Modularity and Delayed Product Differentiation in Assemble-to-order Systems: Analysis and Extensions from a Complexity Perspective

The paper assumes a product design around modular architectures and discusses the suitability of the principle of delayed product differentiation in assemble-to-order environments. We demonstrate that this principle does not enable one to make optimal decisions concerning how variety should proliferate in the assembly process. Therefore, we propose to complement this principle in that we additionally consider the variety induced complexity throughout the assembly process. The weighted Shannon entropy is proposed as a measure for the evaluation of this complexity. Our results show that the delayed product differentiation principle is reliable when the selection probabilities of module variants at each assembly stage are equal and the pace at which value is added in the whole assembly process is constant. Otherwise, the proposed measure provides different results. Furthermore, the entropy measure provides interesting clues concerning eventual reversals of assembly sequences and supports decisions regarding what modules in an assembly stage could be substituted by a common module.modularity; complexity; ATO; delayed product differentiation

Variety Management in Assemble-to-Order Supply Chains

Assemble-to-order refers to a supply chain strategy in which products are not assembled until customer order arrives. It is based on the so-called form postponement that is to hold components at a generic form and to delay the point of product differentiation. The performance of an assem-ble-to-order supply chain depends on two main dimensions, which are responsiveness and achievement level of scale economies. Responsiveness refers to the capability of fulfilling customer requirements in a fast-paced manner, whereas the achievement of scale economies reflects the degree of operations efficiency. Assemble-to-order supply chains induce high product variety, which has adverse effects on performance. We use demand volumes as a proxy for scale economies and lead times as a proxy for responsiveness. A matrix that consists of both dimensions can be defined, in which we distinguish between short/long lead times and low/high demand volumes. This matrix is called performance matrix. On the other hand, the consequence that results from product variety is a high demand variability of end products, which also affects the demand variability of components. An analysis of component demand variability enables one to identify the components with low/high demand variability. These components can further be classified into supplied and in-house made components. Thus, a second matrix (called component matrix) with two dimensions, namely variability (low/high) and supply source (in-house/supplier) can be defined. Due to the supply source dimension in the component matrix, the supply chain perspective is also taken into ac-count. The combination of both matrixes into a single one provides the performance/component matrix for assemble-to-order supply chains. To use the final matrix, it is necessary to compute lead times, demand volumes and demand variability of the supplied and in-house made components. By plotting the components in the matrix, one can determine the problems induced by variety. In order to improve the performance of the assemble-to-order supply chain, the implementation of variety management strategies is necessary. The identified strategies are: commonality, component families, modularity, and platforms. Based on the performance/component matrix, we discuss how these strategies or a combination of them can contribute to derive recommendations that aim to alleviate variety impacts on the as-semble-to-order supply chain.Assemble-to-order; Supply Chain Management; Variety Management

Enabling and sustaining collaborative innovation

This paper extends the principles of open source software development to a non-industry-specific level by introducing the Open Source Innovation (OSI) model. OSI exhibits main differences to other related models and concepts such as the private-collective model, commons-based peer production, R&D networks and is therefore an innovation model in its own right. In order for OSI projects to be successful, numerous factors need to be fulfilled. We make the distinction between four categories of factors: economic, technical, legal, and social. In each category, we differentiate between enabling and sustaining factors. The enabling factors must be met at the beginning of the project, whereas the sustaining factors must be satisfied as the project progresses. --

Open source innovation: Characteristics and applicability outside the software industry

Motivation of this paper is to discuss that the open source model of innovation does not only seem practical in the software industry, but also in various other industrial contexts. We develop the concept of Open Source Innovation (OSI) as a generalisation of the open source model of software development (OSS). Our definition centres on the collaboration of volunteers and the free revelation of knowledge between actors. Since OSI exhibits important differences to several related concepts in the literature, we conclude that it is an innovation model in its own right, deserving more attention and research. We further proceed to identify aspects affecting the application of the OSI model in industry practices, grouping them into economic, technical, legal, and social factors. Based on these results as well as expert interviews, we find that the applicability of OSI is primarily determined by the characteristics of, first, the innovation object and, second, the group of contributors, rather than the industrial sector. Finally, we advance propositions on the employment of OSI in industrial practice, relating its feasibility to the innovation object and the group of contributors. --

