Multiple Product Variants Research Articles

Enhancing multi-facility manufacturing resilience through integrated product and supply chain network design

This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework’s capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience.

THE DESIGN OF ELEMENTS OF AN ADAPTIVE ASSEMBLY SYSTEM

The paper is an overview of the design of elements of an adaptive assembly system. Furthermore, this paper seeks to address with analysis and evaluation of the elements of the adaptive assembly system. The work presents a taxonomy in the field of adaptive assembly. The core of the work consists of the design of the architecture of individual systems. It is necessary to broaden knowledge about this topic because the designed elements can serve as an overview of this issue. Moreover, at the same time, they can use it in industrial practice. The proposal provides an overview of the elements for possible detailed processing of a specific design of an adaptive assembly system, either when changing an existing assembly system or when designing a new system. To ensure the long-term sustainability of the company's competitive ability, it is necessary to be able to respond flexibly to changes in production and assembly requirements. The issue (theme) is current and was defined based on the finding that there was a demand for the ability to offer multiple product variants and the introduction of new products in shorter and shorter time intervals.

Balancing mixed-model assembly lines for random sequences

Assembly lines are the standard choice for the manufacturing of large products and must combine efficiency with flexibility. Since multiple product variants are assembled on the same line, the variation in the processing time and the production sequence must be accounted for. This is especially hard in the planning phase of assembly lines when the demand is only forecast and the production sequence is not yet known. In this paper, an exact method and heuristic extensions are proposed to tackle the paced assembly-line balancing problem considering a random production sequence. Along with the assignment of tasks, the length of the stations is optimized in a Branch-and-Bound algorithm using Markov chains to evaluate the expected costs, which require the solution of a convex subproblem on each node. Product variants are modelled with a given probability, which can be either independent or correlated. Results of the model can be then further improved by optimizing the sequencing once the orders are known. The exact method can solve instances of small and medium-size containing up to 1012 product variants and the heuristic extensions can give good solutions with up to 1020 products. Results show that the optimization of station lengths is more important for the total cost than the difference between optimal and good task assignment solutions.

A Novel Diagnostic Tool for Human-Centric Quality Monitoring in Human–Robot Collaboration Manufacturing

Abstract The manufacturing industry is currently facing an increasing demand for customized products, leading to a shift from mass production to mass customization. As a result, operators are required to produce multiple product variants with varying complexity levels while maintaining high-quality standards. Further, in line with the human-centered paradigm of Industry 5.0, ensuring the well-being of workers is equally important as production quality. This paper proposes a novel tool, the “Human–Robot Collaboration Quality and Well-Being Assessment Tool” (HRC-QWAT), which combines the analysis of overall defects generated during product variant manufacturing with the evaluation of human well-being in terms of stress response. The HRC-QWAT enables the evaluation and monitoring of human–robot collaboration systems during product variant production from a broader standpoint. A case study of collaborative human–robot assembly is used to demonstrate the applicability of the proposed approach. The results suggest that the HRC-QWAT can evaluate both production quality and human well-being, providing a useful tool for companies to monitor and improve their manufacturing processes. Overall, this paper contributes to developing a human-centric approach to quality monitoring in the context of human–robot collaborative manufacturing.

Defect Detection in Multiple Product Variants Using Hammering Test with Machine Learning

Various nondestructive testing (NDT) methods have been proposed to detect defects inside products. The hammering test is an NDT technique widely used for this purpose. In this test method, a worker judges whether a part is defective or not by listening to the sound after hitting the product with a hammer. Conventional research has shown that a classifier using machine learning can discriminate the hammering data with high accuracy. However, to use these machine learning methods, a lot of samples are needed for learning. In actual industrial situations, it is difficult to collect a lot of samples of defective products. Regarding the hammering test, a machine learning method that can correctly discriminate defective products without sample data has not been proposed. This study aims to construct a system that can correctly discriminate the hammering test data even when there are no defective samples. We propose a method using ‘transfer learning.’ We conducted case studies to demonstrate the effectiveness of the proposed method using two variants of a brazed product. First, we verified the effectiveness of normal machine learning in a hammering test. In this study, we succeeded in discriminating brazed products, which were not correctly discriminated by the workers. We then applied the proposed method to brazed products. We succeeded in discriminating a variant of the brazed products by transferring the knowledge learned from another variant of the brazed products.

