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Presents research into the improvement of flow‐line assembly systems. Aims to understand and improve the design and control of manually intensive flow‐line assembly in the clothing industry. A simulation model of the progressive bundle system has been constructed, incorporating operator performance variations and learning effects, machine failure and repair, operator absenteeism, quality failure and supervisory control. While the operator performance data and the stochastic variables are handled satisfactorily within the simulation, the problems of control are not handled well by conventional discrete event modelling techniques. Adopts a knowledge‐based approach to control in which an online computerized supervisor exercises control over the execution of the simulation run. Complex system models are not easy to validate and a four‐stage approach is used to demonstrate conformance with real‐world systems: qualification; face validity; modular validation; and time‐series system behaviour. Discusses applications of the model and the results of experiments with a line starting work on a new style.

The paper discusses the design, implementation and validation of FLEAS, a flowline environment for automated supervision of clothing manufacturing systems. The paper argues for a mixed initiative approach to system control which incorporates both a scheduling component, based on local search, and a simulation component which dynamically tests the validity of the supervisory decisions made by the scheduler.

Explains that the improvement of flowline assembly systems provides the context for this research: to understand and improve the design and control of manually‐intensive flowline assembly in the clothing industry. Constructs a simulation model of the progressive bundle system, incorporating operator performance variations and learning effects, machine failure and repair, operator absenteeism, quality failure and supervisory control. Notes that, while the operator performance data and the stochastic variables are handled satisfactorily within the simulation, the problems of control are not handled well by conventional discrete event modelling techniques. Adopts a knowledge‐based approach to control, in which an online computerized supervisor exercises control over the execution of the simulation run. As complex system models are not easy to validate, uses a four‐stage approach to demonstrate conformance with real‐world systems: qualification, face validity, modular validation and time‐series system behaviour. Discusses applications of the model and the results of experiments with a line starting work on a new style.

Integrating sustainability strategies with business processes is the most challenging task for industry professionals due to the lack of a proper understanding of sustainability concepts. At the same time, a lack of proper guidance restricts them from pursuing such activities. As far as the aspects of implementation are concerned, it is very tough to analyse and pick up key points to start with. The purpose of this paper is to utilize a text mining approach to analyse qualitative data and identify the critical issues for implementing sustainability in the manufacturing sector by focussing on turning processes based on the survey responses of researchers and industry professionals.

An integrated method employing principal component analysis (PCA) and the k-means clustering algorithm has been applied to extract useful information from a set of various suggestions provided by both the groups surveyed. The textual data has also been visualized using word clouds and, thus, it has been compared with the results of the text mining approach.

The results of the study indicate the importance of the role of government organizations and the need for a skilled workforce, which are crucial for enhancing aspects of sustainability in the manufacturing sector, as supported by both researchers and industry professionals. Besides this, researchers have highlighted the need to focus more on environmentally related issues, whereas industry professionals have raised performance-related issues.

The findings of the study present the important concerns of both the groups towards sustainability initiatives and, thus, will help to enhance the understanding of the underlying possibilities of negotiating jointly to enhance the performance of machining processes.

The novelty of this paper lies in its identification of important initiatives that are having a direct impact on the sustainable aspects of the machining process, based on the views of researchers and industry professionals.

Stochastic computer‐simulation models have been constructed of the clothing supply chain and applied to retail inventory control. This quantifies the performance of quick response procedures for seasonal merchandise, thus creating an analytical tool. They are designed to investigate the effects of improved retailing and supply procedures on financial and other performance measures using two supply strategies: fixed quantity re‐ordering and fixed interval re‐ordering. These offer a wide range of options in experimentation. They permit an evaluation of both purchasing systems in relation to different quantities and lead‐time scenarios. Experimental work with both models has shown that if the replenishment time exceeds two weeks, the potential for lost sales greatly increases. This provides a benchmark figure for assessing the responsiveness of clothing industry supply chains.

A logistics‐based project is described which addresses the need for better intermodal transport, whilst balancing economic and environmental gains through the use of Internet technologies. Pipeline intermodal system to support control, expedition and scheduling (PISCES) provides an integrating platform for using these technologies in processing and sharing commercially sensitive data within transport chains (i.e. road, rail and barge). The paper demonstrates how information from an Internet‐based system can be used to drive a scheduling tool to provide appropriate routes for the transport of goods, using a multimodal transport model.

