A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

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

Product Variants Platform Customization Strategies and Performance of Reconfigurable Manufacturing Systems (RMS)

Customers’ demands and needs are changing over time. As a result, manufacturers are seeking new ways to respond to market changes effectively and efficiently. They include offering customers a wide range of product varieties in a reasonable time while reducing associated costs. One of the prime techniques adopted by manufacturers is mass customization and its enablers, such as product family and product platforms. The main objective of this research is to help manufacturers manage a high level of variety by implementing the most suitable manufacturing strategy and product platform design. Customized Platform To Order (CPTO) has been introduced and compared with existing manufacturing/production strategies, such as assemble to order (ATO). CPTO is a hybrid assemble-to-stock (ATS)/assemble-to-order (ATO) strategy that uses a platform customization approach to increase the efficiency and productivity of manufacturers. The platform(s) design is based on customers’ historical demand rather than on commonality between product variants. In this thesis, the CPTO approach was compared to the ATO and hybrid ATS/ATO strategies. A discrete-event simulation model of the learning factory iFactory in the Intelligent Manufacturing System Centre (IMSC) is developed. The results were then compared with a physical implementation conducted in the (IMS) Centre. The results of this investigation indicated that the CPTO approach provides manufacturers the ability to be more responsive by reducing the lead time by 30% and assembly time by 27% as well as lowering inventory and assembly costs by 24% and 18% respectively for the considered case study. This approach is applicable to products with modular and flexible platforms and both flexible and reconfigurable manufacturing systems

Configuration knowledge modeling: How to extend configuration from assemble/make to order towards engineer to order for the bidding process

The bidding process is one of the most important phases for system contractors. A successful bid implies defining and implementing attractive and realistic systems solutions that fulfil customer expectations. An additional challenge arises with the increase in systems diversity resulting from growing customization needs. As a result, for standard customizing offers, bidders find good quality support with configuration software for assemble/make-to-order situations. But when requirements exceed the standard offers, bidders need extended support to fulfil Engineering-to-Order requirements. In this context, this article shows how configuration knowledge models, which support configuration in assemble/make-to-order situations (AMTO), can be extended and used in engineer-to-order situations (ETO). Modeling is achieved assuming that the configuration problem is considered as a constraint satisfaction problem. Six key requirements that differentiate ETO from AMTO are identified and modeling extensions are proposed and discussed. An example illustrates all the contributions

Integrating the common variability language with multilanguage annotations for web engineering

Web applications development involves managing a high diversity of files and resources like code, pages or style sheets, implemented in different languages. To deal with the automatic generation of custom-made configurations of web applications, industry usually adopts annotation-based approaches even though the majority of studies encourage the use of composition-based approaches to implement Software Product Lines. Recent work tries to combine both approaches to get the complementary benefits. However, technological companies are reticent to adopt new development paradigms such as feature-oriented programming or aspect-oriented programming. Moreover, it is extremely difficult, or even impossible, to apply these programming models to web applications, mainly because of their multilingual nature, since their development involves multiple types of source code (Java, Groovy, JavaScript), templates (HTML, Markdown, XML), style sheet files (CSS and its variants, such as SCSS), and other files (JSON, YML, shell scripts). We propose to use the Common Variability Language as a composition-based approach and integrate annotations to manage fine grained variability of a Software Product Line for web applications. In this paper, we (i) show that existing composition and annotation-based approaches, including some well-known combinations, are not appropriate to model and implement the variability of web applications; and (ii) present a combined approach that effectively integrates annotations into a composition-based approach for web applications. We implement our approach and show its applicability with an industrial real-world system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

