Developing assembly line layout for delayed product differentiation using phylogenetic networks

Effective formation of product platforms helps adapt to product demand changes and decrease time-to-market and lead time. The product platform groups the core elements of product family members into a common module used to derive different product variants by combining it with different components. A new delayed product differentiation (DPD) platform network model, which applies median-joining phylogenetic networks (MJPN), is proposed. It is used for forming product platforms and determining the assembly line layout of modular product families. The MJPN is traditionally used for DNA sequences’ mapping, analysis, clustering and tracing evolutionary trends. The concept of assembly/disassembly modular platforms, whereby both assembly and disassembly of components are used to derive the final product variants from the platform, is utilised. The proposed model determines the required number and composition of a product platform and defines the DPD points. The developed dynamic assembly/disassembly platforms enhance routing and product mix flexibility due to having different platforms that can be used to produce the same product variant. A family of household kettles is used to demonstrate the application of the proposed model. A metric is presented for determining the effectiveness of a given platform in delaying the product differentiation, hence increasing the efficiency of mass customisation. The proposed metric, applied to the case study, demonstrated that the proposed platform formation model using MJPN is more capable of postponing the product differentiation point.     

Late customisation: issues of mass customisation in the food industry

The strategy of mass customisation is being increasingly adopted as companies seek to exploit market trends for greater product variety and individualisation. The implications of changing to mass customisation practice are considerable, where traditional contradictions of high volume and extensive product variety require being reconciled. The literature discusses the need for an integrated approach to mass customisation across all business functions if micro-segmentation of markets is to be profitably pursued, and the current paper investigates extending the paradigm of mass customisation into the, until now, poorly represented sector of food processing. Product design and manufacturing system design for mass customisation are reviewed and contrasted with good practice in more traditional mass customisation industries.     

Managing product variety through configuration of pre-assembled vanilla boxes using hierarchical clustering

Postponement strategy and platform-based production are common practices of mass customisation to address supply chain challenges due to the requirement of product variety. This paper focuses on implementing mass customisation through development of semi-finished forms of products (vanilla boxes) to reduce supply chain cost and facilitate the production process. The challenge is that the possible number of vanilla box configurations grows dramatically with the increase in number of product variants. In the solution approach, the basic information of product variety is captured in a matrix format, specifying the component requirements for each product variant. Then, hierarchical clustering is applied over the components with the considerations of demands. The clustering method consists of three major stages: similarity analysis, tree construction and tree-based analysis. The key stage is similarity analysis, in which problem-specific information can be incorporated in the clustering process. Two numerical examples from the literature are used to verify that the clustering approach can yield good-quality solutions.     

An approach for the modelling and quantification of PSS customisation

A Product–Service System (PSS) is a dynamic and multidimensional system that is comprised of products, services, a large network of stakeholders and all necessary supporting infrastructures, aiming towards becoming continuously competitive, satisfying customer needs and improving environmental aspects. The design of such a system and its evaluation throughout the lifecycle are challenging tasks owing to their inherent complexity, which becomes even greater when the aspect of customisation is considered. Nevertheless, the literature work regarding the customisation of PSS is scarce. Towards bridging this gap, the present work proposes a framework for PSS customisation, including the main steps that are required for a successful PSS customisation. For each step in the framework, which has been collected from the literature, PSS approaches have been provided, and could be used in the execution of each step. Next, the present study introduces a method for: (i) the modular representation of PSS based on vector analysis, and then (ii) the quantification of the PSS customisation degree.     

An ontology for relating affective needs to design parameters: development process and content

Product manufacturers are looking for ways to design and refine their customisation offerings, based on knowledge of affective needs. One approach, CATER, has at its heart an ontology, which supports the exchange of data between the relevant sub-systems in a semantically meaningful manner. This article presents the ontology, specifically emphasising the role of Citarasa, product breakdown and customer selection history. It also presents the ontology development process, including a validation exercise, and discussion of the implications for affective customisation that emerge from the ontology and the development process. This includes issues of how generic an affective ontology can be, the importance of iteration in the customer selection and the functional component of affective requirements.     

