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Producers and consumers – who represent opposing sides of the market – have different frames of reference, which may result in differences in classification of the same products. The authors aim to demonstrate that “classification gaps” have a negative effect on the performance of products and that these effects play a role in different stages of consumers’ decision process.

The data collection consisted of three comprehensive parts covering production and consumption in the music festival market in The Netherlands. The first part focused on festival organizers who were asked to classify their own music festival in terms of musical genres. In total, 70 festival organizers agreed to participate. The second part measured the genre classification of 540 consumers. In the third part, the authors interviewed 1,554 potential visitors of music festivals in The Netherlands about their awareness of the festival and if they considered visiting or actually visited the festival.

This paper provides empirical evidence that a classification gap between the production side and the consumption side of the market has negative effects on music festival performance. In addition, the authors found that this is in part because of lower activation of potential consumers in the marketplace.

An important practical implication of this study is that – in general – producers should be aware that classification gaps can occur – even if they are sure about the classification of their products – and that this can have serious consequences. The category membership of products is often seen as a given, whereas it cannot be assumed that the classification perceived by different economic groups is the same – as demonstrated in this paper.

This paper demonstrates that a fundamental – but understudied – disconnect between the two opposing sides of the market (i.e. producers and consumers) regarding the classification of the same products can have negative effects on performance of these products.

Stock keeping unit (SKU) classifications are widely used in the field of production and operations management. Although many theoretical and practical examples of classifications exist, there are no overviews of the current literature, and general guidelines are lacking with respect to method selection for classifying SKUs. The purpose of this paper is to systematically synthesise the earlier work in this area, and to conceptualise and discuss the factors that influence the choice of a specific SKU classification.

The paper structurally reviews existing contributions and synthesises these into a conceptual framework for SKU classification.

How SKUs are classified depends on the classification aim, the context and the method that is chosen. In total, three main production and operations management aims were found: inventory management, forecasting and production strategy. Within the method three decisions are identified to come to a classification: the characteristics, the classification technique and the operationalisation of the classes.

Drawing on the literature survey, the authors conclude with a conceptual framework describing the factors that influence SKU classification. Further research could use this framework to develop guidelines for real‐life applications.

Examples from a variety of industries and general directions are provided which managers could use to develop their own SKU classification.

The paper aims to advance the literature on SKU classification from the level of individual examples to a conceptual level and provides directions on how to develop a SKU classification.

The recent social and economic changes have resulted in a polarization of product standardization. While some products are constantly demanded to be of internationally recognized standards, others are increasingly required to cater for the needs of individual customers. A growing number of manufacturers serving the latter market are therefore being pushed into a Non‐standard Route Manufacturing (NSRM) situation where product volatility is the norm. Common problems facing these firms include marketing strategies, product costing, and corporate business planning. Under the circumstances product classification is identified as an effective tool to tackle these problems. Based on dissection theory, proposes a Cost Based Product Classification (CBPC) system. The establishment of a CBPC system consists of two major stages, system design and system use. Following a step‐by‐step guide, includes a practical example demonstrating how a system of this kind may be implemented.

This study aims to develop a more inclusive working definition and a formalised classification system for offsite construction to enable common basis of evaluation and communication. Offsite manufacturing (OSM) is continuously getting recognised as a way to increase efficiency and boost productivity of the construction industry in many countries. However, the knowledge of OSM varies across different countries, construction practices and individual experts thus resulting into major misconceptions. The lack of consensus of what OSM is and what constitutes its methods creates a lot of misunderstanding across Architecture Engineering and Construction (AEC) industry professionals, therefore, inhibiting a global view and understanding for multicultural collaboration. Therefore, there is a need to revisit these issues with the aim to develop a deep understanding of the concepts and ascertain what is deemed inclusive or exclusive.

A state-of-the-art review and analysis of literature on OSM was conducted to observe trends in OSM definitions and classifications. The paper identifies gaps in existing methods and proposes a future direction.

Findings suggest that classifications are mostly aimed towards a particular purpose and existing classification system are not robust enough to cover all aspects. Therefore, there is need to extend these classification systems to be fit for various purposes.

This paper contributes to the body of literature on offsite concepts, definition and classification, and provides knowledge on the broader context on the fundamentals of OSM.

In the construction industry, building facade systems are gaining significance as reflected in the number of facade product innovations available on the market. While research studies on the adoption of these facade product innovations are comparatively limited in number. An awareness of the advantages and impacts that a product innovation brings is critical in its adoption, and therefore the purpose of this paper is to develop a classification framework for the use of producers/vendors present the advantages and impacts of their innovative facade products to the architects.

The study, in addition to literature review, consisted of four main steps without distinct boundaries, namely, (1) case study sample selection regarding facade products, (2) data collection through review of published documents, analysis of the data via content analysis and generation of a preliminary framework for the evaluation of facade product innovations, (3) further data collection through semi-structured interviews and analysis of the products and (4) redefining innovation types and finalizing classification framework through assessment of new facade products.

Innovation types may vary according to the product groups under investigation and viewpoints of the classifications. Selected facade products were evaluated in terms of their specific characteristics through identified viewpoints: as a physical object and as an agent. It was revealed that assessing a product innovation in terms of changes in its structural composition and impact on other related physical objects/processes can change the type of innovation. Additionally, insights about improved characteristics specific to facade products, forms of change in these products and architects' new actions in the facade design process were obtained.

