Assel Mukhamediyeva, Francesco Gigli Sutcliffe, Sezim Kaitupov
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引用次数: 0
Abstract
The paper describes an innovative approach to performance improvement using Causal Learning (CL), a method based on the general observation that a business performance is largely the outcome of the organization, processes and procedures, ways of working, constraints and norms – the systems that the business applies to itself. These system causes are often remote from physical causes of equipment failures and as such remain hidden until revealed by appropriate analysis. The objective of CL is discovering these system causes that ultimately lead to an undesired outcome or event. CL helps us "learn" the performance system, develop insights from these discoveries and recognize the specific aspects of a system that require change to shift business performance.
The Company adopted this approach to improve problem solving and root cause analysis of machinery failures. The initial decision to apply CL followed several outages of power generation systems that continued to occur after previous analyses of similar events in the past.
An Enhanced Problem Solving Team (EPST) was established and trained to apply Causal Learning principles to reveal the underlying system causes of these outages. In the time since that first analysis the tools and techniques of CL have been applied to other undesired or unexpected business outcomes including HSE and project work with little or no direct technical content.
CL reveals the contribution of well-intended human behaviours behind unwanted outcomes (e.g. hardware failures), and importantly the underlying system causes of these human behaviours. This is predicated on the basis that people do their best to achieve the goals they believe they need to achieve. When it is revealed why those goal were important and why the actions taken were "their best" with the time, tools, processes available to the individual at that time at that place the systems causes can be properly understood. These findings often surprise the organization, particularly when it is made visible to Leaders at all levels how they created, or are responsible for, the systems that influenced those human behaviours.
本文描述了一种利用因果学习(CL)来提高绩效的创新方法,这种方法基于这样一种普遍的观察,即企业绩效在很大程度上是组织、流程和程序、工作方式、约束和规范的结果——企业应用于自身的系统。这些系统原因通常与设备故障的物理原因相距甚远,因此在通过适当的分析发现之前一直是隐藏的。CL的目标是发现最终导致不期望的结果或事件的系统原因。CL帮助我们“学习”绩效体系,从这些发现中获得洞察力,并认识到一个体系中需要改变的特定方面,以改变业务绩效。公司采用这种方法来改进问题解决和机械故障的根本原因分析。在对过去类似事件进行分析后,发电系统接连发生了几次停电,随后决定应用CL。建立并培训了一个增强问题解决团队(Enhanced Problem Solving Team, EPST),以应用因果学习原则来揭示这些中断的潜在系统原因。自第一次分析以来,CL的工具和技术已经应用于其他不希望或意想不到的业务结果,包括HSE和项目工作,这些工作很少或没有直接的技术含量。CL揭示了在不希望的结果(例如硬件故障)背后的善意人类行为的贡献,以及重要的是这些人类行为的潜在系统原因。这是基于人们尽最大努力实现他们认为需要实现的目标。当揭示了为什么这些目标是重要的,以及为什么所采取的行动是“他们最好的”,在时间、工具和过程中,在那个时候,在那个地方,系统的原因可以被正确地理解。这些发现往往会让组织大吃一惊,尤其是当各级领导都能看到他们是如何创建或负责影响这些人类行为的系统时。