모듈러 제품군 운영을 위한 다양성 관리 방법론

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 홍유석.글로벌 제조업체들은 다양한 제품을 출시하기 위해 모듈러 디자인 전략을 제품개발에 적용해왔다. 모듈러 디자인 전략은 제품을 모듈 단위로 구분한 후, 여러 종류의 모듈을 조합하여 새로운 제품을 만드는 전략이다. 모듈러 디자인은 제조업체가 제품다양성을 달성할 수 있도록 하였지만, 제공하는 제품의 수가 무수히 많아지면서 제품다양성으로 인한 안 좋은 영향들이 설계 영역뿐만 아니라, 시장, 생산 영역에서 지속적으로 발생하고 있는 실정이다. 따라서, 본 논문에서는 제품다양성의 안 좋은 영향을 줄일 수 있도록 이를 체계적으로 개발하고 운영하는 다양성 관리(variety management) 방법론을 제안한다. 다양성 관리를 성공적으로 수행하기 위해서는 교차영역 관점과 변종 수준 관점의 접근이 필요하다. 교차영역 관점은 제품다양성이 영향을 미치는 시장, 설계, 생산 영역의 요소들의 연결관계를 정립하는 메커니즘을 제공하며, 변종 수준 관점은 일반적인 요소(elements) 수준에서 한 단계 내려가 다양성 관리에 실제 문제가 되는 각 요소들의 변종들(variants)을 체계적으로 관리할 수 있도록 한다. 이 두 가지 관점에서, 본 논문은 다양성 관리에서 중요하게 다루어야 할 세 가지 과제–예상치 못한 변종의 발생 방지, 설계 복잡성 감축, 시장 점유율과 복잡성 비용 사이의 균형 잡기–를 해결하기 위한 방법론을 제안한다. 첫 번째 주제에서는, 아키텍처 기반의 접근법을 활용한 변종 관리 아키텍처(VA, variation architecture)를 도입하여 예상치 못한 변종의 발생을 방지하고자 한다. 개발 아키텍처는 모듈러 제품군을 개발할 때 사용하는 일종의 참조 아키텍처로, 시장 속성, 설계 모듈, 생산 설비의 연결관계를 정의하는 교차영역 연결 메커니즘을 제공한다. 변종 관리 아키텍처에서는 일반 수준의 계획과 변종 수준의 계획을 함께 세울 수 있다. 일반 수준에서는 요소 간 연결관계의 종류를 정의하여 제품군의 다양성 수준을 결정하고, 변종 수준에서는 변종들 간의 조합 규칙을 설정하여 불필요한 변종의 발생을 최소화한다. 또한, 본 연구에서는 제조업체가 변종 관리 아키텍처를 활용할 수 있도록 아키텍처 구축 프레임워크를 제안한다. 사례 연구에서는 자동차 프론트섀시 제품군을 통해 제품 및 변종의 수를 상당히 줄일 수 있음을 보여 줌으로써 프레임워크의 실용성을 검증한다. 다음으로, 인터페이스 표준화 개념을 적용하여 변종들 간의 복잡한 관계로부터 발생하는 설계 복잡성을 줄이는 연구를 수행한다. 본 연구에서 제안하는 인터페이스 설계 방법론은 하나가 아닌 다수의 표준 인터페이스를 사용하도록 허용한다. 모듈 변종들을 연결하기 위해 다수의 인터페이스를 도입하면, 인터페이스의 수와 적용범위에 따라 모듈러 제품군의 전체 구조가 달라지고 설계 복잡성 또한 다양한 양상으로 발생한다. 이를 측정하기 위해, 본 연구에서는 인터페이스의 선택에 영향을 받는 두 가지 복잡성 지표를–인터페이스 표준화 복잡성과 통합 복잡성을–정의한다. 인터페이스 표준화 복잡성은 표준 인터페이스를 설계할 때, 모듈 변종 설계자 간의 조율에 필요한 맨아워(person-hour)를 계산하고, 통합 복잡성은 각각의 모듈 변종과 인터페이스를 통합된 제품으로 설계하는데 필요로 하는 노력의 양으로, 위상적 복잡성(topological complexity) 지표를 기반으로 측정한다. 본 연구에서는 두 가지 복잡성을 최소화하는 인터페이스 설계 대안을 찾기 위한 프레임워크를 제공한다. 사례 연구에서 이의 적용성을 보여주기 위해 프론트섀시 제품군에 맞는 최적의 인터페이스 수와 제품군 구조를 도출한다. 마지막 주제에서는, 시장 점유율과 복잡성 비용의 균형을 맞추는 최적 제품 종수를 찾기 위한 최적화 모델을 개발한다. 최적화 모델은 제품을 구성하는 모듈 변종을 기반으로 모델링되고, 제품 및 모듈 종수가 증가함에 따라 시장 점유율의 증가분이 줄어들고, 반대로 복잡성 비용의 증가분은 늘어나는 특성을 반영한다. 시장 점유율을 구하기 위해 네스티드 로짓 모델(nested logit model)을 기반으로 하는 수요 모델을 개발한다. 네스티드 로짓 모델에서는 동일 제품군 내 제품들의 유사성을 고려하여 시장 점유율의 증가분이 줄어드는 특성을 반영한다. 다음으로, 제로베이스 원가계산 접근법(zero-based costing approach)을 활용한 복잡성 비용 모델을 도입한다. 이 접근법에서는 제품 혹은 모듈의 종수가 한 단위씩 늘어날 때 발생하는 비용을 단계적으로 계산하는 방법을 사용한다. 마지막으로, 수요 모델과 복잡성 비용 모델을 합친 최적화 모델(optimization model)을 모델링하여 최적 제품 종수와 제품의 모듈 구성을 도출하는 연구를 수행한다. 사례 연구에서는 민감도 분석을 수행하여 각 상황별 최적해가 어떻게 달라지는 지 보여주어 연구에서 제안하는 모델들의 효과를 검증한다.Global manufacturing companies have been achieving product variety by implementing a modular design strategy in which product variants are created by combining, adding, or substituting modules. Providing a high variety of products, however, causes negative effects not only on design but also on market and production. Variety management that defines the right range of variants is one of the most critical issues for most of the manufacturing companies. This thesis aims to propose methodologies that enable companies to systematically reduce negative effects of variety. In order to achieve successful variety management, this study approaches the issue from two viewpoints: cross-domain and variant-level viewpoints. A cross-domain viewpoint supports establishing relationships between elements in market, design, and production domain that are affected by product variety, and a variant-level viewpoint enables to explicitly manage variants of elements that are the main source of negative effects. In these viewpoints, this thesis focuses on dealing with three important challenges in variety management: to prevent unexpected variants, to reduce design complexity, and to balance market share and complexity cost. In the first theme, an architecture-based approach named variation architecture is introduced to prevent unexpected variants. Variation architecture (VA) is defined as a reference architecture for a modular product family providing the scheme by which variants in market, design, and production domain are arranged by cross-domain mapping mechanisms. The VA consists of generic-level and variant-level plans. At the generic-level, mapping types between domain elements are determined, and at the variant-level, combination rules between variants are set to reduce unexpected variants. Then, a framework is proposed to increase the practicality of the VA so that its compositions are well defined. In the case study, the framework is applied to an automobile front chassis family. The result shows that the number of module variants is