Perturbations and dynamic efficiency--论文代写范文

如果创新提高，效率也会变化,可能是一个优势策略，导致更高的效率和更高的适应性。为了达到这个目的,有意变化必须是选择性的。在这篇essay代写范文中继续论述。

Abernathy’s (1978) productivity dilemma arises because mature processes provide few natural opportunities for learning. In mature processes, activities proceed according to plan, accidents and exceptions are rare, and external stimuli are filtered and buffered to prevent them from disrupting routine operation. For example, work-inprocess inventory buffers insulate manufacturing activities from problems in upstream processes. When processes are stable and predictable, organizational activity validates existing knowledge, but provides no new information to enable knowledge creation (Peng et al., 2008). To sustain learning under these conditions, organizations must intentionally re-introduce variance into mature processes. We term this deliberate perturbation (Brunner et al., 2008). 

  Our theory of deliberate perturbation emphasizes dynamic efficiency. By sacrificing some efficiency in the short term, organizations may be able to sustain innovation and adaptability. If innovation yields increased efficiency in subsequent periods, then deliberate perturbation may be a dominant strategy leading to both higher efficiency and higher adaptability (Fine and Porteus, 1989). To achieve this, deliberate perturbation must be selective and strategic. Some processes have little scope for improvement, because their performance is and will likely remain nearly optimal. Perturbing these processes is unlikely to increase dynamic efficiency. 

  Some perturbations indicate bottlenecks or opportunities, while others are simply distractions. Deliberate perturbation is most likely to increase dynamic efficiency when it leads directly to the creation of new knowledge that improves operational performance. While perturbations are necessary to spark exploration, they are not sufficient. Organizations can ignore perturbations or work around them, in which case perturbations only reduce static efficiency. Alternatively, organizations can attempt to understand the causes and implications of perturbations. This choice is analogous to the decision of how to respond to a crash in a software application. A user can simply reboot the computer system, hoping the problem will not reappear, or the user can debug the application in order to understand and resolve the problem. In organizations with streamlined, stable processes, most perturbations carry valuable signals from which the organization can learn. 

  did a defect appear in the output? Why did a design task require more time than expected? Why did a new product exceed sales forecasts? Many organizations assume that such perturbations are chance occurrences and ignore them, thereby passing up valuable opportunities to learn and adapt to changing environment conditions. Organizations learn from perturbations through exploratory interpretation, the process of interpreting perturbations as opportunities to explore new possibilities. Exploratory interpretation requires the active engagement of organization members in detecting, analyzing, and drawing inferences from failures and discrepancies. Indeed such engagement is a key element of many improvement methodologies such as TQM and lean production (Flynn et al., 1994). The more organization members participate in exploratory interpretation, the greater the potential for learning. 

  Engaged en masse, learning by front-line personnel can make substantial contributions to high-level organizational performance (Flynn et al., 1994; Loch et al., 2007; May, 2007). Since interpretation begins within the minds of organization members, it cannot be dictated or micromanaged. To ensure that exploratory interpretation advances organizational goals, members must be committed to those goals (Nonaka, 1994; Loch, 2008). Otherwise, the learning that results from perturbations may be irrelevant or even counterproductive (for example, learning to game incentive systems). Even as variance reduction leads to the productivity dilemma, it also facilitates deliberate perturbation. When processes are under tight control, the effects of perturbations can be seen more clearly, enabling organizations to better identify and understand the causal relationships between organizational activities (Jaikumar and Bohn, 1992; Bohn, 1995; Schroeder et al., 2008). 

  This knowledge guides the design and interpretation of subsequent rounds of deliberate perturbation, and also enables even tighter control over the underlying processes. Variance reduction, targeted re-introduction of variance, and analysis of the results form a virtuous cycle that increases efficiency and sustains adaptability. Together, deliberate perturbation and exploratory interpretation provide mechanisms for achieving superior dynamic efficiency. When the organization deliberately perturbs a process, the resulting variance reduces static efficiency. Exploratory interpretation and ensuing problem-solving activity also imposes costs on the organization. 

  However, the new knowledge deriving from the perturbation can be used to improve the efficiency or efficacy of the process. Our companion paper uses Toyota to illustrate how an organization uses deliberate perturbation and exploratory interpretation to attain exceptional dynamic efficiency (Brunner et al., 2008). For example, Toyota shrinks work-in-progress inventory buffers to identify the weak links in its supply chain. By focusing attention on strengthening these links, Toyota can efficiently reduce the total inventory in its supply chain (Fujimoto, 1999; Fullerton and McWatters, 2001). Based on Toyota’s performance, it appears that the learning occasioned by the perturbations quickly outweighs the costs of the resulting brief disruptions. The increase in dynamic efficiency more than compensates for the decrease in static efficiency. Deliberate perturbation need not be mindful. 

  Organizations can create routines that induce perturbations automatically, without conscious choices by employees. MacDuffie describes how processes at Toyota are designed to draw attention to problems and occasion learning (this article). Toyota’s operating system generates a constant stream of perturbations that employees are tasked with interpreting. Winter theorizes that such routines can underpin dynamic capabilities (this article). While routines that automatically induce perturbations can certainly help sustain exploration, consciously induced perturbations may also be essential, especially for high-level exploration. Radical new product development initiatives like the Lexus luxury brand and the Prius hybrid are not triggered by shrinking buffers or andon cord pulls; rather, they derive from intentional actions by senior managers (Dawson, 2004; Osono et al., 2008). 

  The problem of achieving dynamic efficiency is further complicated by the relationship between process adaptability and high-level exploration. When processes are adaptable, they semi-autonomously reconfigure themselves to support changes in high-level strategy and in other interdependent processes. Thus adaptability in a process increases dynamic efficiency not only by improving the performance of the particular process over time, but also by contributing to better system-level performance. When implications for system-level performance are taken into account, deliberate perturbation may be efficiency-enhancing even when the costs outweigh the value of learning for a particular process in isolation. The challenge for organizations is to design systems that increase dynamic efficiency through deliberate perturbation and exploratory interpretation. In contrast to static efficiency, which can often be framed as a deterministic optimization problem, there are many approaches to deliberate perturbation with the potential to increase dynamic efficiency. Experimentation may be the only way to determine whether a particular approach works. The theory clearly predicts, however, that the absence of perturbations or the failure to learn from them will prove suboptimal over time.

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