Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production

One-of-a-kind production (OKP) aims at manufacturing products based on the requirements from individual customers while maintaining the high quality and efficiency of mass production. This research addresses the issues in identifying the optimal product configuration and its parameters based on individual customer requirements on performance and costs of products. In this work, variations of product configurations and parameters in an OKP product family are modeled by an AND-OR tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained optimization. A case study to identify the optimal configuration and its parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.     

A stochastic model of a reconfigurable manufacturing system Part 2: Optimal configurations

Various products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then, the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. In the design period of a RMS, there may exist several feasible configurations for each family. Then, an important issue in a RMS is the optimal configurations for the families. Based on a stochastic model, an optimization problem stemmed from the issue is formulated. Two algorithms are devised to solve the optimization problem. Numerical examples are presented for evaluating the efficiency of the algorithms.     

Modelling and integration of customer flexibility in the order commitment process for high mix low volume production

With increasing product variety and dynamic demand fluctuation, manufacturing industry is moving towards a high product mix and low order volume production environment. Consequently, the order commitment process is becoming one of the most important processes for manufacturing firms to meet individual customer's needs with limited resources. However, demands for shortened delivery lead time, diverse customer requirements and more frequent customer orders have made the order commitment task more challenging. This paper attempts to tackle these new challenges by incorporating not only manufacturing flexibility but also flexibility from the demand side. Customer flexibility is characterised by customer indifference to certain product attributes and/or delivery schedules. Intuitively, with the consideration of customer flexibility, both manufacturers’ and customers’ interests can be better served since the solution space of matching demand and supply can be extended beyond the traditional domain purely from a manufacturing perspective. To this end, a systematic approach is developed to characterise and model customer flexibility. A mixed-integer-programming model is formulated to provide optimal order commitment decisions.     

Optimal levels of process parameters for products with multiple characteristics

The purpose of off-line quality control is to design robust products using robust manufacturing processes before the actual manufacturing of the product. Most of the research work has focused on determining the optimal level settings of process parameters for products with a single quality characteristic. In this paper, we employ the loss function approach to determine the optimal level settings of the process parameters of the production processes for products with multiple characteristics.     

A stochastic model of a reconfigurable manufacturing system Part 1: A framework

Products required by customers are classified into several product families, each of which is a set of similar products. A reconfigurable manufacturing system (RMS) manages to satisfy customers, with each family corresponding to one configuration of the RMS. Then the products belonging to the same family will be produced by the RMS under the corresponding configuration. The manufacturing system possesses the reconfigurable function for different families. In an RMS there are three important issues: the optimal configurations in the design, the optimal selection policy in the utilization, and the performance measure in the improvement. This paper proposes a framework for a stochastic model of an RMS, which involves the above issues. Two optimization problems and the performance measure stemmed from the issues are formulated. An example is given for illustration. Some discussions are presented for future research work.     

在线大规模定制下基于客户需求模板的产品配置研究

针对目前在线大规模定制模式下产品配置中存在的配置需求获取问题,构建了引导不同类型客户需求获取的客户需求模板模型,建立了基于客户需求模板的产品配置框架,对面向不同类型客户的配置方法进行了研究。以摩托车产品为例说明了上述方法的有效性。     

Prediction of manufacturing resource requirements from customer demands in mass-customisation production

Mass-customisation production is a new manufacturing approach to produce customised products based on requirements of individual customers while maintaining the quality and efficiency of mass production. Due to the large variations of customised products, the traditional methods for planning manufacturing resources based on volumes of mass produced products are not effective for mass-customization production. In this research, a new manufacturing resource planning method is developed by studying the relations between customer demands and manufacturing resource requirements based on the true data from a mass-customisation production company—Gienow Windows and Doors. In this research, first the relations between the customer demands, modeled by sales data at levels of whole company, sales branches, and markets in sales branches, and the manufacturing resource requirements, modeled by labour requirements of different production lines are studied. Fuzzy pattern clustering method is employed for classifying the resource requirements into patterns to further understand the relations. Based on this study, linear regression and neural network are used to model the linear and non-linear relations between customer demands and manufacturing resource requirements, and to predict the manufacturing resource requirements from available customer demands. A manufacturing resource planning system was developed to demonstrate the effectiveness of this introduced approach.     

