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Industrial experiments have shown an unprecedented success for improving the product and process quality in many manufacturing organisations. This paper provides some useful and practical tips to industrial engineers with limited knowledge in experimental design (ED) for making industrial experiments successful in their own organisations. ED is a powerful tool which can be used to achieve breakthrough improvements in product quality and process effectiveness. The purpose of the paper is to stimulate the engineering community to start applying ED for tackling quality control problems in key processes they are dealing with everyday.

The purpose of this paper is to focus the application of design of experiments (DOE) using industrial equipments, reinforcing idea that non‐statistical aspects in planning and conducting experiments are so important as formal design and analysis.

Two case studies are presented to illustrate typical industrial applications and difficulties. Supported on these case studies and literature, this paper presents guidelines to planning, conducting and analysis involving technical and organizational aspects.

Solving problems in industry, including in companies recognized as competent in the respective industrial sector, is not just a question of applying the right technique. Ceramic industry case study illustrates how important are non‐statistical issues in DOE application. Paint industry case study illustrates the strong relationship of the results with incorporating presented guidelines into practice. Moreover, both case studies consolidating a fundamental advantage of DOE: experimentation provides more knowledge about products, processes and technologies, even in unsuccessful case studies.

Unsuccessful cases studies are very useful for identifying pitfalls and others limitations. This paper highlight difficulties aroused from non‐statistical aspects, although it is possible to find unsuccessful case studies due to statistical issues also. So, papers illustrating inadequate application of statistical techniques are welcome.

Successful DOE implementation depends on statistical and non‐statistical aspects. Although none of them shall be neglected, technical skills and technological knowledge about processes and products, management understanding of potential possibilities of statistical techniques and statistical fundamentals and knowledge about techniques of DOE must be ensuring to successful case studies in industrial setting.

This paper highlights non‐statistical aspects instead of the statistical ones. Tob overcome difficulties structured guidelines were designed to support DOE application in industrial setting.

It is widely considered that the advanced statistical quality improvement techniques (ASQIT) such as design of experiments and Taguchi methods form an essential part of the search for effective quality control. These quality improvement techniques are well established methodologies in which statisticians are formally trained. Research has shown that the application of these ASQIT for solving process quality problems by industrial engineers in manufacturing companies are limited and when applied they are often performed incorrectly. Presents a strategic and practical methodology with the aim of assisting industrial engineers to tackle manufacturing quality problems in a systematic and structured manner. The potential benefit of this practical methodology is its simplicity in usage and it is therefore readily accessible to the engineering fraternity for solving quality problems in real life situations. Also highlights the results of five industrial case studies which were used to validate and refine the methodology.

The purpose of this study has been to evaluate the technology of autonomous mobile manipulation in a real world industrial manufacturing environment. The objective has been to obtain experience in the integration with existing equipment and determine key challenges in maturing the technology to a level of readiness suitable for industry. Despite much research within the topic of industrial mobile manipulation, the technology has not yet found its way to the industry. To mature the technology to a level of readiness suitable for industry real-world experience is crucial. This paper reports from such a real-world industrial experiment with two mobile manipulators.

In the experiment, autonomous industrial mobile manipulators are integrated into the actual manufacturing environment of the pump manufacturer Grundfos. The two robots together solve the task of producing rotors; a task constituted by several sub-tasks ranging from logistics to complex assembly. With a total duration of 10 days, the experiment includes workspace adaptation, safety regulations, rapid robot instruction and running production.

With a setup time of less than one day, it was possible to program both robots to perform the production scenario in collaboration. Despite the success, the experiment clearly demonstrated several topics in need of further research before the technology can be made available to the industry: robustness and cycle time, safety investigations and possibly standardization, and robot and workstation re-configurability.

Despite the attention of research around the world, the topic of industrial mobile manipulation has only seen a limited number of real-world integrations. This work reports from a comprehensive integration into a real-world running production and thus reports on the key challenges identified from this integration.

Experiments are fundamental to enhancing understanding of the complex industrial processes which we deal with every day. Experimental Design (ED) is a very powerful tool that assists engineers and scientist to discover a set of variables which are most important for a process and thereby provide a great insight into the way a process or system works. It is superior to traditional scientific approach or One‐Factor‐At‐A‐Time (OFAT) approach to experimentation, still often used today. This paper illustrates some fundamental and practical issues that every industrial engineer should know about ED. These issues include factor effects, interactions, response or quality characteristics, randomisation, replication, analysis of variance and contour plots. The paper also presents an example to demonstrate the above issues. The problems and gaps in ED in the state‐of‐the‐art will also be highlighted.