ReSIde: Reusable service identification from software families

The clone-and-own approach becomes a common practice to quickly develop Software Product Variants (SPVs) that meet variability in user requirements. However, managing the reuse and maintenance of the cloned codes is a very hard task. Therefore, we aim to analyze SPVs to identify cloned codes and package them using a modern systematic reuse approach like Service-Oriented Architecture (SOA). The objective is to benefit from all the advantages of SOA when creating new SPVs. The development based on services in SOA supports the software reuse and maintenance better than the development based on individual classes in monolithic object-oriented software. Existing service identification approaches identify services based on the analysis of a single software product. These approaches are not able to analyze multiple SPVs to identify reusable services of cloned codes. Identifying services by analyzing several SPVs allows to increase the reusability of identified services. In this paper, we propose ReSIde (Reusable Service Identification): an automated approach that identifies reusable services from a set of object-oriented SPVs. This is based on analyzing the commonality and the variability between SPVs to identify the implementation of reusable functionalities corresponding to cloned codes that can be packaged as reusable services. To validate ReSIde, we have applied it on three product families of different sizes. The results show that the services identified based on the analysis of multiple product variants using ReSIde are more reusable than services identified based on the analysis of singular ones.

Joint decision-making of production and maintenance in mixed model assembly systems with delayed differentiation configurations

Mixed model assembly systems (MMASs) can simultaneously manufacture multiple product variants and are developed to satisfy customers’ increasing desire for products with a high variety. This paper investigates the joint decision-making of production and maintenance policies in MMASs with delayed differentiation configurations, where common operations are performed before differentiated processes. The problem is formulated as a Markov Decision Process (MDP) problem that minimises the average cost per unit time. Monte Carlo simulation is used to evaluate the system performance measures (e.g. volume mix ratio, product quality) under the optimal policy. Numerical examples are presented to illustrate the structure of the optimal policy and the impact of different factors on the system performance in an MMAS that produces two types of product variants. Techniques that can potentially solve the problem in large-sized MMASs are also discussed.

Novel approach for a holistic and completely digital represented product development process by using graph-based design languages

Scope of this research is a holistic, completely digital representation of product development processes. The proposed process is implemented by graph-based design languages based on diagrams of Unified-Modelling-Language. The unique approach allows an automatic generation and evaluation of multiple product variants and is illustrated by the development and evaluation of an automotive bonnet. One main research objective is the investigation of model-based integration of an optimization loop and adaptation of the synthesis process based on results of evaluation steps. It was possible to use results of multiple finite-element simulation for fully automated adaptation of inner structure of the automotive bonnet.

Increasing resource efficiency with an engineering decision support system for comparison of product design variants

The development of sustainable and resource-efficient products requires consideration of multiple design targets concerning the whole product life cycle. Taking these factors into account leads to complex decision situations with conflicting targets and trade-offs. To support design engineers in these situations an Engineering Decision Support System (EDSS) has been developed. In this article, the overall concept of the EDSS is presented. Furthermore, one function of the EDSS to assist a systematic comparison of product variants is introduced in detail. It is based on combining an existing PLM solutions (in particular Siemens Teamcenter 11/Siemens NX9) and software for Life Cycle Assessment (GaBi 7). Beyond a proof of concept for information exchange between both systems a methodology is presented which enables design engineers to systematically assess and select multiple product variants based on their resource utilization. The approach is complemented with a comprehensive case study for different design options of a core slide. In the scope of this study, variations of geometry (solid vs. hollow design), materials (hot-working steel vs. nickel-based superalloy) and manufacturing processes (laser metal deposition vs. milling) were considered. Furthermore, a usability study of the decision support tool is shown.

Sustainability impact of digitization in logistics

Today, most enterprises are undergoing a digitization process with the fourth industrial revolution, named industry 4.0. The focus of the digital transformation lies mainly on production, therefore the terms such as “Factory of the Future” or “Smart Factory” are used similar with this concept. However, there are many reasons for considering the impact of digitalization in logistics and the importance of supply chain for industry 4.0. The key promises of this concept are enabling real-time full-transparency from suppliers to customers, small lot sizes, multiple product variants, connected processes and decentralized, autonomous management. These benefits cannot be achieved by production alone, but only along the entire supply chain. Moreover, logistics should gain a greater vision to fulfill the requirements of industry 4.0 as sustainably as possible in terms of using appropriate technologies and enhancing vertical and horizontal integration among the supply chain partners. In this respect, this study highlights the benefits of the digitization of logistics process and examines the sustainability impact of digitization in logistics. The study is pursued as a single case study within the FMCG companies and their transport service providers in Turkey and it is based on a qualitative method and on connected semi-structured interviews.