An approach to identify the optimal configurations and reconfiguration processes for design of reconfigurable machine tools

A reconfigurable machine tool (RMT) is a special machine that can deliver different machining functions through reconfiguration processes among its configurations during the machine utilisation stage. In this research, a new approach is developed to identify the optimal configurations and the reconfiguration processes for design of the RMTs. In this work, a generic design AND-OR tree is used to model different design solution candidates, their machine configurations and parameters of these configurations. A specific design solution is created from the generic design AND-OR tree through tree-based search and modelled by different machine configurations. For a reconfiguration process between two machine configurations, a generic process AND-OR graph is used to model reconfiguration operation candidates, sequential constraints among operations and operation parameters. A graph-based search is used to generate all feasible reconfiguration process candidates from the generic process AND-OR graph. The optimal design is identified by multi-level and multi-objective hybrid optimisation. A case study is developed to show how this new approach is used for the optimal design of a RMT.     

Optimal process parameter determination for computer‐aided manufacturing

Producing high‐quality products at low cost is one of the key factors to survival for manufacturing sectors in today's intense global competition environment. One way to gain competitiveness is to integrate product design and process planning into one activity. This study attempts to determine optimal process parameters for a manufacturing process under given design parameters. The process parameters to be determined in this study include process means and process tolerances for particular manufacturing process sequences. The problem is formulated in constrained non‐linear optimization, considering both quality‐ and manufacturing‐related costs. The proposed application evaluates alternative product designs and process sequences so that the best associated process parameters can be determined during the early stages of design and planning. This makes the link between CAD and CAM systems more useful and effective. As a result, optimal integration of product design and process planning with minimal production costs and maximal product quality is possible. Copyright © 1999 John Wiley & Sons, Ltd.     

Grouping and selecting products: the design key of Reconfigurable Manufacturing Systems (RMSs)

A Reconfigurable Manufacturing System (RMS) is a new paradigm that focuses on manufacturing a high variety of products at the same system. Having specified a design strategy for an RMS as the first design step at the tactical level, products must be grouped to identify and allocate corresponding manufacturing facilities. An interface between market and manufacturing called reconfiguration link is presented to specify and arrange products for manufacturing. The reconfiguration link incorporates the tasks of determining the products in the production range, grouping them into families and selecting the appropriate family at each configuration stage. The proposed approach of (re)configuring products before manufacturing facilitates assigning product families to the required manufacturing facilities in terms of (re)configuring manufacturing systems. This paper contributes an overall approach of grouping products into families based on operational similarities, when machines are still not identified. Since the problem of product family selection consists of quantitative and qualitative objectives, the Analytical Hierarchical Process (AHP) is then used while considering both market and manufacturing requirements. The AHP model is verified in an industrial case study through using Expert Choice software. The solutions take advantage of monitoring sensitivity analysis while changing the priorities of manufacturing and/or market criteria. The concept of the proposed model is generic in structure and applicable to many firms. However, the model must be adapted according to the specific nature of the company under study. For instance, product family choices may differ from one company to another because of the available technology and the volume and type of existing products in the production range.     

Pharmaceutical formulation and manufacturing using particle/powder technology for personalized medicines

Particle/powder technology is used in the manufacture of many pharmaceutical products, and research on the physical properties of particles in the nano- to micro-particle range is important in the pharmaceutical field. The concept of precision medicine will require an increasing shift in pharmaceutical manufacturing toward the design of individualized products. This perspective article focuses on particle design and powder technology for advanced formulations that will be needed in the future for individualized drug formulations and on-demand production. Nanoparticles as drug carriers in drug delivery systems will require particle designs to meet the treatment requirements of individual patients with a particular disease. In pharmaceutical manufacturing, process intensification, such as continuous manufacturing and integrated drug production from drug synthesis to final formulation, has attracted increased attention. Digital design approaches, such as artificial intelligence based on computer-aided development, also will be increasingly used. Continuous production of pharmaceutical products enables downsizing of manufacturing equipment, and on-demand manufacturing equipment has been developed. In addition, additive manufacturing, such as 3D printing, is considered to be suitable for personalized formulations, and small-scale powder handling and predictive modeling of powder characterization will be important for individual preparations.     

