Mass Customization Manufacturing Research Articles

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time.

Incorporating deep learning data analytics techniques in the optimisation of capacitated planned maintenance

Manufacturing systems must be supported by the availability of materials, a streamlined production process and a prepared production line to achieve the production target. In a mass customization manufacturing system, the number of machines required for customization is relatively small. Conse-quently, maintenance on critical machines will impact this manufacturing system the most. Two types of maintenance strategies are implemented: corrective and preventive maintenance. The corrective maintenance requires more resources since the time and cost to repair the breakdown machine will be higher due to fatal failure. For the management to consider preventive maintenance while the binding machines are still operational, it must be equipped with a deep analysis demonstrating that fewer resources will be required. This paper discusses two deep analyses: accurate prediction of the binding machines' breakdown based on Mean Time Between Failure (MTBF) data using a deep learning data analytics technique and optimizing the maintenance total cost in the available capacitated time. The findings and results of this paper show that the proposed deep learning data analytics technique can increase the MTBF prediction accuracy by up to 66.12% and reduce the total maintenance cost by up to 4% compared with the original model.

Production management for mass customization and smart cellular manufacturing system: NSGAII and SMPSO for factory-level planning

Production management for mass customization and smart cellular manufacturing system: NSGAII and SMPSO for factory-level planning

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing.

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00521-020-05657-1.

Dynamic Imaging Solution

The basic economic principle dictates that productivity improvement is the main driving force for raising the living standard of the general public and companies' profit margin. By replacing analog devices with their digital counterparts, the last digital revolution has made huge contribution in boosting labor productivity annually despite the reduction in working hours. However, the labor productivity growth rate has dropped to the lowest level during the Great Recession. Recognizing the long-term sustainability challenges of the current economic development, a new societal paradigm has emerged where artificial intelligence and connected devices take over well-defined tasks to achieving economies of scale and empowering people to innovate and create value. Being a form of mass customization manufacturing process, a dynamic imaging solution is proposed in an electrophotographic printing system with computational calibration capability in the spatial and tonal domains when requested, which will be an enabling technologies for the vision of autonomous printing.

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.

Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights

Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA‐II algorithm. This paper established a six‐tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA‐II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.

Product Family Flexible Design Method Based on Dynamic Requirements Uncertainty Analysis

Developing product families has been recognized as an efficient and effective means to realize sufficient product variety to satisfy a range of customer and support mass customization manufacturing. This paper presents a product family flexible design method based on dynamic requirements uncertainty analysis. The product family dynamic uncertain requirements analysis and forecasting techniques is researched in this paper, aims to improve the dynamic response ability of the product family to the change of the market demand in the future. Firstly, the multi-domain transmission mode of dynamic requirements was discussed and the product family flexible design model was proposed. Then the sensibility of design parameter to dynamic requirements was analyzed and the variation index of design parameter was calculated. As a result, the product platform of product family flexible design was constructed. A product family flexible design prototype system was also developed, and the application verification for flexible design of forging press product family was carried out to demonstrate the validity of the proposed method.

Demographics of mass customization: a global study of manufacturing plants

This study explores the demographic configuration of mass customization (MC) manufacturing plants. It uses cluster analysis, ANOVA and correspondence analysis to test for differences in business unit size, location, and supply chain structure between MC and non-MC producers across the globe. The analysis uses data from an international survey of manufacturers of metal products, machinery, and instruments from 21 countries. Results indicate no significant differences in size, but significant differences in location and supply chain indicators between MC and non-MC plants. We also find renewed evidence of the positioning of MC plants in Hayes & Wheelwright's (1979) product and process matrix.

Driving Model of Implementing Mass-Customization Manufacturing in Cluster Supply Chain

To explore the attributes of cluster supply chain, and provide its driving model (2P&2BP) for more effective and more efficient way in implementing mass customization. The paper analyzes cluster supply chain driving model for implementing mass customization manufacturing using data from case study from 8 garment and IT industrial clusters. In process of implementing mass customization, we found that cluster supply chain has supplying level, marketing level and supplementing level to support its driving model (2P&2BP: Pull/push/burst power/bottleneck press) rather than conventional pull\push model. thus SMEs (small and medium-sized enterprises) in industrial cluster would have chances to extensively participate cooperate with core companies in the form of supply chain, thus expanding gradually.

The design of reconfigurable assembly stations for high variety and mass customisation manufacturing

The economical production of mass customised and high variety goods is a challenge facing modern manufacturers. This challenge is being addressed, in part, by the on-going development of technologies that facilitate the manufacturing of these goods. Existing technologies require either excessive inbuilt flexibility or frequent changes to the machine set up to provide the manufacturing functions required for the customisation process. This paper presents design principles for automated assembly stations within the scope of mass customisation. Design principles are presented that minimise the hardware and operating complexities of assembly stations, allowing stations to be easily automated for concurrent mixed model assembly with a First In First Out (FIFO) scheduling policy. A reconfigurable assembly station is developed to demonstrate how the proposed design methods simplify the creation and operation of an assembly station for a product family of flashlights.

The effects of human resource systems on operational performance in mass customisation manufacturing environments

This study empirically examines the impact of two human resource management (HRM) systems on operational performance in two distinct mass customisation manufacturing (MCM) environments. Using the survey data collected electronically from 148 mass customisation manufacturers in the USA, moderated hierarchical multiple regression was employed to test the research hypotheses. Results suggest that operational performance significantly increases when a bundle of complementary basic HRM practices are integrated in MCM contexts that emphasise module and process standardisation. This finding indicates that these types of mass customisation manufacturers are capitalising on the opportunity to improve operational performance by strategically combining several different, but supportive, traditional HRM practices with a finite MCM system.

