Mass Customization Production Research Articles

Coordination and Enhancement Schemes for Quick Response Mass Customization Supply Chains With Consumer Returns and Salvage Value Considerations

Mass customization (MC) programs are commonly seen in the real world. Many companies offer consumer returns and quick delivery for their MC products to the market. Thus, the quick response (QR) strategy is crucial for MC supply chains. Prior studies in related areas only examine MC supply chains with limited features and topics, such as measures for win-win coordination or profit risk analysis, but without a comprehensive consideration of these aspects together. Besides, the systems enhancement schemes are not yet explored. This paper aims to fill these gaps and generate novel insights. Among the findings, we show that a higher salvage value of the consumer returned items and a higher salvage value of the unused inventories both can reduce the value of QR for the supplier. We illustrate how a simple two-part tariff contract and a sophisticated hybrid contract can achieve win-win coordination with different levels of flexibility. We also conduct a profit risk analysis on the MC supply chain. Finally, we demonstrate how three practical measures, namely technology investment, product design improvement, standardization of component, can further improve the MC supply chain system's performance.

The impact of lead-time uncertainty in product configuration

Product configuration is widely practiced and is very effective when offering mass customisation production. This strategy assists manufacturers in understanding customers' preferences and demands, presenting product relevant rules, choosing alternative components and modules, and constructing theoretical models. However, the uncertainties (such as lead-time uncertainty) are not fully considered in the product configuration. To fill this gap, we develop an uncertain product configuration model based on uncertain lead-time and time-sensitive demand using uncertain programming. Another contribution is that we explored the interdependence between uncertain lead-time and outsourcing strategy. This uncertain mixed-integer programming is solved by using CPLEX 12.8. During a series of numerical experiments, we found that the profit and the sourcing strategies are sensitive to the uncertain lead-time and the outsourcing strategy helps companies reduce losses due to uncertainty.

Laser Transmission Welding of additive manufactured parts: process modifications to reduce cavities inside the weld seam

For small batches, prototypes, and customized mass production, additive manufacturing is a prominent technology in the production of complex thermoplastic parts. A frequently used process is Fused Deposition Modeling (FDM). For more and more applications, additive manufactured components have to be joined in order to form larger functional assemblies. Laser transmission welding (LTW) of thermoplastics is an industrial established technique, e.g. for joining injection molded parts in the automotive sector.Compared to injection molding, the FDM process itself results in an inhomogeneous structure with a significant amount of air inside a component. Because the part is built up layer by layer, it supports the formation of cavities inside the weld seam and thus a reduction in the weld seam strength.To optimize the weld seam quality, new strategies for the design of FDM parts for LTW are being investigated. Welded parts are being analyzed with tensile testing and cross sections.

Development of a Context-Aware Assistive System for Manual Repair Processes - A Combination of Probabilistic and Deterministic Approaches

Looking at the trend of mass customization of products, companies are confronted with an increasing number of different products and product variants. Especially for humans working in assembly and rework domain it is increasingly difficult to maintain an overview of all assembly paths and work steps. To tackle this problem, context aware assistance systems are introduced to the field. Although there is a lot of research in the area of context aware assistive systems, most work focuses on fixed work plans or purely probability-based activity recognition. As a result, these systems either restrict the worker’s personal way of working or the modelling of the work plans is complex and time-consuming. The goal of our approach is a context sensitive support for all performable steps at a given time. That system requires an intuitive modelling of work processes including an activity recognition. Therefore, we present a process model consisting of the combination of a petri net and aspects of Hidden Markov Models (HMM). Based on the modelled work process, the system determines performable work steps at the given time, while the activity recognition selects the most likely work step on the subset of feasible work steps. In this paper we describe the structure of the process model and how petri nets and approaches from the area of HMM are combined in this model. The outlook shows an implementation approach for the model.

