Serial production line performance under random variation: Dealing with the ‘Law of Variability’
Introduction
A primary concern of any operations management department is to correctly manage production resources to achieve the strategic objectives of the company. This is particularly true for companies gaining competitive advantage through operations. In order to align production performance with the strategic vision of the firm, managers must thoroughly understand and prioritise the most impactful performance factors for factory productivity in order to determine the best course of action to attain the desired goals.
Addressing this need, Schmenner and Swink [1] suggested that the ‘Law of Variability’ was one of the main laws used by the Operations Management (OM) field to understand the causes of differences in factory productivity, or more generally, factory performance, since variability can have a significant impact on performance; it postulates that ‘the greater the random variability, either demanded of the process or inherent in the process itself or in the items processed, the less productive the process is.’
The most well-known effect of process variability on performance – the variance of inter-arrival and processing (service) times – has been succinctly described by Queueing Theory. A higher variance of inter-arrival and processing times produces longer queues [2] and, consequently, higher mean waiting times for customers and reduced customer satisfaction.
This and other results are captured in ‘Factory Physics’ by Hopp and Spearman [3]. The tome clearly conveys insights on the fundamental effects of variability on production line performance. The clear insights allow both practitioners and researchers to develop intuition about production line behaviour, a result needed to fully understand the ‘consequences of a design decision and the causes of a specific event’ [4].
Other authors have developed comprehensive reviews on Queueing Theory results applied to production lines [[5], [6], [7]] or on the general modelling of stochastic production lines [8,9]. But, most of those efforts focus more on exact modelling of production systems than on supporting the development of intuition about the behaviour of stochastic production lines and the effects of such behaviour on performance, like Hopp and Spearman.
To the best of our knowledge, there is no single resource that compiles the plethora of interesting and relevant results (currently scattered throughout Queueing Theory and Production Management) that could, if assembled, help others gain a better understanding of production lines under the effects of variability. The aim of this paper is to continue the work of Schmenner and Swink [1] and Hopp and Spearman [3] by presenting a summary of the most relevant research results on the effects of variability in serial production line performance, and provide a reference manual of sorts for practitioners and researchers alike.
We intend that the insights presented here will help readers identify the effects of both diverse variability factors and design features on production line performance. Readers will finish with a better comprehension of the ‘Law of Variability’ tenets, since the more commonly applied phrase ‘random variability’ can represent various factors with varying degrees of impact and does not specifically define any performance measure.
The remainder of this paper is organised as follows: Section 2 presents fundamental definitions and describes the paper’s general scope. Section 3 reviews the most relevant results of the effects of variability in the performance of serial production lines. Section 4 presents a brief discussion and Section 5 suggests opportunities for future research. Finally, Section 6 provides conclusions.
Section snippets
Scope of the study
The fields of Queueing Theory and Production Management have comprehensively investigated the behaviour of serial production lines under variability.
Most research investigating variability has concentrated on studying the effect of a single variable on the performance of a stochastic production line. Although real production lines are generally more complex, the study of single variable effects can provide great insights under relatively controlled conditions. In addition, intuition can be more
How different factors affect the performance of production lines under random variation
The ‘Law of Variability’ posits that the random variability intrinsic to the manufacturing process is detrimental to the productivity of the process. To address this concern, this section first pinpoints how different variability factors inherently associated with the process affect line performance. Second, we relate how some line design factors impact performance. Third, we analyse the effects of some of the most widely known production control techniques on line performance.
Discussion
Table 2’s summary of the effects of variability and various design factors on different performance measures provides clear and concise guidance to better understand the behaviour of simple serial production lines under the effects of variability. It also contributes to a better understanding of the ‘Law of Variability’ [1] by describing the effects of different variability factors inherent to the process, apart from the variance of inter-arrival and processing times, on the productivity of the
Managerial implications and opportunities for future research
For managers, the summaries provided in Table 2, Table 3 and discussed in Section 3.5. provide useful conclusions on the managerial interpretation and application of these results. Complementing those points, some general practical insights can also be inferred. For instance, a common effect for all the performance measurements considered is that both Var(A) and Var(S) negatively impact the performance of serial lines since increasing Var(A) or Var(S) or both always affects performance. Thus,
Conclusions
The main objective and contribution of this paper is to present and summarise some of the most relevant conclusions on the performance behaviour of serial production lines under the effects of variability, and extend the implications of the ‘Law of Variability’ to improve factory and service management and gain and retain competitive advantage. A brief overview of the most meaningful conclusions on different performance measures is presented and serves as a guide to better understand and manage
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