schriesheim, Freed, and Zhou 595 lated to individual performance. Recently, however, researchers have proposed operating policies for flow lines that not only acknowledge individual differences but also rely on them (Doerr, Klastorin, & Magazine, 2000;Zavadlav, McClain, & Thomas, 1996). Unfortunately, the operations management models of individuals are rather like "stick figures": in the m...

We propose a model in which between-individual differences in performance (heterogeneity) and within-individual differences in periormance over time (variability) affect flow line performance. The impact of heterogeneity and variability is contingent upon the flow line context, particularly the rules governing the way work moves between employees (work flow policy). We show how subtle changes in this policy can have a motivational effect on heterogeneity and variability and how these, in turn, can impact the relationship between work flow policy and flow line periormance.In this paper we develop a model of production line performance in a particular operational context, and we integrate elements of the operations management literature on flow lines and the organizational behavior literature on workgroups and motivation to develop a behavioral model of flow line performance. We show that these two bodies of literature interrelate in important ways that have implications for both organizational behavior and operations management theory and research. Given the potential difficulties of integrating these areas, we build our model by focusing narrowly on specific operating policies of production flow lines.A production flow line involves multiple employees completing tasks that are sequenced in a particular way. Flow lines are used to produce such goods as automobiles, jet aircraft, and perWe thank Chris Earley, Kevin Gue, Steven Powell, Ken Schultz, Ruth Wageman, and the anonymous reviewers for their helpful comments on an earlier draft of the manuscript. sonal computers; this is the recommended form of production for discrete-item mass production (Hayes & Wheelwright, 1984). The detailed study of these lines is important, because flow lines are used in situations of high-demand volume, and even small improvements in their per-unit operation can yield large gains in profitability (Wild, 1972). Because of the popularity and efficiency of this type of line, it has been the subject of considerable research in the field of operations management (Gagnon & Ghosh, 1991;Ghosh & Gagnon, 1989). Another reason for the attention given to the context of flow lines is that, to our knowledge, no production method has yet been developed that can rival its efficiency.However, historically, individual attributes have been virtually ignored in operations management flow line models, in spite of a wealth of evidence suggesting that significant individual differences exist, even for simple manual tasks (Hunter, Schmidt, & Judiesch, 1990;Schmidt & Hunter, 1983;Schmidt, Hunter, Outerbridge, & Goff, 1988), and that those differences are re- Mitchell, Schriesheim, Freed, and Zhou 595 lated to individual performance. Recently, however, researchers have proposed operating policies for flow lines that not only acknowledge individual differences but also rely on them (Doerr, Klastorin, & Magazine, 2000;Zavadlav, McClain, & Thomas, 1996). Unfortunately, the operations management models of individuals are rather like "stick figures": in the m...

Most manufacturing processes can benefit from an automated scheduling system. However, the design of a fast, computerised scheduling system that achieves high-quality results and requires minimal resources is a difficult undertaking. Efficient scheduling of a semiconductor device test facility requires an information system that provides good schedules quickly. Semiconductor device testing is the last stage of the long semiconductor manufacturing process, and therefore is subjected to customer service pressures. The cost of an off-the-shelf computerised scheduling system may be prohibitive for many companies. In addition, many companies are taken aback by other characteristics of off-the-shelf scheduling systems, such as code confidentiality, maintenance costs, and failure rates. We draw upon the literature and our field case to discuss some of the trade-offs between in-house development and off-the-shelf acquisition of software. We describe the in-house design and implementation of a scheduling decision support system for one device test facility. Using the design and implementation process of this system as a case study, we discuss how one facility uses in-house design of systems in a strategic way, as a competitive capability.

>IJH=?J The potential for RFID based systems to improve the safety and efficiency of a supply chain with rapidly decaying products and strict health standards is creating pressure to adopt RFID in several agricultural industries. A handful of fresh produce industry leaders currently participate in mandated pilot projects, while the industry as a whole is still intimidated by the perceived cost of RFID. Therefore in this study we attempt to validate the correlation between performance and automated data collection, paving the way to economic justification of investment in data collection technologies, such as barcode and RFID.The majority of product in this industry is identified and tracked using pallet barcode labels at the more progressive facilities, or facility-specific manual identification methods at the less advanced facilities. Most fresh produce facilities in the US have minimal information systems capabilities, and most of their logistics operations are documented on paper only.Thus the form of Automated Data Collection (ADC) used in the more advanced facilities is Barcode-based. This study compares facilities that use ADC with those that do not. Significant advantages of using ADC are found in many areas, especially in product spoilage, administrative labor and space utilization.