• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

When we look at the work of Shigeo Shingo about improvement of production processes, he is not talking about processes as such, but rather at process delays. Essentially, he is discussing lossless transitions between (sub)processes and reduction of avoidable waste in transformation (sub)processes [6]. Any storage that is not required for stabilisation of the product is considered a process delay, and as such a loss to be avoided. However, when the period between order and delivery is shorter than the actual time it takes to manufacture a product, stocks are necessary. Therefore, reduction of process delay is key to Shingo’s thinking. Faster production throughput implies less need for stocks, and shifts production from push to pull.

Process improvement is fundamentally about time and timing. Underutilisation of production capacity is allowed when it reduces significantly throughput time. As an example, imagine a production company where the packaging is the bottleneck. The company has to find a balance between order lead time, customer service levels, idle time of expensive packaging equipment, and scrapped stock waiting for orders that did not materialise. Considerations of production cost would argue against investments in equipment, market considerations would argue against higher lead times. Taiichi Ohno writes “In production, ‘waste’ refers to all elements of production that only increase cost without adding value – for example, excess people, inventory, and equipment” [7]. The company will have to balance excess equipment against excess stocks. “Idle equipment” cannot always be equated to “excess equipment”.

To summarise, this kind of process thinking is primarily about pull, flow and avoidance of delays. This requires balancing on both the design level (production capacities) and the operational level (mechanisms for mutual adjustment/modification of production capacities). Processes can be recognised on different aggregation levels. They can be continuous or discrete. To realise flow through processes mechanisms must be in place that prevent unwanted intermediate stocks and unnecessary waiting between (sub)processes.

The relation of a business process to preceding and subsequent processes is another thing. The classic waterfall approach of IT projects is a prototypical example where each subsequent process is triggered by its preceding process and the chain of processes is carried out linearly, without going back to previous processes, until the end result. A second kind of process structure is linear with feedback, either directly feeding information back to a preceding process, or indirectly via some monitoring process. This process structure is found in conventional production companies. A third kind of process structure is with mutual adjustment between preceding and subsequent processes. Here a kind of reciprocity is to be found between preceding and subsequent processes, and this process structure is more likely to be found in production organisations that are based on the lean ideas.

In order to create constant outputs that are useful for customers or internal subsequent processes, business processes must be able to absorb variability. Irregularities in inputs or in the processing that are not absorbed will be passed on as irregularities in outputs.

Often, there will be a trade-off between extra costs caused by eliminating variability in the processes (creating extra consumption of resources. extra waste, and/or late delivery) and the extra costs of not fulfilling specifications and expectations for customers or for subsequent processes. Dealing with such trade-offs might be subject to coordination processes within the company or between the company and its customers.
In the design and execution of business processes there are different dimensions of variability, and different ways for coping with variability. One dimension is quality and deals with specifications and tolerances. Elimination of output variability can be achieved by elimination of variability of input in combination with standardisation of processes (Mintzberg: standardisation of work) [8]. A second way of elimination of output variability is to allow variability of input and have processes in place that eliminate variability in the processes (Mintzberg: standardisation of output). The third option is to allow variability at the output of the process, and then the question is how much the customer or the next internal process can and will tolerate.

Another dimension of variability is quantity and timing. This dimension is about getting the right amount of output at the right time available out of the process, and this requires the right amounts of resources at the right time available for consumption in the process. Some variability will be absorbed in the process. Variability in quantity and time between processes must be resolved by mutual adjustments of the processes, or by rescheduling. Major readjustments will be made dependent on a broad range of competing values. Will a delivery be on time but incomplete, or late and complete? Will an internal process be on time but generate extra costs, or late without extra production costs? This kind of decision making might also depend on the creativity of experienced people. Sometimes people can find smart ways to lessen the negative effects of product or production variability, by balancing requirements and possibilities of efficiency, specifications, timing, and allowable tolerances. Decision making in this kind of adjustment processes requires that a broad range of experience and competence is represented, because (1) heterogeneous values must be weighed against each other and (2) detailed knowledge about processes is needed to evaluate what is really possible in the given situation. And, where the output for customers is affected, both the specific agreement with the customer and the general conventions are important factors in balancing obligations and costs.

Of course, Morgan has offered not only the machine metaphor, but also the metaphors for the organisation as an organism, as a brain, as a culture, as political system, as psychic prison, as flux and transformation, and as domination. Each metaphor helps to see certain aspects of an organisation by comparing typical organisational features with features of the concept of the machine, organism, brain, et cetera. In this sense each metaphor is “true” in the sense that the organisation can be considered to have similar features as a machine. At the same time, the concepts brought together in the metaphor differ in many other respects. Morgan has described this paradox of the metaphor as the phenomenon that the statement “A is $\mathrm{B}$ ” can be both very useful and patently false at the same time. Taken metaphorically, the statement “the organisation is a machine” or “the organisation is an organism” can generate insights in the workings of an organisation as a consequence of the similarities between machine and organisation or between an organism and an organisation.

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。