Automatic derivation of transition matrix for end-of-life decision making

Recently strengthened environmental regulations have obligated manufacturing companies to treat end-of-life (EOL) products both environmentally consciously and economically. EOL treatment begins with disassembling a product into recyclable or disposable sub-assemblies. Therefore, the economic value of an EOL product is largely a function of the plan for its disassembly: the means by which it is to be disassembled into smaller sub-assemblies, and the choice of sub-assemblies to be disassembled first. In order to make these decisions, a disassembly structure describing every possible sub-assembly division and its disassembly path from the original product has to be presented in a typical form. A widely used form of such a structure is a transition matrix. A transition matrix shows all feasible sub-assemblies and their disassembly hierarchy. Whereas it can be easily transformed into mathematical disassembly planning problem, the tedious work required for its generation limits its practical use. In this paper, we propose an algorithm for automatic derivation of a transition matrix. The proposed algorithm provides an efficient way to derive a transition matrix based on a product's architectural information, which includes the product's physical connections and the relative geometric locations between individual parts. The algorithm was validated in deriving a transition matrix of a car door-trim. Our algorithm can significantly expand the applicability of transition-matrix-based disassembly planning research.     

Real-time scheduling in manufacturing system with machining and assembly operations: a state of art

Manufacturing systems producing multiple products are common in many industries, where products are made from several parts and/or sub-assemblies that require machining operations in first stage and assembly operations at later stage. Several scheduling techniques are proposed in the literature for such manufacturing system to develop near optimal schedule. A disruption in the manufacturing necessitates adjusting previously planned schedule which is known as real-time scheduling. This paper presents a comparative evaluation of different scheduling methods proposed by different investigators for dealing such situations. The literature indicates that real-time scheduling of manufacturing system with machining and assembly operations is hardly attempted. The paper offers a framework for developing rescheduling methodologies for such manufacturing situations.     

Computational modelling of manufacturing choice complexity in a mixed-model assembly line

Manufacturing systems have evolved to adopt a mixed-model assembly line enabling the production of high product variety. Although the mixed-model assembly system with semi-automation (i.e. human involvement) can offer a wide range of advantages, the system becomes very complex as variety increases. Further, while the complexity from different options can worsen the system performance, there is a lack of quantifiable models for manufacturing complexity in the literature. Thus, in this paper, we propose a novel method to quantify manufacturing choice complexity for the effective management of semi-automated systems in a mixed-model assembly line. Based on the concept of information entropy, our model considers both the options mix and the similarities between options. The proposed model, along with an illustrative case study, not only serves as a tool to quantitatively assess the impact of choice complexity on total system performance, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.     

Proactive assessment of basic complexity in manual assembly: development of a tool to predict and control operator-induced quality errors

A major challenge for manufacturing companies today is to manage a huge amount of product variants and build options at the same time in manufacturing engineering and in production. The overall complexity and risk of quality errors in manual assembly will increase placing high demands on the operators who must manage many different tasks in current production. Therefore, methods for decreasing and controlling assembly complexity are urgent because managing complex product and installation conditions will result in distinct competitive advantages. The objective of this paper is to present a method for predictive assessment of basic manual assembly complexity and explain how included complexity criteria were arrived at. The verified method includes 16 high complexity and 16 low complexity criteria to aid designers in preventing costly errors during assembly and create good basic assembly conditions in early design phases of new manufacturing concepts.     

A graph-based model for manufacturing complexity

This paper presents a graph-based model to measure the relative manufacturing complexity of and the manufacturing similarity of products in job shop manufacturing systems. This model depicts the impact of the complexity factors on the profit realisable from products based on their manufacturing process and required resources/skills. These resources deal with the process required for a component to reach assembly, the process of assembling the components to a whole product. This relative manufacturing complexity measure not only can support assembly and production cost estimation, but also can provide a guideline for creating a product with the most effective balance of manufacturing and assembly. Also, the results of this study can help improve budgeting and resource allocation, and the product life cycle cost estimation for future products. A numerical example is also presented to demonstrate the application of the proposed approach.     

Optimal concurrent product design and process planning based on the requirements of individual customers in one-of-a-kind production

One-of-a-kind production is a new manufacturing paradigm for producing customised products based on the requirements of individual customers while maintaining the quality and efficiency of mass production. This research addresses the issues in optimal concurrent product design and process planning based on the requirements of individual customers. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in a generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimisation are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study is introduced to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products of an industrial company to demonstrate the effectiveness of the developed method.     

