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The purpose of this paper is to apply group assembly (GA) considerations to the construction industry and to provide evidence of construction sector industrialization with quantitative results. Moreover, a flexible assembly system is proposed, especially designed to cope with variability: this can be easily extendable to other industrial sectors, especially when dealing with extremely variable environments.

The paper presents a case study conducted at an Italian company leader in the design, manufacture and installation of architectural claddings and lightweight continuous facades.

The research empirically demonstrates how the application of GA and the creation of project families lead to consistent enhancement also within the construction industry. The case study reveals great improvement in terms of both operating and ergonomic performances, agile assembly system reconfiguration design and make span reduction. The possibility of correlating a new project to an identified family gives the opportunity to understand the best assembly line layout configuration which should be assigned to the project, to improve the throughput time and the controllability of the assembly process and to guarantee efficient floor space utilization, lead‐time control, accuracy and reliability.

The novelty of the study lies in the way the assembly layout is designed to cope with variability: the assembly line, which is dedicated to more stable processes, is coupled with pre‐assembly stations, easily reconfigurable, meant to be “variability absorbers”. As far as the authors know, this is also the first time GA is applied to the construction industry. Moreover, a timely topic such as construction sector industrialization is confirmed by quantitative results.

Dimensional variation management is a major challenge in multi‐station sheet metal assembly processes involving complex products such as automotive body and aircraft fuselage assemblies. Very few studies have explored it at a preliminary design phase taking into consideration effects of part deformation on variation propagation, since early design phase involves the development of imprecise design models with scant or incomplete product and process knowledge. The objective of this paper is to present a variation model which can be built into the preliminary design phase taking into consideration all of the existing interactions between flexible parts and tools in multi‐station sheet metal assembly process.

The paper addresses this problem by first, presenting a beam‐based product and process model which shares the same data structure of the B‐Rep CAD models, and therefore can be embedded in CAD systems for automatic product skeletal design; second, determining the influence of part deformation, for various, differing joining and releasing schemes, on variation propagation; and third, utilizing this information to generate a vector‐based variation propagation model for multistation sheet metal assemblies.

This paper presents a beam‐based product and process model which shares the same data structure of the B‐Rep CAD models, and therefore can be embedded in CAD systems for automatic product skeletal design; determines the influence of part deformation, for various, differing joining and releasing schemes, on variation propagation; and utilizes this information to generate a vector‐based variation propagation model for multistation sheet metal assemblies.

A truck cab assembly is presented to demonstrate the advantages of the proposed model over the state‐of‐the‐art approach used in industry for sheet metal assemblies.

Looks at the advantages of dimensional management in improving quality and reducing cost through controlled variation and robost design as opposed to the more traditional tolerance assignment. Lists the limitations of tolerance assignment and details the six basic steps of the dimensional management process. Gives practical advice on how to undertake the process of dimensional management.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Traditional production management systems are often designed to support manufacturing based on a limited number of product variants. With the emerging trend of producing customized products to meet diverse customer needs, the number of product variants increases exponentially in mass customization. In a situation of assembly‐to‐order production, production planning and control involve not only product variety, but also process variety. It is imperative to synchronize product and process variety in a coherent manner.

This paper discusses integrated product and production data management for assembly‐to‐order production. An integrated BOM and routing generator is proposed for the purpose of unifying BOM and assembly‐planning data in order to accommodate a wide range of product variability and production variations.

An integrated BOM and routing generator excels in variety synchronization for assembly‐to‐order production planning.

Variety synchronization opens many opportunities for research into mass customization production. It is important to deal with not only the results of high variety production but also the causes of process variations.

The proposed methodology is applicable to manage high variety production like mass customization.

The paper proposes the variety synchronization issue in mass customization. An object‐oriented methodology is applied to manage variety of BOMs and variety of routings.

The purpose of this paper is to describe new methods to manage variation in complex manufacturing process chains and to show synergies between the variation risk management (VRM) and six‐sigma approaches.

The research methodology was experimental prototyping conducted in collaboration with industry partners. A prototype IT system was developed and tested to implement the approach. A quality cost‐based system was used to assess variation at each operation stage, for every product characteristic.

A comprehensive approach to the management of manufacturing variation is introduced, based on a new process risk matrix which can be used to specify an individual variation risk for every manufactured characteristic, throughout a manufacturing process chain. The approach has been implemented in a prototype software system and is aimed at the complex products such as those manufactured by the aerospace industry.