Optimising product configurations with a data-mining approach

Customers benefit from the ability to select their desired options to configure final products. Manufacturing companies, however, struggle with the dilemma of product diversity and manufacturing complexity. It is important, therefore, for them to capture correlations among the options provided to the customers. In this paper, a data mining approach is applied to manage product diversity and complexity. Rules are extracted from historical sales data and used to form sub-assemblies as well as product configurations. Methods for discovering frequently ordered product sub-assemblies and product configurations from ‘if-then’ rules are discussed separately. The development of the sub-assemblies and configurations allows for effective management of enterprise resources, contributes to the innovative design of new products, and streamlines manufacturing and supply chain processes. The ideas introduced in this paper are illustrated with examples and an industrial case study.     

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.     

Strategic configuration of flexible electronics assembly facilities facing stochastic requirements

Flexible configuration of manufacturing facilities is a key strategy for efficiently improving market responsiveness and market share in the face of uncertain future product demand. Flexible facility configurations can produce an efficient production system that allows higher capacity utilization given uncertainty in product demand and mix. The aim of this paper is to consider the alternative choices of flexible equipment available at the strategic planning level and to make decisions about the facility design and configuration that best suits the specific needs of the manufacturing system under consideration. A chance constrained mixed integer programming model for strategic configuration and capacity planning of flexible multiple-stage production facilities under time-varying production requirements is introduced in this paper. It is an integrated model that determines the number of assembly lines required, the flexible automation levels required in each line, capacity levels, and product assignments/reassignments for a multiple-stage production system, considering the stochastic nature of the demand. A two-step heuristic based on genetic algorithms is proposed and tested. Experimental results indicate that the two-step heuristic performs well in terms of both computation speed and solution accuracy.     

Platform design variable identification for a product family using multi-objective particle swarm optimization

The variability of products affects customers’ satisfaction by increasing flexibility in decision-making for choosing a product based on their preferences in competitive market environments. In product family design, decision-making for determining a platform design strategy or the degree of commonality in a platform can be considered as a multidisciplinary optimization problem with respect to design variables, production cost, company’s revenue, and customers’ satisfaction. In this paper, we investigate evolutionary algorithms and module-based design approaches to identify an optimal platform strategy in a product family. The objective of this paper is to apply a multi-objective particle swarm optimization (MOPSO) approach to determine design variables for the best platform design strategy based on commonality and design variation within the product family. We describe modifications to apply the proposed MOPSO to the multi-objective problem of product family design and allow designers to evaluate varying levels of platform strategies. To demonstrate the effectiveness of the proposed approach, we use a case study involving a family of General Aviation Aircraft. We show that the proposed optimization algorithm can provide a proper solution in product family design process through experiments. The limitations of the approach and future work are also discussed.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

Optimizing the configuration of a keyboard assembly cell

This paper describes how the configuration of a three station serial keyboard assembly cell can be optimized using a combination of discrete event simulation modelling and experimental design techniques. The investigation is based on a discrete event simulation model written in ARENATM. Optimum buffer sizes and number of pallets are initially determined by factorial design to identify the most significant factors affecting the throughput of the cell. Response surface methodology is then used to determine the optimal settings. The paper illustrates the methodology of using a combination of discrete event simulation modelling and experimental design techniques to design the optimal configuration of serial assembly cells and similar configurations in the manufacturing environment.     

面向大规模定制的供应链驱动模型的研究与应用

大规模定制是在高效率的大规模生产的基础上,通过产品结构和制造过程的重组,运用现代信息技术,新材料技术、柔性技术等一系列高新技术,以大规模生产的成本和速度,为单个顾客或小批量多品种市场定制任意数量的产品的一种生产模式,本文提出了面向大规模定制的推拉型供应链驱动模式,构建了以装配为分离点的供应链模型,并进行了案例研究。     

A primal decomposition method for the integrated design of multi-period production–distribution systems

We study the integrated design of strategic supply chain networks and the determination of tactical production-distribution allocations in the case of customer demands with seasonal variations. Given a set of potential suppliers, potential manufacturing facilities and distribution centers with multiple possible configurations, and customers with seasonal demands, the goal is to determine the configuration of the production-distribution system with the lowest sum of supply, production, transportation, inventory, and facility costs such that seasonal customer demands are met. We develop a mixed integer programming formulation and an integrated design methodology based on primal (Benders) decomposition. For a case study in the packaging industry, specialized acceleration techniques reduced the running times by a factor of 480. The company projects savings of 2% or $8.3 million by using the integrated rather than the optimal hierarchical configuration.