Only the products stated by the producers/vendors as being new to the Turkish market at the time they were introduced were evaluated in terms of all aspects proposed in the framework. For these products and others that were evaluated, the product data obtained from the producers/vendors have been deemed to be accurate. Research on equivalent products in the market has not been conducted.

Studies identifying innovation types in the facade industry are very rare. The proposed framework can be regarded as a detailed investigation of product innovation types in the facade industry which offers component-system level improvement/change analysis. Moreover, it can be an effective tool for producers/vendors to ensure that architects have knowledge about their innovative products and their impacts on facade design and also to facilitate the adoption of these products.

A consumer's post‐purchase product usage behaviour influences future decision making. Particularly, for fashion products, in which usage behaviour is highly observable, impacts of usage behaviour on future purchases and other consumers are more conspicuous. This study investigated usage behaviour for clothing fashion products. Behavioural aspects (use frequency and use variety) of usage behaviour were considered with psychological aspects (use satisfaction). The study focused on the differences in usage behaviour (use frequency, use variety and use satisfaction) by product types (fashionability and classification). Data was collected from a questionnaire survey. The results revealed that product types affected the post‐purchase usage behaviour of fashion products. There were significant differences in the use frequency by the product classification, in the use variety by the product fashionability and in the use satisfaction by the product classification and fashionability. Further, significant differences by product types were discussed.

The purpose of this paper is to critically evaluate the state of the art of applications of organisational systematics and manufacturing cladistics in terms of strengths and weaknesses and introduce new generic cladistic and hierarchical classifications of discrete manufacturing systems. These classifications are the basis for a practical web-based expert system and diagnostic benchmarking tool.

There were two stages for the research methods, with eight re-iterative steps: one for theory building, using secondary and observational data, producing conceptual classifications; the second stage for theory testing and theory development, using quantitative data from 153 companies and 510 manufacturing systems, producing the final factual cladogram. Evolutionary relationships between 53 candidate manufacturing systems, using 13 characters with 84 states, are hypothesised and presented diagrammatically. The manufacturing systems are also organised in a hierarchical classification with 13 genera, 6 families and 3 orders under one class of discrete manufacturing.

This work addressed several weaknesses of current manufacturing cladistic classifications which include the lack of an explicit out-group comparison, limited conceptual cladogram development, limited use of characters and that previous classifications are specific to sectors. In order to correct these limitations, the paper first expands on previous work by producing a more generic manufacturing system classification. Second, it describes a novel web-based expert system for the practical application of the discrete manufacturing system.

The classifications form the basis for a practical web-based expert system and diagnostic benchmarking tool, but also have a novel use in an educational context as it simplifies and relationally organises extant manufacturing system knowledge.

The research employed a novel re-iterative methodology for both theory building, using observational data, producing the conceptual classification, and through theory testing developing the final factual cladogram that forms the basis for the practical web-based expert system and diagnostic tool.

The purpose of this paper is to propose a multivariate-based method to classify products in replenishment categories based on principal component analysis (PCA) along with two classification algorithms, k-nearest neighbor (KNN) and linear discriminant analysis (LDA).

In the propositions, PCA is applied to data describing products’ features and demand behavior, and a variable importance index (VII) is derived based on PCA parameters. Next, products are allocated to inventory replenishment models applying the KNNs to all original variables; the classification accuracy is then assessed. The variable with the smallest VII is removed and a new classification is carried out; this iterative procedure is performed until a single variable is left. The subset yielding the maximum classification accuracy is recommended for future classification. The aforementioned procedure is repeated replacing the KNN by the LDA.

When applied to real data from a consulting company, the KNN classification technique led to higher performance levels than LDA, yielding 89.4 percent average accuracy and retaining about 80 percent of the original variables. On the other hand, LDA reached 87.1 percent average accuracy and retained 95 percent of the variables. Based on such results, the authors’ findings suggest that 14 out of the 24 variables are crucial in determining an inventory replenishment model for a product in a specific location replacement. Several of the retained variables were typically used in reorder point estimation or associated to market profile in specific locals.

The idea of this paper is to remove irrelevant and noisy market metrics that jeopardize the correct allocation of products to the most appropriate replenishment model.

The title for this paper was selected advisedly, for it will serve to correct a popular misconception about the part the Chemical Industries Association plays in the collection and dissemination of statistics about the chemical industry.

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the electronic commerce. To evaluate the validity of the model, the structural equation modeling technique is employed.

A method has been suggested to evaluate the impact of customer history, product classification and prices on the success of the recommender systems in electronic commerce. After that, the authors investigated the relationship between these factors. To achieve this goal, the structural equation modeling technique was used for statistical conclusion validity. The results of gathered data from employees of a company in Iran is indicated the impact of the customer history on the success of recommender systems in e-commerce which is related with the user profile, expert opinion, neighbors, loyalty and clickstream. These factors positively influence the success of recommender systems in ecommerce.

The obtained results demonstrated the efficiency and effectiveness of the proposed model in term of the success of the recommender systems in the electronic commerce.

In this paper, the effective factors of success of recommender systems in electronic commerce are pointed out and the approach to increase the efficiency of this system is applied into a practical example.