Model-Based Requirements Management in Gear Systems Design Based On Graph-Based Design Languages

For several decades, a wide-spread consensus concerning the enormous importance of an in-depth clarification of the specifications of a product has been observed. A weak clarification of specifications is repeatedly listed as a main cause for the failure of product development projects. Requirements, which can be defined as the purpose, goals, constraints, and criteria associated with a product development project, play a central role in the clarification of specifications. The collection of activities which ensure that requirements are identified, documented, maintained, communicated, and traced throughout the life cycle of a system, product, or service can be referred to as “requirements engineering”. These activities can be supported by a collection and combination of strategies, methods, and tools which are appropriate for the clarification of specifications. Numerous publications describe the strategy and the components of requirements management. Furthermore, recent research investigates its industrial application. Simultaneously, promising developments of graph-based design languages for a holistic digital representation of the product life cycle are presented. Current developments realize graph-based languages by the diagrams of the Unified Modelling Language (UML), and allow the automatic generation and evaluation of multiple product variants. The research presented in this paper seeks to present a method in order to combine the advantages of a conscious requirements management process and graph-based design languages. Consequently, the main objective of this paper is the investigation of a model-based integration of requirements in a product development process by means of graph-based design languages. The research method is based on an in-depth analysis of an exemplary industrial product development, a gear system for so-called “Electrical Multiple Units” (EMU). Important requirements were abstracted from a gear system specification list and were analyzed in detail. As a second basis, the research method uses a conscious expansion of graph-based design languages towards their applicability for requirements management. This expansion allows the handling of requirements through a graph-based design language model. The first two results of the presented research consist of a model of the gear system and a detailed model of requirements, both modelled in a graph-based design language. Further results are generated by a combination of the two models into one holistic model.

A Transformable Manufacturing Concept for Low-Volume Aerospace Assembly

This paper presents a vision for a transformable manufacturing system based on reconfigurable and adaptive intelligent technologies, anchored in the description of a real automation cell demonstrating the assembly of various aircraft structures. This production environment developed by the University of Nottingham is a new reconfigurable production environment for the complete manufacturing of high-accuracy high-complexity low-volume aerospace products. This agile reconfigurable cell can accommodate multiple product variants and families which ensures that the capital invested in fixturing is not tied to one application. By compressing the assembly of an aircraft wing structure into a single automated cell, the Future Automated Aircraft Assembly Demonstrator features a flexible, holistic, and context-aware solution that includes automated positioning, drilling, and fastening processes. The assembly cell is based on industrial robots for the handling of aircraft components while an intelligent metrology and control system monitors the cell and ensures that the operations carried out are safe and adhere to the tight tolerances required. These modules are integrated into a single, standardised interface requiring only one operator to control the cell. Performance analyses have shown that, using the reconfigurable production environment described hereafter, an assembly part location accuracy better than ±0.1 mm can be achieved.

Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines

Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets.

Design for Changeability: Incorporating Change Propagation Analysis in Modular Product Platform Design

Modular product platforms (MPP) enable the derivation of multiple product variants while realizing beneficial effects like economies of scale. In order to preserve the benefits throughout the platform's lifecycle it is necessary to design a changeable system. This can be achieved by defining degrees of freedom (DoF) within the platform design that are suitable for the realization of future product variants and changes. In this paper an approach will be presented that supports the design of a MPP achieving an increased changeability by incorporating a change propagation analysis in an early stage. The approach enables the systematic identification and evaluation of DoFs in a system. Using a matrix-based change propagation analysis the effects on the system caused by a variation of a DoF will be calculated and used for the definition of a changeable system design.

A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration

Considering the need for more effective decision support in the context of distributed manufacturing, this paper develops an advanced analytics framework for configuring supply chain (SC) networks. The proposed framework utilises a distributed multi-agent system architecture to deploy fuzzy rough sets-based algorithms for knowledge elicitation and representation. A set of historical sales data, including network node-related information, is used together with the relevant details of product families to predict SC configurations capable of fulfilling desired customer orders. Multiple agents such as data retrieval agent, knowledge acquisition agent, knowledge representation agent, configuration predictor agent, evaluator agent and dispatching agent are used to help execute a broad spectrum of SC configuration decisions. The proposed framework considers multiple product variants and sourcing options at each network node, as well as multiple performance objectives. It also captures decisions that span the entire SC simultaneously and, by implication, represents multiple network links. Using an industry test case, the paper demonstrates the effectiveness of the proposed framework in terms of fulfilling customer orders with lower production and emissions costs, compared to the results generated using existing tools.