significantly reduced compared to the current number of variants in operation. Secondly, the concept of interface standardization is introduced to manage design complexity caused by complicated combinations between module variants. This theme proposes an interface design methodology that addresses multiple standard interfaces in a modular product family. A product family structure is changed by implementing multiple standard interfaces, generating design complexity. This study defines two complexities resulting from the introduction of multiple standard interfaces: standardization effort and integration effort. Standardization effort is estimated as a required person-hours for coordinating module variants to design a standard interface, and integration effort is measured as an effort to integrate all design elements based on the concept of topological complexity. A framework is proposed to identify an optimal product family structure that minimizes the two complexities. In the case study, the proposed framework identifies an optimal structure and the number of standard interfaces for the front chassis family. Then, the study conducts a sensitivity analysis to demonstrate the methodologys applicability in interface management. In the last theme, an optimization model is developed to identify an optimal product variety to balance market share and complexity cost. The model focuses on module variants, not just product variants, because a modular product family creates product variants by combining module variants. The model reflects the trends of concave increase in market share and convex increase in complexity cost as the number of variety increases. A demand model is developed by the nested logit model that shows the concavity of market share based on the similarity of product variants in the same family, and a complexity cost model is constructed by the zero-based costing approach that an incremental cost is estimated as a variant is added. Combining the models, an optimization model is formulated to find an optimal variety and configurations of product variants. The case study demonstrates the models effectiveness by analyzing optimal solutions in various situations.Abstract i Contents iv List of Tables viii List of Figures ix Chapter 1 Introduction 1 1.1 Variety Management 1 1.2 Variety Management Challenges 5 1.3 Research Proposal: How to Deal with the Challenges? 7 1.4 Structure of Thesis 10 Chapter 2 Literature Review 11 2.1 Variety Management Methodologies 11 2.1.1 Modular product family design 11 2.1.2 Product family architecture 13 2.1.3 Classification of the contributions 15 2.2 Modular Design and Complexity 17 2.2.1 Modular design 17 2.2.2 Interface design 19 2.2.3 Design complexity 20 2.3 Product Family Design and Variety 22 2.3.1 Product family design 22 2.3.2 Variety optimization 25 Chapter 3 Variation Architecture for Reducing the Generation of Unexpected Variants 29 3.1 Introduction 29 3.1.1 Generation of unexpected variants 29 3.1.2 Needs for a systematic approach 31 3.2 Variation Architecture (VA) 33 3.2.1 Generic-level planning 34 3.2.2 Variant-level planning 41 3.3 Framework for Planning Product Variety 46 3.4 Application 47 3.4.1 Case description 47 3.4.2 Construction of variation architecture (VA) 49 3.4.3 Result and discussion 53 3.5 Summary 57 Chapter 4 Variant-level Interface Design for Reducing Design Complexity 59 4.1 Introduction 59 4.2 Variant-level Interface Design 61 4.3 Interface Design Complexity 64 4.3.1 Standardization effort 66 4.3.2 Integration effort 71 4.4 Framework for Variant-level Interface Design 76 4.5 Case Study 79 4.5.1 Application of the framework 79 4.5.2 Analysis and discussion 84 4.6 Summary 88 Chapter 5 Optimizing Product Variety for Balancing Market Share and Complexity Cost 91 5.1 Introduction 91 5.2 Evidence of the impact of variety on market share 94 5.3 Planning of Product Configurations 96 5.3.1 Product family architecture 96 5.3.2 Product configuration 98 5.4 Variety Optimization Model 100 5.4.1 Demand model 100 5.4.2 Complexity cost model 104 5.4.3 Optimization model 108 5.5 Case Study 110 5.5.1 Case description 110 5.5.2 Data source 112 5.5.3 Optimization setting 113 5.5.4 Result 115 5.5.5 Discussion 118 5.6 Summary 122 Chapter 6 Conclusion 125 6.1 Summary of Contributions 125 6.2 Limitations and Future Research Directions 127 Bibliography 129 Appendix A Variant-level Plan of a Front Chassis Family 147 Appendix B Adjacency and Combination Matrices of a Front Chassis Family 151 국문초록 155Docto