The purpose of this paper is to address misconceptions about the design of experiments (DoE) usefulness, avoid bad practices and foster processes’ efficiency and products’ quality in a timely and cost-effective manner with this tool.

To revisit and discuss the hindrances to DoE usage as well as bad practices in using this tool supported on the selective literature from Web of Science and Scopus indexed journals.

A set of recommendations and guidelines to mitigate DoE hindrances and avoid common errors or wrong decisions at the planning, running and data analysis phases of DoE are provided.

Errors or wrong decisions in planning, running and analyzing data from statistically designed experiments are always possible so the expected results from DoE usage are not always 100 percent guaranteed.

Novice and intermediate DoE users have another perspective for developing and improving their “test and learn” capability and be successful with DoE. To appropriately plan and run statistically designed experiments not only save the user of DoE from incorrect decisions and depreciation of their technical competencies as they can optimize processes’ efficiency and products’ quality (reliability, durability, performance, robustness, etc.) in a structured, faster and cheaper way at the design and manufacturing stages.

DoE usefulness will be increasingly recognized in industry and academy and, as consequence, better products can be made available for consumers, business performance can improve, and the link between industry and academy can be strengthened.

A supplemental perspective on how to succeed with DoE and foster its usage among managers, engineers and other technical staff is presented.

“All things are in a constant state of change”, said Heraclitus of Ephesus. The waters if a river are for ever changing yet the river endures. Every particle of matter is in continual movement. All death is birth in a new form, all birth the death of the previous form. The seasons come and go. The myth of our own John Barleycorn, buried in the ground, yet resurrected in the Spring, has close parallels with the fertility rites of Greece and the Near East such as those of Hyacinthas, Hylas, Adonis and Dionysus, of Osiris the Egyptian deity, and Mondamin the Red Indian maize‐god. Indeed, the ritual and myth of Attis, born of a virgin, killed and resurrected on the third day, undoubtedly had a strong influence on Christianity.

The purpose of this article is to provide an extensive review of the barriers faced by engineers when applying design of experiments (DoE). The aim is to help new practitioners learn from the past and avoid possible barriers that they may encounter when applying DoE in industry.

An exhaustive literary review was carried out to find articles in which hindrances to the application of DoE were mentioned. The information is organised and grouped into 16 barriers with this end in mind.

The 16 barriers can be classified into three different groups: business barriers; educational barriers; and technical barriers. It is shown that DoE can be successfully applied without overcoming every barrier, although it is inconvenient to do so.

Although DoE is commonly found in statistics and quality literature, it is clearly underused in industry. The paper brings together ideas from those with experience in DoE to detect the reasons behind this anomaly.

Very little material has been published regarding the difficulty of applying DoE. Unfortunately, what is available is repetitive, unstructured and incomplete. The paper is intended to encourage discussion between practitioners and experts, in order to find a way to define, categorise and eventually overcome the most problematic barriers.

The reported mathematical models of gas–liquid flow in single snorkel Rheinstahl–Heraeus (SSRH) are based on the assumption of steady Ar-molten steel flow. The purpose of this paper is to develop a mathematical model to describe the unsteady turbulent flow (CO-Ar-molten steel) with nonequilibrium decarburization reaction.

On the base of the finite volume method, the computational fluid dynamics software CFX is used to predict the unsteady fluid flow, the spatial distributions of CO/argon gas and carbon element. The water model experiment and the industrial experiment are carried out to verify the mathematical models.

A two-way coupling model (T-WCM) based on algebraic slip model is developed to investigate the coupling phenomena. The related results show that T-WCM is more rigorous and accurate than one-way coupling model in predicting carbon content of molten steel. The amount of CO gas, which can enhance turbulent flow and mass transfer, is about three times the argon gas blown into SSRH.

CO gas is the key factor in investigating the transport phenomena. This study fully reveals the truth about the unsteady gas-liquid flow in SSRH. It is necessary to adopt T-WCM based on algebraic slip model to describe the CO-Ar-molten steel flow phenomenon.

The purpose of this paper is to focus on the characteristics, efficiencies and interaction of many alternative market governance modes, by the approach of comparative institutional experimentation.

First, a highly simplified model of market economy is developed, which is embedded in a three‐layer governance structure. Then the model is transplanted into laboratory experimentation, so the characteristics and efficiencies of different governance modes can be identified by observing the subjects' behaviors under them.

The experimental results show that the market with governance structure based on rule is more efficient than the market with governance only based on long relation and based on preference or belief, and the dynamic improvement of governance based on rule has a destructive effect on the governance based on relation and governance based on preference or belief.

These results have profound implications for the development or enhancement of market institutions in transition or developing countries.