Marketing/Manufacturing Trade-Offs in Product Line Management

A critical decision facing firms across industries is the selection of a mix of products to offer in the marketplace. Both in practice and in the academic literature, the product line design problem has typically been considered from a marketing perspective, with a focus on how alternative sets of products interact and compete in the marketplace. The operational implications of product line decisions have been largely ignored, even while the importance and complexity of interactions among products in the manufacturing environment increase with broadening product lines. Furthermore, consideration of manufacturing synergies among products in product line design is increasingly beneficial given efforts in many industries to improve co-ordination of manufacturing activities across products. In this work we examine the benefits of integrating marketing implications of product mix with more detailed manufacturing cost implications. Traditional product line models are extended to capture both individual product costs and relevant cost interactions among products. The relevant marketing and manufacturing elements are considered in a mathematical programming formulation that identifies a profit maximizing mix of products. The resulting normative model of the product line design problem is used to generate insights into important cross-functional issues in product line management. Specifically, we examine the impact of alternative manufacturing environment characteristics on the composition of the optimal product line.     

Product portfolio planning with customer-engineering interaction

A critical decision that faces companies across all industrial sectors is the selection of an optimal mix of product attributes to offer in the marketplace, namely product portfolio planning. The product portfolio planning problem has to date generally been considered from a marketing perspective, with the focus being on customer concerns i.e., how alternative sets of product attributes and attribute-level options interact and compete among the target customer segments. From the engineering perspective, the operational implications of product portfolio decisions have been tackled with a primary concern about the cost and complexity of interactions among multiple products in a manufacturing environment with increasing variety. Consideration of the customer and engineering interaction in product portfolio planning is becoming increasingly important, manifested by the efforts observed in many industries to improve the coordination of marketing, design and manufacturing activities across product and process platforms. This paper examines the benefits of integrating customer concerns over product offerings with more engineering implications. To leverage both the customer and engineering concerns, a maximizing shared-surplus model that considers customer preferences, choice probabilities and platform-based product costing, is proposed to address the product portfolio planning problem. A heuristic genetic algorithm procedure is applied to solve the mixed-integer combinatorial optimization problem involved in product portfolio planning. Initial findings from a case study on notebook computer portfolio planning suggests the importance of the research problem, as well as the feasibility and potential of the proposed framework.     

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives

Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field.     

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.     

Methods for the capture of manufacture best practice in product lifecycle management

The capture of manufacturing best practice knowledge in product lifecycle management systems has significant potential to improve the quality of design decisions and minimise manufacturing problems during new product development. However, providing a reusable source of manufacturing best practice is difficult due to the complexity of the viewpoint relationships between products and the manufacturing processes and resources used to produce them. This paper discusses how best to organise manufacturing best practice knowledge, the relationships between elements of this knowledge plus their relationship to product information. The paper also explores the application of UML-2 as a system design tool which can model these relationships and hence support the reuse of system design models over time. The paper identifies a set of part family and feature libraries and, most significantly, the relationships between them, as a means of capturing best practice manufacturing knowledge and illustrates how these can be linked to manufacturing resource models and product information. Design for manufacture and machining best practice views are used in the paper to illustrate the concepts developed. An experimental knowledge based system has been developed and results generated using a power transmission shaft example.     

Choices in manufacturing strategy decision areas in batch production system – six case studies

The configuration of a production system can be described by the choices a firm makes in its manufacturing decision areas. Manufacturing strategy literature lacks empirical research in manufacturing decision areas. The current paper is an exploratory study using six case companies on alternative configurations that can exist in a batch production system. Choices made in decisions such as layout, shop floor control, etc., were found to be similar for all six companies that use batch process. However, there were a number of decisions that were found to be non-process specific and are influenced by product complexity, important competitive priorities, strategic orientation of manufacturing, top management decisions and the size of the company. The paper concludes with managerial implications and future research directions.     

Creating targeted initial populations for genetic product searches in heterogeneous markets

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct ‘targeted’ initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.     

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.     

Measuring complexity in mixed-model assembly workstations

In an effort to maintain or increase their market share and at the same time prevent costs from escalating, manufacturing organisations are increasingly using their current manufacturing system to produce custom output. As a consequence, the large number of product variants increases significantly the complexity of manufacturing systems, both for the operators as for the support services. This is especially true in automotive industry, where customisation is increasing at a rapid pace. To counter the ensuing loss of productivity, a more fundamental approach to dealing with this complexity in manufacturing processes is required. In order to investigate the impact of complexity on production performance, one must first delineate the concept and then identify as unambiguously as possible highly complex workstations. This article defines complexity at the workstation level and proposes a complexity measure for mixed-model assembly workstations. Based on data from several leading automotive companies from Belgium and Sweden, some statistical models are proposed to characterise workstations complexity. The models are described and their validity and accuracy are discussed.     

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.     

