Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Mass Customization Strategies And Their Relationship To Lean Production In The Homebuilding Industry

Current housing trends point to an increasing interest from homebuyers to demand houses that reflect their personal and unique styles, and which are individually configured according to these needs (NAHB, 2004). These homebuyers in turn are unwilling to settle for standard models that sacrifice what they really want in a home. At the same time this creates pressure on builders who are reluctant to sacrifice production efficiencies by deviating from standard models. Such customization desired by demanding customers can disrupt the entire estimating, production, delivery and management process, making it even more difficult to manage homebuilding efficiently and effectively. The question faced by homebuilders in this conditions is, how to manage this trade-off and deliver exactly what homebuyers want, at reasonable prices and lead times with minimal disruptions in efficiencies. Mass Customization (MC) is an emerging production paradigm that seeks to manage the trade-offs between product variety and mass efficiency, while fulfilling individual customer requirements. The general purpose of this research is to improve the effectiveness and efficiency of housing production through the implementation of mass customization strategies. More specifically, this research focuses on the study of the production system through the application of lean production principles, as an approach to enable mass customization. This study first characterizes how much product choice is currently being offered by U.S. homebuilders and what is the impact of customization on production efficiency; and then focuses on the evaluation of the relationships between mass customization and lean production principles. Results revealed that homebuilders offering increased product choice are likely to suffer poorer labor productivity, greater inventory, higher production costs, more quality issues, less satisfied homebuyers, and lower space efficiency. In general, operational performance deteriorated with an increase in product choice. Therefore, industrialized housing manufacturers have not reached the ideal of mass customization and are paying a price for offering more choices to their customers. Homebuilders could mitigate these challenges by using lean concepts. In general, case studies showed that product choice does not necessarily make the implementation of lean concepts more difficult. Some lean concepts, like workload balancing and standardizing tasks, clearly facilitated the handling of product choice. Other lean concepts, like creating a continuous process flow, can be made to work well, even with increased choice. Case study results suggested that good concepts for lean (e.g., efficient continuous flow, effective pull system, workload leveling, defect-free processes, standard tasks, good visual controls, and reliable technology) were also good concepts for (or easily accommodated) handling a range of product choice

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 Steering Concept for Mass Customization

In this paper we make the distinction between subjective and objective 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

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Contextual variety, Internet-of-things and the choice of tailoring over platform : mass customisation strategy in supply chain management

This paper considers the implications for Supply Chain Management from the development of the Internet of Things (IoT) or Internet Connected Objects (ICO). We focus on the opportunities and challenges arising from consumption data as a result of ICO and how this can be translated into a provider’s strategy of offering different varieties of products. In our model, we consider two possible strategies: tailoring strategy and platform strategy. Tailoring strategy implies that a provider produces multiple varieties of a product that meet consumers’ needs. Platform strategy depicts the provider’s actions in offering a flexible and standardised platform which enables consumers’ needs to be met by incorporating personal ICO data onto various customisable applications independently produced by other providers that could be called on in context and on demand. We derive conditions under which each of the strategies may be profitable for the provider through maximising consumers’ value. We conclude by considering the implications for SCM research and practice including an extension of postponement taxonomies to include the customer as the completer of the product