User Behavior Analysis by Cross-Domain Log Data Fusion

Modern mass customization production allows user interaction activities to be distributed in the full product life cycle via multiple information systems. Investigating user behaviors across the boundaries of different domains helps to deeply integrate isolated fragmental profiles into a comprehensive one, and therefore can provide multi-dimensional, high-quality and valuable services. However, traditional user behavior analysis models are based on individual user profile information derived from separate domains, such as requirement analysis, design, supply chain, logistics, marketing, etc., which have not considered the whole complexity of mass customization manufacturing. In this paper, we introduce the concept of multi-dimensional semantic activity space, where user behavior features are merged and represented as combined vectors. User behavior patterns are discovered by mining action data extracted from log files in different subsystems in the corresponding domains. We also identify distinct categories of user behaviors in various modules and subsystems in the context of an intelligent manufacturing environment. Experiment results show a strong indication that the proposed approach can be applied to reveal variations in typical behavioral aspects of cross-domain participants, in terms of patterns in resource access, operation tasks, performance assessment, etc.

Influences of supply chain finance on the mass customization program: risk attitudes and cash flow shortage

AbstractIn recent years, to satisfy diversified customer needs in the target market, an increasing number of brands have launched mass customization (MC) programs as one of their core operations strategies. The production process flexibility and personalization degree of customized products (i.e., the MC products) have thus become key to the success of MC schemes. In the meantime, however, considering the high market uncertainty in consumer preferences and the challenges from the production process, there are plenty of risks in MC operations. Hence, in this paper we develop a game‐theoretic model consisting of an MC brand and an upstream manufacturer to identify the influences of supply chain finance (SCF) on MC programs. The influences brought by the risk attitudes of both MC brand and manufacturer are analyzed. Besides, an extra interest rate for raising the working capital for production (e.g., from the bank) at the manufacturing level is also considered in this paper to address the impact of cash flow shortage. We find that when the positive sensitivity of the market demand with respect to the modularity level of the MC product is sufficiently high, the optimal product modularity level of the MC product increases with the MC brand's degree of risk averse. In addition, it can also be observed that when both the market demand's positive sensitivity to the modularity level of the MC product and the MC brand's opportunity cost rate for placing the advance order are sufficiently high, any increase in the risk‐averse degree of the MC brand or the manufacturer will lower the manufacturer's optimal wholesale price of the MC product as well as the MC brand's optimal rate of the advance order. Furthermore, the higher the interest that the manufacturer needs to pay (such as to the bank) for raising the working capital for the MC production process, the lower the optimal wholesale price of each finished MC product will be to help the manufacturer raise more money through the advance order placed by the MC brand. These observations complete the findings in the extant literature and provide important managerial insights into managing MC operations.

Socio-Technical Perspectives on Smart Working: Creating Meaningful and Sustainable Systems

Technological advances have made possible industrial and commercial applications of artificial intelligence, virtual reality and highly integrated manufacturing systems. It has also freed business activity from a focus on place, as both work activities and markets have been able to harness information and communication technologies in order to operate remotely. As a result, researchers have highlighted a phenomenon of ‘smart’ working. Some have pointed to a fourth Industrial Revolution (Industry 4.0) in which ‘smart’ factories use robotics to achieve high performance. There is now a suggestion of progress towards Industry 5.0, in which technological and social systems work in harmony to deliver personalised mass customization of products and services. This paper examines these developments from the perspective of unique, individual understandings of work roles and sustainability, posing the questions ‘Smart’ from whose point of view? Do smart systems promote sustainable organizations? How should design of smart systems be approached? It suggests that contemporary socio-technical systems approaches to organizational analysis are the best way to support harnessing of smart technologies in organizations.

Patent analysis and classification prediction of biomedicine industry: SOM-KPCA-SVM model

This paper proposed the application of a combinatorial model of machine learning to patent quality classification and forecasting in the biomedical industry. The model consists of three methods: Self-Organizing Map (SOM), Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM), and names it SOM-KPCA-SVM model. The model proposed in this paper is implemented in two steps. First, the SOM groups the patent data and defines the patent level. Second, the patent data is reduced by KPCA to decrease noise, and SVM is applied to KPCA’s patent data to derive the classification results. The study collected 11,251 biopharmaceutical patent data from the patent transaction news. After training the patent quality model, 2196 historical patents were used to verify the performance of the training model. The accuracy of the match between experimental results and actual transaction status reached 84.13%. Therefore, the proposed patent quality method as a preliminary screening solution automatically and effectively evaluates the quality of patents. This method saves valuable time for reviewing experts, facilitates the rapid identification of high-quality patents, and can be used for the development of commercialization and mass customization of products.

