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This paper introduces a schema for the product assembly feature data in an object-oriented and module-based format using Unified Modeling Language (UML). To link production with product design, it is essential to determine at an early stage which entities of product design and development are involved and used at the automated assembly planning and operations. To this end, it is absolutely reasonable to assign meaningful attributes to the parts’ design entities (assembly features) in a systematic and structured way. As such, this approach empowers processes such as motion planning and sequence planning in assembly design.

The assembly feature data requirements are studied and definitions are analyzed and redefined. Using object-oriented techniques, the assembly feature data structure and relationships are modeled based on the identified requirements as five UML packages (Part, three-dimensional (3D) models, Mating, Joint and Handling). All geometric and non-geometric design data entities endorsed with assembly design perspective are extracted or assigned from 3D models and realized through the featured entity interface class. The featured entities are then associated (used) with the mating, handling and joints features. The AssemblyFeature interface is realized through mating, handling and joint packages related to the assembly and part classes. Each package contains all relevant classes which further classify the important attributes of the main class.

This paper sets out to provide an explanatory approach using object-oriented techniques to model the schema of assembly features association and artifacts at the product design level, all of which are essential in several subsequent and parallel steps of the assembly planning process, as well as assembly feature entity assignments in design improvement cycle.

The practical implication based on the identified advantages can be classified in three main features: module-based design, comprehensive classification, integration. These features help the automation and solution development processes based on the proposed models much easier and systematic.

The proposed schema’s comprehensiveness and reliability are verified through comparisons with other works and the advantages are discussed in detail.

The main features of evolvable systems include distributed control, a modularized, intelligent and open architecture, a comprehensive and multi‐dimensional methodological support that comprises the reference architecture. Furthermore, integration of legacy subsystems and modules has been addressed in the methodology. This paper aims to present the latest developments, applications and conclusions drawn to date.

Evolvable assembly system is a new methodology in itself, and is currently being applied within several European projects. Evolvable assembly goes beyond reconfigurability and offers continuous evolution of the system.

The work has been, and is being, implemented through large European research projects. Evolvability, being a system concept, is envisaged addressing every aspect of an assembly system throughout its life cycle, i.e. design and development, operation and evolution.

This paper presents the latest developments, applications and conclusions drawn to date.

The paper presents the methodology and the latest application of it, which is industrial. This is the first application that offers self‐configuration of the equipment.

The Assembly Systems Unit at the Royal Institute of Technology and IVF Stockholm has developed several Flexible Automatic Assembly (FAA) cell solutions over the years (Mark I, Mark II, Mark IIF and Mark III). The industrial reality, however, clearly points out that the basic notions of flexibility must be extended and be enhanced without increasing the complexity. This has led our research team to revise the ideas and solutions available for manual and automatic assembly, resulting in the Hyper Flexible Automatic Assembly (HFAA) project. The paper describes the driving factors behind the needs and objectives for the HFAA project, as well as how it will present a standardised set of assembly process‐oriented system components. The paper also describes the new Mark IV application. This industrial HFAA system is being developed in order to test the concept’s industrial viability. The HFAA concept will allow the user to start from a manual assembly station and gradually add assembly equipment. The basic concepts of stepwise automation, standard assembly machine and sub‐batch principle emanate from our previous research.

This paper aims to provide a method and decision support tool to enhance swift reconfiguration of Plug&Produce (P&P) systems in the presence of continuously changing production orders.

The paper reviews different production scenarios and system design and configuration methods and more particularly specifies the need of decision support tools for P&P systems that integrate configuration and planning activities. This problem is then addressed by proposing a method that helps reduce the solution space of the reconfiguration problem and allows the timely selection of the most promising reconfiguration alternative.

The proposed method was found to be helpful in reducing the reconfiguration alternatives that need to be considered and in selecting the most promising one for different orders. The advantages and limitations of this method are identified, and an illustrative test case of the approach is presented, corroborating the method applicability in the absence of large queues in the system.

This paper addresses a less explored domain within the P&P systems research field, which is the system reconfiguration. It proposed a method to support system validation and reconfiguration jointly with an illustrative test case. This represents an original contribution to the P&P research field, and it can have impact in improving agility and decreasing the complexity of reconfiguration activities to cope with constantly changing production orders.

This roadmap is primarily concerned with the adaptive assembly technology situation in Europe, a topic of particular interest as assembly is often the final process within manufacturing operations. Being the final set of operations on the product, and being traditionally labour‐intensive, assembly has been considerably affected by globalisation. Therefore, unlike most technology roadmaps, this report will not focus solely on particular technologies, but will strive to form a broader perspective on the conditions that may come to influence the opportunities, including political aspects and scientific paradigms. The purpose of this paper is to convey a complete view of the global mechanisms that may come to affect technological breakthroughs, and also present strategies that may better prepare for such a forecast.

The paper describes a technological roadmap.

This paper provides a complete overview of all aspects that may come to affect assembly in Europe within the next 20 years.

The paper gives an original Evolvable Ultra Precision Assembly Systems FP6 project result which will be of general interest for strategic R&D.

Evolvable production systems enable fully reconfiguration capabilities on the shop floor through process‐oriented modularity and multi‐agent‐based distributed control. To be able to benefit architectural and operational characteristics of evolvable systems, there is a need of a new planning approach which links shop floor characteristics and planning operations. This paper seeks to address these issues.

Evolvable production system has a structured methodology in itself. Consistent to this, a reference planning architecture is developed aiming to achieve agility on planning activities. Besides a workload control method is proposed and implemented as a part of the planning architecture.

First applications of evolvable systems have been implemented through European research projects. Shop floor working principles and architectural characteristics are consistent to facilitate more agility on planning activities which are framed at a planning reference architecture called demand responsive planning. As an implementation case, an agent‐based workload control method is proposed and implemented. The characteristics of EPS and proposed planning architecture enable continuous and dynamic workload control of the shop floor to be implemented.

This paper presents a new planning model compatible with evolvable production systems targeting to agility to demand on planning and control activities benefiting shop floor enhancements of a fully reconfigurable system which enables to relax constraints imposed from production systems to planning. In addition, a continuous and dynamic workload control method is proposed and implemented.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

Industry 4.0 (I40) is an open window of opportunity for Turkey, a developed country, to eliminate technological dependence and produce with maximum productivity. However, I40, which corresponds to the fourth wave of industrial revolutions, brings both opportunities and challenges. In this context, this study aims to reveal the foresight of managers in the Turkish white goods industry (TWGI) regarding the advantages and challenges of I40 and compare them with the literature.

The Delphi method was used for the study. Data were collected from managers of companies that are members of the White Goods Suppliers Association (BEYSAD). Seventy managers from 55 companies participated in the first round, and 19 managers participated in the second round of Delphi.

The results show that the most frequently cited advantages are productivity/resource efficiency, data and information-enabled effectiveness/productivity, quality 4.0 and competitiveness/strategy. The most frequently mentioned challenges are financial resources/investment, employee qualification/training, technical/processual challenges and organizational transformation/leadership.

The sample was limited to the managers of the TWGI.

Players in similar ecosystems and policymakers should consider the advantages and respond to potential challenges when creating roadmaps, taking the necessary steps and positioning themselves in the marketplace. In particular, the TWGI – Turkey’s showcase in international markets – should consider the undeniable benefits of the I40 transition to increase innovation.

The findings for the first time highlight the advantages and challenges of I40 in an industry in Turkey, and they will benefit the TWGI, which is among the leaders in Turkey in terms of digital maturity and innovation in its journey to I40.