A Quantitative Approach to Assessing System Evolvability

When selecting a system from multiple candidates, the customer seeks the one that best meets his or her needs. Recently the desire for evolvable systems has become more important and engineers are striving to develop systems that accommodate this need. In response to this search for evolvability, we present a historical perspective on evolvability, propose a refined definition of evolvability, and develop a quantitative method for measuring this property. We address this quantitative methodology from both a theoretical and practical perspective. This quantitative model is then applied to the problem of evolving a lunar mission to a Mars mission as a case study

Adaptive Big Data Pipeline

Over the past three decades, data has exponentially evolved from being a simple software by-product to one of the most important companies’ assets used to understand their customers and foresee trends. Deep learning has demonstrated that big volumes of clean data generally provide more flexibility and accuracy when modeling a phenomenon. However, handling ever-increasing data volumes entail new challenges: the lack of expertise to select the appropriate big data tools for the processing pipelines, as well as the speed at which engineers can take such pipelines into production reliably, leveraging the cloud. We introduce a system called Adaptive Big Data Pipelines: a platform to automate data pipelines creation. It provides an interface to capture the data sources, transformations, destinations and execution schedule. The system builds up the cloud infrastructure, schedules and fine-tunes the transformations, and creates the data lineage graph. This system has been tested on data sets of 50 gigabytes, processing them in just a few minutes without user intervention.ITESO, A. C