The IT approach described was developed during the research and is not commercially available.

Manufacturing industry should be able to use this approach, in particular the process risk matrix concept, to develop more effective management of product variation and resultant cost, in complex process chains.

The paper describes a novel approach to combine VRM and six‐sigma concepts, and introduces the process risk matrix as a structure to understand process variation.

Overview All organisations are, in one sense or another, involved in operations; an activity implying transformation or transfer. The major portion of the body of knowledge concerning operations relates to production in manufacturing industry but, increasingly, similar problems are to be found confronting managers in service industry. It is only in the last decade or so that new technology, involving, in particular, the computer, has encouraged an integrated view to be taken of the total business. This has led to greater recognition being given to the strategic potential of the operations function. In order to provide greater insight into operations a number of classifications have been proposed. One of these, which places operations into categories termed factory, job shop, mass service and professional service, is examined. The elements of operations management are introduced under the headings of product, plant, process, procedures and people.

This study aims to present a model and analysis of automotive body outer cover panels (OCPs) assembly systems to predict assembly variation. In the automotive industry, the OCPs assembly process directly influences the quality of the automobile body appearance. However, suitable models to describe variation propagation of OCPs assembly systems remain unknown.

An adaptive state space model for OCPs assembly systems is introduced to accurately express variation propagation, including variation accumulation and transition, where two compliant deviations make impacts on key product characteristics (KPCs) of OCP, and the impacts are accumulated from welding process to threaded connection process. Another new source of variation from threaded connection is included in this model. To quantify the influence of variation from threaded connection on variation propagation, the threaded connection sensitivity matrix is introduced to build up a linear relationship between deviation from threaded connection and output deviation in KPCs. This matrix is solved by homogeneous coordinate transformation. The final deviation of KPCs will be transferred to ensure gaps and flushes between two OCPs, and the transition matrix is considered as a unit matrix to build up the transition relationship between different states.

A practical case on the left side body structure is described, where simulation result of variation propagation reveals the basic rule of variation propagation and the significant effect of variation from threaded connection on variation propagation of OCPs assembly system.

The model can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase. The calculated results of assembly variation guide designers or technicians on tolerance allocation, fixture layout design and process planning.

Problem solving and continuous process improvement are key elements to achieve business excellence. Many problem solving and process improvement methodologies have been proposed and adopted by organisations, with DMAIC being the most widely used. The purpose of this paper is to present an empirical application of a modified version of DMAIC which enabled a world-class organisation to achieve an optimum reduction in the lead time of its aerospace engine assembly process.

The paper reviews the most commonly used problem solving and process improvement methodologies and specifically, DMAIC, its variations and limitations. Based on this, it presents define, measure, analyse, improve, review, control (DMAIRC). Finally, DMAIRC is empirically applied through a case study, in a world-class manufacturing organisation.

The results obtained from the case study indicate that DMAIRC is an effective alternative to achieve the maximum improvement potential of a process. In particular, DMAIRC helped the organisation studied to achieve a 30 percent reduction in the lead time of its engine assembly process.

The novel problem solving and process improvement methodology presented in this paper can be used by organisations to undertake a more effective improvement project by assuring that the maximum potential of their improvement initiatives and processes is achieved.

The purpose of this paper is to investigate the benefit of pre-emptive disruption management measures for assembly systems towards the target dimension adherence to delivery times.

The research was conducted by creating simulation models for typical assembly systems and measuring its varying throughput times due to changes in their disruption profiles. Due to the variability of assembly systems, key influence factors were investigated and used as a foundation for the simulation setup. Additionally, a disruption profile for each simulated process was developed, using the established disruption categories material, information and capacity. The categories are described by statistical distributions, defining the interval between the disruptions and the disruption duration. By a statistical experiment plan, the effect of a reduced disruption potential onto the throughput time was investigated.

Pre-emptive disruption management is beneficial, but its benefit depends on the operated assembly system and its organisation form, such as line or group assembly. Measures have on average a higher beneficial impact on group assemblies than on line assemblies. Furthermore, it was proven that the benefit, in form of better adherence to delivery times, per reduced disruption potential has a declining character and approximates a distinct maximum.

Characterising the benefit of pre-emptive disruption management measures enables managers to use this concept in their daily production to minimise overall costs. Despite the hardly predictable influence of pre-emptive disruption measures, these research results can be implemented into a heuristic for efficiently choosing these measures.