A bilevel production planning using machine learning-based customer modeling

Mass customization is an important strategy to improve production systems to satisfy customers’ preferences while maintaining production efficiency for mass production. Module production is one of the ways to achieve mass customization, and products are produced by combining modules. In the module production, it becomes much more important for manufacturing companies to reflect customers’ preferences for selling products. The manufacturer can increase its total profit by providing customized products that satisfy customers’ preferences by increasing customers’ satisfaction. In conventional production planning, there are some cases where module production is conducted by the demands from customers’ preferences. However, the customer decision-making model has not been employed in the production planning model. In this paper, a production planning model incorporating customers’ preferences is developed. The customers’ purchasing behavior is generated by using a machine learning model. Customer segmentation is conducted by clustering data that uses the purchase data of multiple customers. The resulting production planning model is a bilevel production planning problem consisting of a single company and multiple customers. Each company can sell products that combine modules that customers require in each segment. We show that the proposed model can obtain higher customers’ satisfaction with greater profits than the model that does not employ the customers’ purchasing model

Platform Determination Modeling for Existing and New Product Family

Today\u27s automotive market is a highly competitive industry as many global manufacturing enterprises are competing to increase and dominate market shares. Automotive and other major manufacturers must focus on product differentiation to fulfill customer demands and expectations, increase market share globally and domestically, and reduce design and manufacturing cost. To meet market demand, enterprises must understand current and future customer expectations as perceptions evolve overtime. Product platform and products family strategies have been implemented widely to offer variations. Assessing and benchmarking platforms and families differentiations - within an enterpriser -are tools used to support and create the most effective balance between market demands and product variations; to avoid self-competition. It has been noted that there has been insufficient researches to identify the gaps in products differentiations within an enterprise and the market. Differentiations with consideration of the dynamic market, market share analysis, globalization factors, functions, function attributes, and sales prices. The focus of this research is to identify the ultimate number of product platforms and product families of existing and prospective products of an enterprise. The mathematical model discovers the top features and functions needed in the market, and eliminates weak car models which do not meet customer expectations. This identification is achieved through analyzing current products diversification, degree of diversification, product saturation and ability to accommodate more functions. The developed mathematical model is demonstrated and validated using case studies based on examples from actual situations. It applies to both product platforms and product families. The results showed that the developed model is not limited to the automotive industry only, but it can be applied to other products and industries as well. This work supports the product designer and strategy-makers in the activity decision process to identify needed functions and features to increase market shares and allocate resources efficiently

Powertrain Assembly Lines Automatic Configuration Using a Knowledge Based Engineering Approach

Technical knowledge and experience are intangible assets crucial for competitiveness. Knowledge is particularly important when it comes to complex design activities such as the configuration of manufacturing systems. The preliminary design of manufacturing systems relies significantly on experience of designers and engineers, lessons learned and complex sets of rules and is subject to a huge variability of inputs and outputs and involves decisions which must satisfy many competing requirements. This complicated design process is associated with high costs, long lead times and high probability of risks and reworks. It is estimated that around 20% of the designer’s time is dedicated to searching and analyzing past available knowledge, while 40% of the information required for design is identified through personally stored information. At a company level, the design of a new production line does not start from scratch. Based on the basic requirements of the customers, engineers use their own knowledge and try to recall past layout ideas searching for production line designs stored locally in their CAD systems [1]. A lot of knowledge is already stored, and has been used for a long time and evolved over time. There is a need to retrieve this knowledge and integrate it into a common and reachable framework. Knowledge Based Engineering (KBE) and knowledge representation techniques are considered to be a successful way to tackle this design problem at an industrial level. KBE is, in fact, a research field that studies methodologies and technologies for capturing and re-using product and process engineering knowledge to achieve automation of repetitive design tasks [2]. This study presents a methodology to support the configuration of powertrain assembly lines, reducing design times by introducing a best practice for production systems provider companies. The methodology is developed in a real industrial environment, within Comau S.p.A., introducing the role of a knowledge engineer. The approach includes extraction of existing technical knowledge and implementation in a knowledge-based software framework. The macro system design requirements (e.g. cycle time, production mix, etc.) are taken as input. A user driven procedure guides the designer in the definition of the macro layout-related decisions and in the selection of the equipment to be allocated within the project. The framework is then integrated with other software tools allowing the first phase design of the line including a technical description and a 2D and 3D CAD line layout. The KBE application is developed and tested on a specific powertrain assembly case study. Finally, a first validation among design engineers is presented, comparing traditional and new approach and estimating a cost-benefit analysis useful for future possible KBE implementations

