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This study is aimed at exploring the problem of quantification of process modularity degree. Modularity as a system design principle is apprehended here as the extent to which processes can be decomposed into modules to be executed in parallel and/or in series. For this purpose, a new method is proposed to measure relative modularity of different assembly process structures. This method is compared with other relative modularity measures, namely singular value modularity index, degree of process module, and process module independence, in order to verify its effectiveness. For this purpose, selected representative types of assembly process structures are used. This testing proved that the proposed relative modularity indicator for manufacturing and/or assembly process structures reflects the expected system property in adequate way.

Manufacturing processes are considered as dynamic systems and controlled objects that deal with scheduled/planned changes and unplanned changes due to several possible perturbations. This paper firstly describes potential measures of operational complexity of manufacturing processes. Subsequently, after comparison of their properties, one of them is selected as the most representative complexity measure. This operational complexity measure is based on the information theory and it evaluates the manufacturing processes based on the number of parts, machines and operations. The main goal of the paper is to outline a concept, by which complexity levels of discrete manufacturing processes can be explored. Accordingly, the paper proposes complexity categorization, based on which manufacturing processes can be categorized into the five complexity levels.

When focusing on business models in the context of Industry 4.0 transformation, it is quite obvious that such models will need to adopt new businesses trends, such as mass customization, platform-based businesses, networking manufacturing or creativity-based businesses, respectively. This chapter aims to analyse implementation success factors of Industry 4.0 especially from business models perspective, and also to address some features of human-centred manufacturing in terms of small- and medium-sized enterprises (SMEs). The motivation of this research is awareness of the importance that just a combination of the selected decisive success factors can significantly help businesses to become more competitive and improving their performance.

In service industries where mass operations along with mass customization are demanding, they are keen in minimizing all factors such as total process time, waiting time, and movement time for a cost-effective environment and customer satisfaction. In such situations, maintaining customers to a satisfied level, optimal due date plays a vital role. Also the customers may not be ready in waiting for more than just in time. Keeping all these in mind a model is proposed to find out optimal batch size and corresponding makespan that satisfies required due date(s) for all possible clients of municipal services, such as patients of hospitals. A simple heuristics-based algorithm for mass customization is developed that finds the near-optimal solution for different categories of patients. The search algorithm offers better results which show that the model is effective in such given conditions. The results show that the proposed method performs well in such type of problems.

The chapter, in its first part, describes the questionnaire self-assessment method, which has been applied for testing of ten SMEs with the aim of mapping the requirements for introducing the concept Industry 4.0. Subsequently, based on obtained findings, the key requirement of the SMEs is identified. This output is further used for development of readiness maturity model of organization capabilities for mass customized manufacturing. Then, generic organizational model of a mass customized manufacturing system is proposed. Finally, conclusion comments on the results and future research directions are outlined.

This chapter deals with an implementation of advanced vision technologies for contactless parts of product inspection and automatic object identification using RFID during the assembly process in experimental assembly line to improve quality control over the assembly of the different product parts. Both, vision technologies and UHF RFID system are used for digitization of quality control, and automatic identification for the future world of the Internet of Things (IoT). Moreover, all quality control data are stored in Cloud Platform for the purpose of data analysis and visualization. Subsequently, digital twin of quality control system is generated from its 3D model and transformed into virtual reality device for remote monitoring of quality control.

An important role in product variety management is finding an accurate variety extent to which the product matches the consumer&rsquo, s expectations. In principle, customers prefer to have more rather than less versions of a product from which to choose. This motivates producers to offer a richer variety of goods. As a consequence, it brings a large amount of manufacturing complexity, and configuration conflicts may frequently occur. In order to avoid a situation in which a customer will select mutually incompatible components, product configurators usually recommend corrective actions for generating valid configurations. Nevertheless, the presence of infeasible configurations in customer options are negatively perceived by customers, and therefore it has an unfavorable impact on the sustainability of mass customization. One way to solve this problem is to eliminate, or at least reduce, mutually incompatible components. When considering the fact that eliminating all incompatible components may cause a rapid decrease in product variety, then the reduction of incompatible components can help to solve the product configuration problem. The proposed method aims to find a trade-off solution between minimizing configuration conflicts and maintaining a sufficient level of mass customization. Moreover, two supplementary methods for the determination of infeasible product configurations are proposed in this paper. The applicability and effectiveness of the proposed methods are demonstrated by two practical examples.