Optimisation of product configuration in consideration of customer satisfaction and low carbon

Product configuration is one of the key technologies in the environment of mass customisation, and it has been emphasised and concerned by much research. However, previous studies mostly focus on the cost or the customer utility, but ignore the environmental concern which becomes an important design criterion due to the rising awareness of environmental protection. Moreover, various preferences of customers are also not considered. In this paper, we develop a new bi-objective optimisation model with simultaneous consideration of customer satisfaction and the environmental impact in product configuration. Two objectives of our model are the customer satisfaction index (CSI) and greenhouse gas (GHG) emissions of products. The CSI is presented for the evaluation of customer satisfaction and the GHG emission model is developed to assess the environmental impact of the product. Essential constraints, such as selection, cost and compatibility, are also considered in the model. In addition, a two-phase approach is proposed to solve the optimisation model. Finally, the effectiveness of the proposed method is demonstrated through a case study.     

Assembly systems layout design model for delayed products differentiation

Frequently changing customers’ needs and market pressures motivate manufacturers to offer a wide variety of their products. Poor demand prediction and increased manufacturing complexities and managerial burdens are just a few symptoms of products variety proliferation. Applying a postponement strategy is an effective method for mitigating the complexities arising due to increased products variety and customisation. Delayed product differentiation (DPD) is a prerequisite for applying form postponement strategies, where the unique features that distinguish each product are added at the final stages of production. This paper introduces an innovative design methodology to derive and represent an assembly line schematic layout for delayed products differentiation. The proposed methodology incorporates product commonality analysis and feasible assembly sequences to synthesise the assembly line layout in a single integrated logical design framework, without pre-defining either the number or the positions of differentiation points along the line. Products commonality analysis is performed using cladistics techniques commonly used for biological classification. The classical cladistics was modified to take into consideration the precedence constraints, which are required to ensure the feasibility of assembly sequences. Real product variants are used to validate the proposed DPD assembly line layout design methodology and demonstrate its merits.     

Functional characterisation of mechanical joints to facilitate its selection during the design of open architecture products

New trends in product design require the use of modularity as key feature aimed to improve functional performance and the generation of open architecture products. For mechanical systems, one of the challenges during early design stages of these products involves the proper selection of joining methods among their constructive components. A robust joint selection process must consider product requirements, life cycle analysis and eventual procedures for assembly and disassembly. However, the general approach towards a Design-for-Assembly (DFA)/Design-for-Disassembly (DFD) only considers design, manufacturing and in some cases final disposal stage. Additionally, most of the works found in the literature are merely focused on assembly operations, disregarding economic and environmental benefits from optimising disassembly complexity. Herein, a functional characterisation of mechanical joint methods for the assembly and disassembly activities that take place throughout the product life cycle is proposed, focusing on open architecture products. Additionally, a classification of joining methods, a joint complexity metric valuation and a selection process are proposed for the conceptual design stage. The approach integrates both DFA and DFD principles in a formal methodology. The proposed selection roadmap can be implemented to increase product sustainability positively regarding resources optimisation, operational time and costs in reuse, remanufacturing and recycling tasks.     

Development of a metric to assess the complexity of assembly/disassembly tasks in open architecture products

This paper describes two metrics to measure the complexity involved in assembly and disassembly tasks for open architecture products during its use phase. The approach proposed is based on the summary of all tasks required to assemble and disassemble a predefined set of modules to generate several product variants, which comprise different working levels or functionalities. The aim of the method is to provide a useful tool to designers in the analysis of product complexity regarding use and further phases in which the assembly and disassembly of modules are required. The benefits and usefulness of the metrics are oriented to enhance the sustainability performance of products through the measurement of complexity in modular systems for the decision-making during the design stage. The reduction of complexity involves significant benefits in all lifecycle phases of product, especially when the user or customer is responsible for many related tasks (maintenance, upgrading, reconfiguration and final disposal of modules). The metrics and their calculation process are illustrated using two case study products.     

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.     