Modern reliability assurance of integrated circuits—A strategy based on technology capability assessment and production reproducibility control

Technological innovations provide integrated circuits of increased functionality and complexity, and modern design tools facilitate a new multiplicity of products, such as application-specific products (ASICs). Traditional qualification procedures cannot keep pace with this evolution with respect to requirements of product reliability, ability of qualifying the multiplicity of future products, and market demands for saving cost and time. A further development of a reliability assurance concept, which will be discussed here, considers design tools, basic product elements, materials, manufacturing process and controls as a ‘system’, which has to be qualified with respect to the consistency and efficiency of all of the implemented reliability assurance measures. The concept is based on the manufacturer's ‘system’ knowledge and responsibility. It is compatible with the relevant requirements of ISO 9000 and recent military standard proposals. The procedure is applied to commercial products. The main part of this concept is the qualification of the manufacturing technology. The procedure is organized as a continuous process starting at the concept phase of a new technology and its pilot product. The various steps then follow the development, the pre-series and series production phases. The reliability aspects concentrate on the physical properties of product elements relevant to their stability and endurance, i.e. the potential failure mechanisms and their root causes as reliability risks. Thus a major part of reliability testing for the qualification of the pilot product of a new technology can be performed without the use of the final product version. The benefits derivable from this approach are savings in time and cost as well as the capability to handle future product multiplicity.     

Open manufacturing: a design-for-resilience approach

Open systems have been of interest to the research and industrial community for decades, e.g. software development, telecommunication, and innovation. The presence of open manufacturing enterprises in a cloud calls for broadly interpretable models. Though there is no global standard for representation of digital models of processes and systems in a cloud, the existing process modelling methodologies and languages are of interest to the manufacturing cloud. The models residing in the cloud need to be configured and reconfigured to meet different objectives, including complexity reduction and interpretability which coincide with the resilience requirements. Digitisation, greater openness, and growing service orientation of manufacturing offer opportunities to address resilience at the design rather than the operations stage. An algorithm is presented for complexity reduction of digital models. The complexity reduction algorithm decomposes complex structures and enhances interpretability and visibility of their components. The same algorithm and its variants could serve other known concepts supporting resilience such as modularity of products and processes as well as delayed product differentiation. The ideas introduced in the paper and the complexity reduction algorithm of digital models are illustrated with examples. Properties of the graph and matrix representations produced by the algorithm are discussed.     

Assessing and improving commonality and diversity within a product family

At a time when product differentiation is a major indicator of success in the global market, each company is looking to offer competitive and highly differentiated products. This differentiation issue is restricted by the design of platform-based products that share modules and/or components. It is not easy to differentiate products in a market that is often overwhelmed by numerous options. A platform-based approach can be risky because competition in the global market can become an internal competition among similar products within the family if there is not enough differentiation in the family. Thus, the goal for the product platform is to share elements for common functions and to differentiate each product in the family by satisfying different targeted needs. To assess commonality in the family, numerous indices have been proposed in the literature. Nevertheless, existing indices focus on commonality and reflect an increase in value when commonality increases but do not positively reflect an increase in the value as a result of diversity; hence, the commonality versus diversity index (CDI) is introduced in this paper to assess the commonality and diversity within a family of products or across families. The CDI has variable levels of depth analysis to help designers design or improve the product family. Two case studies using single-use cameras and power tool families highlight the usefulness of this new index.     

A novel approach to include sustainability concepts in classical DFMA methodology for sheet metal enclosure devices

Nowadays sustainable design is a mandatory requirement in the product development process. For this reason, design methodologies are addressed to establish a close relationship between environmental, social and economic impact indicators and product features from early design stages, especially in those features related to its manufacturing. In this paper, the design for manufacturing and assembly—DFMA methodology is adapted to sheet metal enclosure devices, integrating functional and component relationships to minimize particular sustainability indicators such as energy consumption, carbon footprint, number of parts, required amount of material, assembly time and manufacturing costs. Savings with the proposed method are achieved following specific sub-tasks oriented to define new simplified product components, considering changes in manufacturing processes and re-defining mechanical connections between parts. Traditional DFMA approaches consider manufacturing and assembly issues related to a reduction of product complexity and economic savings. The proposed method aims to examine the benefits in life cycle stages such as raw material consumption, service, maintenance, upgrading and end of life—EOL. The methodology is validated through a redesign of a sheet metal industrial clock, in which the sustainability impacts are calculated from a comparison of an existent product vs. a new product development. The implementation of the method in the case study demonstrate reductions of more than 25% in product mass, consumed energy and CO₂ footprint, and more than 50% in theoretical assembly time and product complexity. Sustainability indicators of the proposed method are selected from literature analysis and taking into account attributes of sheet metal enclosure devices.