Enabling and Sustaining Collaborative Innovation

This paper extends the principles of open source software development to a non-industry-specific level by introducing the Open Source Innovation (OSI) model. OSI exhibits main differences to other related models and concepts such as the private-collective model, commons-based peer production, R&D networks and is therefore an innovation model in its own right. In order for OSI projects to be successful, numerous factors need to be fulfilled. We make the distinction between four categories of factors: economic, technical, legal, and social. In each category, we differentiate between enabling and sustaining factors. The enabling factors must be met at the beginning of the project, whereas the sustaining factors must be satisfied as the project progresses.OSI, open source innovation, R&D

A Multi-Agent based Configuration Process for Mass Customization

Large product variety in mass customization involves a high internal complexity level inside a companyís operations, as well as a high external complexity level from a customerís perspective. In order to reach a competitive advantage through mass customization, it is necessary to cope with both problems. This is done within the scope of variety formation and variety steering tasks: Variety formation supports customers during the configuration task according to their preferences and knowledge, variety steering tasks internally deal with finding the customizerís optimal offer. Driven by this economic background, we present a comprehensive multi-agent based design for a configuration process in this paper. It is identified as a suitable solution approach integrating both perspectives. The mass customized products are assumed to be based on a modular architecture and each module variant is associated with an autonomous rational agent. Agents must compete with each other in order to join product variants which suit real customersí requirements. The negotiation process is based on a market mechanism supported by the target costing concept and a Dutch auction.Multi-agent systems; Configuration process; Market mechanism; Mass Customization

Mass Customization vs. Complexity: A Gordian Knot?

Mass customization is a business strategy that aims at satisfying individual customer needs, nearly with mass production efficiency. It induces a high complexity level because of various customer requirements and a steadily changing environment. However, mass customization has some potential to reduce complexity. These interdependencies between mass customization and complexity form a Gordian knot that should be cut in order to point out that mass customization is not just an oxymoron linking two opposite production concepts, but a business strategy that contributes towards reaching a competitive advantage. On the one hand, mass customization increases the production program, manufacturing and configuration complexities. On the other hand, mass customization can contribute to reduce complexity at the levels of order taking process, product and inventories. The main results attained through the analysis are integrated in a comprehensive framework that shows the complexity increasing and complexity decreasing aspects due to mass customization.complexity; mass customization

Dynamic Multi-Agent Based Variety Formation and Steering in Mass Customization

Large product variety in mass customization involves a high internal complexity level inside a company’s operations, as well as a high external complexity level from a customer’s perspective. To cope with both complexity problems, an information system based on agent technology is able to be identified as a suitable solution approach. The mass customized products are assumed to be based on a modular architecture and each module variant is associated with an autonomous rational agent. Agents have to compete with each other in order to join coalitions representing salable product variants which suit real customers’ requirements. The negotiation process is based on a market mechanism supported by the target costing concept and a Dutch auction. Furthermore, in order to integrate the multi-agent system in the existing information system landscape of the mass customizer, a technical architecture is proposed and a scenario depicting the main communication steps is specified.Product Configuration, Mass Customization, Variety Formation and Steering, Multi Agent System

Product Configuration Systems: State of the Art, Conceptualization and Extensions

Product configurators are considered to be among the most successful applications of artificial intelligence technology. In this paper, we determine different conceptualizations of configurators and condense them in a comprehensive morphological box, which should support configurator designers as well as decision makers in selecting the right system. The analysis of the criteria according to which configurators that are designed thus far reveals a neglect of the front-end perspective. Therefore, it is relevant to extend configurators with a front-end component assisting customers during product configuration through advisory. We develop a framework describing the main requirements on an advisory system and propose the technical infrastructure for its implementation. Finally, the advisory system and the configurator are integrated into a comprehensive interaction system.product configurators; advisory system; product personalization

Key Metrics System for Variety Steering in Mass Customization

The main goal of this paper is to provide a key metrics system for variety steering in mass customization. We distinguish between objective and subjective customer needs. The subjective needs are the individually realized and articulated requirements, whereas the objective needs are the real ones perceived by a fictive neutral perspective. We show that variety in mass customization has to be orientated on the objective needs. In order to help mass customizers better evaluate the degree to which they can fulfill the objective needs as well as their internal complexity level, we have developed a key metrics system model. We also present a conceptual application showing how to use this model to support decision making related to the introduction or reduction of product variants.Variety Management; Complexity; Production/Operations Management