Development of a product design and supply-chain fulfillment system for discontinuous innovation

Given the lengthy product development lifecycle process, high cost, and low success rate, many firms avoid considering discontinuous innovation strategies, in spite of their increase in frequency and importance in many markets. Even with advances in automation and technology, many of the techniques being utilised in product development are relatively unchanged, and the definition of discontinuous innovation itself lacks a structural component. To address this problem, the authors developed a methodology for generative customisation to implement discontinuous innovation. Using the emerging technologies of generative design and agent-based modelling, the authors developed a methodology to create product inventions and measure product innovations using a complex adaptive system (CAS) model. This appears to be the first model that represents a complex adaptive system environment to measure the success of discontinuous innovation in the development of a market equilibrium agent model.     

An analytical framework for evaluating the value of enhanced customisation: an integrated operations-marketing perspective

In this paper we present a formal approach to evaluate the value of enhanced customisation. The suggested approach is based on a new perspective addressing the important alignment between the operations and marketing functions, under which we are able to obtain a more holistic understanding of the extent to which enhanced customisation may influence profitability. The model we develop captures the interaction between several factors including the inventory level, number of product variety, price, and delivery lead time. The numerical findings provide insights regarding some important aspects that may inhibit as well as facilitate the progress towards the manufacturing concept with a greater degree of personalised offerings.     

Coordinated optimisation of platform-driven product line planning by bilevel programming

Product line planning (PLP) aims at an optimal combination of product feature offerings, suggesting itself to be a determinant decision for a company to satisfy diverse customer needs and gain competitive advantages. Fulfilment of planned product lines must make trade-offs between product variety and production costs. To balance the costs of product lines, manufacturers often adopt a product platform configuration (PPC) approach to redesign product and process platforms by adding new modules to the legacy platforms. The PPC is an effective means of providing product variety while controlling the manufacturing costs. The PLP and PPC problems have traditionally been investigated separately in the marketing research and engineering design fields. It is important to coordinate PLP and PPC decisions within a coherent optimisation framework. This paper proposes a bilevel mixed 0–1 nonlinear programming model to formulate coordinated optimisation for platform-driven product line planning. The upper level deals with the PLP problem by maximising the profit of an entire product line, whilst the lower level copes with the multiple product platforms optimisation for the optimal PPC in accordance with the upper level decisions of product line structure. To solve this bilevel programming model, a bilevel genetic algorithm is developed to find the optimal solution. A case study of coordinated optimisation between an automobile line and its product platforms is presented to demonstrate the feasibility and effectiveness of the proposed bilevel programming in comparison with a typical ‘all-in-one’ approach and a non-joint optimisation programming.     

Simultaneous design of a product family and its related supply chain using a Tabu Search algorithm

Product family design is currently facing a multitude of challenges, the main problem stemming from the diversity offered to consumers. To design a product family, designers have to define an efficient bill of materials which ensures product assembly within a predefined length of time in order to satisfy the synchronised delivery principle. In addition, the modules used to assemble the finished products have to be competitive in terms of logistical costs. The ability to anticipate the constraints associated with the production process and with transportation is consequently of great interest. In this paper, we focus on the process of identifying a set of modules to be used in the assembly of the finished product. The objective is to define the bill of materials for each product from the modules belonging to that set, and to assign these modules to distant facilities where they will be manufactured and then shipped to a nearby facility for final assembly within a specific time. We use a set partitioning formulations to represent the problem, and solve it by adapting a Tabu Search algorithm in which the assembly process and the supply chain design are considered at the same time.     

Attribute selection for product configurator design based on Gini index

Customer needs play a critical role in product design. Matching diversified customer needs with a company’s product offerings has been a challenge for academia and industry. To this end, product configurator systems have been accepted as important tools to elicit customer needs to meet the challenge. However for complex products with many product possibilities and intricate inter-product relationships, current product configuration systems may become tedious and time consuming for customers’ choice navigation process. They cannot adapt to each individual customer’s preferences by leveraging on the attribute specification information captured in previous configuration steps. This paper presents a Gini index-based attribute selection approach for configurator design. Product configuring is modelled as a sequential query–answer process. In each configuring step, the Gini index is deployed to quantify the clarity of the designer’s beliefs about the customer’s needs. The attribute which contributes most to the clarity will be selected for the customer to configure. A product recommendation module is also integrated with the configurator to further improve the efficiency. As a result, designers obtain clarification on the customer’s needs and preferences in an accelerated manner. An example is presented to test the viability of the method.     