Characterising existing approaches to operational complexity, it can be stated that for those metrics are characterful different factors used as variables such as product structure, machine composi...

Process modularity in terms of mass customized manufacturing is considered as a key criterion in designing process architecture. The impact of process modularity on effectiveness of mass customized can be almost predicted based upon experience with traditional mass production. This study firstly analyses relationship between product modularity and process modularity. Then, in order to identify possible tolls for process modularity management, potential process modularity indices are compared and assessed on testing examples. Subsequently, theoretical principles for designing manufacturing and assembly process structures are formulated. The results showed that management of process architecture in mass customized environment might not be conducted in isolation from management of product development.

Even though several researches have focused on exploration of mutual relations between batch size and throughput, there is still relevant research to be done and considerations to be made as organizational problems in manufacturing are not irreversible. That is because practitioners want to know how to optimize input parameters to reach acceptable objectives. The main goal of this paper is to show easily-applicable procedure to analyse mutual relations between batch-size, throughput rate and complexity via a simulation. The exploration has been performed by an analysis of batch siześ impact on performance of production processes organized into two different basic designs – product and process layouts. Moreover, impact of process parameter changes on complexity of production process will be treated.

In this paper dynamic lot sizing problems with the single level multi-item lot sizing with due date, random demands and limited machine capacities is discussed. The focus is on a multi-model parallel assembly lines, where different products are assembled in the same line in runs with varying quantities of daily orders of products, whose sequence and length should be optimally determined. The main goal of this paper is to present a method to determine the sufficient lot sizes released to the shop in order to optimize schedules that conform to due process requirements.

Assembly supply chain systems are becoming increasingly complex and, as a result, there is more and more need to design and manage them in a way that benefits the producers and also satisfies the interests of community stakeholders. The structural (static) complexity of assembly supply chain networks is one of the most important factors influencing overall system complexity. Structures of such networks can be modeled as a graph, with machines as nodes and material flow between the nodes as links. The purpose of this paper is to analyze existing assembly supply chain complexity assessment methods and propose such complexity metric(s) that will be able to accurately reflect not only specific criteria for static complexity measures, but also selected sustainability aspects. The obtained results of this research showed that selected complexity indicators reflect sustainability facets in different ways, but one of them met the mentioned requirements acceptably.

As well-known good planning is a prerequisite to scheduling. This paper outlines methodology framework to optimize schedules in order to employ earliest due date criterion that plays crucial role in mass customization environment. For this purpose, the new concept for production scheduling is described by using proprietary software. In the second part of the paper, the proposed methodology framework was applied and explained on the theoretical example. Enumeration and analysis of the selected production indicators are also provided in this part. The last section of the paper summarizes useful findings.

In the mass customization environment, product platform development includes several aspects. One relates to the extent to which products are customized. Usually, a high level of product variety brings significant benefits to customers. On the other hand, a high degree of product customization may have a negative environmental impact during production, due to higher material usage. One possible way to reduce the impact is eliminating infeasible configuration options, caused by incompatibilities between optional component types, within a product platform. Such optional components are a source of negative complexity. However, a reduction of optional component types within a product platform can lead to decreasing the extent of the variety of a product to an undesirable level. An effective way of finding this optimal level of reduction is to quantify and analyze the rates between positive and negative complexities, which are related to the numbers of configuration options. The method for this purpose is presented in this paper.

The main objective of this paper is to propose an approach to resolve product configuration conflicts based on the previously developed framework for product component modelling in terms of mass customization and original matrix method, by which it is possible to determine the number and structural designs of all relevant product configurations.