Stabilisation and control of configurable product manufacturing through Biased Decision Feedback decoupling

Mass Customisation Manufacturing (MCM) attempts to deliver efficient manufacturing outputs, initiated by unique design input into a configurable product platform. In this paper, customers’ selection behaviour in terms of product configuration is characterised and metrics thereof used in presenting a new manufacturing flow control method. The novel setup scheduling and flow control method, termed Biased Decision Feedback (BDF), is tested in a simulation study of a custom fly fishing reel manufacturing facility. Also, BDF is shown to posses an operating point when the statistical behaviour of customers’ selections is stable.

Order-oriented cooperative sequencing optimisation in multi-mix-model assembly lines

Order-oriented products assembly sequence among different assembly lines becomes a critical problem for mass customisation manufacturing systems. It significantly affects system productivity, delivery time, and manufacturing cost. In this paper, we propose a new approach to extend the traditional products sequencing from mixed model assembly line (MMAL) to multi-mixed model assembly lines (MMMALs) to obtain the optimal assembly sequence with the objectives of minimising consumption waviness of each material in the lines, assembly line setup cost, and lead-time. A multi-objective optimisation algorithm based on variable neighbourhood search methods (VNS) is developed. We perform an industrial case study in order to demonstrate the practicality and effectiveness of the proposed approach.

Supply Chain Management in Domestic Mobile Phone Industry under Mass Customization

The detailing of professional division of mobile phone components and parts lays a solid foundation to implement mass customization manufacturing mode in real mobile phone industry. Under mass customization, effective operation of the supply chain cannot do without advanced information system as well as progressive supply chain management methods and technology application. This article states the method of mass customization in mobile phone enterprises mainly from aspects like customer relation management, differentiation delay strategy, products structure and supply chain management in the stage of process development design, logistics management of mobile phones and supply chain coordination management, etc.

GASD Based Relayout Method in Mass Customization Manufacturing Quality Assurance

Mass Customization is the most advanced production mode. In Mass Customization Manufacturing (MCM), facility relayout is needed frequently in an instant, costless, seamless and frictionless mode. Three kinds of production line layouts are brought forward which are probably suitable for the MCM. These three types are the child production lines type, the C-shape lines type and the flower type, whose nature characteristics (the optimization of weak coupling relations) are also analyzed. Using a Genetic Algorithm with Sex Differentiation under the concept of quality loss, its model in MCM is built. Then a real small MCM production line in our lab and its digital model in e-Factory are described. Finally, the algorithm was integrated in the model and a petal type production line layout was implemented in a QUEST based simulation system.

Modeling and Analysis of Operator Effects on Process Quality and Throughput in Mixed Model Assembly Systems

With the increase of market fluctuation, assembly systems moved from a mass production scheme to a mass customization scheme. Mixed model assembly systems (MMASs) have been recognized as enablers of mass customization manufacturing. However, effective implementation of MMASs requires, among other things, a highly proactive and knowledgeable workforce. Hence, modeling the performance of human operators is critically important for effectively operating these manufacturing systems. But, certain cognitive factors have seldom been considered when it comes to modeling process quality of MMASs. Thus, the objective of this paper is to introduce an integrated modeling framework by considering the factors—both intrinsic (such as work experience, mental deliberation time, etc.) and extrinsic (such as task complexity)—that affect the operator’s performance. The proposed model is justified based on the findings presented in the psychological literature. The effect of these factors on process operation performance is also investigated; these performance measures include process quality, throughput, and process capability in regard to handling complexity induced by product variety in MMASs. Two examples are used to demonstrate potential applications of the proposed model.

Inventory-shortage driven optimisation for product configuration variation

Inventory control is a critical problem in manufacturing systems. Inventory shortage significantly affects system productivity, while excessive stocks increase the operation cost. It is difficult to avoid fully inventory shortage under mass customisation manufacturing based on product configuration. In this paper, we propose a new approach for inventory-shortage driven optimisation of dynamic product configuration variation to meet the requirements of product configuration change and find suitable combination of parts by considering cost, lead-time and inventory variation. The multi-objective optimisation model uses a multi-objective genetic algorithm and adds impact cost, lead-time and inventory factors to the normal configuration optimisation model. An industrial case study demonstrates the practicality and effectiveness of the proposed approach. By means of this research, valid solutions for configuration variation are available to the decision makers.

Corrections to “Agent-Based Control Framework for Mass Customization Manufacturing With UHF RFID Technology”

Corrections were made to Table 1 in the above titled paper (ibid, Vol. 3, no 3, 343-359 Sept 09).

Measuring the Costs of Custom Products through Better Technology Utilization

This case study details the process of determining the economic feasibility of implementing lean manufacturing in a small mass customization manufacturing company in the medical industry. The case study investigates the information required for a lean implementation and proposes a number of changes to the work procedures (including instrumentation) to obtain the data. A large number of enterprise resource planning systems were evaluated for their suitability to track data in the mass customization environment and the top candidates are compared in the article. The current labor and materials costs are estimated to determine the magnitude of potential savings to support the changes. Based on the analysis the recommended solution has a payback period of approximately two years.