Grouping workers with similar learning profiles in mass customization production lines

Mass Customization (MC) implies in large product variety and reduced production batch sizes. Tasks that rely on human ability are especially affected in this context as workers must quickly adapt to the requirements of new models. That adaptation process takes place differently in each worker, justifying the development of strategies to group workers with similar learning profiles. This paper proposes a framework to form homogeneous groups of workers based on their learning profiles by integrating learning curve (LC) modeling and cluster analysis. For that, performance data are collected and modeled through LCs, such that LC parameters quantify workers’ adaptation to tasks. Principal Components Analysis (PCA) is applied to the dataset consisting of the LC parameters; PCA outputs give rise to an importance index that guides a backward parameter selection process. After each LC parameter is removed from the dataset, a new grouping using the Fuzzy C-Means (FCM) clustering technique is carried out using the remaining LC parameters, and the quality of the formed groups is assessed by means of three metrics. When applied to a shoe manufacturing process, 8 out of the original 29 LC parameters were retained, and used to insert workers into two clusters. The reduced subset of parameters elevated the quality of the clustering procedure by 29.4% (from 0.476 to 0.616) according to the Silhouette Index; similar improvements were indicated by the other two metrics. The retained parameters are related to workers’ performance at the first repetition and previous experience, highlighting the importance of well-designed training programs.

Real-Time asset tracking; a starting point for Digital Twin implementation in Manufacturing

Mass customization and shortening product life cycles pose a heightened set of requirement on modern production systems. Fast response to changing conditions has been found to be a key to competitive advantage for manufacturing firms. In order to cope with this, modern firms are entering in the fourth industrial revolution. Smart pieces of equipment, based on ubiquitous computation and reliable communication, are being deployed in the shop-floors and supply information to the whole enterprise. The work presented in this paper belong to a pilot project to explore promising technologies in this domain and deploying them in a test-line at a large vehicle manufacturer in Sweden. Traceability and visibility of assets plays a critical role in the process of improving shop-floor performance, contributing to better control, planning and scheduling decisions. In detail this article focuses on the collection of requirement and design of a real-time positioning system for asset tracking including its implementation with other technologies in the company demonstrator.

Combining Simulation and Augmented Reality Methods for Enhanced Worker Assistance in Manual Assembly

Due to mass customization product variety increased steeply in the automotive industry, entailing the increment of worker’s cognitive load during manual assembly tasks. Although worker assistance methods for cognitive automation already exist, they proof insufficient in terms of usability and achieved time saving. Given the rising importance of simulation towards autonomous production planning, a novel approach is proposed using human simulation data in context of worker assistance methods to alleviate cognitive load during manual assembly tasks. Within this paper, a new concept for augmented reality-based worker assistance is presented. Additionally, a comparative user study (N=24) was conducted with conventional worker assistance methods to evaluate a prototypical implementation of the concept. The results illustrate the enhancing opportunity of the novel approach to save cognitive abilities and to induce performance improvements. The implementation provided stable information presentation during the entire experiment. However, with regard to the recentness, there has to be carried out further developments and research, concerning performance adaptions and investigations of the effectiveness.

Using Serious Games to Inform Mass Customization Production Methods from the Fuzzy Front-End of New Product Development

Mass customization means to offer products or services which meet the demands of each individual customer, but which still can be produced and delivered with mass production efficiency. The buzz surrounding technologies like 3D printing and artificial intelligence has many start-ups hoping to capitalise on this dream of creating personalised products at an affordable price, and well-established companies scrambling to innovate and maintain their market share. However, the majority of them are failing as they struggle to understand one key question – where does customization make sense? Customization and personalisation only make sense where the value of the perceived benefit outweighs the cost to implement it. In other words, will people pay for it? Looking at the Kano Model makes it clear that it depends on the product. In products where customization is an inherent need, like prosthetics, mass customization technologies can be highly beneficial. However, for products that already sell as a standard, like headphones, offering customization is likely only an added bonus, and so the product development team must figure out if the customers’ perception of the added value of this feature will outweigh its premium price tag. This can be done through the use of a ‘serious game,’ whereby potential customers are given a limited budget to collaboratively buy and bid on potential features of the product, before it is developed. If the group choose to buy customization over other features, then the product development team should implement it into their design. If not, the team should prioritize the features on which the customers have spent their budget. The level of customization purchased can also be translated to an appropriate production method. For example, the most expensive type of customization would likely be free-form design and could be achieved through digital fabrication, while a lower level could be achieved through short batch production. Twenty-eight teams of final year students from design, engineering, construction and technology tested this methodology when bringing a product from concept through to production plan and found that it allowed them to confidently decide what level of customization, if any, would be worth offering for their product, and what would be the best method of producing it. They also found that the discussion and negotiations between players during the game led to invaluable insights, and almost all teams said they would use the methodology in future product development projects, especially those from non-design backgrounds.

Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries

In order to respond to today’s dynamic needs of customers, customized mass production systems have been more and more developed that, are facing with different challenges. Maintenance planning and scheduling is one of the most important manufacturing components in such systems, due to importance of availability and high investment for this kind of system. In order to consider real machine operation state, recently, predictive maintenance method is proposed. However, in traditional methods, historical failure data is the main source for this planning. In this paper, we propose a methodology for dynamic predictive maintenance for a real case in automotive industries with considering multi-component structural and positive economic dependencies between them. In our methodology, we propose to gather data science with mathematical optimization method. Prediction of Remaining Useful Life (RUL) of machine parts has been made by Artificial Neural Network method with considering sensors data. With this RUL values and other cost values and optimization model parameters, and by solving proposed mathematical model, an optimal schedule is achieved with minimization of maintenance costs. Through a dynamic proposed procedure, when a new data is received, RUL values and model parameters are readjusted and new optimal solution for maintenance planning and scheduling can be achieved. Further, some scenarios are defined for analyzing the dynamicity of the proposed procedure and relating results, conclusion and perspectives of these researched are discussed.

Yığın Kişiselleştirme Yaklaşımı Ve Örnek Uygulama - Mass Customization And Sample Application

Unpredictable demand, heterogeneous customer requests, short product life cycles, increase in the demand for customized and quality product and services and technological and innovative discoveries that enable all these have boosted the rivalry between enterprises. The role of mass customization system in defining and meeting the requests and necessities of the customers who are the focal point of this rivalry in an efficient and adequate manner has increased since 1980’s. Mass customization means offering products that meet the requirements and the necessities of the customers at mass production costs. Mass customization system which provides for the customer participation at different points of product value chain provides the opportunity to customize products in different levels. In the kitchen production industry where design-production-assembly and distribution stages of the production are completely determined according to the specifications of the product which is going to be presented to the customer, mass customized products provide cost advantages. Aim of this study is to be able to present the cost advantage by comparing the costs of Enterprise B which custom produces kitchens and Enterprise A which produces modular kitchens with mass customization approach. To that end, cost data of the two enterprises analyzed and it was seen that the enterprise which uses mass customization production system gains cost advantage by using scale and scope economies efficiently.

Quality monitoring by special charts for highly customized production systems

This study presents alternative specials control charts formulation to deal with the flexibility in the quality features due to a mass customization production system. The proposed charts have capacity to detect small shifts from process target faster than traditional approaches for flexible systems, thus improving the quality monitoring process. The proposed charts have been compared with other conventional charts using simulation scenarios generated from Monte Carlo experimentation. Results show that the proposed EWMA chart for flexible systems is the most robust among the evaluated control charts to apply in Mass Customization environment. The performance of the best chart was analyzed in a flexible process and managerial implications were discussed.

Optimal pricing in mass customization supply chains with risk-averse agents and retail competition