Integrating Kansei engineering with conjoint analysis to fulfil market segmentation and product customisation for digital cameras

Diverse customer desires coupled with technological advances have forced companies to manufacture products with ultimate performance, low cost, high quality and much shorter time-to-market. Recently, the popularity of smart phones has given rise to seriously declined product sales of digital cameras. In this paper, a two-phase framework is presented to offer decision supports on developing next-generation cameras. In the phase of market segmentation, Kansei engineering is employed to capture customer perceptions of affective features. Then, rough set theory is conducted to generate decision rules for partitioning the whole market into the consumer segment and the professional segment, respectively. In the phase of product customisation, conjoint analysis is applied to extract customer preferences for functional features. Furthermore, Grey relational analysis is conducted to select the top three varieties with regard to two distinct segments. In particular, this paper is capable to help brand companies or camera manufacturers better capture customer perceptions and preferences for digital cameras, effectively perform market segmentation (based on affective features) and efficiently conduct product customisation (based on functional features).     

Organisation design strategies for mass customisation: an information-processing-view perspective

The need to transform the organisation for mass customisation has long been recognised in literature, but the discussion has largely relied on anecdotal evidence or case studies and has limitedly taken advantage of insights from organisation theory. In this paper, we draw on organisational information-processing theory to develop solid theoretical links between mass customisation capability and four organisation design strategies aimed at either increasing an organisation's information-processing capacity or decreasing its information-processing needs. We then test the hypothesized relationships using a sample of 238 manufacturing plants from three industries and eight countries, and find that mass customisation capability is positively related to self-containment of tasks, environmental management, and use of lateral relations. We conclude by discussing implications of our results for both research and practice.     

Asynchronous stochastic learning curve effects in engineering-to-order customisation processes

As industrial technology progresses from tailored customisation and mass production all the way to mass customisation, the demand for faster order-to-delivery time keeps increasing, especially for those products that require lengthy customised engineering-to-order (ETO) tasks among multiple partners. Products such as commercial airplanes and ocean vessels, which have delivery demands before the integrated production system has reached a steady production pace, involve partners progressing through their engineering tasks at asynchronous stochastic learning curve (ASLC) rates. This paper aims to contribute to the research by revealing the novel ASLC model and addressing ETO-related processes. An application of the ASLC model in an ETO case study by using sanitised real production data is also presented.     

The impact of social capital on mass customisation and product innovation capabilities

Social capital with customers has three dimensions: structural, relational and cognitive capital. We propose a research model on the joint effects of the three dimensions of social capital on mass customisation capability (MCC) and product innovation capability (PIC). The hypotheses are empirically tested using structural equation modelling and data collected from 276 manufacturing firms in China. The results show that the three dimensions of social capital contribute to MCC and PIC development through different mechanisms. In particular, cognitive capital has a significant impact on MCC; relational capital significantly influences PIC; and structural capital indirectly associates with both MCC and PIC. We also find that structural capital enhances both relational and cognitive capital. MCC improves PIC and fully mediates cognitive capital’s effect on PIC. The findings extend current understanding about the complex interrelationships among structural, relational and cognitive capital and how to develop MCC and PIC by investing in social capital.     

Strategic orientations and mass customisation capability: the moderating effect of product life cycle

This study examines the influence of three strategic orientations (customer, competitor and innovation) on mass customisation (MC) capability via the organisational learning process known as customisation knowledge utilisation (CKU). The moderating effect of product life cycle (PLC) is also investigated. Using data collected from 204 manufacturers, we show that all three strategic orientations positively contribute to MC capability, both directly and indirectly through CKU. Further, our empirical findings suggest that the effects of customer, competitor and innovation orientation on MC capability are contingent on PLC. The effects of customer orientation on CKU and the effects of competitor orientation on CKU and MC capability are stronger when a product is in the maturity stage of its life cycle, whereas the effect of innovation orientation on CKU and MC capability are stronger in the growth stage. These findings suggest that to effectively improve MC capability, managers need to leverage external and internal orientations at different stages of the PLC.