The Bass model was created to analyse the product life cycle (PLC) in order to help sales and marketing departments in their business decision making. The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

In this research, the authors analysed the 223,577 clients assisted by a nationwide network of car dealerships, who acquired 36,819 vehicles, during a 24-month period. In the analysis, the Bass model was applied to define the PLC phases; and nonlinear regression models were used to carry out the estimations.

The results show that more consistent estimates of the PLC phases are obtained from the clients assisted variable. This work has theoretical and practical implications that can help business management.

The most remarkable thing about this research is that we have shown that the functionality of the clients assisted variable is greater than the sales variable for the Bass model and, therefore, for PLC estimation.

The results of this research are very useful, since they allow marketing decision makers to obtain more consistent estimations of the PLC phases using the Bass model and the clients assisted variable. This is based on the fact that the use of this variable helps to detect if there is any deficiency in the design of the marketing strategy when the client does not make the purchase.

The data on clients assisted are as easily available to companies as sales data. However, the use of this variable improves PLC analysis and this allows an improvement in company forecasting. Thus, making the clients assisted variable a tool to strategically plan investments in innovation and marketing would reduce uncertainty in business management.

The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

The purpose of this study is to understand how business-to-business organizations use social media during the sales process.

The meta-synthesis steps methodology (Hoon, 2013) was applied.

This study presents a theoretical framework and contributes to improved understanding of how business can use social media in the sales process stages. The results allow identifying stages, discussing the integration between marketing and sales and generating benefits for the organization.

The proposed framework helps in understanding the previously performed fragmented studies. This study shows that social media use not only influences the sales process stages and increases the benefits to the business but also works as a mediator in the relation between sales process stages and identified benefits.

Assembly line balancing problems that occur in real world situations are dynamic and are fraught with various sources of uncertainties such as the performance of workers and the breakdown of machinery. This is especially true in the clothing industry. The problem cannot normally be solved deterministically using existing techniques. Recent advances in computing technology, especially in the area of computational intelligence, however, can be used to alleviate this problem. For example, some techniques in this area can be used to restrict the search space in a combinatorial problem, thus opening up the possibility of obtaining better results. Among the different computational intelligence techniques, genetic algorithms (GA) is particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. In this paper, we present the details of a GA and discuss the main characteristics of an assembly line balancing problem that is typical in the clothing industry. We explain how such problems can be formulated for genetic algorithms to solve. To evaluate the appropriateness of the technique, we have carried out some experiments. Our results show that the GA approach performs much better than the use of a greedy algorithm, which is used by many factory supervisors to tackle the assembly line balancing problem.

This paper aims to investigate the bond strength of metakaolin-based geopolymer mortar with cement mortar.

The mortar-mortar bond strength is assessed by slant shear and split tensile tests; pure shear strength is evaluated by Mohr’s criterion for result validation. Metakaolin-based geopolymer mortar is cast over the cured cement mortar specimen with two levels of surface roughness: smooth or grooved interface. The influence of the alkaline solution to metakaolin ratio on geopolymer bond strength is studied. Compressive strength, ultrasonic pulse velocity, permeability and flow table tests are also performed.

The paper’s findings are highlighted as follows: (1) strong mortar-mortar bond properties achieved for geopolymer mortar in all tests and conditions and validated by Mohr’s criterion and pure shear, (2) a lower alkaline solution to metakaolin ratio achieves higher bond strength to Portland cement mortar and (3) geopolymer mortar has higher compressive strength and ultrasonic pulse velocity than cement mortar at all curing ages; additionally, it is more flowable and less permeable.

The full replacement of Portland cement with metakaolin, a more sustainable cementitious material, will contribute to the decarbonization of the construction industry.

Limited research has been carried out on the bond strength of metakaolin-based geopolymer mortar to Portland cement mortar. Also, computing the pure shear using Mohr’s circle criterion of metakaolin-based geopolymer to validate the results can be considered original.