Définition des familles de produits à l'aide de la logique floue

Dans cette thèse, la contribution principale porte sur la conception des familles de produits par l'application de la logique floue, ceci afin d’améliorer le processus de prise de décisions. Nous considérons que la formation des familles de produits, permet aux entreprises d'offrir une grande variété de produits. Cela permet alors de satisfaire une grande variété de différents types de clients sur un marché cible, et d’éviter une diversification coûteuse par la conception et la fabrication de produits personnalisés pour chaque client. La logique floue permet d’entrer l'information à fournir en des termes linguistiques familièrement exprimés par les personnes. C’est-à-dire qu’elle permet de considérer une information plus conforme à celle exprimée par les consommateurs; elle n'est pas limitée au maniement de variables binaires comme la logique booléenne. La logique floue à travers la formulation de différentes fonctions d'appartenance, est capable d'évaluer une variété de réponses pour une variable et pas seulement un «oui» ou un «non». Après l'analyse de littérature en ce qui concerne la logique floue et le développement des familles de produits. Nous concluons que le processus de prise de décisions est fondamental pour une formation effective des familles de produits et que le classement flou représente la base des processus de prise de décisions aidés par la logique floue. Pour cela, dans ce travail, différents outils assistés par la logique floue ont été développés et appliqués en cherchant à atteindre l'objectif principal. Premièrement, une procédure de classement flou a été améliorée pour permettre d'évaluer les relations de préférences entre plusieurs nombres flous avec différentes fonctions d’appartenance. L’amélioration de cette procédure a été la définition de vingt-neuf cas généraux pour représenter les différentes situations qui peuvent se présenter entre deux nombres flous. Ces cas généraux ont été aussi présentés comme un cadre de référence qui permet d'inclure d'autres fonctions d’appartenance. Postérieurement, en ce qui concerne la conception de familles de produits, différents outils ont été développés, appliqués et finalement intégrés dans une méthodologie globale pour la formation de familles de produits.----------ABSTRACT In this thesis, the main contribution is concerned to the design of product families by applying fuzzy logic, in order to improve the decision making process. We consider that the formation of product families enables companies to offer a wide variety of products allowing the satisfaction of different types of customers into the target market, and avoiding a costly diversification by designing customized products for each customer. Fuzzy logic allows entering information provided in linguistic terms familiarly expressed by the people. That is to say, it allows considering more consistent information close to the expressed by customers and it is not limited to handle binary variables as the Boolean logic. Fuzzy logic through the formulation of different membership functions can evaluate more answers of a variable instead of a just a “yes” or a “not”. After carrying out the literature review, regarding to the fuzzy logic and to the product family development. We concluded that the process of decision making is fundamental for the effectively formation of families of products, and that the fuzzy ranking is the basis of such process. In this work, various fuzzy logic-aided tools have been developed and applied aiming at achieving the main objective. First, an improved fuzzy ranking procedure for decision making in product has been proposed to permit the evaluation of the fuzzy preference relations among several fuzzy numbers with different membership functions. This fuzzy ranking procedure has been supported by the definition of twenty-nine general cases, which is enough to consider all the possible situations between two normal fuzzy numbers. These general cases have been presented as a framework to facilitate the inclusion of other membership functions. Later, regarding the design of product families, different tools have been developed, implemented, and integrated into a global methodology to form families of products. These tools include: a ranking procedure for fuzzy decision-making in product design to compare different products, a method to select products based on the fuzzy preferences of the customers, an iterative method to configure products for specific customers, a method to configure different products to satisfy the different segments of the market, and finally the integration of all these tools in a global methodology for designing families of products by using fuzzy logic

Architectural disruption in aerospace

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 70-71).Distinctive technology and customer / supplier relationships are currently the primary sources of competitive advantage in the Aerospace industry. Modular Open System Architecture (MOSA) requirements represent a significant disruption to this mode of competition. The United States Department of Defense intends to accelerate the rate of aerospace innovation and inject additional competitiveness into the procurement process through the modularization of its products and effective intellectual property management. This combination of architectural disruption and new customer capabilities has the potential to reduce the industry's opportunity to capture value from innovative technologies or a position as first supplier. Historical examples such as Polaroid and IBM demonstrate the organizational paralysis that often results from disruptions in product architecture. The competitive formula becomes ingrained in the processes, resources, and culture of mature companies and is no longer explicit knowledge, which limits the company's ability to develop the capabilities required to compete in its new environment. Competing in a MOSA environment will require the development of new organizational capabilities such as rapid experimentation, fighting standards wars, and protecting system-level knowledge. Defining the disruptive threat and the foundations of current core competencies will enable firms to develop the organizational capabilities essential for this shift in competitive context.(cont.) The author will present several historical examples of architectural disruption, a framework for evaluating the disruptive change, and an identification of organizational anchors that may hinder a particular competitor's ability to respond to MOSA. The goal of the thesis is to start a dialogue within an identified incumbent with in hopes of beginning the organizational transformation required to effectively compete in this new era.by Geoffrey Ashworth.S.M

Factories of the Future

Engineering; Industrial engineering; Production engineerin