Hierarchical clustering for structuring supply chain network in case of product variety

To compete in the market, manufacturers often need to offer multiple product variants to different customers. Given such a challenge of product variety to supply chain, the postponement strategy has been adapted that the differentiating modules are handled at the later stage of the process to minimize the impacts from demand variations. Given the information of product variants and their mix ratios, this paper focuses on the problem of structuring supply chain network that indicates the precedence orders of suppliers and sub-assemblers. The solution approach is based on hierarchical clustering, in which the tree structure is applied to construct the supply chain network. One core technique is to investigate the coupling values between the modules by characterizing the grouping condition in the structuring process. A complexity measure is also adopted to compare the quality of resulting supply chain networks. Five numerical examples are utilized to demonstrate the applicability and effectiveness of the proposed clustering method.

Efficient testing of software product lines via centralization (short paper)

Software product line~(SPL) engineering manages families of software products that share common features. However, cost-effective test case generation for an SPL is challenging. Applying existing test case generation techniques to each product variant separately may test common code in a redundant way. Moreover, it is difficult to share the test results among multiple product variants. In this paper, we propose the use of centralization, which combines multiple product variants from the same SPL and generates test cases for the entire system. By taking into account all variants, our technique generally avoids generating redundant test cases for common software components. Our case study on three SPLs shows that compared with testing each variant independently, our technique is more efficient and achieves higher test coverage.

Effects of product platform development: fostering lean product development and production

In the lean management literature, it is mentioned that product platforms foster lean product development and production. Although product platforms are well known for several effects like reducing costs and attaining multiple product variants, there is still little understanding of how these effects are interrelated and how product platforms support lean product development and production. From a content analysis, we found 27 effects; but only a few effects are mentioned per paper and they vary from paper to paper. Given reasonable indications that common hierarchies and other relationships exist, we define a consistent framework for the effects of product platforms that describes the influence on product development projects and production. An adaptation of this framework to a literature case also illustrates how cost reductions and competitive advantages are achieved. Therefore, the developed framework shows how product platforms’ lean thinking helps companies to become more competitive and profitable in the market.

Product variety, pricing and differentiation in a supply chain

Manufacturers typically sell consumer products through retailers and the presence of intermediaries has interesting ramifications for their product variety and pricing decisions. Retailers may want higher variety to help reduce price competition but the costs of variety are borne by the manufacturer. The increased variety may increase demand and profits for the manufacturer too but this depends on market-specific factors as well as costs. We explore these interactions through a model wherein a manufacturer sells multiple product variants at a wholesale price to two retailers who in turn compete for consumers. Consumers choose between the retailers based on the price and variety offered by each retailer and the search or transportation cost incurred by the consumer, equivalent to the level of retailer differentiation in our model. Several insights emerge from the analysis. The manufacturer offers the same variety to both retailers and this variety increases with market size and consumer sensitivity to variety. We find that some retailer differentiation benefits the retailers (not the manufacturer) but too much differentiation hurts both the retailers and the manufacturer. If the market is fully covered, then the channel is coordinated even with a simple wholesale pricing contract. If the retailers incur costs to sell the product, the manufacturer surprisingly loses out more than the retailers and in fact absorbs some or all of the retailer costs. Finally, asymmetry between retailers has some unexpected consequences. For example, variety is not impacted by asymmetry in consumer preferences for a retailer and the manufacturer offers the same variety to both retailers.

Problem solving for multiple product variants

Pneumatic control systems are built using various types of valves of which each single one is to meet specific constraints given by the environment a brake system is operated in. In the perspective of a manufacturer of pneumatic appliances this results in a high number of product variants, while each variant requires several product versions. Complexity of product range inflicted by e.g. functional requirements might increase with the growth of a company, if the products of absorbed enterprises are retained and employed as a basis for new developments as well as the firm's original products. Thus the aim of the approach presented in this paper is the identification of highly similar or even redundant products and the design of standardized components in order to facilitate the development process and to reduce current manufacturing costs. Thereby the approach should give the means to determine what has to be standardized and optimized in product architectures and how external and internal constraints should be taken into consideration. By the use of functional modelling the products embodying physical solutions of identical function structures are recognized. Further the function structures of all product types are compared with the objective of discovering identical substructures. By the analysis of substructures, contradictory as well as consistent ones, standardized patterns and problem statements can be derived. Finally standardized designs can be defined for each problem statement to form a construction kit to be used over all product variants.