We analytically investigate the optimal pricing decisions in a mass customization (MC) supply chain with one risk averse manufacturer and two risk averse competing retailers. The manufacturer is the Stackelberg leader, which offers a wholesale pricing contract to the retailers. After receiving the wholesale price, each retailer decides the retail selling price for the MC product simultaneously. We first prove the existence of a unique pricing equilibrium and then derive the optimal prices. After that, we focus our attention on exploring how the degree of risk aversion of each supply chain agent affects the optimal prices as well as consumer welfare, supply chain profitability, and credit deposit under a competitive setting. We find that a more risk averse manufacturer will offer a lower wholesale price, which leads to lower retail selling prices offered to the market. For the retailers, if a retailer is more risk averse, it will make the manufacturer offer a higher wholesale price and it will set a lower retail selling price; however, whether the competing retailer will increase or decrease the retail selling price depends on the level of competition. We examine the impacts brought by the market demand uncertainties as well as the respective demand correlation. We conclude by revealing that in the sufficiently competitive market environment, consumers enjoying MC services are benefited more but the supply chain profitability may decrease more (depending on how risk averse the agents are) when (i) the manufacturer and retailers are more risk averse, (ii) demand uncertainties and the correlation between market demands are higher. We also find that the retailers need to pay more credit deposit if the manufacturer is more risk averse or the demand correlation is higher. Finally, we consider the MC product improvement scheme in the extended model and reveal that it is a Pareto improving optimal measure if the supply chain agents are not too risk averse and the increment in production cost is sufficiently small.

A MULTI-STAGE OPTIMISATION METHOD FOR THE MANAGEMENT OF AN ON-DEMAND FIXTURE MANUFACTURING CELL FOR MASS CUSTOMISATION PRODUCTION SYSTEMS

The implementation of reconfigurable fixtures is a major facilitator of mass customisation. Traditional scheduling techniques do not consider reconfigurable fixtures comprehensively. This paper describes a multi-stage optimisation method that manages the recirculation of reconfigurable fixtures in a mass customisation production system. The method was based on an on-demand fixture manufacturing cell that served a part processing cell. The method consists of three stages: using established techniques such as k-means clustering and hierarchical clustering; and a novel mixed integer linear programming (MILP) model to optimise operation sequences. Minimisation of total idle time (and thus makespan) was used as the measure of performance.

Label-/tag-free traceability of electronic PCB in SMD assembly based on individual inherent surface patterns

Individual traceability of components enables efficient quality control, improving operational efficiency, or smart manufacturing approaches like big data analysis as well as customized mass production. Conventional track and trace solutions however, require a tag or label which is often not attachable due to e.g., spatial or economic reasons. A promising alternative is the identification of components based on individual, inherent surface patterns. For a set of 115 printed circuit boards, it is shown that fiducial markers, which are commonly used for alignment of components, can possess such individual surface patterns and hence serve as unique identifier. As the investigations are based on industrial conditions, the paper transfers the approach of label-/tag-free traceability from laboratory to an application in an industrial environment. Furthermore, faultless traceability of all samples within a SMD assembly line is achieved demonstrating the robustness of the identification against changes of the surface.

Research on Reconfigurable Mechanism of Manufacturing System for Printing Process Based on ACP

The reconfiguration manufacturing system (RMS) allows mass manufacturing to be changed into customized mass production and short-series production. However, the existing RMS models are not applicable to the printing production process, which is coexistence of the hybrid nature of being continuous and discrete. With the printing manufacturing system as the object of the research, this paper proposes the Agent-Resource-Workstation (ARW) model based on agent by analyzing the characteristics of “Production and Consumption of Resources” in the printing process. And then, each component of the ARW model is analyzed and defined. Finally, after systematically abstracting the reconfiguration of workstation models (RWMs), the reconfigurable mechanism of RWMs is established based on the algebra of communicating process (ACP), and formal verification was conducted by applying axioms of process algebra. The verification results show that RWM can show its external behavior well. Meanwhile, our research provides a theoretical approach for the reconfiguration of the printing manufacturing system.

Collaborative Robots in the Workplace [From the Guest Editors

The articles in this special section report on the most advanced applications of human– robot collaboration in relevant environments. 2016 saw the highest number of industrial robots installed worldwide, nearly 300,000 units. Despite this constant sales growth, industrial manipulators are mainly used only in traditional markets such as automotive and electronics. The sizable investment required to acquire, install, commission, and train industrial robots still deters small- and medium-sized companies from implementing this technology. The limited flexibility of such platforms remains the most critical entry barrier for potential users to adopt robotized solutions. In response to the increasing demand for the mass customization of products in the global marketplace, a completely new class of robots has recently appeared, representing an affordable solution to this problem. Designed to safely collaborate with human workers, these robots are presenting new opportunities to partially automate manufacturing processes.