Roger J. Jiao

Affective and cognitive design for mass personalization: status and prospect

The prevailing practice of design for mass customization manifests itself through a configure-to-order paradigm, which means to satisfy explicit customer needs (CNs) and built upon legacy design. With pervasive connectivity and interactivity of the Internet and sensor networks, personalization has been witnessed in a number of industry sectors as a promising strategy that makes the market of one a reality. Mass personalization entails a strategy of producing goods and services to satisfy individual customer’s latent needs with values outperforming costs for both customers and producers. This review paper envisions an affective and cognitive design perspective to mass personalization. By exploiting implicit market demand information and revealing latent CNs, mass personalization aspires to assist customers in making better informed decisions, and to the largest extent, to anticipate customer satisfaction and adapt to customer delight. The key dimensions of mass personalization are identified and discussed. By capitalizing on user experience, affective and cognitive design for mass personalization is expected to address individual customer’s latent CNs. The decisions of affective and cognitive design, involving affective and cognitive needs elicitation, affective and cognitive analysis, and affective and cognitive fulfillment, are reviewed with a wide range of interests, including engineering design, human factors and ergonomics, engineering psychology, marketing, and human-computer interaction. Recent trends and future research directions are also speculated to inspire more meaningful research in this area.

Joint optimization of product family configuration and scaling design by Stackelberg game

Product family design is generally characterized by two types of approaches: module-based and scale-based. While the former aims to enable product variety based on module configuration, the latter is to variegate product design by scaling up or down certain design parameters. The prevailing practice is to treat module configuration and scaling design as separate decisions or aggregate two design problems as a single-level, all-in-one optimization problem. In practice, optimization of scaling variables is always enacted within a specific modular platform; and meanwhile an optimal module configuration depends on how design parameters are to be scaled. The key challenge is how to deal with explicitly the coupling of these two design optimization problems.

Integrated Vehicle Configuration System-Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.

Mass customization in the product life cycle

This study presents an introduction to mass customization in the product life cycle—the goal of mass customization, mass customization configurations, and new customer integration techniques, modular design techniques, flexible manufacturing systems (FMSs), and supply chain management methods. The study reviews three selected books and twenty-one selected papers—early papers that describe the goal of mass customization, early papers that describe mass customization configurations, and recent papers that describe new customer integration techniques, modular design techniques, FMSs, and supply chain management methods. The study shows that the goal of mass customization is to create individually customized products, with mass production volume, cost, and efficiency, that most companies use ‘assemble-to-order’ configurations to create standardized products, and that more work is needed on interactive customer integration techniques, collaborative modular design techniques, reconfigurable manufacturing systems, and integrated supply chain management methods to achieve the goal of mass customization.

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.

Mass customization design of engineer-to-order products using Benders' decomposition and bi-level stochastic programming

Leveraging product differentiation and mass production efficiency in mass customization basically entails a configure-to-order paradigm. In the engineer-to-order (ETO) business, however, companies build unique products in response to ‘foreseeable’ customer specifications. The key challenge of ETO mass customization lies in the complexity of accommodating future design changes when customers are involved in customizing design specifications. This paper proposes a two-stage, bi-level stochastic programming framework to tackle ETO mass customization. At the first stage, product platform configuration is integrated with production reconfiguration, which is formulated as a shortest path problem with resource constraints (SPPRC) to optimize production delays within the capabilities of the process platform. Benders’ decomposition algorithm is applied to solve this optimal configuration problem owing to its high computational efficiency. The second stage scrutinizes the optimal configuration resulting from the first stage for scaling optimization of design parameters (DPs) for each module. All DPs are differentiated by standard or customizable DPs. A bi-level stochastic program is implemented to leverage conflicting goals between the producer (leader) and consumer (follower) surpluses. As a result, ETO customization design is anchored with optimal values of standard DPs and optimal value ranges of customizable DPs. A case study of ship engine and power generator ETO design is presented, demonstrating the feasibility and potential of the ETO mass customization framework.

Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews

Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite to keywords mining for explicit customer needs. This paper proposes a two-layer model for latent customer needs elicitation through use case reasoning. The first layer emphasizes sentiment analysis, aiming to identify explicit customer needs based on the product attributes and ordinary use cases extracted from online product reviews. Fuzzy support vector machines are developed to build sentiment prediction models based on a list of affective lexicons. The second layer is geared towards use case analogical reasoning, to identify implicit characteristics of latent customer needs by reasoning the semantic similarities and differences analogically between the ordinary and extraordinary use cases. Case-based reasoning is utilized to perform case retrieval and case adaptation. A case study of Kindle Fire HD 7 inch tablet is developed to illustrate the potential and feasibility of the proposed method.

A system level product configurator for engineer-to-order supply chains

System level configuration is a common need for engineering businesses.Engineer to order processes and incomplete configurations present challenges.A prototype system for system level configuration management is presented. Supply chains in construction, infrastructure building, ship building, factory design and conveyor systems are operating in an engineer-to-order type of environment. Companies in these project-based businesses have special requirements for product configuration. Products have configuration dependencies with each other and there are system level configuration dependencies between several products. Incomplete product configuration items that are subject to change or require engineering work prior to production can occur. This paper introduces the requirements for system level configuration and proposes a prototype solution for ship projects and engine-room related supply chains.

Accountability-based order fulfillment process reengineering towards supply chain management: A case study at a semiconductor equipment manufacturer

Purpose – The purpose of this paper is to provide a methodology to industry and academia on how to reengineer the order fulfillment process (OFP) by capitalizing on integration and coordination across the entire supply chain to sustain supply chain management.Design/methodology/approach – A case study at a semiconductor equipment manufacturer in Singapore is undertaken.Findings – This paper confirms that the traditional OFPs present companies a challenge to satisfy the demanding customer requirements while achieving performance optimization of each supply chain partner; and it is imperative to reengineer the OFPs to stay competitive. To successfully reengineer OFPs, the efforts should be systematically organized to, for example, exploit potential processes as many as possible, determine an optimal process based on mathematically sound grounds.Research limitations/implications – The use of a single case study may limit the generalizability of the findings.Practical implications – A methodology incorporatin...

User Experience Modeling and Simulation for Product Ecosystem Design Based on Fuzzy Reasoning Petri Nets

Product ecosystem design entails complex user experience (UX) that involves interactions among multiple users, products, and the ambience. This paper aims to capture causal relationships between UX and design elements and in turn to provide decision support to product ecosystem analysis. A fuzzy reasoning Petri net is developed to deal with the uncertainty, complexity, and dynamics associated with UX modeling. Reasoning of diverse constructs of UX is embedded in the fuzzy production rules that are derived from self-report UX data based on rough set mining. A fuzzy reasoning algorithm is implemented to perform parallel inference by multicriteria rules and to simulate most likely UX under different ambient factors. A case study of subway station UX design demonstrates the potential of product ecosystem FRPN formulation.