Consumers’ market is made up of huge number of products as a direct response to the ever increasing demands of customers. This situation is closely related to constantly evolving technical and technological industries. The rapidly increasing demand on products and variety of functional properties enabled a series of fast-growing and emerging processes and operations all over the industry. Unfortunately for companies, such variety results with a complexity of both, products and related processes. This paper deals with the external-product and internal-process complexity in the view of its usability within a mass customized manufacturing. A novel method to identify and measure product and process complexity of an assembly station is proposed on the basis of the number of product and process variants. Subsequently, the aggregated complexity ratios can provide a complexity map of an entire production line. The novel complexity assessment method is applied and verified on the case automotive manufacturing plant. As a result of this application, the newly-proposed complexity assessment methodology is highly useful especially in customized automotive production as an effective tool for assessing the level of complexity of a selected department, or when introducing a new installation. The newly-proposed methodology can be also adopted by a company to decide on what part or sub-module can be produced by external supplied—transfer of complexity—in order to decrease own process complexity and to keep the extent of variety offered to customers. Finally, based on the case application and verifications, the complexity assessment methodology proposed in this paper is highly applicable in the mass customized manufacturing.

Manufacturing systems can be considered as a network of machines/workstations, where parts are produced in flow shop or job shop environment, respectively. Such network of machines/workstations can be depicted as a graph, with machines as nodes and material flow between the nodes as links. The aim of this paper is to use sequences of operations and machine network to measure static complexity of manufacturing processes. In this order existing approaches to measure the static complexity of manufacturing systems are analyzed and subsequently compared. For this purpose, analyzed competitive complexity indicators were tested on two different manufacturing layout examples. A subsequent analysis showed relevant potential of the proposed method.

A frequent question with regards to product variety in terms of mass customization concerns searching for the optimum level of variety. One way to do so is through quantification of product variety. This paper proposes another approach to measure so called variety induced complexity. This measure is based on Axiomatic design theory and is derived from degree of disorder. The most important finding of this study is that the proposed Axiomatic design-based indicator brings more realistic values than absolute number of available product configurations.

Industry 4.0 as a new manufacturing paradigm brings in a new wave of networked manufacturers and smart factories, which will determine future competitiveness of manufacturing companies. The aim for researchers should thus be to generate and optimize innovative solutions for different types of producers including SMEs in order to support them in meeting the challenges of Industry 4.0. The paper presents the readiness self-assessment method and roadmap model as a tools to secure a consistent implementation of technologies and devices supporting smart logistics and smart production. Proposed method has been applied by selected SMEs and it was proved that the model is easy to use in real production environment.

The sharing economy is becoming increasingly popular in many sectors also thanks to the fact that not only young generation, but people of almost all ages are digitally literate, and therefore they readily participate in sharing economy practices. A very important part of their motivation comes from the knowledge that it is a great way to save money, but a no less important reason is the philosophical aspect, which is consistent with efforts to promote more sustainable consumption practices that prioritize access over ownership. The sharing economy has become a subject of interest not only among researchers and practitioners but also among policymakers and stakeholders who are concerned about the further extension of digital ecosystems. Fortunately, theorists from many fields, such as the commercial economy, gift economy, entrepreneurship, digital transformation, etc., pay due attention to this phenomenon. However, there is still room for focused exploration of this entrepreneurial and innovative approach from specific views in order to bring useful findings and insights on given issues. The intention of this study is to analyze the existing related literature and categorize sharing economy-based business models in relation to traditional business models. Potentially, this study can also contribute to a better understanding of the perspectives of the sharing economy in the transition of SMEs towards the Industry 4.0 paradigm.

Definitions of complexity often depend on several circumstances, such as the nature of investigated complex system, the kind of complexity, the conceptual framework used for a study, the theoretical approach taken, and the like. In this paper, two complexity measures that are based on Boltzmann’s entropy concept and AD theory are proposed and described. The first measure quantifies product variety complexity and the second one focuses on process structure complexity. Such complexity techniques will be used to determine product platform complexity and related process complexity for early stage of design decision-making. The method focused on product platform complexity assumes that the distribution of FR-DP couplings offers a suitable complexity concept, which prescribes that coupled designs should be decoupled, if possible, since uncoupled design is ideal and a decoupled design is less good, while a coupled design is the least satisfactory. Analogically, the same principle is used for the purpose to quantify topological process complexity by transforming input components into process variables and product modules including final product into design parameters. Subsequently, relevant properties of these measures will be analysed by computational experiments. Finally, practical findings for mass customization practice will be presented.