We, I mean colleagues in academia, like to develop models and want to get them published. In fact, a large proportion of the submissions to QREI contain newly developed models. In this editorial, I would like to share with you what are the key elements that we look for in screening/reviewing these submissions. I hope this will serve as a guide for young colleagues in preparing their research work for submission.

The key purpose of a model is to solve a real problem; preferably a significant, recurring problem that is experienced by people in a certain sector of the industry. For example, profile monitoring was motivated by process control in semiconductor industry to ensure uniformity across a piece of wafer. In the other words, the context from which the model was conceived must be well articulated. This will address the concern that “…while the paper contains interesting theory development but will there ever be an application” or simply put, the classic case of “hammer looking for nails”. This has been well‐expounded in an editorial piece contributed by Professor Brombacher, one of the Co‐Editor‐in‐Chief, in 2015 that commented on “Conceptual studies”.

The next consideration is that the model should be implementable. Very often, we encounter papers that presented a model, followed by a numerical example in which the values of the unknown parameters were given. Unless there is an obvious means of obtaining these values, the research work is deemed to be incomplete. As the “test of the pudding is in the eating”, the real test of a model is how does it work in the face of data arising from the problem (i.e. when it is applied to the actual scenarios). The parameters should be estimated from some real‐life data or pseudo data if the associated data are confidential. This will also help to identify the data field and the preferred or correct data collection scheme for the model parameters to be estimatable. Sometimes, from the data structure, one could provide additional insight on the ease of implementation of the proposed model in relation to other competing models. Moreover, the associated sampling error and the variability of the estimated parameter could also be quantified. A classic example on this implementation issue is the Phase 1 problem for SPC.

For a model to be adopted, it should outperform other competing models that address the same or similar problem. Inevitably, a comprehensive comparison is expected and the strength of the model in some aspects (not necessarily all aspects) should be clearly illustrated. In particular, the circumstances in which the model works best against others should be convincingly put across. The weaknesses of the model should also be discussed.

The above is a summary of three key ingredients in preparing a model‐centric paper. It is not meant to be a standard or template. Any other ways of presenting the relevance and utilities of a model to solving practical problems are welcome.

我的意思是，我们学术界的同事喜欢开发模型并希望将其发布。实际上，向QREI提交的大部分内容都包含新开发的模型。在这篇社论中，我想与您分享我们在筛选/审查这些提交内容时需要寻找的关键要素。我希望这将为年轻同事准备研究工作提供指导。

模型的主要目的是解决实际问题。最好是该行业某些部门的人们所经历的重大的反复发生的问题。例如，轮廓监测是由半导体工业中的过程控制推动的，以确保整个晶片的均匀性。换句话说，必须很好地阐明构想模型的上下文。这将解决以下问题：“……虽然本文包含了有趣的理论发展，但仍会得到应用”，或者简单地说就是“锤子寻找钉子”的经典案例。2015年，共同主编之一的布罗姆巴赫尔（Brombacher）教授撰写的社论中对此作了很好的阐述，该文章评论了“概念研究”。

下一个考虑因素是该模型应该是可实施的。很多时候，我们会遇到介绍模型的论文，然后是一个数值示例，其中给出了未知参数的值。除非有明显的手段获得这些价值，否则研究工作将被认为是不完整的。正如“布丁测试就在吃中”一样，模型的真实测试是面对问题产生的数据（即，将其应用于实际情况时）如何工作。如果相关数据是机密的，则应从一些实际数据或伪数据中估计参数。这也将有助于识别数据字段以及模型参数可估计的首选或正确的数据收集方案。有时候，从数据结构来看，一个可以提供关于其他竞争模型所提出的模型易于实施的更多见解。而且，相关的采样误差和估计参数的可变性也可以被量化。关于此实现问题的经典示例是SPC的第一阶段问题。

对于要采用的模型，它应优于其他解决相同或相似问题的竞争模型。不可避免地，需要进行全面的比较，并且应该清楚地说明模型在某些方面（不一定是所有方面）的优势。特别是，应该令人信服地提出该模型在与他人的对抗中发挥最佳作用的情况。该模型的弱点也应该讨论。

上面总结了准备以模型为中心的论文的三个关键要素。它并不意味着是标准或模板。欢迎提出表示模型与解决实际问题的相关性和实用性的任何其他方式。